diff --git a/NOTICE.txt b/NOTICE.txt index 4494992..0cefe3e 100644 --- a/NOTICE.txt +++ b/NOTICE.txt @@ -6,4 +6,18 @@ Technische Universität Darmstadt ------------------------------------------------------------------------------- Third party legal information +docs/notebooks/world_cities_continents.csv + + This file is a DERIVATIVE WORK of the simplemaps World Cities Database, + Basic edition (https://simplemaps.com/data/world-cities), which is licensed + under the Creative Commons Attribution 4.0 International licence + (https://creativecommons.org/licenses/by/4.0/). + + Changes made to the original: the city list was filtered to administrative + centres (plus US and Canadian cities above 100,000 inhabitants); a + "continent" column was added by joining on country name and, for cities the + join did not cover, by majority vote over the three nearest labelled cities; + duplicate city names were collapsed to the most populous entry; and only the + city, country, continent, lat, lon and population columns were retained. + diff --git a/README.md b/README.md index bfa26f2..4db82ee 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,15 @@

- +

# Supervised Multi-Dimensional Scaling -[![Arxiv](https://img.shields.io/badge/Arxiv-2510.01025-red?style=flat-square&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2510.01025) +[![Paper](https://img.shields.io/badge/paper-Transactions%20on%20Machine%20Learning%20Research%20(TMLR)-112467?style=flat-square&logoColor=white)](https://openreview.net/forum?id=vCKZ40YYPr) [![License](https://img.shields.io/github/license/UKPLab/supervised-multidimensional-scaling)](https://opensource.org/licenses/Apache-2.0) [![Python Versions](https://img.shields.io/badge/Python-3.12-blue.svg?style=flat&logo=python&logoColor=white)](https://www.python.org/) [![CI](https://github.com/UKPLab/supervised-multidimensional-scaling/actions/workflows/main.yml/badge.svg)](https://github.com/UKPLab/supervised-multidimensional-scaling/actions/workflows/main.yml) -This is a stand-alone implementation of Supervised Multi-Dimensional Scaling (SMDS) from the paper "Shape Happens: -Automatic Feature Manifold Discovery in LLMs". It contains a plug-and-play class written with the +This is a stand-alone implementation of Supervised Multi-Dimensional Scaling (SMDS) from the paper "Hypothesis-Driven Feature Manifold Analysis in LLMs via Supervised Multi-Dimensional Scaling". It contains a plug-and-play class written with the familiar [scikit-learn](https://scikit-learn.org) interface. SMDS supports several template shapes to discover manifolds of various shape. @@ -37,7 +36,7 @@ pip install smds ## Usage The `SupervisedMDS` class provides a scikit-learn style interface that is straightforward to use. Unlike standard MDS, -it requires selecting a stage-1 strategy and target `manifold` by name (for example: `"cluster"`, `"circular"`). +it requires selecting a parametrization strategy and target `manifold` by name (for example: `"cluster"`, `"circular"`). ### Fit & Transform @@ -52,9 +51,9 @@ X = np.random.randn(100, 20) # 100 samples, 20 features y = np.random.randint(0, 5, size=100) # Discrete labels (clusters) # Instantiate and fit -# stage_1: "computed" (default) or "user_provided" +# parametrization: "computed" (default) or "user_provided" # manifold: one of the built-in shape names, e.g. "cluster", "circular", "log_linear" -smds = SupervisedMDS(stage_1="computed", manifold="cluster", alpha=0.1) +smds = SupervisedMDS(parametrization="computed", manifold="cluster", alpha=0.1) smds.fit(X, y) # Transform to low-dimensional space @@ -62,7 +61,7 @@ X_proj = smds.transform(X) print(X_proj.shape) # (100, 2) ``` -If you set `stage_1="user_provided"`, `manifold` is ignored and a warning is raised. +If you set `parametrization="user_provided"`, `manifold` is ignored and a warning is raised. ### Manifold Discovery @@ -206,13 +205,15 @@ mkdocs serve Please use the following citation: ``` -@misc{tiblias2025shapehappensautomaticfeature, - title={Shape Happens: Automatic Feature Manifold Discovery in LLMs via Supervised Multi-Dimensional Scaling}, - author={Federico Tiblias and Irina Bigoulaeva and Jingcheng Niu and Simone Balloccu and Iryna Gurevych}, - year={2025}, - eprint={2510.01025}, - archivePrefix={arXiv}, - primaryClass={cs.AI}, - url={https://arxiv.org/abs/2510.01025}, +@article{tibliasHypothesisDrivenFeatureManifold2025, + title = {Hypothesis-Driven Feature Manifold Analysis in LLMs via Supervised Multi-Dimensional Scaling}, + author = {Tiblias, Federico and Bigoulaeva, Irina and Niu, Jingcheng and Balloccu, Simone and Gurevych, Iryna}, + year = 2025, + month = oct, + journal = {Transactions on Machine Learning Research}, + issn = {2835-8856}, + urldate = {2026-04-26}, + url={https://openreview.net/forum?id=vCKZ40YYPr}, + langid = {english}, } ``` diff --git a/assets/favicon.png b/assets/favicon.png new file mode 100644 index 0000000..3e595a0 Binary files /dev/null and b/assets/favicon.png differ diff --git a/logo.png b/assets/logo.png similarity index 100% rename from logo.png rename to assets/logo.png diff --git a/assets/logo_white.png b/assets/logo_white.png new file mode 100644 index 0000000..1e1c6b7 Binary files /dev/null and b/assets/logo_white.png differ diff --git a/docs/api/core.md b/docs/api/core.md new file mode 100644 index 0000000..5467ed0 --- /dev/null +++ b/docs/api/core.md @@ -0,0 +1,52 @@ +# Core Estimators + +## SupervisedMDS + +::: smds.smds.SupervisedMDS + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +--- + +## HybridSMDS + +::: smds.hsmds.HybridSMDS + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +--- + +## Parametrization Strategies + +### SMDSParametrization + +::: smds.smds.SMDSParametrization + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### ComputedSMDSParametrization + +::: smds.smds.ComputedSMDSParametrization + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### UserProvidedSMDSParametrization + +::: smds.smds.UserProvidedSMDSParametrization + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true diff --git a/docs/api/shapes.md b/docs/api/shapes.md new file mode 100644 index 0000000..53b9e64 --- /dev/null +++ b/docs/api/shapes.md @@ -0,0 +1,169 @@ +# Shapes + +All shapes inherit from `BaseShape` and implement the same callable interface: +calling an instance with an array of labels returns a pairwise distance matrix. + +## Base + +::: smds.shapes.base_shape.BaseShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +--- + +## Continuous Shapes + +### CircularShape + +::: smds.shapes.continuous_shapes.circular.CircularShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### SemicircularShape + +::: smds.shapes.continuous_shapes.semicircular.SemicircularShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### EuclideanShape + +::: smds.shapes.continuous_shapes.euclidean.EuclideanShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### LogLinearShape + +::: smds.shapes.continuous_shapes.log_linear.LogLinearShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### SpiralShape + +::: smds.shapes.continuous_shapes.spiral_shape.SpiralShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### KleinBottleShape + +::: smds.shapes.continuous_shapes.klein_bottle.KleinBottleShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### TorusShape + +::: smds.shapes.continuous_shapes.torus.TorusShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +--- + +## Discrete Shapes + +### ClusterShape + +::: smds.shapes.discrete_shapes.cluster.ClusterShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### ChainShape + +::: smds.shapes.discrete_shapes.chain.ChainShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### DiscreteCircularShape + +::: smds.shapes.discrete_shapes.discrete_circular.DiscreteCircularShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### HierarchicalShape + +::: smds.shapes.discrete_shapes.hierarchical.HierarchicalShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### GraphGeodesicShape + +::: smds.shapes.discrete_shapes.graph_geodesic.GraphGeodesicShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### PolytopeShape + +::: smds.shapes.discrete_shapes.polytope.PolytopeShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +--- + +## Spatial Shapes + +### SphericalShape + +::: smds.shapes.spatial_shapes.spherical.SphericalShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### CylindricalShape + +::: smds.shapes.spatial_shapes.cylindrical.CylindricalShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true + +### GeodesicShape + +::: smds.shapes.spatial_shapes.geodesic.GeodesicShape + options: + members_order: source + show_bases: true + show_signature_annotations: true + separate_signature: true diff --git a/docs/api/stress.md b/docs/api/stress.md new file mode 100644 index 0000000..f907a94 --- /dev/null +++ b/docs/api/stress.md @@ -0,0 +1,47 @@ +# Stress Metrics + +## StressMetrics + +::: smds.stress.stress_metrics.StressMetrics + options: + show_signature_annotations: true + separate_signature: true + +--- + +## Functions + +### scale_normalized_stress + +::: smds.stress.scale_normalized_stress.scale_normalized_stress + options: + show_signature_annotations: true + separate_signature: true + +### normalized_stress + +::: smds.stress.normalized_stress.normalized_stress + options: + show_signature_annotations: true + separate_signature: true + +### non_metric_stress + +::: smds.stress.non_metric_stress.non_metric_stress + options: + show_signature_annotations: true + separate_signature: true + +### shepard_goodness_stress + +::: smds.stress.shepard_goodness_score.shepard_goodness_stress + options: + show_signature_annotations: true + separate_signature: true + +### kl_divergence_stress + +::: smds.stress.kl_divergence.kl_divergence_stress + options: + show_signature_annotations: true + separate_signature: true diff --git a/docs/assets/favicon.png b/docs/assets/favicon.png new file mode 100644 index 0000000..3e595a0 Binary files /dev/null and b/docs/assets/favicon.png differ diff --git a/docs/assets/logo.png b/docs/assets/logo.png new file mode 100644 index 0000000..1b2b4c6 Binary files /dev/null and b/docs/assets/logo.png differ diff --git a/docs/assets/logo_white.png b/docs/assets/logo_white.png new file mode 100644 index 0000000..1e1c6b7 Binary files /dev/null and b/docs/assets/logo_white.png differ diff --git a/docs/index.md b/docs/index.md index 1d2f74f..bb47f51 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,12 +1,15 @@ -# SMDS by Shape Wizards +

+ +

-[![Arxiv](https://img.shields.io/badge/Arxiv-2510.01025-red?style=flat-square&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2510.01025) +# Supervised Multi-Dimensional Scaling + +[![Paper](https://img.shields.io/badge/paper-Transactions%20on%20Machine%20Learning%20Research%20(TMLR)-112467?style=flat-square&logoColor=white)](https://openreview.net/forum?id=vCKZ40YYPr) [![License](https://img.shields.io/github/license/UKPLab/supervised-multidimensional-scaling)](https://opensource.org/licenses/Apache-2.0) [![Python Versions](https://img.shields.io/badge/Python-3.12-blue.svg?style=flat&logo=python&logoColor=white)](https://www.python.org/) [![CI](https://github.com/UKPLab/supervised-multidimensional-scaling/actions/workflows/main.yml/badge.svg)](https://github.com/UKPLab/supervised-multidimensional-scaling/actions/workflows/main.yml) -This is a stand-alone implementation of Supervised Multi-Dimensional Scaling (SMDS) from the paper "Shape Happens: -Automatic Feature Manifold Discovery in LLMs". It contains a plug-and-play class written with the +This is a stand-alone implementation of Supervised Multi-Dimensional Scaling (SMDS) from the paper "Hypothesis-Driven Feature Manifold Analysis in LLMs via Supervised Multi-Dimensional Scaling". It contains a plug-and-play class written with the familiar [scikit-learn](https://scikit-learn.org) interface. SMDS supports several template shapes to discover manifolds of various shape. @@ -19,18 +22,21 @@ Don't hesitate to report an issue if you have further questions or spot a bug. ## Getting started With uv (recommended): + ```shell uv add smds ``` With pip: + ```bash pip install smds ``` ## Usage -The `SupervisedMDS` class provides a scikit-learn style interface that is straightforward to use. Unlike standard MDS, it requires selecting a stage-1 strategy and target `manifold` by name (for example: `"cluster"`, `"circular"`). +The `SupervisedMDS` class provides a scikit-learn style interface that is straightforward to use. Unlike standard MDS, +it requires selecting a parametrization strategy and target `manifold` by name (for example: `"cluster"`, `"circular"`). ### Fit & Transform @@ -45,9 +51,9 @@ X = np.random.randn(100, 20) # 100 samples, 20 features y = np.random.randint(0, 5, size=100) # Discrete labels (clusters) # Instantiate and fit -# stage_1: "computed" (default) or "user_provided" +# parametrization: "computed" (default) or "user_provided" # manifold: one of the built-in shape names, e.g. "cluster", "circular", "log_linear" -smds = SupervisedMDS(stage_1="computed", manifold="cluster", alpha=0.1) +smds = SupervisedMDS(parametrization="computed", manifold="cluster", alpha=0.1) smds.fit(X, y) # Transform to low-dimensional space @@ -55,7 +61,7 @@ X_proj = smds.transform(X) print(X_proj.shape) # (100, 2) ``` -If you set `stage_1="user_provided"`, `manifold` is ignored and a warning is raised. +If you set `parametrization="user_provided"`, `manifold` is ignored and a warning is raised. ### Manifold Discovery @@ -70,12 +76,12 @@ from smds.pipeline import open_dashboard # Evaluates default shapes (Cluster, Circular, Hierarchical, etc.) # Returns a DataFrame sorted by best fit (lowest stress / highest score) df_results, save_path = discover_manifolds( - X, - y, - smds_components=2, # Target dimensionality - n_folds=5, # Cross-validation folds - experiment_name="My_Exp", # Name for saved results - n_jobs=-1 # Use all available cores + X, + y, + smds_components=2, # Target dimensionality + n_folds=5, # Cross-validation folds + experiment_name="My_Exp", # Name for saved results + n_jobs=-1 # Use all available cores ) print(f"Best matching shape: {df_results.iloc[0]['shape']}") @@ -86,87 +92,95 @@ open_dashboard.main(save_path) ``` The discovery pipeline handles: + - **Hypothesis Testing**: Iterates through a default or custom list of manifold shapes. - **Cross-Validation**: Uses k-fold CV to ensure robust scoring. - **Caching**: Caches intermediate results to resume interrupted experiments. - **Visualization**: Generates interactive plots for the dashboard. -## Optimization & GPU Support - -For manifolds with undefined distances (e.g. `ChainShape`), SMDS falls back to a generic SciPy solver. -For large datasets, this can be slow. - -SMDS provides an **optional accelerated solver** based on **PyTorch**, which is significantly faster on CPU and can transparently leverage GPUs when available. +### Statistical Validation -### Enabling the Accelerator +Standard cross-validation provides a mean score, but it does not tell you if one manifold is *statistically* better than +another. SMDS includes a robust **Statistical Testing (ST)** wrapper that runs repeated experiments to perform a * +*Friedman Rank Sum Test** and **Nemenyi Post-Hoc Analysis**. -Install SMDS with the optional `fast` extra: +#### Running a Statistical Test -```bash - pip install smds[fast] -``` +Instead of `smds/pipeline/run_pipeline`, use the `smds/pipeline/run_statistical_test.py` wrapper: -Then enable it in your model: ```python -smds = SupervisedMDS( - ..., - manifold="chain", - gpu_accel=True, +from smds.pipeline.statistical_testing.run_statistical_test import run_statistical_validation + +# Runs the pipeline 10 times (10 repeats), each with 5-Fold CV +pivot_dfs, output_path = run_statistical_validation( + X=my_data, + y=my_labels, + n_repeats=10, + n_folds=5, + experiment_name="my_robust_experiment" ) ``` -If a compatible GPU is available, PyTorch will use it automatically. -Otherwise, the accelerated solver will run on CPU. ->💡 GPU support (CUDA on NVIDIA, MPS on Apple Silicon) depends on your PyTorch installation. -See the official PyTorch documentation for platform-specific setup. +#### Viewing Results + +Open the dashboard to view the **Friedman Statistic**, **P-Value Heatmap**, and **Critical Difference (CD) Diagram**: -## Development (for contributors) +```python +from smds.pipeline import open_dashboard -This seciton is especially usefull if you consider contributing to the library! +# Launch the interactive Streamlit dashboard to explore results and plots +open_dashboard.main() +``` -### Documentation +### Testing -To build and serve the documentation locally: +Run the test suite using pytest: ```bash -mkdocs serve + make test ``` -### Statistical Validation +### Optimization & GPU Support -Standard cross-validation provides a mean score, but it does not tell you if one manifold is *statistically* better than another. SMDS includes a robust **Statistical Testing (ST)** wrapper that runs repeated experiments to perform a **Friedman Rank Sum Test** and **Nemenyi Post-Hoc Analysis**. +For manifolds with undefined distances (e.g. `ChainShape`), SMDS falls back to a generic SciPy solver. +For large datasets, this can be slow. -#### Running a Statistical Test +SMDS provides an **optional accelerated solver** based on **PyTorch**, which is significantly faster on CPU and can +transparently leverage GPUs when available. -Instead of `smds/pipeline/run_pipeline`, use the `smds/pipeline/run_statistical_test.py` wrapper: +#### Enabling the Accelerator -```python -from smds.pipeline.statistical_testing.run_statistical_test import run_statistical_validation +Install SMDS with the optional `fast` extra: -# Runs the pipeline 10 times (10 repeats), each with 5-Fold CV -pivot_dfs, output_path = run_statistical_validation( - X=my_data, - y=my_labels, - n_repeats=10, - n_folds=5, - experiment_name="my_robust_experiment" +```bash + pip install smds[fast] +``` + +Then enable it in your model: + +```python +smds = SupervisedMDS( + ..., + manifold="chain", + gpu_accel=True, ) ``` -#### Viewing Results +If a compatible GPU is available, PyTorch will use it automatically. +Otherwise, the accelerated solver will run on CPU. -Open the dashboard to view the **Friedman Statistic**, **P-Value Heatmap**, and **Critical Difference (CD) Diagram**: +> 💡 GPU support (CUDA on NVIDIA, MPS on Apple Silicon) depends on your PyTorch installation. +> See the official PyTorch documentation for platform-specific setup. -```bash -python smds/pipeline/open_dashboard.py -``` +### Documentation -### Testing +The full online documentation is available +at [SMDS Documentation](https://ukplab.github.io/supervised-multidimensional-scaling/). -Run the test suite using pytest: +To build and serve the documentation locally: ```bash -make test +mkdocs serve ``` ## Contributors (the Shape Wizards team) @@ -175,18 +189,31 @@ make test +## Contribution Matrix + +| Category | Anton | Arwin | Jan | Simon | Vinayak | +|------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------|--------------------------------------------------------------------| +| Shape Implementation | Geodesic, Cylindrical, Spherical | KleinBottle, HierarchicalShape | Shapes:
CircularShape
SpiralShape | ClusterShape, DiscreteCircular, ChainShape | LogLinear, Euclidean, SemiCircular, Torus, Polytope, GraphGeodesic | +| Architectural Extensions | BaseShape API design/implementation, Precomputed manifolds (Y bypass) | BaseShape, AlternativeReducer | Design base shape abstraction. Design of stress metrics following sklearn conventions following research papers | | | +| Stress Metrics | | Non-metric stress | Implement abstraction of stress metric. Shepard score. | | Normalized stress, KL divergence | +| Discovery, Visualization & Validation | | Discovery Pipeline, png-Visualization | | Discovery Pipeline, Dashboard, Statistical Testing | | +| Infrastructure, Tooling, Testing & Performance | Default normalization in BaseShape, General refactoring ([PR](https://github.com/UKPLab/supervised-multidimensional-scaling/pull/34)) Shapes API | Unified stress tests, sklearn compatibility tests | Leading engineering efforts: Implementing CI/CD pipelines
Implementing environment reproducibility
Selection of tools used by our team for coding and organisation
Deploying package to PyPi
Designing rules for Pull Requests review process
Creating documentation | Shape Integration Test Framework, GPU acceleration for ChainShape | | +| Experiments | Manifold Activation Patching | Hour of Manifold Experiment | Experimental notebook | Manifold Location Experiment | Color Manifolds in VLMs | + ## Cite Please use the following citation: ``` -@misc{tiblias2025shapehappensautomaticfeature, - title={Shape Happens: Automatic Feature Manifold Discovery in LLMs via Supervised Multi-Dimensional Scaling}, - author={Federico Tiblias and Irina Bigoulaeva and Jingcheng Niu and Simone Balloccu and Iryna Gurevych}, - year={2025}, - eprint={2510.01025}, - archivePrefix={arXiv}, - primaryClass={cs.AI}, - url={https://arxiv.org/abs/2510.01025}, +@article{tibliasHypothesisDrivenFeatureManifold2025, + title = {Hypothesis-Driven Feature Manifold Analysis in LLMs via Supervised Multi-Dimensional Scaling}, + author = {Tiblias, Federico and Bigoulaeva, Irina and Niu, Jingcheng and Balloccu, Simone and Gurevych, Iryna}, + year = 2025, + month = oct, + journal = {Transactions on Machine Learning Research}, + issn = {2835-8856}, + urldate = {2026-04-26}, + url={https://openreview.net/forum?id=vCKZ40YYPr}, + langid = {english}, } -``` \ No newline at end of file +``` diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js new file mode 100644 index 0000000..1cea476 --- /dev/null +++ b/docs/javascripts/mathjax.js @@ -0,0 +1,18 @@ +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"], ["$", "$"]], + displayMath: [["\\[", "\\]"], ["$$", "$$"]], + processEscapes: true, + processEnvironments: true, + }, + options: { + skipHtmlTags: ["script", "noscript", "style", "textarea", "pre", "code"], + }, +}; + +document$.subscribe(() => { + MathJax.startup.output.clearCache(); + MathJax.typesetClear(); + MathJax.texReset(); + MathJax.typesetPromise(); +}); diff --git a/docs/notebooks/city_geography_manifold.ipynb b/docs/notebooks/city_geography_manifold.ipynb new file mode 100644 index 0000000..d7683a7 --- /dev/null +++ b/docs/notebooks/city_geography_manifold.ipynb @@ -0,0 +1,41996 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6f57977f", + "metadata": {}, + "source": [ + "# Does an LLM Store Cities on a Globe?\n", + "\n", + "When a language model reads *\"I live in Tokyo\"*, does the hidden state at the city token encode\n", + "**where Tokyo is on Earth** — and with what geometry?\n", + "\n", + "The pipeline: label `\"I live in {city}\"` sentences with (lat, lon), capture the residual stream at the\n", + "city token, then fit three competing SMDS geometries — **spherical**, **cylindrical** and **flat** — and\n", + "score them on cities they did not see. That comparison is the point: SMDS does not ask *\"is there\n", + "structure?\"* but *\"which of these shapes explains it best?\"*\n", + "\n", + "> **Dependencies.** `nnsight` and `torch` are not part of the base `smds` install:\n", + "> `uv add nnsight torch` (or `pip install nnsight torch`)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7638dc5d", + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "import torch\n", + "from nnsight import LanguageModel\n", + "from numpy.typing import NDArray\n", + "from scipy.linalg import orthogonal_procrustes\n", + "from sklearn.model_selection import StratifiedKFold, cross_validate, train_test_split\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from tqdm import tqdm\n", + "\n", + "from smds import SupervisedMDS" + ] + }, + { + "cell_type": "markdown", + "id": "e8e6a9f2", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "aff38436", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on mps\n" + ] + } + ], + "source": [ + "# Gated repo -- `huggingface-cli login` (or HF_TOKEN) first. Any causal LM works.\n", + "MODEL_ID = \"meta-llama/Llama-3.2-1B\"\n", + "BATCH_SIZE = 128\n", + "SEED = 123\n", + "PROMPT_TEMPLATE = \"I live in {city}\"\n", + "\n", + "# Derived table shipped with the notebook; see the provenance note in Stage 1.\n", + "CITIES_CSV = \"world_cities_continents.csv\"\n", + "# The one knob on the city list: dataset size against how likely the model is to know a place.\n", + "# 50k leaves 5,705 cities.\n", + "MIN_POPULATION = 50_000\n", + "# Prototyping knob: cap the dataset at roughly this many cities (None = use all of them).\n", + "MAX_CITIES = None\n", + "\n", + "TEST_SIZE = 0.5 # the split behind the 3D figure: half trains the projection, half is drawn held out\n", + "N_FOLDS = 20 # the stress table cross-validates instead of trusting one split\n", + "N_COMPONENTS = 3 # same dimensional budget for every geometry -- what makes their stress comparable\n", + "ALPHA = 1.0 # ridge on W; non-zero matters, d >> N would otherwise interpolate the training half\n", + "\n", + "# Memory, not cores, is what caps the cross-validation. Every fold holds several n_train x n_train\n", + "# matrices -- the classical-MDS eigendecomposition peaks at ~2.6 GB for 5.7k cities, ~3.8 GB once the\n", + "# train fold is scored -- and each parallel worker pays that in full. Budget ~4 GB per worker against\n", + "# total RAM: `n_jobs=-1` asks 12 workers for ~45 GB, the OS kills them, and joblib reports\n", + "# \"A worker stopped while some jobs were given to the executor\".\n", + "N_JOBS = 3\n", + "\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else (\"mps\" if torch.backends.mps.is_available() else \"cpu\")\n", + "\n", + "# Cache key includes MAX_CITIES: a subsampled run must not reuse the full run's activations.\n", + "CACHE_DIR = Path(\"activations\")\n", + "CACHE_TAG = MODEL_ID.replace(\"/\", \"_\") + (\"\" if MAX_CITIES is None else f\"_n{MAX_CITIES}\")\n", + "CACHE_PATH = CACHE_DIR / f\"city_manifold_{CACHE_TAG}.npz\"\n", + "\n", + "print(f\"Running on {DEVICE}\")" + ] + }, + { + "cell_type": "markdown", + "id": "39981fa5", + "metadata": {}, + "source": [ + "## Stage 1 — Dataset\n", + "\n", + "One row per sentence, labelled with the city's latitude, longitude and continent. The sentence ends on\n", + "the city name, so the capture anchor is always the last token.\n", + "\n", + "> **Data provenance.** `world_cities_continents.csv` is a **derivative of the\n", + "> [simplemaps World Cities Database, Basic edition](https://simplemaps.com/data/world-cities)**\n", + "> (CC BY 4.0), post-processed to attach a **continent** to every city. The post-processing kept\n", + "> administrative centres plus US/Canadian cities above 100k, joined a country→continent lookup,\n", + "> filled the ~444 cities the join missed by majority vote over their 3 nearest labelled neighbours,\n", + "> and collapsed duplicate city names to the most populous entry.\n", + "\n", + "That leaves 14,776 cities; `MIN_POPULATION` thins them to 5,705 spanning the full ±180° of longitude.\n", + "Set `MAX_CITIES` to thin further — the cut is stratified by continent — for a quicker run." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ef15565a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5705 cities across 6 continents\n", + "latitude -55..69 longitude -158..178\n", + "continent\n", + "Asia 1843\n", + "Europe 1267\n", + "South America 1001\n", + "North America 972\n", + "Africa 600\n", + "Oceania 22\n" + ] + }, + { + "data": { + "text/html": [ + "
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sentencecitycountrylatloncontinent
0I live in A CoruñaA CoruñaSpain43.3650-8.4100Europe
1I live in A Yun PaA Yun PaVietnam13.3939108.4408Asia
2I live in AachenAachenGermany50.77566.0836Europe
3I live in AalborgAalborgDenmark57.03379.9166Europe
4I live in AalenAalenGermany48.833310.1000Europe
\n", + "
" + ], + "text/plain": [ + " sentence city country lat lon continent\n", + "0 I live in A Coruña A Coruña Spain 43.3650 -8.4100 Europe\n", + "1 I live in A Yun Pa A Yun Pa Vietnam 13.3939 108.4408 Asia\n", + "2 I live in Aachen Aachen Germany 50.7756 6.0836 Europe\n", + "3 I live in Aalborg Aalborg Denmark 57.0337 9.9166 Europe\n", + "4 I live in Aalen Aalen Germany 48.8333 10.1000 Europe" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A derivative of the simplemaps World Cities Database (CC BY 4.0), already filtered to\n", + "# administrative centres and annotated with continents -- see the provenance note above.\n", + "df = pd.read_csv(CITIES_CSV)\n", + "df = df[df[\"population\"] >= MIN_POPULATION]\n", + "\n", + "df = df[[\"city\", \"country\", \"continent\", \"lat\", \"lon\"]].sort_values(\"city\").reset_index(drop=True)\n", + "df[\"sentence\"] = df[\"city\"].map(lambda city: PROMPT_TEMPLATE.format(city=city))\n", + "df = df[[\"sentence\", \"city\", \"country\", \"lat\", \"lon\", \"continent\"]]\n", + "\n", + "if MAX_CITIES is not None:\n", + " # Stratified thinning: keep each continent's share, but never fewer than N_FOLDS of it.\n", + " frac = MAX_CITIES / len(df)\n", + " thinned = [\n", + " group.sample(min(len(group), max(N_FOLDS, round(frac * len(group)))), random_state=SEED)\n", + " for _, group in df.groupby(\"continent\")\n", + " ]\n", + " df = pd.concat(thinned).sort_values(\"city\").reset_index(drop=True)\n", + "\n", + "print(f\"{len(df)} cities across {df['continent'].nunique()} continents\")\n", + "print(f\"latitude {df['lat'].min():.0f}..{df['lat'].max():.0f} longitude {df['lon'].min():.0f}..{df['lon'].max():.0f}\")\n", + "print(df[\"continent\"].value_counts().to_string())\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "2cc0842e", + "metadata": {}, + "source": [ + "## Stage 2 — Activation capture\n", + "\n", + "Inside `model.trace`, `nnsight` saves the residual-stream output of the middle layer without altering\n", + "the forward pass. Batching needs padding, so position `-1` is not the city token for every row and each\n", + "row's anchor is read from its own last non-pad index instead.\n", + "\n", + "That produces the two inputs SMDS needs — $X$, one activation per sentence, and $y$, the matching\n", + "(latitude, longitude) pairs — cached to `.npz` so the model is loaded only once." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "43e83615", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded cached activations from activations/city_manifold_meta-llama_Llama-3.2-1B.npz\n", + "X: (5705, 2048) (N x d)\n", + "y: (5705, 2) (N x 2, degrees)\n" + ] + } + ], + "source": [ + "def resolve_residual_layers(model: LanguageModel):\n", + " \"\"\"Return the list of residual-stream blocks for GPT-style or LLaMA-style checkpoints.\"\"\"\n", + " if hasattr(model, \"transformer\") and hasattr(model.transformer, \"h\"):\n", + " return model.transformer.h\n", + " if hasattr(model, \"model\") and hasattr(model.model, \"layers\"):\n", + " return model.model.layers\n", + " raise ValueError(\"Unsupported model structure for residual stream access.\")\n", + "\n", + "\n", + "def capture_city_token_activations(sentences: list[str]) -> NDArray[np.float32]:\n", + " \"\"\"Trace the sentences in batches, returning the hidden state at each city token.\"\"\"\n", + " model = LanguageModel(MODEL_ID, device_map=DEVICE, dtype=torch.float32, dispatch=True)\n", + " tokenizer = model.tokenizer\n", + " if tokenizer.pad_token is None:\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + " tokenizer.padding_side = \"right\" # trailing pads are invisible to earlier positions under causal attention\n", + "\n", + " layers = resolve_residual_layers(model)\n", + " mid_layer = len(layers) // 2\n", + " print(f\"Capturing layer {mid_layer} of {len(layers)} (exact middle of the network)\")\n", + "\n", + " activations: list[NDArray[np.float32]] = []\n", + "\n", + " for start in tqdm(range(0, len(sentences), BATCH_SIZE)):\n", + " batch = tokenizer(sentences[start : start + BATCH_SIZE], return_tensors=\"pt\", padding=True).to(DEVICE)\n", + "\n", + " with model.trace(batch):\n", + " hidden = layers[mid_layer].output.save()\n", + "\n", + " # Older transformers versions return (hidden_states, ...) from a decoder layer.\n", + " hidden = hidden[0] if isinstance(hidden, tuple) else hidden\n", + " hidden = hidden.detach().float().cpu() # (B, T, d)\n", + "\n", + " # Per row, the last non-pad position: the final sub-token of the city name.\n", + " last_index = batch[\"attention_mask\"].sum(dim=1).cpu() - 1\n", + " rows = torch.arange(hidden.shape[0])\n", + "\n", + " activations.append(hidden[rows, last_index].numpy())\n", + "\n", + " return np.concatenate(activations).astype(np.float32)\n", + "\n", + "\n", + "if CACHE_PATH.exists():\n", + " X = np.load(CACHE_PATH)[\"X\"]\n", + " print(f\"Loaded cached activations from {CACHE_PATH}\")\n", + "else:\n", + " X = capture_city_token_activations(df[\"sentence\"].tolist())\n", + " CACHE_DIR.mkdir(parents=True, exist_ok=True)\n", + " np.savez(CACHE_PATH, X=X)\n", + " print(f\"Saved activations to {CACHE_PATH}\")\n", + "\n", + "y = df[[\"lat\", \"lon\"]].to_numpy(dtype=np.float64)\n", + "\n", + "print(f\"X: {X.shape} (N x d)\")\n", + "print(f\"y: {y.shape} (N x 2, degrees)\")" + ] + }, + { + "cell_type": "markdown", + "id": "df84c162", + "metadata": {}, + "source": [ + "## Stage 3 — Fitting three competing geometries\n", + "\n", + "Half the cities train the projection, half are held out. SMDS learns a linear map with no intercept, so\n", + "the features are standardised first, fitted on the training half only.\n", + "\n", + "| Geometry | Ideal distance between two cities | What it assumes |\n", + "|---|---|---|\n", + "| **Spherical** | chord length on the unit sphere | a globe: both coordinates wrap |\n", + "| **Cylindrical** | Euclidean on a cylinder | longitude wraps, latitude does not |\n", + "| **Flat (linear)** | Euclidean in the (lat, lon) plane | a flat map: no wrap at the antimeridian |\n", + "\n", + "All three manifolds are computed from the same `(lat, lon)` labels, and all three get\n", + "`n_components=3`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "be8e6126", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spherical fitted n_components=3 W: (3, 2048)\n", + "flat (linear) fitted n_components=3 W: (3, 2048)\n", + "cylindrical fitted n_components=3 W: (3, 2048)\n", + "\n", + "Train: 2852 cities Test: 2853 cities\n" + ] + } + ], + "source": [ + "idx_train, idx_test = train_test_split(\n", + " np.arange(len(df)),\n", + " test_size=TEST_SIZE,\n", + " random_state=SEED,\n", + " stratify=df[\"continent\"],\n", + ")\n", + "\n", + "scaler = StandardScaler().fit(X[idx_train])\n", + "X_scaled = scaler.transform(X)\n", + "\n", + "X_train, y_train = X_scaled[idx_train], y[idx_train]\n", + "X_test, y_test = X_scaled[idx_test], y[idx_test]\n", + "\n", + "models: dict[str, SupervisedMDS] = {\n", + " \"spherical\": SupervisedMDS(manifold=\"spherical\", n_components=N_COMPONENTS, alpha=ALPHA),\n", + " \"flat (linear)\": SupervisedMDS(manifold=\"euclidean\", n_components=N_COMPONENTS, alpha=ALPHA),\n", + " \"cylindrical\": SupervisedMDS(manifold=\"cylindrical\", n_components=N_COMPONENTS, alpha=ALPHA),\n", + "}\n", + "\n", + "for name, model in models.items():\n", + " model.fit(X_train, y_train)\n", + " n_comp = model.parametrization_fitted_.n_components\n", + " print(f\"{name:<14} fitted n_components={n_comp} W: {model.W_.shape}\")\n", + "\n", + "print(f\"\\nTrain: {len(idx_train)} cities Test: {len(idx_test)} cities\")" + ] + }, + { + "cell_type": "markdown", + "id": "8b575f7d", + "metadata": {}, + "source": [ + "## Stage 4 — Visualising the embedding\n", + "\n", + "Every city, train and held out alike, is projected through the spherical model's learned $W$ into three\n", + "dimensions. If the hypothesis holds, the points sit near the surface of a unit sphere with continents\n", + "grouped as they are on Earth. Colour encodes continent, open diamonds mark held-out cities, hovering\n", + "names the city, and **Lisbon** is called out as a landmark.\n", + "\n", + "**Note:** An SMDS embedding is only pinned down **up to an isometry**, so two linear corrections are\n", + "applied to recover the correct centring and orientation of the globe." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5e07641b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "template sphere: fitted centre [0.085 0.278 0.379], radius 1.000\n", + "alignment to the Earth frame: rotation\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "colorscale": [ + [ + 0, + "#9aa0a6" + ], + [ + 1, + "#9aa0a6" + ] + ], + "hoverinfo": "skip", + "name": "template sphere", + "opacity": 0.12, + "showscale": false, + "type": "surface", + "x": { + "bdata": 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Europe · %{customdata[4]}
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", 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t4j/S7cVyM+TaPx8inkmUZ7k/+NQOKbZEcb9U2J+ykpjcPyoeS+3IV9U/j+q7dKIV0T9q3U/tVXO4vxbp1Ca24tg/47BQiV0c2z+wjirMykjdP4tpu/roouc/c7zFzeSf6z+ayAWIyT3jP9KAWAePNto/UlL7vIq44T/xKsOgGDHfP7vdo3DBEuc/24HyvNnhzT+XwaTBjl3UP4oR3UH3J+Q/AHBn4BhY6j/UUrKCaY3kPzhDJLbvUeU/zQXX9SKswT9o+ejVo4DhP2xcN+lllNY/m7SubLjW6z+IFk9CJCLpP3kLNo0Ka+E/669CJQU13j82itN2RdbmP53fvfzaDtg/66hvoudr4z9xMf9xwEzLP9bq/wg0a+M/xSoxvmLzwz+TZ86i9UbSP0zr3yq3EeY/W/0A953v3D8YvBNM1xzeP+jivNYmU+I/s29PWisW6j8aqWX/vU3UP2llZcryv9M/2dHcJnkTsT9Lb+lBJ37oP0JySemV+OU/i6kqFKiQ4j8cxb/jplrgPxK9Eilg+Nk/e3FI9seE2z8eZYJNm4faP/vX93juQ9w/+Ow2sZCq3j+yfQXTlkrkP4K4dD5jqc8/AomXoJ1p4D+ATgKDFovRPyuO7ERdX+s/TCctRh/q4T/7ika49prgP1e71qfj/uI/F6ZYOiVd5D9TDxwHSznfPyalRrgLOuc/kcD4ldA56z/b3ycxEf/oP+jpi3E/aNo/6AEBf0F57j+TPuwlYhzsPy7do7iyRus/v+U3/xJH5D86OQp+NzLlPznfNmyiu+g/egtfGCk96D+CdnQnnNzpP2+EGnE5v98/hnJjdKXC6z8NmHK1fCPpP0aOSaPmv8s/8A3D4fAN6j9uDLlrU7rlP6ctpQ9UvZK/C1QxK+qL3T/zhAyupx3lP7i2GbH30NE/v5Yqqa5qlz9ZJXLSlcCVvwUdQ07eRoW/M1aWiHkclL+ZuPbRwrLjP6QwEnavYeQ/777w8Sqk0j+dzoSEqgjTP415rhA6yaI/hQFXi7oWx7+RJHJz7innPzdMeFMx0Nc/cTvkVVWUzj+RPwH0ZkTTPx21cVifbcw/Zq/JhxRWxz+3VwBzXZ/QP+SKPEoVtNU/2dj38HjI4D8DZJrsyBTbP2ypBDkIbt8/HLvn3fjQ4D8x/RSOZF7tP+Tzx377wto/wjiRBEUY7D84g9I2ya+1v1dEf4k4vec/DQR+QMVxsT81LiyOgVjuPxFoA25nb8c/V8b6Je30yT8a/22Io4jAvwXzS9xDMek/iIr5v/E14T9gwU6dLFTgP21ZrvTrmeg/MgEmRNwy3j+wHIvIwQTlPyeeq28qQuM/IdHUr1f24j+E70XbnRDcP6CqiWwxlOQ/Te19RzfL4j9WEoOP1JTiPzbmIgpvad0/sHXLxF7k5D+OZSxVH6TiP2l3mUWPzNc/TzvgES0U4j8eWaiRRaToPxy5YRjvEto/INotAStxwj9qYoHgFfjjP2nHL93wJOQ/k6d/W50Cyz9V08cWdzLWPxb8mfRrIOQ/brzrT1WT4z+ICxs8iDDRP7UAXhMlhNs/QkqZPO0q5j+Cl+dDv9fgP/1kg2Rm++I/DfgRO6FL2z8pmrx7e0bqPzzOVd9GnuE/weoDHvCD4j9tGwFfgs3oP0vSQkjBCOk/w2UbNM0O1z9waDuk72XiPx9Qvuxt8NY/BMbdz19Z4T9GR+Qzruq6P2rwE67yDOI/1D9NnuVe4j/dXfGRqS/jPxuZ6oertuQ/QKHatGtN3D/KZ4J42zLrP6wJ/N/Cn+U/QidTEMlq6T8gFMPSnoKjv+u1h2PTCuQ/GdNHJJiurj+8zevlBhvQP3dPhR5/ZcE/M1TDkykQ1j/e7zTT0cfdP9ByzNNHl9M//G3bAsTS5T/vDn3zYpPnPzUXFBEdI84/gmnLR1X74j9OCtDouhviPzBxUIvW8+k/tapJs/U24z+amZYboSfjP9AjOkZUFL+/FNLTMIJ6hL+RRchffXHpP0XeSulvbOw/aHOmYP4W4T9ajby99WThP7kZBE/HK+Y/zpPz6I4A4D+/+jZY5CvoPxJnrQRS+eQ/W8s49Ivy2T+Rbwc0nJvVP86p/kRjd9w/VIw9AnAC4T9XDZ9HdQfeP5rtVUpQQdw/8UW7Ujvc6z8VAR2Zl0/dP29kYUURidc//5DcgpLF4T/7nh8DlafhP0uQp5bzutc/eeoqYWqX1j90g8iC5VvhP0WG+YQQKOI/dFkR2ut15T9SKQoDO9LiPzfXHBKD9uQ/wPImoSsH5j/NDpLBnDvdPzsmH/4Old8/Q0c51Ep62z8uKAspkY3gPzgFwg8Uxuk/lrmnbrY8w78YsYw6zrzlP9Se4MUjadc/H7oMeApS7D8hQqhl/ZznP+cidh98uOM/6nz2Tq8a4T/YeHlFt1e7P3tmALV0QuI/qG8cVUEezT8TB26JFTXlPw92CXDneOc/QmzXDDTw7D+k3zYAj2qxv53hMqyJoeI/ZBhrrc2s4D+i/IllxB22v+bGQ+p0Ube/Op1KQyyE4D96d/VTSVi2vyEvo3KbBu4//QSHPLN+4z/27Vv7oUXqP9CM9dsQmNw/Ls7WXndB6D+CzW/Niz3QP4mdKuPdbd8/3xa85apc2D+uaR2B8czYP6AO1Sbrvt0/kdfTmd0z5z/2qVJnYinQPylLnwPa0co/TIM9tF+O0j8jtR6ndPrBPzqwhhXt3Nw/oN0pJYJ54D/Do6ElvmTpPxW4ijGggOY/+P2L1GEa4j9ruiNnX869P6k6Xep0lOc/xDzHxMLFzz9k1VzCxcjBvz+zQ+4FoOM/OqfC5Z8jzz+qQ+8iNL3hP+dgt83Iydo/iXe/p+Vb5D+mFAR3Elenv4K42+wyfeI/v2pI59NC3j8tbiPEMo/mPx26PxkNvtE/aLduIGHI4j/B0041ECXcP0uAqFiPWNQ/a9ZtdT6E3j8yJI7YiovEP95O2YgF2NU/E1gKQIdR1z+QP1S9OUDNP1rg1AKTLOI/7mKdcC2u5D/Zd4MEEk3qP9rjjvR1u90/jdlOpEMu5T9CquV9QKftP4yHtUoiC+I/KxALa+0c6j9ZjcQkLV7nP6rF7bXhGuE/2ekqaM0V4j/ofx86mJPiP7/hHGs/0NE/zfpsCKKisL8AIn+umsjBP3UHftLxu9Q/U3MqnOdr4z9ZVrT1qAKMv6meyB9jKtw/Ee6jLXKB3D/RB6QRS97dP9AKWDeGM+U/mlDzZRpO0j8TfidEqMHIP403OTtyWtQ/P6GQ2WS25j+EJ40LU0vlP10sPM+cxeE/fBe72P834j8O9JKJv+nkP0qfOoWU2tM/+0YNhhP76D+aUTrG9P3uPyDg9y9QxuA/un9dpKMY6z/hPhyMSyDmP/dECmS6a+g/MsnSLLtC2j+U5LVkwkvlP2ct/EdjM+M/4vcxpcKo6T8a2g9Y4bfoP71ZtFCcEMg/3LCfenzs6j9uvMvcNZLpP1uAkJJ5O9Y/Yau4sfbE5z9qaX0QhSnoP+L66fNFT+s/t+903kHC6j+rSXpXAt7qP93x1lKJWe4/hM13nfnNzz97LKXrs/PWP2Ix6Ab/Qd8/xrtrAFxetL9Yxe63zl/kPzvBqEirAam//qawFKUG5j+M9DIJshfkPz7cGvaVw+Q/bia3Ps464D9E7lrDwzPcP7MQ+Jq9ENo/1GR6MaS62z/SPu861RXkP/g0IXrcq+U/rmxwycnw3j/gqe1MCZ/jP9FF2to0huI/Btzs7i1b4T94tBjuoPjlP/ZSuTT0VOA/EIirM1dM5z/f/DnG3NDjP0q51wb2OuQ/Vh8taWfa2z/Q0nGF1lngP2N75IOZJuM/PwWhPrzw4z806Yj0+bLiP7Bj3jh6JuE/Gtm4U2aA4T8M2Lxn/aPeP0zYbGwzeNs/OzsWrwzv2j9SZOLipc3hPxvjCRIEk94/ixn/vAbc3D89MwDS8tnjP0bJUToQcuM/kS0SwCw/3z+0FjsIClDYP+Avdplrw+E/y5MyKsz04z80hsEC9nzfP5w5GoY61eQ/4NtV46E04T+Y438NbtrhP2MNh8Ktz+Y/yKa3hrq94j8ITHrtKDrhP/jZ4qpMt98/g9+J3c8e4z94jRTtCAHhP5lTt2GDtu4/L1HWAfq04T9luQiHTwLoP3AwFIjaW+Q/BOhI5/d85D+8FChfNlvdP9FZFIOlN98/MTcuD9T53z/mYGudAcXiP4bSXWbAEd0/jyz/KID5lr+9C7ZEgY3mPziA8pRtxuI/4mmM3otu2z/MIJEPLavdPxGdEeA9T90/giTe1N+X3z8xyLTP8xHhP6cb6w7L4uo/307jDsCx3D9Jz2qkYK3pP9CyPl5m3t0/1KIvjqo05D8wIfqJ08HmP2neN1cSCr0/DlpXFsc84j9WuWeUsNTjP//kV+6vlOE/imdbeBc44j+tvqVoQxjiP/3X18Hw6Ns/h5NxFAT/5j/f//a17mDYP0ABqawwx+U/u+Ybfn6b3j9Bi7ZcmDziP5MavYpPwOU/4fGYsxAJ4z/wX0nvfp/jP10Fdxk1Yd0/KpZ0Od0X2T8Y4wY2w1/dPzmJhjt2Y94/VzcWubde0j8h5fBALCHiP6IXotSE2eM/APBRsotO4z82TW+IDGHsPxwIfoP9/+I/zyt5T9al4j80bUGOUnzmP8C1wn5rd+U/qxyaWFla5D9ZhkeHpjPCP15JgmeRq8c/xB6/xIYZzD9cAUEqORfMP3/vVItzuNI/9OSOAz1R3T9sslyXOZXjPzI5xEx+T9M/iWs9sfLOyz/4o7KSBqvfPwEaQyvYhOE/EpEa4VOV3T/zjPcBtyHkP/OXznt+n+I/", 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Asia · %{customdata[4]}
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", 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Africa · %{customdata[4]}
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South America · %{customdata[4]}
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North America · %{customdata[4]}
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\"#e87ba4\",\n", + " \"Oceania\": \"#4a3aa7\",\n", + "}\n", + "\n", + "def fit_sphere(points: NDArray[np.float64]) -> tuple[NDArray[np.float64], float]:\n", + " \"\"\"Least-squares sphere through the points, as (centre, radius).\"\"\"\n", + " design = np.c_[2 * points, np.ones(len(points))]\n", + " solution, *_ = np.linalg.lstsq(design, (points**2).sum(axis=1), rcond=None)\n", + " centre = solution[:3]\n", + " radius = float(np.sqrt(solution[3] + centre @ centre))\n", + " return centre, radius\n", + "\n", + "\n", + "def globe_coords(coordinates: NDArray[np.float64]) -> NDArray[np.float64]:\n", + " \"\"\"Map (lat, lon) in degrees onto the unit sphere, z pointing at the North Pole.\"\"\"\n", + " lat, lon = np.radians(coordinates[:, 0]), np.radians(coordinates[:, 1])\n", + " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=1)\n", + "\n", + "\n", + "spherical = models[\"spherical\"]\n", + "\n", + "# The template's own sphere. Classical MDS centres the embedding on the centroid of the sampled\n", + "# cities, which is not the centre of the globe, so the fitted centre is offset from the origin.\n", + "centre, radius = fit_sphere(spherical.Y_)\n", + "proj = spherical.transform(X_scaled) - centre\n", + "\n", + "# An SMDS embedding is only defined up to an isometry: rotations and reflections leave every pairwise\n", + "# distance untouched, so the raw axes point in arbitrary directions and the globe comes out mirrored.\n", + "# Procrustes onto the true coordinates of the *training* cities puts the cloud in the Earth frame --\n", + "# z through the North Pole, x through (0 N, 0 E) -- which is what makes the picture readable.\n", + "rotation, _ = orthogonal_procrustes(proj[idx_train], globe_coords(y[idx_train]))\n", + "proj = proj @ rotation\n", + "\n", + "print(f\"template sphere: fitted centre {np.round(centre, 3)}, radius {radius:.3f}\")\n", + "print(f\"alignment to the Earth frame: {'reflection + rotation' if np.linalg.det(rotation) < 0 else 'rotation'}\")\n", + "\n", + "plot_df = df.assign(\n", + " x=proj[:, 0],\n", + " y_=proj[:, 1],\n", + " z=proj[:, 2],\n", + " split=np.where(np.isin(np.arange(len(df)), idx_train), \"train\", \"held-out\"),\n", + ")\n", + "\n", + "u, v = np.mgrid[0 : 2 * np.pi : 60j, 0 : np.pi : 30j]\n", + "fig = go.Figure(\n", + " go.Surface(\n", + " x=radius * np.cos(u) * np.sin(v),\n", + " y=radius * np.sin(u) * np.sin(v),\n", + " z=radius * np.cos(v),\n", + " colorscale=[[0, \"#9aa0a6\"], [1, \"#9aa0a6\"]],\n", + " opacity=0.12,\n", + " showscale=False,\n", + " hoverinfo=\"skip\",\n", + " name=\"template sphere\",\n", + " )\n", + ")\n", + "\n", + "# One city is called out on its own so it can be found instantly among several thousand markers.\n", + "HIGHLIGHT_CITY = \"Lisbon\"\n", + "highlight = plot_df[plot_df[\"city\"] == HIGHLIGHT_CITY]\n", + "\n", + "for continent, group in plot_df[plot_df[\"city\"] != HIGHLIGHT_CITY].groupby(\"continent\", sort=False):\n", + " for split, marker in ((\"train\", \"circle\"), (\"held-out\", \"diamond-open\")):\n", + " subset = group[group[\"split\"] == split]\n", + " if subset.empty:\n", + " continue\n", + " fig.add_trace(\n", + " go.Scatter3d(\n", + " x=subset[\"x\"],\n", + " y=subset[\"y_\"],\n", + " z=subset[\"z\"],\n", + " mode=\"markers\",\n", + " name=f\"{continent} ({split})\",\n", + " legendgroup=continent,\n", + " marker=dict(size=2, symbol=marker, color=CONTINENT_COLORS[continent], line=dict(width=0)),\n", + " customdata=np.stack(\n", + " [subset[\"city\"], subset[\"country\"], subset[\"lat\"], subset[\"lon\"], subset[\"split\"]], axis=1\n", + " ),\n", + " hovertemplate=(\n", + " \"%{customdata[0]}, %{customdata[1]}
\"\n", + " + continent\n", + " + \" · %{customdata[4]}
\"\n", + " \"lat %{customdata[2]:.2f}, lon %{customdata[3]:.2f}\"\n", + " ),\n", + " )\n", + " )\n", + "\n", + "# Scatter3d offers no star glyph, so the call-out is a heavy \"x\" in ink rather than a series colour.\n", + "fig.add_trace(\n", + " go.Scatter3d(\n", + " x=highlight[\"x\"],\n", + " y=highlight[\"y_\"],\n", + " z=highlight[\"z\"],\n", + " mode=\"markers+text\",\n", + " name=HIGHLIGHT_CITY,\n", + " marker=dict(size=5, symbol=\"x\", color=\"#111827\", line=dict(width=1, color=\"#ffffff\")),\n", + " text=highlight[\"city\"],\n", + " textposition=\"top center\",\n", + " textfont=dict(size=13, color=\"#111827\"),\n", + " customdata=np.stack(\n", + " [highlight[\"city\"], highlight[\"country\"], highlight[\"lat\"], highlight[\"lon\"], highlight[\"split\"]], axis=1\n", + " ),\n", + " hovertemplate=(\n", + " \"%{customdata[0]}, %{customdata[1]}
\"\n", + " \"%{customdata[4]}
\"\n", + " \"lat %{customdata[2]:.2f}, lon %{customdata[3]:.2f}\"\n", + " ),\n", + " )\n", + ")\n", + "\n", + "fig.update_layout(\n", + " title=\"Spherical SMDS embedding of city activations (aligned to the Earth frame)\",\n", + " template=\"plotly_white\",\n", + " height=820,\n", + " margin=dict(l=0, r=0, t=60, b=0),\n", + " # Small legend floated inside the plotting area, so the globe keeps the full width of the figure\n", + " # instead of being squeezed into whatever a legend sitting beside it leaves over.\n", + " legend=dict(\n", + " itemsizing=\"constant\",\n", + " title=dict(text=\"Continent · split\", font=dict(size=10)),\n", + " font=dict(size=9),\n", + " itemwidth=30,\n", + " xanchor=\"right\",\n", + " x=1,\n", + " yanchor=\"top\",\n", + " y=1,\n", + " bgcolor=\"rgba(255,255,255,0.6)\",\n", + " ),\n", + " scene=dict(\n", + " xaxis_title=\"toward 0°N 0°E\",\n", + " yaxis_title=\"toward 0°N 90°E\",\n", + " zaxis_title=\"toward the North Pole\",\n", + " aspectmode=\"data\",\n", + " domain=dict(x=[0, 1], y=[0, 1]),\n", + " camera=dict(eye=dict(x=1.05, y=1.05, z=0.85)), # default eye ~1.25 each; closer = bigger globe\n", + " ),\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7a12637d", + "metadata": {}, + "source": [ + "### Stress comparison\n", + "\n", + "The figure rests on one split; the numbers should not, so each geometry is refitted across `N_FOLDS`\n", + "continent-stratified folds and scored on the fold it did not see.\n", + "\n", + "`SupervisedMDS.score` returns $1 - \\text{stress}$, so the table reports **stress** directly — **lower is\n", + "better** — as both a metric (*scale-normalised*) and a rank-only (*non-metric*) measure.\n", + "\n", + "> Only the held-out columns carry evidence: with far more features than cities, the ridge fit drives\n", + "> train stress to ~0 for every geometry. The two curved templates finish close together and well ahead\n", + "> of the flat one, which is the takeaway — the model wraps longitude." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3ef165f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best geometry over 20 folds: cylindrical\n", + "Ahead of spherical by 0.004, against a fold-to-fold sd of 0.013\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def stress_scorer(metric: str):\n", + " \"\"\"Scorer reporting stress directly: SMDS returns 1 - stress, and the pipeline's scaler is\n", + " applied by hand because `Pipeline.score` does not forward the `metric` argument.\"\"\"\n", + "\n", + " def scorer(estimator, X_eval: NDArray[np.float32], y_eval: NDArray[np.float64]) -> float:\n", + " return 1 - estimator[-1].score(estimator[:-1].transform(X_eval), y_eval, metric=metric)\n", + "\n", + " return scorer\n", + "\n", + "\n", + "cv = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED)\n", + "folds = list(cv.split(X, df[\"continent\"])) # materialised so every geometry sees the same folds\n", + "\n", + "scoring = {\n", + " \"stress\": stress_scorer(\"scale_normalized_stress\"),\n", + " \"nm_stress\": stress_scorer(\"non_metric_stress\"),\n", + "}\n", + "\n", + "rows = []\n", + "for name, model in models.items():\n", + " # The scaler sits inside the pipeline, so each fold refits the centring on its own training\n", + " # cities. N_JOBS folds run at once -- see the memory note where it is defined.\n", + " cv_results = cross_validate(\n", + " make_pipeline(StandardScaler(), model),\n", + " X,\n", + " y,\n", + " cv=folds,\n", + " scoring=scoring,\n", + " return_train_score=True,\n", + " n_jobs=N_JOBS,\n", + " pre_dispatch=\"n_jobs\", # queue one fold per worker; the default 2x doubles peak memory\n", + " )\n", + "\n", + " rows.append(\n", + " {\n", + " \"geometry\": name,\n", + " \"n_components\": model.parametrization_fitted_.n_components,\n", + " \"train stress\": cv_results[\"train_stress\"].mean(),\n", + " \"held-out stress\": cv_results[\"test_stress\"].mean(),\n", + " \"held-out stress (sd)\": cv_results[\"test_stress\"].std(),\n", + " \"held-out non-metric stress\": cv_results[\"test_nm_stress\"].mean(),\n", + " }\n", + " )\n", + "\n", + "stress_df = pd.DataFrame(rows).set_index(\"geometry\")\n", + "\n", + "best = stress_df[\"held-out stress\"].idxmin()\n", + "runner_up = stress_df[\"held-out stress\"].drop(best).idxmin()\n", + "gap = stress_df.loc[runner_up, \"held-out stress\"] - stress_df.loc[best, \"held-out stress\"]\n", + "print(f\"Best geometry over {N_FOLDS} folds: {best}\")\n", + "print(f\"Ahead of {runner_up} by {gap:.3f}, against a fold-to-fold sd of {stress_df.loc[best, 'held-out stress (sd)']:.3f}\\n\")\n", + "\n", + "stress_df.style.format({col: \"{:.3f}\" for col in stress_df.columns if col != \"n_components\"}).background_gradient(\n", + " cmap=\"RdYlGn_r\", subset=[\"held-out stress\", \"held-out non-metric stress\"]\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "smds (3.13.11.final.0)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/notebooks/emergence_of_color_manifold_in_VLM.ipynb b/docs/notebooks/emergence_of_color_manifold_in_VLM.ipynb deleted file mode 100644 index a1a2c1e..0000000 --- a/docs/notebooks/emergence_of_color_manifold_in_VLM.ipynb +++ /dev/null @@ -1,2090 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "86913566", - "metadata": {}, - "source": [ - "# Emergence of the Color Manifold in Qwen2.5-VL\n", - "\n", - "## Neural Scan: From Raw Activations to Causal Geometry\n", - "\n", - "This notebook investigates whether Qwen2.5-VL learns a **circular hue manifold** as an internal abstraction. We perform a layer-wise neural scan, compare manifold hypotheses, and test causal steering in latent space.\n", - "\n", - "### Design note \n", - "We used:\n", - "- Multiple dataset contexts (`qwen_full_scan.pkl` and `qwen_wild_scan.pkl`)\n", - "- Cross-validated shape ranking (`discover_manifolds`)\n", - "- Aggregated summary across runs (mean and consistency)\n", - "- Geometry-aware arbitration for periodic variables (hue seam closure)\n", - "\n", - "The goal is not only best numeric score, but a topology that is faithful to full 0-360 hue periodicity." - ] - }, - { - "cell_type": "markdown", - "id": "6cd80b8b", - "metadata": {}, - "source": [ - "## 1) Environment Setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "e3c70e31", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Repository root: /Users/vinayakjoshi/Documents/Masters/Sem_3/DASP/Code\n" - ] - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "import colorsys\n", - "import re\n", - "import sys\n", - "from pathlib import Path\n", - "\n", - "import joblib\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa: F401\n", - "from PIL import Image\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "\n", - "def find_repo_root(start: Path) -> Path:\n", - " for candidate in [start, *start.parents]:\n", - " if (candidate / \"supervised-multidimensional-scaling\" / \"smds\").exists():\n", - " return candidate\n", - " raise FileNotFoundError(\"Could not locate repository root containing supervised-multidimensional-scaling/smds\")\n", - "\n", - "repo_root = find_repo_root(Path.cwd().resolve())\n", - "smds_root = repo_root / \"supervised-multidimensional-scaling\"\n", - "if str(smds_root) not in sys.path:\n", - " sys.path.insert(0, str(smds_root))\n", - "\n", - "from smds import ComputedSMDSParametrization, SupervisedMDS, UserProvidedSMDSParametrization\n", - "from smds.pipeline.discovery_pipeline import discover_manifolds\n", - "import smds.shapes.continuous_shapes as shapes\n", - "\n", - "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", - "print(f\"Repository root: {repo_root}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "9c1a4772", - "metadata": {}, - "source": [ - "## 2) Data Preview: Simple vs Wild Color Stimuli\n", - "\n", - "Before touching activations, we visualize source images for both scans:\n", - "- `qwen_full_scan.pkl`: simple color stimuli\n", - "- `qwen_wild_scan.pkl`: in-the-wild textured stimuli\n", - "\n", - "This gives the reader immediate visual intuition for the two regimes." - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "1fddbe43", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def parse_hue_from_name(path: Path) -> int:\n", - " match = re.search(r\"hue_(\\d+)\", path.stem)\n", - " return int(match.group(1)) if match else 10**9\n", - "\n", - "def collect_images(dir_candidates: list[Path], max_images: int = 12) -> list[Path]:\n", - " for directory in dir_candidates:\n", - " if directory.exists():\n", - " images = sorted(directory.glob(\"hue_*.png\"), key=parse_hue_from_name)\n", - " if images:\n", - " return images[:max_images]\n", - " return []\n", - "\n", - "simple_candidates = [\n", - " repo_root / \"experiments\" / \"vlm_color\" / \"color_manifold\",\n", - " repo_root / \"experiments\" / \"Qwen\" / \"color_manifold\",\n", - "]\n", - "wild_candidates = [\n", - " repo_root / \"experiments\" / \"vlm_color\" / \"wild_manifold\",\n", - " repo_root / \"experiments\" / \"Qwen\" / \"wild_manifold\",\n", - "]\n", - "\n", - "simple_imgs = collect_images(simple_candidates, max_images=12)\n", - "wild_imgs = collect_images(wild_candidates, max_images=12)\n", - "\n", - "if not simple_imgs and not wild_imgs:\n", - " print(\"No image folders found for preview. Continuing with scan data only.\")\n", - "else:\n", - " n_cols = 6\n", - " fig, axes = plt.subplots(4, n_cols, figsize=(14, 7), dpi=130)\n", - " axes = np.asarray(axes).reshape(4, n_cols)\n", - "\n", - " def render_row(row_axes: np.ndarray, image_paths: list[Path], title: str) -> None:\n", - " for idx, ax in enumerate(row_axes):\n", - " if idx < len(image_paths):\n", - " with Image.open(image_paths[idx]) as img:\n", - " ax.imshow(np.asarray(img))\n", - " ax.set_title(f\"{parse_hue_from_name(image_paths[idx]):03d}\", fontsize=8)\n", - " ax.axis(\"off\")\n", - " row_axes[0].text(\n", - " -0.12,\n", - " 0.5,\n", - " title,\n", - " transform=row_axes[0].transAxes,\n", - " rotation=90,\n", - " va=\"center\",\n", - " ha=\"center\",\n", - " fontsize=10,\n", - " weight=\"bold\",\n", - " )\n", - "\n", - " render_row(axes[0], simple_imgs[:6], \"Simple\")\n", - " render_row(axes[1], simple_imgs[6:12], \"Simple\")\n", - " render_row(axes[2], wild_imgs[:6], \"Wild\")\n", - " render_row(axes[3], wild_imgs[6:12], \"Wild\")\n", - "\n", - " plt.suptitle(\"Qwen Color Stimuli (Simple vs Wild)\", fontsize=13, weight=\"bold\", y=1.02)\n", - " plt.tight_layout()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "69f81896", - "metadata": {}, - "source": [ - "## 3) Load Both Scans with `joblib`\n", - "\n", - "We load both files explicitly and keep them as separate experimental domains." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "f30117d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datasetfilen_samplesembedding_dimn_layersmin_layermax_layerangle_minangle_max
0full_simple/Users/vinayakjoshi/Documents/Masters/Sem_3/DA...602048280270.0354.0
1wild_textured/Users/vinayakjoshi/Documents/Masters/Sem_3/DA...3602048280270.0359.0
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" - ], - "text/plain": [ - " dataset file \\\n", - "0 full_simple /Users/vinayakjoshi/Documents/Masters/Sem_3/DA... \n", - "1 wild_textured /Users/vinayakjoshi/Documents/Masters/Sem_3/DA... \n", - "\n", - " n_samples embedding_dim n_layers min_layer max_layer angle_min \\\n", - "0 60 2048 28 0 27 0.0 \n", - "1 360 2048 28 0 27 0.0 \n", - "\n", - " angle_max \n", - "0 354.0 \n", - "1 359.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def resolve_scan_path(filename: str) -> Path:\n", - " candidates = [\n", - " repo_root / \"experiments\" / \"vlm_color\" / filename,\n", - " repo_root / \"experiments\" / \"Qwen\" / filename,\n", - " ]\n", - " for candidate in candidates:\n", - " if candidate.exists():\n", - " return candidate\n", - " checked = \"\\n\".join(str(path) for path in candidates)\n", - " raise FileNotFoundError(f\"Missing required scan file {filename}. Checked:\\n{checked}\")\n", - "\n", - "scan_files = {\n", - " \"full_simple\": \"qwen_full_scan.pkl\",\n", - " \"wild_textured\": \"qwen_wild_scan.pkl\",\n", - "}\n", - "\n", - "scans = {}\n", - "for tag, filename in scan_files.items():\n", - " path = resolve_scan_path(filename)\n", - " payload = joblib.load(path)\n", - " layer_data = payload[\"layer_data\"]\n", - " layers = sorted(int(layer) for layer in layer_data.keys())\n", - " angles_deg = np.asarray(payload[\"angles\"], dtype=np.float64)\n", - " hue_norm = (angles_deg % 360.0) / 360.0\n", - "\n", - " scans[tag] = {\n", - " \"path\": path,\n", - " \"payload\": payload,\n", - " \"layer_data\": layer_data,\n", - " \"layers\": layers,\n", - " \"angles_deg\": angles_deg,\n", - " \"hue_norm\": hue_norm,\n", - " }\n", - "\n", - "summary_rows = []\n", - "for tag, bundle in scans.items():\n", - " sample = np.asarray(bundle[\"layer_data\"][bundle[\"layers\"][0]], dtype=np.float64)\n", - " summary_rows.append(\n", - " {\n", - " \"dataset\": tag,\n", - " \"file\": str(bundle[\"path\"]),\n", - " \"n_samples\": int(bundle[\"angles_deg\"].shape[0]),\n", - " \"embedding_dim\": int(sample.shape[1]),\n", - " \"n_layers\": int(len(bundle[\"layers\"])),\n", - " \"min_layer\": int(min(bundle[\"layers\"])),\n", - " \"max_layer\": int(max(bundle[\"layers\"])),\n", - " \"angle_min\": float(bundle[\"angles_deg\"].min()),\n", - " \"angle_max\": float(bundle[\"angles_deg\"].max()),\n", - " }\n", - " )\n", - "\n", - "summary_df = pd.DataFrame(summary_rows)\n", - "display(summary_df)\n" - ] - }, - { - "cell_type": "markdown", - "id": "96d70fb0", - "metadata": {}, - "source": [ - "## 4) Helper Functions for Neural Scan and Topology Arbitration\n", - "\n", - "Topological stress is computed as `1 - score`.\n", - "For periodic variables, we also evaluate **seam closure**: the distance between low-hue and high-hue endpoints in learned coordinates, normalized by local neighbor spacing." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "6bdf8b0d", - "metadata": {}, - "outputs": [], - "source": [ - "METRIC_COL = \"mean_scale_normalized_stress\"\n", - "CANDIDATE_SHAPES = [shapes.CircularShape(), shapes.SemicircularShape(), shapes.EuclideanShape()]\n", - "SHAPE_FACTORIES = {\n", - " \"CircularShape\": lambda: shapes.CircularShape(),\n", - " \"SemicircularShape\": lambda: shapes.SemicircularShape(),\n", - " \"EuclideanShape\": lambda: shapes.EuclideanShape(),\n", - "}\n", - "\n", - "def fit_shape_smds(X: np.ndarray, y: np.ndarray, shape_obj) -> SupervisedMDS:\n", - " stage_1 = ComputedSMDSParametrization(manifold=shape_obj, n_components=2)\n", - " model = SupervisedMDS(stage_1=stage_1, alpha=1e-4)\n", - " model.fit(X, y)\n", - " return model\n", - "\n", - "def run_layerwise_circular_stress(layer_data: dict, layers: list[int], hue_norm: np.ndarray) -> pd.DataFrame:\n", - " records = []\n", - " for layer in layers:\n", - " X_layer = np.asarray(layer_data[layer], dtype=np.float64)\n", - " model = fit_shape_smds(X_layer, hue_norm, shapes.CircularShape())\n", - " score = float(model.score(X_layer, hue_norm))\n", - " records.append({\"layer\": layer, \"score\": score, \"stress\": 1.0 - score})\n", - " return pd.DataFrame(records).sort_values(\"layer\").reset_index(drop=True)\n", - "\n", - "def periodic_train_test_split(\n", - " angles_deg: np.ndarray,\n", - " test_fraction: float = 0.2,\n", - " offset: int = 0,\n", - ") -> tuple[np.ndarray, np.ndarray]:\n", - " \"\"\"Deterministic split preserving coverage around the hue circle.\"\"\"\n", - " angles = np.asarray(angles_deg, dtype=np.float64)\n", - " if angles.ndim != 1:\n", - " raise ValueError(\"angles_deg must be a 1D array\")\n", - "\n", - " n = angles.shape[0]\n", - " if n < 8:\n", - " raise ValueError(f\"Not enough samples for stable split: n={n}\")\n", - "\n", - " order = np.argsort(angles)\n", - " step = max(int(round(1.0 / test_fraction)), 2)\n", - "\n", - " test_mask = np.zeros(n, dtype=bool)\n", - " test_mask[order[offset::step]] = True\n", - "\n", - " if test_mask.sum() == 0 or test_mask.sum() >= n:\n", - " test_mask[:] = False\n", - " test_mask[order[::step]] = True\n", - " if test_mask.sum() >= n:\n", - " test_mask[order[-1]] = False\n", - "\n", - " train_idx = np.where(~test_mask)[0]\n", - " test_idx = np.where(test_mask)[0]\n", - " return train_idx, test_idx\n", - "\n", - "def discover_on_train(X_train: np.ndarray, y_train: np.ndarray, label: str) -> pd.DataFrame:\n", - " n_folds = min(5, max(2, int(X_train.shape[0] // 10)))\n", - " results_df, _ = discover_manifolds(\n", - " X=X_train,\n", - " y=y_train,\n", - " shapes=CANDIDATE_SHAPES,\n", - " n_folds=n_folds,\n", - " n_jobs=1,\n", - " save_results=False,\n", - " create_png_visualization=False,\n", - " clear_cache=True,\n", - " experiment_name=f\"qwen_color_train_{label}\",\n", - " )\n", - " if results_df.empty or METRIC_COL not in results_df.columns:\n", - " raise RuntimeError(f\"Discovery failed for {label}.\")\n", - " return results_df.sort_values(METRIC_COL, ascending=False).reset_index(drop=True)\n", - "\n", - "def seam_closure_ratio(coords: np.ndarray, angles_deg: np.ndarray) -> float:\n", - " angles = np.asarray(angles_deg, dtype=np.float64)\n", - " unique = np.unique(np.sort(angles))\n", - " step = float(np.median(np.diff(unique))) if unique.size > 1 else 1.0\n", - " low_value = float(unique.min())\n", - " high_value = float(unique.max())\n", - "\n", - " low_idx = np.where(np.abs(angles - low_value) <= step / 2.0 + 1e-9)[0]\n", - " high_idx = np.where(np.abs(angles - high_value) <= step / 2.0 + 1e-9)[0]\n", - "\n", - " low_center = coords[low_idx].mean(axis=0)\n", - " high_center = coords[high_idx].mean(axis=0)\n", - " seam_distance = float(np.linalg.norm(low_center - high_center))\n", - "\n", - " order = np.argsort(angles)\n", - " sorted_coords = coords[order]\n", - " local_steps = np.linalg.norm(sorted_coords[1:] - sorted_coords[:-1], axis=1)\n", - " local_steps = local_steps[local_steps > 0]\n", - " local_scale = float(np.median(local_steps)) if local_steps.size > 0 else 1.0\n", - "\n", - " return seam_distance / (local_scale + 1e-12)\n", - "\n", - "def hue_coverage_ratio(angles_deg: np.ndarray) -> float:\n", - " unique = np.unique(np.sort(np.asarray(angles_deg, dtype=np.float64)))\n", - " if unique.size <= 1:\n", - " return 0.0\n", - " step = float(np.median(np.diff(unique)))\n", - " span = float(unique.max() - unique.min() + step)\n", - " return span / 360.0\n", - "\n", - "def periodicity_score(closure_ratio: float) -> float:\n", - " return float(np.exp(-closure_ratio))\n" - ] - }, - { - "cell_type": "markdown", - "id": "c9543f1c", - "metadata": {}, - "source": [ - "## 5) Layer-wise Stress Scan for Both Domains\n", - "\n", - "We run the circular stress scan independently on `full_simple` and `wild_textured`." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "6f2a7131", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datasetoptimal_layermin_stressmax_score
0full_simple270.0000060.999994
1wild_textured270.0000010.999999
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" - ], - "text/plain": [ - " dataset optimal_layer min_stress max_score\n", - "0 full_simple 27 0.000006 0.999994\n", - "1 wild_textured 27 0.000001 0.999999" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "stress_tables = {}\n", - "optimal_rows = []\n", - "\n", - "for dataset_name, bundle in scans.items():\n", - " stress_df = run_layerwise_circular_stress(\n", - " layer_data=bundle[\"layer_data\"],\n", - " layers=bundle[\"layers\"],\n", - " hue_norm=bundle[\"hue_norm\"],\n", - " )\n", - " stress_tables[dataset_name] = stress_df\n", - " best_idx = int(stress_df[\"stress\"].idxmin())\n", - " optimal_rows.append(\n", - " {\n", - " \"dataset\": dataset_name,\n", - " \"optimal_layer\": int(stress_df.loc[best_idx, \"layer\"]),\n", - " \"min_stress\": float(stress_df.loc[best_idx, \"stress\"]),\n", - " \"max_score\": float(stress_df.loc[best_idx, \"score\"]),\n", - " }\n", - " )\n", - "\n", - "optimal_layers_df = pd.DataFrame(optimal_rows).sort_values(\"dataset\").reset_index(drop=True)\n", - "display(optimal_layers_df)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "d5301574", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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KCQkJ8rSy6dOn44knngAA+Y/a6wUEBGD48OFYt24dtm3bJv/x3aFDB3kRZwBo06aN/PXvv/+O8PBwAKY1d86ePYu2bdtCp9PZ7N9ecWfFihWV9v/WW2/F0qVLkZGRgbVr1wIA+vXrh7i4OGg0GmRlZWH16tUATE92U6vVle6rvLwcp06dwunTp9G6dWv5j/j09HS89tpr2LdvH7Zs2YLMzEy0bt1a/rnx48dbFVdOnDiBoKCgSqd7mY0YMQLvvvsuSktLsWrVKtx///1WBZDMzEysX78eeXl5SE5OtruQuz32MoyIiJCv819//SUX40pLS/HQQw/hhhtuwNChQ61GswGm62VWVFRkdwqdPT/99BOOHTsGwFRIGzlypNzuaN/tKSwslEefrVq1yqYwYjl6zLz4uWVbZSO6fHx8bNYhc+ReTk9Px6lTp1BcXIzZs2cDMBWoli5diuXLlyM1NRU7d+7EqFGjcPr0aZw5cwZ+fn547733AJiu+XvvvYcNGzbgzz//xJEjRypdYL02GThyH3z00UeoqKhAVFQUvv/+e3l/7777rt3+EBEREYtQRETUwP3111826ymZdezYEYWFhfL3O3fuxKBBg5CdnW31h+b1IybMj7HPycnBzz//DADyOk5mo0ePxhdffAEAmDFjBqZPnw6NRoPPPvsMP/30ExQKBT799FMMGTKkTud34403olmzZrh48aJcEOvbty+0Wi1iY2Oxb98+ub26qXjmP8Tz8/MRGhqK2bNnIzo6GpmZmfIT2xQKBXx8fBAQEICbb74ZO3bswNq1a9G5c2f07t0bycnJeOWVV1BUVISuXbtizZo1lU6nCw8Px6RJk/Dhhx/i/PnzePDBB/Hcc8+hXbt2OHHiBN5//3153Z3x48ejadOmtc5p5MiRcjHvzTffxGuvvYYmTZpg2bJl8pPbbrrpJgCwOs5///tfjB49GkqlEvPmzbNax6oqlvfVli1bEB0djbS0NLz//vtye033Zc+wYcMQHh6OK1euYO3atQgMDMSYMWPg7++Po0ePYsGCBfK25utuWVxLTU3F6dOnoVQqrdapsseRe/n111/H3r17oVQqcfnyZdx0000oKSmxWg/JXGibNGkSUlNTodVq8cYbbyA2Nha5ubnIycmx2dZZGThyH5ivYXp6Onbu3IlWrVrh22+/RUpKCgBOryMiIrKHRSgiImrQ5s2bV+lrn376KeLi4hAWFoasrCwcOHAAo0ePttnu+mlgvXv3Rps2beT1ZXx8fDBmzBirbbp06YL7778fq1atwr59+6yeWgcAAwcOxM0331zb05IpFAoMHToUK1euBGAqoERGRgIwjYgyH9fPzw8DBgyocl/+/v547bXX8NJLLyE7O9vuGjn333+/PBLmhRdeQFJSEnJzc/H6669bbafVavHCCy9Uu57T008/jZycHHz55ZdITk7G008/bbNN//798eKLL1a5n+rExsbikUcewddff41Tp07hscces3p9wIABuP322wGYpgm2bNkS586dw99//40+ffpAkiT4+/ujffv2chGiKoMGDcL777+PkpISbNmyBVu2bLHZpi4LVZtHD/3jH/9ASUkJvvrqK3z11Vc2202bNg3R0dEATFPlmjdvjgsXLuDQoUMYOXIkHnjgAcyaNavKYzlyL7/88st49NFHkZOTg3nz5tm8/zp37owhQ4ZAoVDgjTfewDPPPIOSkhK7C9jfdNNNVU59q00GjtwHt956K77//nsUFxfjmWeesdlvTk4OysvLazx6jYiIqCHgmlBERERVCAkJwfLlyzFgwAAEBQUhMDAQN954I9599130798fALBr1y6bUSuW6z/169cPTZo0sdn3rFmz8Pbbb6NHjx7w9/eHTqdDVFQUXnjhBSxevNhm2lNtmdeFAkyjoMzM/QeAwYMHw9fXt9p9jRkzBt988w1uvfVWtGjRAmq1Gv7+/ujRowfeeustvPbaa/K2kZGRWLNmDe666y40a9YMarUajRs3xvDhw/Gf//xHfmJeVRQKBWbOnImVK1di+PDhaNy4MVQqFQIDA9G7d2/MnTsXy5Ytq1Hfq/Pqq69i/vz5iImJgZ+fH/z9/REdHY0ZM2Zg0aJF8lRFjUaD5cuX4+abb4a/vz8CAgLkgoS5wFediIgILFu2DLGxsfD390dISAji4uKwdOlSeeTRr7/+Wqfz6devH9avX4/x48cjIiJCfvpcs2bNMHz4cHz55ZeYNGmSvL1CocBbb72FLl26wNfXF6GhofKT7qpT03s5Ojoaa9aswUMPPYSIiAjodDr4+vqiffv2eOaZZ7BixQp5RFbfvn3x/fff44477kCrVq2g0Wig0+nQqVMnzJgxA5988onTMwBqfh+8+uqrmDhxIlq0aAFfX1+0bt0aDz/8MN544w0AQFlZmbwOGxEREZkoJEce40JEREQ1smTJEnzwwQcAgAULFtgdQUVERERE1JBwfDAREZGT5ObmwmAw4O+//8bSpUsBmBY8Hjp0qIt7RkRERETkeixCEREROcnPP/+Mf/3rX1ZtkyZNsvuUOyIiIiKihoZFKCIiIidp27YtQkJCUFxcjJYtW2L8+PF45JFHXN0tIiIiIiK3wDWhiIiIiIiIiIhIOD4dj4iIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioiIiIiIiIiIhGMRioi83sMPP4zo6Gi7/82ZM6dG+zh37hyio6ORkJAAAHj55Zfx8MMPV/kze/bswSOPPILY2Fh0794do0ePxmeffQaDwWDVt5dffrn2J+dgv4mIiMi9TJ48Gffee69N+/3334/o6GgkJiZatW/ZsgXR0dG4ePGi1eeRhIQEREdH49y5c5Ueq6rPHbfcckuln5eio6Or/dxTHYPBgC+//LJO+6itmmRDRPVD5eoOEBHVh5EjR+LVV1+1adfpdEKOt3fvXjzzzDOYOnUqZs2aBbVajT///BPz5s3DmTNnMG/ePADAJ598Ah8fHyF9ICIiIvfXr18/zJs3DyUlJdBqtQCAgoICHD58GM2bN8dvv/2G3r17y9sfPHgQ7dq1Q7NmzfDqq6+ioqLCKf1Ys2aNvK+kpCRMmTIF33//PZo3bw4AUKvVddr/xo0bMW/ePDz22GN17SoReTAWoYioQdBqtWjcuHG9HW/16tUYMGAAnnrqKbmtdevWKCkpwRtvvIGZM2ciKCgIISEh9dYnIiIicj99+/aFwWDAkSNH0KtXLwCmf8wKCgrCPffcg23btmHGjBny9gcOHED//v0BAIGBgU7rR2hoqPx1cHCw3Oasz0+SJDllP0Tk2Tgdj4gI9oen12TKXWUUCgWOHz+OixcvWrWPHTsWmzZtgp+fn81x165di2HDhmHz5s245ZZb0K1bNzz++OPIzMzEnDlz0KtXL/Tr1w9Lliyx6uM///lPzJ07F7Gxsejbty/eeecdlJWVVdq3//73vxg5ciS6deuGkSNH4quvvoLRaKzVeRIREVHdmEc1/fnnn3Lb7t270a9fPwwcOBDJycm4dOkSACA/Px8nTpzAgAEDAFT9WaWsrAxz585F3759ERcXh/fff79O/7+XJAlLly7FkCFD0L17d4wdOxbr16+XX3/rrbcQExOD8+fPAwCKi4sxYsQIPPPMM1i7di1mzpwJAPIyAZ988gluueUWq2OsXbsW0dHR8ve33HIL5s6di9tuuw3x8fHYv39/tf0ATKPF7rnnHnTr1g133HEHjh8/XuvzJiLnYhGKiEiAxx57DFlZWRgyZAgeeeQRLFq0CAkJCVCr1YiMjIRKZX8g6oULF7Bq1SosXrwYX3zxBY4cOYIxY8ZApVLhu+++w/jx4/HBBx/gxIkT8s9s27YNGRkZWLVqFd5++2388MMPla519e2332L+/PmYPHkyNm3ahOnTp2Pp0qVYsGCBkByIiIioen379kVSUpL8/Z49ezBw4EB07doVISEh2L17NwDgjz/+gI+Pj9X0vMq8/fbb2Lx5M9555x2sWrUKGRkZOHjwYK37+OGHH+I///kP/vWvf2HDhg145JFH8MYbb+Cbb74BALz44oto2rQpXn/9dQDAvHnzUFhYiHnz5uG2227DK6+8Ip9bTExMjY+7atUq/Otf/8KyZcvQs2fPavuRnp6OiRMnolOnTli3bh3+8Y9/4NNPP631eRORc3E6HhE1CBs2bMDWrVut2mJiYrB8+XIhx4uJicG6devw1VdfYdeuXfLC4E2aNMGsWbMwdOhQuz9nMBjw2muvISoqCoDpQ+mhQ4fw4osvQqFQ4Omnn8ann36KkydPytsEBwfjvffeg06nQ1RUFC5duoQ5c+bghRdesNn/4sWL8fTTT2PUqFEAgFatWkGv1+PNN9/EtGnT4OvrKyIOIiIiqkLfvn0xd+5cSJKEU6dO4eLFi+jfvz+USiX69u2L3bt3Y9y4cThw4ABiYmLkEdWV0ev1WLt2LWbNmoXBgwcDAObOnVvrB5UUFRXhyy+/xLvvvoubb74ZgGmZgfPnz+Pzzz/Hgw8+CK1Wi/feew/jx4/HK6+8gnXr1uGLL75Ao0aNAFybOujo9L7BgwejX79+Ne7Hd999h/DwcMyaNQs+Pj6IjIzEhQsX5PU4ici1WIQiogbhlltusVpPAYC8+KcokZGReOuttwAAqamp2LNnD77++mtMmzbNZri5pbZt28pf63Q6tGzZEgqFAgDkIlFpaam8zY033mi1wHpMTAwMBgPOnDkjf/ADgOzsbFy8eBEff/wxFi1aJLcbjUaUlpbi3LlziIyMdMKZE1FDkZ2djfvuuw9vv/024uPjnbLPxMREvPfee0hJSUFQUBAeeOABPP30007ZN5G76tu3L3Jzc3H69Gns2bMHHTt2lIs1AwYMwAcffADANM3s+ils9pw5cwYGgwE33nij3Obr64tOnTrVqn8pKSkoLS3FSy+9JE+rA4Dy8nKUlZXJi6rfeOON8j+YPfroo+jTp0+tjmepTZs2DvXjxIkT6Ny5s9WDX3r27FnnfhCRc7AIRUQNgr+/v9WHGHuuXzDTYDDU6lhFRUX48MMPMW7cOHTs2BEAEBERgYiICIwaNQqDBg3Cnj17Ki1CXf/0GaWy6pnT129vXu/h+qfumdtnzpwp/4uiJfPTb4iIauKPP/7Ayy+/jLNnzzptn6dOncJTTz2FWbNmyeu4PProo2jTpg1GjBjhtOMQuZsmTZqgffv2SEpKwp49e+Q1nwBTEerVV1/FX3/9hWPHjuG1116r9XEqWw6gOubPSB999BHatWtn87pGo5G/Pnr0KFQqFRISElBWVmb1WmX7NSsvL7fZxvIfDWvaj+v3W9vzJiLn45pQREQwFXIKCgqs2mr7h5VWq8X69euxevVqm9d0Oh1UKhXCwsJqtW97jh49avV45qSkJOh0OqsRVQAQFhaGsLAwnD17Fm3atJH/O3r0KD766COn9YeIvN+6deswY8YMPPfcczav7d27F3fffTfi4uJw++232ywYXJX//Oc/GDJkCO68804oFAp07NgRq1evRmxsrDO7T+SWzOtC/fHHH1ZFqGbNmqF9+/ZYvXo1/P390aVLl2r3FRkZCV9fX/zxxx9yW3l5OZKTk2vVt3bt2kGlUiEjI8PqM8SuXbvw+eefy/9gtnr1avz+++/4/PPP5dHXZuZR3WZqtRp6vd6qYJSWllbnfnTq1AlHjhyxekjLkSNHanXeROR8LEIREcE0THvv3r349ddfkZ6ejoULF1ot/u0IpVKJGTNmYNWqVZg1axYOHz6Mc+fOYe/evZg8eTKaNWvm1H/RP3/+PN544w2cOnUKP//8MxYuXIiHHnrIaooeYPrw98QTT2DFihVYsWIFzp49i+3bt+PNN9+ERqOp8l8qiYgsDRgwAD///DNuu+02q/bk5GT84x//wFNPPYWEhATMnj0bc+fOlRdVzszMRFpams1/Fy5cAAAcPnwYLVu2xD//+U/Ex8dj5MiRSExMdNoj4oncWd++fbF582YAttPHBgwYgE2bNqFfv37VjpAGAD8/Pzz00ENYuHAhtm3bhlOnTmHWrFnIzMysVd8CAwMxfvx4fPTRR/jhhx+Qnp6OdevW4b333kN4eDgAUwFp/vz5ePbZZ9GnTx+8+uqrWL58OQ4cOCD3CQD++usvlJSUoGfPnsjPz8dnn32Gc+fOYcOGDVi7dm2d+3H//fejuLgYr7zyCk6dOoUdO3ZYLUNARK7FcYlERDA9zS49PR0vvPACFAoFbrvtNjz22GNWj0t2xD333IPw8HB8/fXXePLJJ1FYWIjw8HAMGTIE7777rlPXo+rRowcUCgXGjRuHoKAgPPLII/jHP/5hd9uJEyfC19cXK1aswPz58xEWFoa77rrL7mgGIqLKVFYUWr16NYYMGYJbb70VgOkP6XvvvRfffPMNBg4ciBkzZiAxMdHm5zp27Igff/wReXl5+Prrr/Hhhx/i3XffRVJSEp5++mkEBwdzOh55vfj4eJSVlWHgwIE2/zA0YMAAfPnll+jfv3+N9/f888/D19cXb731FgoLCzFy5MgarSdVmZkzZyI0NBQLFy7EpUuX0KxZMzz77LN46qmnUFFRgRdffBFt27bFE088AQAYM2YMNm/ejJdeegnr169Hnz590L17d4wfPx7vvfceRo4cieeeew4rV67Ep59+il69euGll17CSy+9VOt+AEDTpk3x1VdfYe7cubjzzjvRvHlz/OMf/8Cbb75Z63MnIudRSNdPmCUiIo/x8ssv4/z581ixYoWru0JEDVR0dDS+/vprxMfH48knn8T+/futnrRZUVGB1q1b48cff6x2X6NGjULHjh2xYMECue3NN99Edna21bQeIiIi8kwcCUVERERETtGsWTPceeed8pNBAeDSpUs2iwRXJjIy0modF8BUxOK/mRIREXkHrglFRERERE5x9913Y+PGjdizZw+MRiNSU1Px0EMPYfny5TX6+fHjx+OXX37Bjz/+CEmScODAAWzYsAFjx44V3HMiIiKqD5yOR0RERES1ZjkdDwB27tyJhQsXIi0tDTqdDqNGjcI///nPGj/8YNeuXVi4cCHOnDmD0NBQPPHEExg/frzIUyAiIqJ6wiIUEREREREREREJx+l4REREREREREQkHItQREREREREREQkHJ+OV0/Ky8uRl5cHX19fKJWs/RERETV0RqMRpaWlCA4OhkrlGR/J+HmGiIiILDn6ecYzPvF4gby8PKSmprq6G0RERORmIiIiEBYW5upu1Ag/zxAREZE9Nf08wyJUPfH19QVgujA6nc7FvSEiIiJXKy4uRmpqqvwZwRPw8wwRERFZcvTzDItQ9cQ8ZF2n08HPz8/p+5ckCaWlpfD19YVCoXD6/hsyZisOsxWL+YrDbMVqaPl60rQ2fp7xXMxWHGYrFvMVh9mK1dDyrennGc/51EPVKikpcXUXvBazFYfZisV8xWG2YjHfhovXXhxmKw6zFYv5isNsxWK+tliEIiIiIiIiIiIi4ViEIiIiIiIiIiIi4ViE8iIajcbVXfBazFYcZisW8xWH2YrFfOsmOTkZEyZMQO/evdG/f3+8+OKLyM7OtrttYmIi7rnnHsTExGDw4MFYsmRJPffWGq+9OMxWHGYrFvMVh9mKxXxtsQjlJRQKBfz8/BrEgmf1jdmKw2zFYr7iMFuxmG/dlJSU4IknnkBMTAz27NmDjRs3Ijc3F6+88orNtqdOncJTTz2FBx54AH/++SeWLFmC5cuXY8uWLS7oOa+9SMxWHGYrFvMVh9mKxXztYxHKS0iShKKiIkiS5OqueB1mKw6zFYv5isNsxWK+dZORkYGOHTti8uTJ0Gg0aNSoEe677z4cOHDAZtv//Oc/GDJkCO68804oFAp07NgRq1evRmxsrAt6zmsvErMVh9mKxXzFYbZiMV/7VK7uADlPWVkZdDqdq7vhlZitOMxWLOYrDrMVi/nWXrt27bBs2TKrtq1bt6JLly422x4+fBj9+vXDP//5T/z+++8IDQ3FY489hvvuu6/KY0iSZPWhWqFQ2P2Q7Wg7AJSWlkKr1Va7vbOO6U7tIvctSRLKyspssvXkc3JV+/Vt5sew63Q6t+ljXc/JndrN+Wq1Wq85p9q2O3vfltl6yzm5Q7s75evsc6rpPqrCIhQRERGRl5IkCR999BF27NiBlStX2ryel5eHr7/+Gh9++CHeffddJCUl4emnn0ZwcDBGjBhR6X71ej0MBgMA03oXfn5+KC4uRllZmbyNVquFVqtFYWEhysvL5XY/Pz9oNBro9XpUVFTI7f7+/lCpVCgqKgIAefpCYGAglEol8vLyrPoQHBwMo9GIgoICuU2hUCA4OBjl5eUoLCyU2318fBAYGAiDwSDvHwBUKhUCAgJQWlpq9RhtZ56TWq1Gfn6+1Yd0V5yTWq0GABQXF8vXztPPyV2uk3m0Q0hIiNecE+A+18mcr7+/PzQajVeck7tcp+LiYvk1X19frzgnd7pO+fn5cj+VSqVXnFNl18kRColjw+pFUVER/v77b3Tq1Al+fn5O378kScjLy0NwcDDnnDoZsxWH2YrFfMVhtmI1lHxFfzbQ6/WYOXMmjh49in//+9+Ijo622WbUqFHo2LEjFixYILe9+eabyM7Oxscff1xpnzt27GjVZ2eOhMrNzbW59g3tX89F7FuSJOTn5yMoKMjmfeWp5+SqdnsjofLy8hASEmLz8+7W95qekzu1W/4/QalUesU51bZdxEgoy//fesM5uUO7O+Xr7HOyt21hYSGSk5Nr/HmGI6G8iL3h1eQczFYcZisW8xWH2YrFfOvm7NmzePLJJ9GiRQusWbMGoaGhdreLjIy0+pdUAPLIg6qYP0xf31bZtjVtlyQJOp2uxvt3xjHdrV3kvs3Tmer7uO6Ur7Par28zTx92pz462u5Ofbm+3fx7wdH9uEPfnd3u7H1f/zvXG87JHdrdKV9ntTu6j8pwYXIvoVAo5A8W5FzMVhxmKxbzFYfZisV86yYvLw+PPvooevbsic8//7zSAhQAjB8/Hr/88gt+/PFHSJKEAwcOYMOGDRg7dmw99vgaXntxmK04zFYs5isOsxWL+drHIpSXkCQJer3e4UXBqHrMVhxmKxbzFYfZisV862bt2rXIyMjATz/9hNjYWMTExMj/AUBMTAzWr18PAOjbty8WL16Mr7/+GrGxsZg5cyZeeuklDBkyxCV957UXh9mKw2zFYr7iMFuxmK99nI7nRRxdEIxqjtmKw2zFYr7iMFuxmG/tTZgwARMmTKj09aSkJKvvBw8ejMGDB4vuVo3x2ovDbMVhtmIxX3GYrVjM1xZHQnmBc5dysOzHPUg5d8XVXSEiIiIiIvI6JWUGfLMlAbv+POHqrhB5NI6E8gLLftiDP5LTsPd/KVjeJdLV3SEiIiIiIvIq/7f2N/ySmAyFAmjTPAwRzcNc3SUij8SRUF6gqNT0VJvLuXqUGjjcTwQRj84mE2YrFvMVh9mKxXwbLl57cZitOMxWLFfne/r8Ffx6IBkAIEnA/06ku7Q/zuTqbL0d87XFIpQXCA3yB6CAQqFEXkGxq7vjdRQKBTQaDZ9qIACzFYv5isNsxWK+DRevvTjMVhxmK5ar85UkCcs3/A7LtaWPnbngkr44m6uz9XbM1z6XFaGysrIwadIkxMXFIT4+HnPmzKl00a5du3Zh9OjR6NGjB0aOHIkdO3ZYvb506VIMGjQIPXr0wMMPP4zTp0/LrxUVFWHmzJmIj49HbGwsXnzxRRQWFtocIy0tDfHx8Th37pzc9sQTT1g9USYmJgbR0dF4/fXXAQBGoxExMTHo0aOH1TZFRUXOiKjGTEUoCRXGClzJ09frsRsCSZJQUFDApxoIwGzFYr7iMFuxmG/DxWsvDrMVh9mK5ep8/0w+i/+dOGfVduzMBa+43q7O1tsxX/tcVoSaPn06/Pz8sHv3bqxZswb79u3Dl19+abNdamoqpkyZgmnTpuHgwYOYMmUKpk+fjszMTADAunXrsGLFCnz++edISEhAly5dMHXqVPlCz549GxcuXMDWrVuxbds2XLhwAQsWLLA6xi+//IL7778fubm5Vu3Lli1DUlKS/N+rr76K5s2b49lnnwUApKSkwGAwIDEx0Wq7+h5yFxrsb/pCkpCTX78FsIaioqLC1V3wWsxWLOYrDrMVi/k2XLz24jBbcZitWK7K12g0YvmGvfL3AX6+AIDcgmJczMp3SZ+cjfeuWMzXlkuKUGlpaUhMTMQLL7wAnU6HVq1aYdKkSfjmm29stl23bh3i4uIwdOhQqFQq3HbbbejVqxe+/fZbAMB3332HBx54AB06dICvry+ef/55ZGRkICEhAcXFxdiwYQOmTp2KkJAQhIWFYcaMGVi7di2Ki03T1hYtWoQPPvgAzz33XJV9Pn36NGbPno0FCxagSZMmAIAjR44gOjoaGo3GyQk5JjToWtErO992lBcRERERERE5ZvuBZJy9mA0A6NC6Ce4Y3EN+zVum5BHVN5cUoU6ePImQkBA0bdpUbouMjERGRgby860ryikpKYiKirJqa9++PZKTk+2+rlarERERgeTkZKSlpcFgMFi9HhkZiZKSEqSmpgIA7rnnHmzcuBF9+/atss9vvvkm7rjjDsTFxcltR44cQWlpKcaNG4c+ffrgwQcfxJ9//ulYGE5gmo5nkpXHIhQREREREVFdlJQasPKnBPn7iWP6o0u75vL3x85kuKJbRB5P5YqDFhYWQqfTWbWZvy8qKkJQUFCV22q1Wnndpape1+tN6yNZTo8zb2teF8qyEFaZgwcP4n//+5/NND6tVotu3bph2rRpCA4OxjfffIPHH38c69evR6tWrezuS5IkqzmhCoXC7hxRR9obBZrOT6lUIju/sNr9O+OY7tYuct+SJMHf39+rzslV7de3SZIkvz/dpY91PSd3ajfnK0mS15xTbdudvW/LbL3lnNyh3Z3ydfY51XQfBPj7+1e/EdUKsxWH2YrlinzX7TokL3XS58a26NquBUrKDFAqFTAaJRw77R0joXjvisV8bbmkCOXn5ydPhzMzf3/9RdLpdCgpKbFqKykpkber6nXzH7fFxcXy9ubjBAQE1Li/3377LUaOHInGjRtbtb/88stW3z/++ONYu3Ytdu3ahYceesjuvvR6PQwGAwBAo9HIWZSVlcnbaLVaaLVaFBYWWi3W7ufnB41GA71ebzW3NNBPA0ABo9GIi1dykJeXZ2oPDIRSqZS/NwsODobRaERBQYHcplAoEBwcjPLycquF2318fBAYGAiDwWC14LpKpUJAQABKS0ut8nfWOfn7+0OtViM/P9/qQ7orz6moqMjrzsldrpNGo4HBYPCqc3K366RUKr3unLzxOvGcGt45kTWFQgG1Wu3qbnglZisOsxXLFfnm5Bdh7Q7TDBelUoFHbzfNmtFq1GjfqglOpGXi3KVc5OmLERygq2pXbo33rljM1z6XFKE6dOiA3NxcXLlyBeHh4QCAU6dOoVmzZggMDLTaNioqCkePHrVqS0lJQdeuXeV9nTx5EjfffDMAwGAwIDU1FVFRUWjbti3UajVSUlLQvXt3+TjmKXs1UV5ejl9++QWffvqpzWsffvghhg8fjs6dO8ttZWVl8PX1rXR/AQEBNguX63Q6m9FcQOVV0+sLaJIkQa3yQUVFOQpLyhEcHCy/Zv6ga0mhUECpVNq0A6YP7/ba1Wq13XZfX1+751vXczKzHBVn2f/6PCdJkpCXl4fAwECvOSczV18nSZLkP+S85ZwsufqczPn6+Ph4zTldz1XnpNFokJ+fj6CgIPmxu55+Tu50nYKCgmzy9fRzquw6kTXz7y3La0/OwWzFYbZiuSLf/2xNREmp6R8KRvbripZNGsmvdW7bHCfSTA/JSk67iPgubeulTyLw3hWL+drnkjWhIiIiEBsbi7lz50Kv1yM9PR2LFy/G3XffbbPtmDFjkJiYiM2bN6O8vBybN29GYmIixo4dCwAYN24cVq5cieTkZJSWluL9999HeHg44uLioNPpMHLkSCxYsADZ2dnIzs7GggULMGrUKGi12hr19fjx4ygtLUXPnj1tXjtx4gTmzJmDy5cvo6ysDIsWLYJer8ewYcMq3Z9CobD6z16bo+1KpRKNri5OnlNQVO32zjimu7WLPqZ5OpM3nZO7XKfK3hvu1ndPvU7m773pnNzlOlX2tSefk6vb3Snf+rpOZIvTFMVhtuIwW7HqM9+zF7OxLeEYAEDnq8b9t/ayer1zW4t1obxgSh7vXbGYry2XFKEAYOHChSgvL8eQIUNw7733YuDAgZg0aRIAICYmBuvXrwdgWkj8008/xZIlS9CrVy8sXrwYn3zyCdq2NVWc7777bjz22GOYPHky+vTpg2PHjmHJkiXysLdZs2YhIiICo0ePxogRI9CyZUu8/vrrNe5neno6goOD7f5r6rx589C6dWuMHTsW8fHxSExMxBdffIGQkJA6puO4sKuLk+uLSlBm4PB+IiIiIiIiR32xcS+MRlPh4O4hsTbT7TpFNJO/5hPyiBznsnHg4eHhWLhwod3XkpKSrL4fOHAgBg4caHdbhUKBiRMnYuLEiXZfDwgIwOzZszF79uwq+9OyZUscP37cpn3EiBEYMWKE3Z8JCQnBvHnzqtxvfTGPhAJMc5ibhtlOEyAiIiIiIiL7/nfyHA4eSwMAhIX4Y+zg7jbbhAT6oUXjYGRczsPJ9EsoNZTDV83p1UQ15bKRUORcocH+UF5d9yW7oLCarclR169VRs7DbMVivuIwW7GYb8PFay8OsxWH2YpVH/kajUYsX/+7/P3DI/tUWlwyT8mrqDDiZPol4X0TifeuWMzXFotQXiI0yB9XV5hAdh6LUM6kUJjW3eLaHc7HbMVivuIwW7GYb8PFay8OsxWH2YpVX/nu+vMkTp+/AgBoe0MYbo6NqnTbzm1byF978rpQvHfFYr72sQjlJUKD/K4+QlpCFotQTmV+Oh4XlXM+ZisW8xWH2YrFfBsuXntxmK04zFas+si31FCOrzfvl7+fOLo/lMrK/1Tu3M5icfIzGcL6JRrvXbGYr30sQnmJ0KBrj5bOKShyYU+IiIiIiIg8x4bdh3ElVw8AiO3YGj2iWlW5fYvwYHnB8uTUizAajcL7SOQtWITyEpZFKI6EIiIiIiIiql6evhjfbT8IAFAqFZgwul+1P6NQKOR1oQqLy5CemSO0j0TehEUoL2H9dDwWoYiIiIiIiKqzetsBFJcYAABDe3dEm+ZhNfq5Tm2byV8fO+O560IR1TcWobxEoJ8WWl9fAApkswjlVAqFAsHBwVxQTgBmKxbzFYfZisV8Gy5ee3GYrTjMViyR+Z6/nIvNe/8CAPhqVHhwRHyNf9Y8Egrw3CIU712xmK99LEJ5kUaBOgASsvO5JpQzSZIEo9HIBeUEYLZiMV9xmK1YzLfh4rUXh9mKw2zFEpnv15v2wWg07feum2OsljipTrsbGkOj9gEA/O2hRSjeu2IxX/tYhPIiAToNAEBfVIoyQ7mLe+NdCgoKXN0Fr8VsxWK+4jBbsZhvw8VrLw6zFYfZiiUi36OnM7D38GkAQEigH+68qYdDP69W+SCqTVMAQGZ2gbywuafhvSsW87XFIpQXMY2EMsnhaCgiIiIiIiIbkiThiw175e8fHNEbOl+Nw/vpHOH5U/KI6huLUF4kxKIIlV3AdaGIiIiIiIiu9/v/TuF4WiYAoHWzRhjWu1Ot9mO5LpSnTskjqm8sQnkRyznM2XksQjkTF5MTh9mKxXzFYbZiMd+Gi9deHGYrDrMVy5n5lhnK8eWmffL3E0b3h49P7f4s7hjRDOaueepIKN67YjFfWyxCeQmFQoGWzcIBmG5yLk7uPHyqgTjMVizmKw6zFYv5Nly89uIwW3GYrVjOzvenvX8hMysfANCtww2I7di61vvy1/kiokUYACD1QhaKSsqc0sf6wntXLOZrH4tQXkKSJATqfAGYVt7PzudIKGeRJAkGg4FPNRCA2YrFfMVhtmIx34aL114cZisOsxXLmfkWFJVg9c8H5e8njO5X5wJBp6vrQhmNEpLTLtZpX/WN965YzNc+FqG8iE597Rcoi1DOVVjIPEVhtmIxX3GYrVjMt+HitReH2YrDbMVyVr7f//IH9EWlAICbY6PQvmWTOu/T09eF4r0rFvO1xSKUF7FamJxrQhEREREREQEAMrPysWH3YQCAWuWDh27r45T9dm7HJ+QROYJFKC/ir9VArVIB4JpQREREREREZl9v3o/yciMAYOzg7mjSKNAp+20cEojGjQIAAMfTMlFeUeGU/RJ5KxahvIhKpUKjID8AnI7nbD4+Pq7ugtditmIxX3GYrVjMt+HitReH2YrDbMWqa74nz2bit6STAIAgfy3uvqWnM7olM68LVVpWjjPnrzh136Lx3hWL+dpiEcpLKBQKBAYGIjTIHwCgLypFmaHcxb3yDuZs+VQD52O2YjFfcZitWMy34eK1F4fZisNsxaprvucu5eCT73fI398/vBf8db7O6h4Az52Sx3tXLOZrH4tQXkKSJJSVlaFRoJ/clsMpeU5hzpZPNXA+ZisW8xWH2YrFfBsuXntxmK04zFas2uZbUmbAis378ex7q3HmfBYAoEXjYIzo28XpfbRcnNyTilC8d8VivvaxCOVFioqKEBrsL3+fXcApec5SVMSCnijMVizmKw6zFYv5Nly89uIwW3GYrViO5ptw9Awmv7sK323/AxUVpnWgGjcKwAsP3QqVgOlRbZqFQqdVAzAVoTyp6MB7Vyzma0vl6g6Qc4UFXStCcSQUERERERE1FBez8vDZuj04cCxVbvPxUeKum3rg3qFx0PqqhRxXqVSic0Rz/JF8FrkFxbhwJQ8tGocIORaRp2MRysuYFyYHgKw8joQiIiIiIiLvVmYox9odSfhu+x8wlF97Ol33qJZ4+s5BaNW0kfA+dG5nKkIBwN+pF1mEIqoEi1BeRKVSWU3Hy+ET8pxGpeJbRRRmKxbzFYfZisV8Gy5ee3GYrTjMVqyq8v0j+SyWrP0NF67kyW2NgvzwxNgBGNijfb0tCm29LlQGhvTqWC/HrSveu2IxX1tMxEsoFAoEBAQgLKhUbstiEcopzNmS8zFbsZivOMxWLObbcPHai8NsxWG2YlWW7+XcAiz7YQ/2Hj4ttymVCowe2A0PDO8NP62mPruJ9q2aQKlUwGiUcOy0ZyxOzntXLOZrH4tQXkKSJJSWllpNx+OaUM5hztbX15eP13QyZisW8xWH2YrFfBsuXntxmK04zFas6/M1lFdg/W//w+qfD6CktFzernPb5vjH3YMR0TzMJf3UatRo36oJTqRl4tylXOTpixEcoHNJX2qK965YzNc+Ph3Pi5SUlCBA5wu1yvTEB64J5TwlJSWu7oLXYrZiMV9xmK1YzLdukpOTMWHCBPTu3Rv9+/fHiy++iOzs7Cp/5sSJE+jevTsSEhLqqZf28dqLw2zFYbZimfM9knIe097/Fl9u3CcXoEICdXjugSF459k7XVaAMrOckpecdtGFPak53rtiMV9bLEJ5GYVCgdCro6GyOR2PiIiowSkpKcETTzyBmJgY7NmzBxs3bkRubi5eeeWVSn+muLgYzz//PD8sE5FbyikowoJvfsYri39AemYOANPUu9sH3Ih/v/Qgbonr6BYjTazWhfKQKXlE9Y3T8bxQoyB/ZGYXQF9UijJDOTRqXmYiIqKGIiMjAx07dsTkyZPh4+MDjUaD++67Dy+++GKlP/Pmm29i6NChOHHiRD32lIioapIkYeOeI/hywx6UlRsBmApNHVo3waS7B6N9yyau7eB1OkU0k78+doZFKCJ7OBLKi2g0psX3QrkulNOZsyXnY7ZiMV9xmK1YzLf22rVrh2XLlsHHx0du27p1K7p06WJ3+x9++AFpaWl49tln66uLVeK1F4fZisNsxdiW8Dc+W/cbSsoqAAABfr6YfM9NWDB1nNsVoAAgJNAPLRoHAwBOpl9CqaG8mp9wPd67YjFfWxwi4yUUCgX8/EzFp9Agf7k9u6AQTcOCXNUtr2CZLTkXsxWL+YrDbMVivs4jSRI++ugj7NixAytXrrR5/dSpU/jwww+xatUqq6JVTfYrSZL8vUKhsPq+Lu06nU4+RlXbO/OY7tIu+ph+fn42187Tz8ldrpNOp3OrPjrjnNyhffPvRwCYpt4Ni++MR2/viyB/LQD3/R3RuW1zZFzORUVFBU6ezUSXdi2ctn8Rfbf8nct7z/nn5Op8RZxTTdqrwiKUl5AkCcXFxdDpdGhkUYTiSKi6s8zWHeaaexNmKxbzFYfZisV8nUOv12PmzJk4evQoVq5ciejoaKvXS0tL8dxzz+GVV15BixYtHN63wWAAYPpXXj8/PxQXF6OsrEzeRqvVQqvVorCwEOXl10YD+Pn5QaPRQK/Xo6KiQm739/eHSqXC5cuXoVar5WsfGBgIpVKJvLw8qz4EBwfDaDSioKBAblMoFAgODkZ5eTkKC6+tjenj44PAwEAYDAYUFV37bKRSqRAQEIDS0lKr9bCceU5qtRr5+flWH9JdcU7mTCVJkq+dp5+Tu1wnc6aNGzf2mnMCXH+dkk+fw8mzFyEBiGjWCM/cOQAajQZ5eXlufU6d27bA1v1HAUnCwaOn0TLM322vk7n/Go0Gvr6+vPecfE75+flyvkql0ivOqbLr5AiF5GjZimqlqKgIf//9Nzp16iTkX3clSUJeXh6Cg4Px68Hj+GjVLwCAp+8aiFEDujn9eA2JZbb8Y8i5mK1YzFccZitWQ8lX5GeDs2fP4sknn0SLFi3w/vvvIzQ01GabgwcPYuLEiVZTBQoKCuDn54exY8fijTfeqLTPHTt2tOqzs/71FQByc3Ntrn1D/NdzZ7dLkoT8/HwEBQXZvK889Zxc1X59m/l3VkhIiM3Pu1vfa3pO7tD+9aZ9+P6XPyABeGBYT4wf3gdKpdLtzynjSh6emWcaeRrbqQ1mPTHKaft3dt+v//8t7z3nnpM75Ovsc7K3bWFhIZKTk2v8eYYjobxQI4s1obLz+IQ8IiKihiQvLw+PPvoo+vTpgzlz5kCptL8EaFxcHA4fPmzVFh0djf/7v/9DfHx8lccwf5i+vq2ybWvabp6uUNP9O+OY7tZeH8es7+O6U77Oaq/s/nSnPjra7k59kSQJvyWdBKCAUgn0u7FtrTJ2Rd9bhAcjOMAPefpiJKdehCRJ8u9hd7xO1//Obej3nrPa3SlfZ7U7uo/KcGFyLxRmtSYUp+MRERE1JGvXrkVGRgZ++uknxMbGIiYmRv4PAGJiYrB+/XoX95KIqHLJaReRmW2a9tOtfUuEBOhc3KOaUygU6Ny2OQCgsLgM6Zk5Lu4RkXvhSCgvotWaFumzXBOKI6Gcw5wtOR+zFYv5isNsxWK+tTdhwgRMmDCh0teTkpIqfe348eMiuuQQXntxmK04zNa5dv5xQv76pp5RHpdvp7bNsO/IaQDAsTMX0KZ5mIt7VDlPy9bTMF9bHAnlJRQKBbRaLRQKBQL9fKFSmS5tFotQdWaZLTkXsxWL+YrDbMVivg0Xr704zFYcZutc5RUV2HMoBQCgVvmgX7dIj8vXPBIKMBWh3BXvXbGYr30sQnkJSZKg1+vltRRCr46GyilgEaquLLMl52K2YjFfcZitWMy34eK1F4fZisNsnSvpeDryC01PA4vv2hY6X7XH5dvuhsbQqH0AAH+7cRGK965YzNc+FqG8iOWjEc1FqILCUpQZHHtkItly9LGTVHPMVizmKw6zFYv5Nly89uIwW3GYrfNYTcWLjQLgefmqVT6IatMUAJCZXYAruXoX96hynpatp2G+tliE8lKhFk/Iy+Xi5ERERERE5OaKS8uQcPQMACDAzxc9o1u7uEe11znCM6bkEdU3FqG8lOXi5Fn5nJJHRERERETubf9fZ1BaZho5MqB7e6hVPi7uUe15yrpQRPXNZUWorKwsTJo0CXFxcYiPj8ecOXMqHaq2a9cujB49Gj169MDIkSOxY8cOq9eXLl2KQYMGoUePHnj44Ydx+vRp+bWioiLMnDkT8fHxiI2NxYsvvojCQtuiTFpaGuLj43Hu3Dmr9ieeeAI33nij1eONf/vttxodu775+V0b/RRqUYTKyedIqLqyzJaci9mKxXzFYbZiMd+Gi9deHGYrDrN1jl0WU/EGX52KB3hmvh0jmsG8HrU7rwvlidl6EuZry2VFqOnTp8PPzw+7d+/GmjVrsG/fPnz55Zc226WmpmLKlCmYNm0aDh48iClTpmD69OnIzMwEAKxbtw4rVqzA559/joSEBHTp0gVTp06VF/+aPXs2Lly4gK1bt2Lbtm24cOECFixYYHWMX375Bffffz9yc3Ntjv/XX3/h888/R1JSkvzfoEGDanTs+qRQKKDRaOSV9y2n42VzJFSdXJ8tOQ+zFYv5isNsxWK+DRevvTjMVhxm6xy5BUVIOpEOAGjcKACdI5oB8Nx8/XW+iGgRBgBIvZCFopIyF/fIlqdm6ymYr30uKUKlpaUhMTERL7zwAnQ6HVq1aoVJkybhm2++sdl23bp1iIuLw9ChQ6FSqXDbbbehV69e+PbbbwEA3333HR544AF06NABvr6+eP7555GRkYGEhAQUFxdjw4YNmDp1KkJCQhAWFoYZM2Zg7dq1KC4uBgAsWrQIH3zwAZ577jmbY6enpyMvLw+dO3e2ex5VHbu+SZKEgoICuQAWGsyRUM5yfbbkPMxWLOYrDrMVi/k2XLz24jBbcZitc+z5XwqMRlOGg2I6QKk0/anqyfl2uroulNEoITntoot7Y8uTs/UEzNc+lxShTp48iZCQEDRt2lRui4yMREZGBvLz8622TUlJQVRUlFVb+/btkZycbPd1tVqNiIgIJCcnIy0tDQaDwer1yMhIlJSUIDU1FQBwzz33YOPGjejbt69NP48cOQJ/f38899xz6NOnD0aNGoU1a9ZU2jfLY1dGkiSr/+y11ba9vLxc/r5RoB8AU3tWnl7YMd2lXfQxKyoqvO6c3OU6mafhuksfve06mX8veNM5uct1svyd6y3n5Op2d8q3vq4T2aqoqHB1F7wWsxWH2dad9VPxoq1e89R8LdeFctcpeZ6aradgvrZUrjhoYWEhdDqdVZv5+6KiIgQFBVW5rVarRVFRUbWv6/WmR2FazsM0b2teF8qyEHa9srIy9OjRA8899xw6dOiAhIQETJkyBf7+/hg5cmS1fbNHr9fDYDAAADQaDfz8/FBcXIyysmvDM7VaLbRaLQoLC63WyfLz84NGo4Fer7e6mf39/aFSqeTjKhQKqFABCYACEi5czkFeXp68fXBwMIxGIwoKCuQ2hUKB4OBglJeXW62Z5ePjg8DAQBgMBqvzUqlUCAgIQGlpKUpKSuR2Z56TWq1Gfn6+1Yf0wMBAKJVKq/MRfU5qtRoAUFxcLF87Tz8nd7lOkiShqKgIISEhXnNOgPtcJ3O+/v7+0Gg0XnFO7nKdiouL5dd8fX294pzc6Trl5+fL/VQqlV5xTpVdJyIiAi5cycPxNNNyK22ahyKieZiLe+QcndtxcXKi67mkCGX+EGjJ/L2/v79Vu06ns/oQCQAlJSXydlW9bi4+FRcXy9ubjxMQEFBtP++44w7ccccd8vcDBgzAHXfcgZ9++gkjR46stm/2BAQE2CxOptPpbIpZgG0Wlvuwx8/PD8HBwVAoFAgKkqBW+aC83IjC0nIEBwfL2ykUCvlD/fVUKpXddrVabbfd19cXvr6+Nu3OOifLgqRl/6/vi8hzkiQJBoMBOp3O7sJynnhOZq6+TpZ/wHnLOVly9TmZ81WpTL/qveGcrueqc9JoNAAg/84FPP+c3PE6WebrLedkyfzeJCJq6Hb9WfkoKE/WOCQQjRsF4HKOHsfTMlFeUQGVj+c+8Y/IGVzy6adDhw7Izc3FlStXEB4eDgA4deoUmjVrhsDAQKtto6KicPToUau2lJQUdO3aVd7XyZMncfPNNwMADAYDUlNTERUVhbZt20KtViMlJQXdu3eXj2OeNledNWvWyKOezMrKyuQPtVUduzIKhcJmYbLKFipzpF2SJAQEBMj7VygUCA3yx6XsAmTnFwo5pru1i9y3v7+/3Wsn+rjulK+z2q9vM/+B5059dLTdnfpyfbv594Kj+3GHvju73dn7tvydK2L/NWl3p3yd1e5O+Tqr3dF9NHRV/WMe1Q2zFYfZ1p4kSdhpUYQaFNPBZhtPzrdTRHNczjmJ0rJynDl/BR1aVz4TxxU8OVtPwHxtuWRNqIiICMTGxmLu3LnQ6/VIT0/H4sWLcffdd9tsO2bMGCQmJmLz5s0oLy/H5s2bkZiYiLFjxwIAxo0bh5UrVyI5ORmlpaV4//33ER4ejri4OOh0OowcORILFixAdnY2srOzsWDBAowaNQparbbafur1esyePRvHjh2D0WjEzp07sXHjRtx3333VHru+KRQKqNVqqw+0oUGmG76gsBRlBg75ry172ZJzMFuxmK84zFYs5ttw8dqLw2zFYbZ1c+rcZZy/lAsA6BrZAk0aWQ9K8PR83XlKnqdn6+6Yr30uKUIBwMKFC1FeXo4hQ4bg3nvvxcCBAzFp0iQAQExMDNavXw/AtJD4p59+iiVLlqBXr15YvHgxPvnkE7Rt2xYAcPfdd+Oxxx7D5MmT0adPHxw7dgxLliyR1/GZNWsWIiIiMHr0aIwYMQItW7bE66+/XqM+Pvroo3jooYfw7LPPIiYmBgsWLMD8+fPlIlN1x65PkiQhLy/PanpTaNC1qWO5BXxCXm3Zy5acg9mKxXzFYbZiMd+Gi9deHGYrDrOtG8tRUIN72o6C8vR8LRcnd7cilKdn6+6Yr30uW4wgPDwcCxcutPtaUlKS1fcDBw7EwIED7W6rUCgwceJETJw40e7rAQEBmD17NmbPnl1lf1q2bInjx4/b7HvSpElycczRY9e362/uRkHXhv5l5xehSajt+hVUM/zFIQ6zFYv5isNsxWK+DRevvTjMVhxmWztGoxG/JZ0EAPj4KNG/e3u723lyvm2ahUKnVaO4xIBjZy5AkiS3Ghnjydl6AuZry2UjoUg8y5FQ2fmFVWxJRERERERUvw6nnEdOvmnGRlynNgj0q37JFE+jVCrROcI0Giq3oBgXruRV8xNE3o1FKC8WajUSikUoIiIiIiJyH1ZPxetZ+cOdPJ3lulB/p150YU+IXI9FKC9y/ZMFLafjmf+FgWrn+mzJeZitWMxXHGYrFvNtuHjtxWG24jBbx5UZyvH74VMAAJ2vGr26RFS6rafna70uVIYLe2LL07N1d8zXFotQXkKhUECpVFrNLw4LvlaEyuJIqFqzly05B7MVi/mKw2zFYr4NF6+9OMxWHGZbOweOpaK4xAAA6NutHXzV9pcr9oZ827dqAqXS1P9jp91ncXJvyNadMV/7WITyEvZW3m8UeG1NqBwWoWqNTzUQh9mKxXzFYbZiMd+Gi9deHGYrDrOtnZ1WU/GiK93OG/LVatRo36oJAODcpVzk6Ytd3CMTb8jWnTFf+1iE8mJB/lr4+JgucVYei1BEREREROR6BUUlOPh3GgAgJNAP3Tvc4OIeiWc5JS85jetCUcPFIpQXUygU8hPyuCYUERERERG5g9//dwrl5UYAwOCeHaBUev+fpVbrQrnRlDyi+ub97/YGzvyEvPzCEhjKK1zcGyIiIiIiauh2JV2bijc4poMLe1J/OkU0k78+doZFKGq4WITyEgqFAsHBwTaLnlkuTp5TwNFQtVFZtlR3zFYs5isOsxWL+TZcvPbiMFtxmK1jruTqcfSU6QlxLRoHy2slVcZb8g0J9EOLxsEAgJPpl1BqKHdxj7wnW3fFfO1jEcpLSJIEo9Fos+hZo6BrRahsrgtVK5VlS3XHbMVivuIwW7GYb8PFay8OsxWH2Tpm158nYI7qptjoav9A96Z8zVPyKiqMOJl+ycW98a5s3RHztY9FKC9SUFBg02ZeEwoAsvmEvFqzly05B7MVi/mKw2zFYr4NF6+9OMxWHGZbc7WZiuct+XZu20L+2l3WhfKWbN0V87XFIpSXaxRoMRKKRSgiIiIiInKRsxezceZ8FgAgqk1TtGgc4toO1bPO7a4tTv5b0glUVBhd2Bsi12ARysuFWq4JxSfkERERERGRi+z847j89U09o1zYE9doER6MDlfXwEq7kI3Ne/9ycY+I6h+LUF7E3nxqTsdzDi4mJw6zFYv5isNsxWK+DRevvTjMVhxmWz2j0YhdSScBAEqlAgN7tK/xz3pLvgqFAk/eOUD+fsVP+5Hr4odHeUu27or52mIRyktUtvJ+aBCn49UVn2ogDrMVi/mKw2zFYr4NF6+9OMxWHGZbM3+nXsSlbNP6OD2iWiIk0K+anzDxtnw7RTTH0N4dAQDFJQZ8sXGvy/ribdm6G+ZrH4tQXkKSJBgMBpuV94P8tfDxMV3mbE7Hq5XKsqW6Y7ZiMV9xmK1YzLfh4rUXh9mKw2xrZuef1xYkv6lndI1/zhvzffT2vvDXaQAAvx44jr9TXbNIuTdm606Yr30sQnmRwkLbkU4KhUKekpedx5FQtWUvW3IOZisW8xWH2YrFfBsuXntxmK04zLZqhvIK7DmUAgDQqH3Q58a2Dv28t+UbEuiHh0bGy9//+7+7YDS6ZpFyb8vW3TBfWyxCNQDmKXn5hSUwlFe4uDdERERERNSQJB0/C31RKQAgvmtb6Hw1Lu6R643s2xVtbwgDAJw5n8VFyqnBYBGqAbBcFyrHxQvfERERERFRw2I9Fa/hPRXPHh8fJZ65a7D8/cqfEpCnL3Zhj4jqB4tQXsTHx8due2iwRRGKi5PXSmXZUt0xW7GYrzjMVizm23Dx2ovDbMVhtpUrLi1Dwl9nAACB/r7o2bG1w/vw1nw7t22OW3qZ1scqLC7Dly5YpNxbs3UXzNcWi1BeQqFQIDAw0O7K++Y1oQAgi+tCOayqbKlumK1YzFccZisW8224eO3FYbbiMNuq7T9yBmUG05IgA7p3gMrBP8q9Pd8Jo/rB7+oi5dsTk5GcerHeju3t2boa87WPRSgvIUkSysrK7K683yjw2kiobI6EclhV2VLdMFuxmK84zFYs5ttw8dqLw2zFYbZV2/nncfnrm2Idn4rn7fmGBPrhweG95e//b+1v9bZIubdn62rM1z6Hi1BGoxGHDx/Gtm3bsH37dhw7dkxEv6gWiorsr/dkORIqJ59rQtVGZdlS3TFbsZivOMxWLObbcPHai8NsxWG29uUWFOHQiXMAgKahgegU0axW+/H2fG/vfyMiWpgWKT917jK27Dtab8f29mxdjfnaUtV0w5ycHCxbtgzfffcdioqK0KhRI5SXlyM/Px+hoaG466678MQTTyAoKEhkf6kWLNeE4kgoIiIiIiKqD78lnYTRaBoFMiimA6clVcLHR4mn7xqEmYvWAQBW/JSA/t3bIzhA5+KeETlfjUZC/fzzz7j33ntRVFSEf//730hKSsKePXuwf/9+HDp0CO+99x5yc3MxduxYbNu2TXSfyUFWT8fjSCgiIiIiIqoHVk/Fi412YU/cX9d2LXDz1emK+qJSfL15n4t7RCRGjUZC7d69G99//z1CQkJsXtNoNOjbty/69u2L7OxsfPDBB7j11lud3U+qAZXK/uUM9POFj48SFRVGZHEkVK1Uli3VHbMVi/mKw2zFYr4NF6+9OMxWHGZr6/zlXJw8ewkA0PaGMLRuFlrrfTWUfB8b3Q/7/zqD4lIDfk74G8P7dEFU66ZCj9lQsnUV5murRiOh3nrrLbsFqOuFhobi7bffrmufqBYUCgUCAgLsDnFVKpXyulAcCeW4qrKlumG2YjFfcZitWMy37pKTkzFhwgT07t0b/fv3x4svvojs7Gy7265atQrDhw9HTEwMhg8fjm+++aaee3sNr704zFYcZmvfb0kn5a8Hxzi+ILlZQ8o3NMgfD44wLVIuScD//VfsIuUNKVtXYL72ObwweXZ2Nr788kvMmTMHer0eO3bsENEvcpAkSSgpKal05f1GV6fk5emLYSivqM+uebzqsqXaY7ZiMV9xmK1YzLduSkpK8MQTTyAmJgZ79uzBxo0bkZubi1deecVm2+3bt+ODDz7A/Pnz8eeff+Kdd97BRx99hK1bt7qg57z2IjFbcZitLX1RKX5J/BsAoFAAg3vWvgjV0PK9fcCNaN2sEQDgZPolbEsQ9yCwhpZtfWO+9jlUhDp69ChGjBiBLVu2YM2aNcjJycG0adPw3//+V1T/yAElJSWVvhZmuS5UAUdDOaqqbKlumK1YzFccZisW8629jIwMdOzYEZMnT4ZGo0GjRo1w33334cCBAzbbZmZm4sknn0SPHj2gUCgQExOD+Ph4u9vWF157cZitOMz2mqKSMrz+2XpkZhcAALp1aInwkIA67bMh5avy8cE/xg2Wv/9q037kF4o7/4aUrSswX1sOFaHmzZuHl19+GatXr4ZKpUKrVq3w6aef4vPPPxfVP3KSRlen4wFADteFIiIi8lrt2rXDsmXL4OPjI7dt3boVXbp0sdn2wQcfxFNPPSV/n5WVhQMHDqBr165VHkOSJKv/7LWJbnfFMXlOPCd364u7nVNxaRlmfbYeJ89mApAQFKDF03cM9OhzckV7l3YtMLhnBwAS9EUl+HrTPrfrI68Tz8my3REOrZJ14sQJjB07FgDkeY0DBw7E9OnTHToo1b+w4GsjobLyWIQiIiJqCCRJwkcffYQdO3Zg5cqVVW57+fJlPP300+jatStGjRpV5bZ6vR4GgwGA6SE1fn5+KC4uRllZmbyNVquFVqtFYWEhysvL5XY/Pz9oNBro9XpUVFxbIsDf3x8qlQpFRaYR2+bPmoGBgVAqlcjLy7PqQ3BwMIxGIwoKCuQ2hUKB4OBglJeXo7Dw2ucdHx8fBAYGwmAwyPsHTAvGBgQEoLS01Opfq515Tmq1Gvn5+VYf0l1xTmq1GgBQXFwsXztPPyd3uU6SJKGoqAghISFec06A49ep3Cjh/VU7cezMBUhGIwL8fPHSg7cgLEgLALU+J3O+/v7+0Gg0Debee2xUP+z9XwpKygzYvPcI+nZphZ6dI516TsXFxfJrvr6+Hnvvuev7KT8/X+6nUqn0inOq7Do5wqEiVGhoKE6fPo0OHTrIbadPn0Z4eLhDByUxNBpNpa81CuR0vLqoKluqG2YrFvMVh9mKxXzrTq/XY+bMmTh69ChWrlyJ6OjKH49+6NAhTJs2DXFxcZg3b161T/MJCAiAn5+fVZtOp4NOp7PZ1t/f36bNvA97QkJCoNPprBZyNX/QtaRQKOQP9ddTqVR229Vqtd12X19f+Pr62rQ765yCgoJs2ur7nCRJgkKhgFartbl2gGeek5mrr5MkSfLvLG85J0s1OadSQzlmf74Jf53KgFKhQECgP95+Ziza3XDt78TanpM5X/PvpYZy74WHBOCh2/riiw2/AwC++TkJMZ3aOfWcNBoNNBqN1e9cT7v3rj8nd3s/XZ+vN5zT9Rx9AqBDWz/wwAN4+umn8cwzz6C8vBybN2/Gv//9b9x3330OHZScT6FQ2P1AYRZqMR0vmyOhHFJdtlR7zFYs5isOsxWL+dbd2bNn8eSTT6JFixZYs2YNQkMrfzT6mjVr8Pbbb2Pq1KmYOHFijfavUChsnvZT2dN/HG2v7AO0ve2ddUx3ahe17+reV554Tq5sv75IWtl968o+Otpe232UGcox94ufcPjkeQCAn84Xs58eg8iWjZ2y/+vzbUj33phB3fDLgb9x9mIOTp69hJ8TkzG8T2enHdPevetJ9547t7tTvs5qd3QflXFoTahHHnkEjz/+OL766isYjUYsXLgQd955Jx577DGHDkrOZx6mWtl8zEYWC5Nnc00oh1SXLdUesxWL+YrDbMVivnWTl5eHRx99FD179sTnn39eZQFq69ateOONN/DJJ5/UuAAlEq+9OMxWnIacbZmhHHO//AlJx9MBADpfNd56ajTat2ritGM05HxVPj54+s5B8vdfbdqLgiLnLXTdkLOtD8zXPoeKUMuWLcOdd96JTZs24dChQ9iyZQsmTpwIpdKh3ZAglnNCr2e5JlROPqfjOaqqbKlumK1YzFccZisW8629tWvXIiMjAz/99BNiY2MRExMj/wcAMTExWL9+PQBg0aJFqKiowNSpU622e/31113Wf157cZitOA0x2/KKCry7Yhv++PssAEDrq8IbT41GdJtmTj9WQ8zXrFuHlhgY0x4AUFBYihWbE5y6/4acbX1gvrYcmo732WefYcKECaL6QgIF+vnCx0eJigojsgs4EoqIiMhbTZgwocrPa0lJSfLXGzZsqI8uEZGXqagw4r0V25Dw1xkAgEbtg1lPjELnts1d3DPvNHF0fxw4loqS0nJs2fcXbu3TCe1bOm+0GVF9cmgI08CBA7F06VJcunRJVH9IEKVSiUaBpnUAsvM4EoqIiIiIiBxXUWHE+//5GXsPnwYAqFU+eO3x29E18gYX98x7hYcEYPywXgAASQL+77+/wWg0urhXRLXjUBHqjz/+wEcffYTBgwejU6dOVv+R62m12ipfD706JS9PXwxDeUWV25K16rKl2mO2YjFfcZitWMy34eK1F4fZitNQsjUajfh49S/YnZQCAFCplHh14m3oEdVK6HEbSr5VGTOoO1o2CQEAHE/LxC8Hjjtlv8xWLOZry6HpeO+++66oflAdmR+5WxXLJ+Tl6ovQOCRQdLe8Qk2ypdphtmIxX3GYrVjMt2E6k3EFW/cfw9BeHZ26qDGZ8H0lTkPJ1mg04pPvdmDHHycAAD4+Ssx8dARiO7YWetyGkm911CofPH3XILz2f6Y1/b7atA99bmyLQL/aZ8NsxWK+9jk0Eqp3796Ii4uDVqvFlStXoFQqERcXh969e4vqH9WQJEnQ6/VVrrwfavmEvDyuC1VTNcmWaofZisV8xWG2YjHfhumzdbuxac9hvLdiC6+9AHxfidMQspUkCf/+72/YnpgMAFAqFXjpkeHo3aVtvRzb2/OtqR5RrdC/eyQA0+yWb7Yk1ml/zFYs5mufQ0Woy5cv45577sH999+POXPm4JFHHsGoUaNw8eJFhw+clZWFSZMmIS4uDvHx8ZgzZw7Ky8vtbrtr1y6MHj0aPXr0wMiRI7Fjxw6r15cuXYpBgwahR48eePjhh3H69Gn5taKiIsycORPx8fGIjY3Fiy++iMJC2wJMWloa4uPjce7cOav2VatWYfjw4YiJicHw4cPxzTffyK8ZjUbExMSgR48eVk+UKSpyzZpLleVnZlWE4hPyHFJdtlR7zFYs5isOsxWL+TY85qUC0jNzUFpmcHFvvBPfV+J4c7aSJGHJut3Ysu8oAFMB6sWHb0XfG9vVWx+8OV9HPT62P3w1pglNP+39CxmXc+u0P2YrFvO15VARav78+YiIiEBiYiJ+//13JCQkoFOnTpg3b57DB54+fTr8/Pywe/durFmzBvv27cOXX35ps11qaiqmTJmCadOm4eDBg5gyZQqmT5+OzMxMAMC6deuwYsUKfP7550hISECXLl0wdepUudo4e/ZsXLhwAVu3bsW2bdtw4cIFLFiwwOoYv/zyC+6//37k5uZatW/fvh0ffPAB5s+fjz///BPvvPMOPvroI2zduhUAkJKSAoPBgMTERCQlJcn/+fn5wR1ZF6E4EoqIiIjcR4vwYPnri1n5LuwJEZlJkoTP1/+OTXuOADAVoP75wFD0797exT1ruBqHBOLuW3oCAIxGCdsT/3Zxj4gc41ARav/+/XjzzTfh728qZgQGBuKNN97Avn37HDpoWloaEhMT8cILL0Cn06FVq1aYNGmS1Sgjs3Xr1iEuLg5Dhw6FSqXCbbfdhl69euHbb78FAHz33Xd44IEH0KFDB/j6+uL5559HRkYGEhISUFxcjA0bNmDq1KkICQlBWFgYZsyYgbVr16K4uBgAsGjRInzwwQd47rnnbI6dmZmJJ598Ej169IBCoUBMTAzi4+Nx4MABAMCRI0cQHR0NjUbj0Pm7iuWaUCxCERERkTtp3jhE/jrjSp7rOkJEAEwFqC837sOPu/4HAFAogGnjb8HgnlEu7hmN6NsFSqUCAPBzYjIqKvikPPIcDhWhjEYjFAqFVZtCoYBarXbooCdPnkRISAiaNm0qt0VGRiIjIwP5+db/8pWSkoKoKOtfdO3bt0dycrLd19VqNSIiIpCcnIy0tDQYDAar1yMjI1FSUoLU1FQAwD333IONGzeib9++Nv188MEH8dRTT8nfZ2Vl4cCBA+jatSsAUxGqtLQU48aNQ58+ffDggw/izz//dCgLZ6puBFYjrglVa+46us0bMFuxmK84zFYs5tvwNL86EkqpVLIIJQjfV+J4Y7YrtyRg7Y4k+ftn770Zt8R1dElfvDHfuggJ9EP81fW4cguKcDA5rdb7YrZiMV9bDhWh4uPj8cYbb8hrHhUWFuKNN95weGHywsJC6HQ6qzbz99evp2RvW61Wa9WHyl7X6/UArC+8eVvzulBNmza1KazZc/nyZTz55JPo2rUrRo0aJR+nW7duWLx4MXbu3IlbbrkFjz/+ONLT0yvdjyRJVv/Za6tNu2UxsLLtTSOhTG3Z+UXC+uKKdtH7No9285ZzcpfrBJgKxwqFwm366E3XyZyvmTeck7tcJ8tsveWc3KHdnfKtr+tE19wQHgxAAYVCiYssQjmdQqGARqOp0edecow3Zrt62wF89/Mf8veT7h6MW+M7u6Qv3pivM9za59r12Lb/WK32wWzFYr72qRzZ+IUXXsCECRPQu3dvhISEIDc3F5GRkfjss88cOqifn588Hc7M/L15qp+ZTqdDSUmJVVtJSYm8XVWvm4tPxcXF8vbm4wQEBNS4v4cOHcK0adMQFxeHefPmQaUyxfbyyy9bbff4449j7dq12LVrFx566CG7+9Lr9TAYTIttajQaOYuysjJ5G61WC61Wi8LCQquFzPz8/KDRaKDX61FRUSG3+/v7Q6VSITMzE76+vvJNHhgYCKVSibw80wc5o9H0wVehALLz9XI7YHqDBAcHo7y83Grhdh8fHwQGBsJgMFgVCFUqFQICAlBaWmqVvzPPSa1WIz8/3+pD+vXnZBYcHAyj0YiCggKnn5NarYbRaIRSqZSvnaefk7tcJ0mSUFpaiqZNm3rNOQHuc50kSUJJSQnCwsKg0Wi84pzc5ToVFxejpKQEWq0Wvr6+XnFO7nSd8vPz5XyVSqVXnFNl14muaRYeDEBChdGIjCu5ru6O15Ek01OaAgIC+AeRk3lbtglHz1g9de2pOwdiZL+uLuuPt+XrLD2jWyEsxB9ZuYU4+HcaruTqER5S879xAWYrGvO1z6EiVIsWLbBp0yYcPHgQWVlZuOGGG3DjjTfCx8fHoYN26NABubm5uHLlCsLDwwEAp06dQrNmzRAYGGi1bVRUFI4ePWrVlpKSIk+J69ChA06ePImbb74ZAGAwGJCamoqoqCi0bdsWarUaKSkp6N69u3wc85S9mlizZg3efvttTJ06FRMnTrR67cMPP8Tw4cPRufO1KnRZWRl8fX0r3V9AQIDNkDydTmczmguwLchZ7sMeX19fBAcHW93g5g+6Zo0bBeJKbiGy84us2s1UKpXddrVabbfd19fX7vk665yCgoJs2q4/J3Ob+Q+V69X1nCRJQn5+vlVh05InnpOZq6+TJEnyH2zeck6WXH1O5j+QzYVzbzin67nqnMyjIy1/53r6ObnjdbLM11vOyZL5vUkmgX5aBPppkVtQiIzLHAklgmVBlZzLm7Lde/iU/PVjo/pi9MBuLuyNiTfl6yxKpRJDe3XCtz8fhNEo4deDybh3aJzD+2G2YjFfWw5Nx8vPz8dLL72Exo0b4/bbb8euXbvw8ssvW/3LZE1EREQgNjYWc+fOhV6vR3p6OhYvXoy7777bZtsxY8YgMTERmzdvRnl5OTZv3ozExESMHTsWADBu3DisXLkSycnJKC0txfvvv4/w8HDExcVBp9Nh5MiRWLBgAbKzs5GdnY0FCxZg1KhR0Gq11fZz69ateOONN/DJJ5/YFKAA4MSJE5gzZw4uX76MsrIyLFq0CHq9HsOGDat0nwqFwuo/e20i283rQuXrS1BxdY0vV/XFme3u1BeeE8/J09vdqS88J56Tp7dXty1ZM68LlZWnR6mBI8WIXOFEmukp5D4+SrcoQFHlhvXuBPP/Sn5O/BtGIxcoJ/fnUBHqjTfeQF5eHkJCQgAAo0aNQkFBAebOnevwgRcuXIjy8nIMGTIE9957LwYOHIhJkyYBAGJiYrB+/XoApoXEP/30UyxZsgS9evXC4sWL8cknn6BtW9NCbHfffTcee+wxTJ48GX369MGxY8ewZMkSeS2JWbNmISIiAqNHj8aIESPQsmVLvP766zXq46JFi1BRUYGpU6ciJiZG/s/88/PmzUPr1q0xduxYxMfHIzExEV988YWcjzsKC772L705BUVVbElERERUv1pYPCGP60IR1b/C4lKcu5QLAGjXIhwaNUdsurOmYUHo3qElAODilXz8dSrDxT0iqp5Dv1X27t2LX375RR6yHhkZiQULFlQ58qcy4eHhWLhwod3XkpKSrL4fOHAgBg4caHdbhUKBiRMn2h2pBJiG0c+ePRuzZ8+usj8tW7bE8ePHrdo2bNhQ5c+EhIRg3rx5VW5TnyqbSmCpUaBFESq/CI1DAqvYmsxqki3VDrMVi/mKw2zFYr4NU7OwICiVpmUeMq7koU3zMBf3yLvwfSWOt2R7Mv2S/HVUm6ZVbFm/vCVfEYbFd8ahE+cAANsSjqHb1aJUTTFbsZivLYdGQhmNRps5jZIkObwmFDmfQqGQnzJWFcuRUFl5jk2jbKhqmi05jtmKxXzFYbZiMd+G64Ymja5edwUyLue6ujtehe8rcbwp2xNnM+Wvo1u7RxHKm/IVoU/XtgjwM62BuPfwaRQUlVTzE9cwW7GYr30OFaEGDRqEl156CWfPnoXBYMDZs2cxc+ZMDBgwQFT/qIbMCzxX97jnRkHXFtbOyWcRqiZqmi05jtmKxXzFYbZiMd+Gq3lY0NV/8JRwMSvf1d3xKnxfieNN2Z44e20kVIfWTVzYk2u8KV8RNGoVhvTqCAAwlFdg5x8navyzzFYs5mufQ0WoV155BXq9Hrfeeiu6deuGW2+9FcXFxXjppZdE9Y8cUJObOyzIYiQUi1A1xl8c4jBbsZivOMxWLObbMJkWJjdd+/McCeV0fF+J4w3ZSpKE41cXJffTadAi3Papnq7iDfmKNKx3J/nrbQnHHMqL2YrFfG05tCZUaGgoVqxYgYyMDFy+fBnNmjVD06buMUyTaqZRkPWaUERERETuItBPC3+dL0rKynGBC5MT1asreXrkXn1wUVSrJlAqHRqvQC7UpnkYots0xfG0TKRmZCEl/RI6uMl0SqLrObwm1M8//4wWLVqgWbNmmDdvHt566y3o9XpR/SMnC7WYjpfNNaGIiIjIzTQLCwIAXMnVo9RQ7uLeEDUcJ9KurQcVxQKGx7m1T2f5620Jf7uwJ0RVc6gI9c477+Dtt98GAMyaNQtXrlzB6dOnq33yHNWPwMDqn3QX5K+FUmlaGC27gEWomqpJtlQ7zFYs5isOsxWL+TZcrZtdeyJeJteFciq+r8Txhmwt14NypyfjAd6Rr2gDe7SH1tc00WnXnydQUmqo0c8xW7GYry2HilC7du3CqlWrUFhYiD179mDOnDlYtGgRdu7cKah7VFMKhQJKpbLalfeVSqW8ODmn49VMTbMlxzFbsZivOMxWLObbcCkUCrRoHALAdO25LpTz8H0ljrdk645PxgO8J1/RdL4aDOzRAQBQXGrAnv+lVPszzFYs5mufQ0WonJwctGjRAgcOHECTJk3Qpk0b6HS6q08xIVdyZOX90KvrQuUWFKOc165afKqBOMxWLOYrDrMVi/k2XJIkIVinhnlxcq4L5Tx8X4njDdkajUaknDONhGoSGoiQQL9qfqL+eEO+9eXWeMspeceq3Z7ZisV87XOoCNWqVSv88MMPWL16NQYMGACj0Yjly5ejffv2ovpHAoRaLE6eW1Dswp4QERERWTOvCQWwCEVUX85m5qCk1LQGW4dWTVzcG6qt6DZN0bpZIwDA32cuIj0zx8U9IrLlUBHq5ZdfxsKFC3H27Fk8++yz2L9/Pz7//HO8/PLLovpHAlgWobLzuS4UERERuY9mYdfWz8i4kuu6jhA1IMe5KLlXUCgUVqOhfk6sfjQUUX1TObJxr1698Ouvv8rfh4SE4LfffoNGo3F6x0gcqyfksQhFREREbiRA54sAPy30RaXIuMyRUET14aTFelDutig5Oeam2Gh8uWkfysuN+PXAcTw8sg/UKh9Xd4tI5tBIqOtpNBoWoNyEQqFAcHBwjRY9Cw22GAmVxyJUdRzJlhzDbMVivuIwW7GYb8NlvvbNw4MBAFdy9Sg1lLu4V96B7ytxvCHbE+mmIpRSqUD7Gxq7uDfWvCHf+hQcoEOfru0AAHn6Yhw4llrptsxWLOZrX52KUOQ+JEmC0Wis0aJnYVbT8fiEvOo4ki05htmKxXzFYbZiMd+Gy3ztW1wtQgFAZla+C3vkPfi+EsfTsy0pNSDtQjYAoE3zUGh91S7ukTVPz9cVrBYo31/5lDxmKxbztY9FKC9SUFBQo+0acU0oh9U0W3IcsxWL+YrDbMVivg1XQUGBPBIK4LpQzsT3lTienG3K+cswGk1/JEe1cs+peJ6cryt073ADmoSa1tf78/hZXMqpPD9mKxbztVXrItTFixed2Q+qR1ZrQnE6HhEREbkZy5FQXBeKSKwTFouSd+Ci5F5BqVRiWO9OAABJAn45kOziHhFdU+si1G233ebMflA9CvLXQqk0zUvNLmARioiIiNxL8/AQ+esLV1iEIhLphMWi5NFclNxrDOndUf6b7+eEYzAajS7uEZFJrYtQnNfofmq64JlSqUSjq6OhcrgmVI1wMTlxmK1YzFccZisW8224FAoFWjTmdDwR+L4Sx5OzPZl+CQCg9VWhddNGLu6NfZ6cr6s0DglETHQrAMDlHD0OnTxndztmKxbztcU1obyEoyvvhwaa1oXK0xejvKJCZNc8Hp9qIA6zFYv5isNsxWK+DZf52gf56xDg5wuAI6Gche8rcTw529yCIlzKNq1Z075lEyiV7vfnoSfn62rVLVDObMVivvbV+rfMmDFjnNkPqiNJkmAwGGo8Qi002P/qzwG5BcUiu+bxHM2Wao7ZisV8xWG2YjHfhsvy2psXJ7+Sq0eZodzFPfN8fF+J48nZHreYihflputBeXK+rtarcwRCAnUAgP1/nUGe3vrvPmYrFvO1r9ZFqDfffNOZ/SAnKCys+fpOjSwWJ8/hE/Kq5Ui25BhmKxbzFYfZisV8Gy7ztTcvTi5JwMWsfFd2yWvwfSWOp2ZruSi5O68H5an5uppa5YNb4joCACoqjNhx8LjNNsxWLOZry/3GW1K9CAvyl7/OYhGKiIiI3EyLxiHy11wXikgMy0XJO7Ru4sKekCjD4jvJX/+ceIyjcsjlWIRqoMzT8QAuTk5ERETup3nYtcXJuS4UkfMZjUacuLooeUigH8KDA1zcIxKhZZNG6NKuOQDg7MUcJKdddHGPqKFjEcqL+Pj41HjbUMuRUHkcCVUdR7IlxzBbsZivOMxWLObbcJmvvdUT8i6zCOUMfF+J44nZZlzJQ1FxGQDTVDx3XjzZE/N1J7f2qXyBcmYrFvO1xSKUl1AoFAgMDKz50/EsilDZnI5XJUezpZpjtmIxX3GYrVjMt+GyvPbNw0Pk9gtZLELVFd9X4nhqtifPXpK/jnLjqXiemq876dctEjqtGgCw+1AKikpMxUdmKxbztY9FKC8hSRLKyspq/nQ8i4XJWYSqmqPZUs0xW7GYrzjMVizm23BZXvsgfy0C/HwBABmXc13bMS/A95U4npqtJzwZD/DcfN2JVqPG4J5RAIDSsnL8lnQSALMVjfnap6rJRosWLap2m2effbbOnaG6KSoqQnBwcPUbAgjy10KpVMBolFiEqgFHsiXHMFuxmK84zFYs5ttwWV775mHBOFl0CVdy9SgzlEOjrtFHV6oE31fieGK2lk/G69DKfUdCAZ6Zr7sZHt8ZW/YeBQD8nPg3RvTtAoDZisZ8bdXo/+TJycn45ZdfEBMTY3dOI4eXeR6lUolGQX7Iyi3kwuREREReJjk5GfPnz8fRo0ehVqvRv39/vPzyywgNDbXZdteuXViwYAHS09PRvHlzvPjii7j55ptd0GtbLRoH42T6JUgScDErH62b2fafiBxXZijH6YwrAICWTULgr/N1cY9ItMiWjdH2hjCcOZ+FE2mZSL2QhTb8nUouUKPpeAsXLkS/fv3Qr18/rFixwua/r7/+WnQ/SYDQQNO6UHn6YlRUGF3cGyIiInKGkpISPPHEE4iJicGePXuwceNG5Obm4pVXXrHZNjU1FVOmTMG0adNw8OBBTJkyBdOnT0dmZqadPde/5uEWT8jjulBETnMm44r8+T+qjftOxSPnUSgUuDX+2gLlPyccq2JrInFqVIRSKpWYPXs2Vq5cCb1eL7pPVEsqlWND1BtdXRdKkoCcAo6Gqoqj2VLNMVuxmK84zFYs5lt7GRkZ6NixIyZPngyNRoNGjRrhvvvuw4EDB2y2XbduHeLi4jB06FCoVCrcdttt6NWrF7799lsX9NzE8tq3sFicnOtC1R3fV+J4WrYnrBYld/8ilKfl664G94yCWmWa2fTrweMoM5QzW8GYr60aL0zeokULrF69Gkol1zJ3RwqFAgEBAQ5NjQwNvvaEvByuC1Wp2mRLNcNsxWK+4jBbsZhv3bRr1w7Lli2zWkJh69at6NKli822KSkpiIqKsmpr3749kpOThffTnuuvveVIqIwrHAlVF3xfieOJ2Z6wWJQ82s2LUJ6Yr7sK9NOif/dIAIC+qBQJR1OZrUC8d+1zqCwXEREhqBtUV5IkobS0FL6+vjW+ycOCrhWhsvIL0UFU5zxcbbKlmmG2YjFfcZitWMzXeSRJwkcffYQdO3Zg5cqVNq8XFhZCp9NZtWm1WhQVVT1CWpIkq6f9KBQKu0//cbQdME0nNF97UxFKAqDAhcu5Qo7pTu0i921+SpNGo7F5X3nqObmq/fo28+8srVZr8/Pu1ndzu6kIJUGtUslrrblbH83tlv9PUCqVvPfquI9hvTth5x8nAEjYuv8oenVsKf/O9dRzcrd2c9v1n2fcqY+1Paea7qMqNSpCffbZZ5g4cWK1Q8kMBgOWL1+Op59+2qFOkHOYP7TVVKMgy5FQnI5XFUezpZpjtmIxX3GYrVjMt+70ej1mzpyJo0ePYuXKlYiOjrbZRqfToaSkxKqtpKQE/v7+Nttev2+DwQAA0Gg08PPzQ3FxMcrKyuRttFottFotCgsLUV5eLrf7+flBo9FAr9ejoqJCbvf394dKpUJ2djb8/PzkD7Z+Ol8UFZfi7MUs5OVdGw0VHBwMo9GIgoICuU2hUCA4OBjl5eUoLLw2ytvHxweBgYEwGAxWBTaVSoWAgACUlpZa5eDMc1Kr1cjPz7f6kB4YGAilUml1PqLPSa1Ww2AwoKKiQr52nn5O7nKdJElCUVERWrRo4RHnVFhShvSLWQCAdi3CUFSoh9qNr5M538aNG0Oj0fDeq+M5tW0WjObhwTh/OQd/Jqfh+Ol0tGnRGL6+vh57Tu56nfLz81FUVAQ/Pz8olUqvOKfKrpMjalSEUqvVGDNmDO666y6MHj0aTZtaD9k8f/48fvrpJ3z//fe49957HeoAuU6YxXS8rDxOxyMiIvIWZ8+exZNPPokWLVpgzZo1dp+KBwBRUVE4evSoVVtKSgq6du1a5f4DAgLg5+dn1abT6WxGVQGotKAVEBBgt93Pzw/BwcHyaJ0W4cFISb+MXH0JdH7+0KhNH18VCoX8of56KpXKbrtarbbb7uvra7fo6axzCgoKsmkzf3i/vk3UOUmSBIPBAJ1OZ3PtAM88JzNXXyfLP+A84ZxOpl+Wp+t2iWwpv9/c9TqZ8zUPiOC9V/dzGhbfCV9v2geFjw8Sks+hW6f28u9cTz0nwH2vk+X/07zlnCw5uu5VjbaeMGECBg0ahIULF+KDDz5A06ZN0bRpUxiNRmRmZiIrKwtDhgzB4sWLERkZ6VAHyHUaBV77AJJTwCIUERGRN8jLy8Ojjz6KPn36YM6cOVWu5zlmzBh88cUX2Lx5M2699VZs27YNiYmJePXVV6s8hnlawfVtlW1b03ZJkuR9XytChSAl/TIkCbiUU4BWTUOr3Ient9fHMev7uO6Ur7PaK7v/3amPlbWfPJsJwNQe1aapR/Td8r7lvVf3fQzp1RErf0qA0WjEb4dO4/Gxg6FSKYX3vbJ2d8rXWe2W92tt7193a3d0H5WpcckqMjISH3/8MbKyspCQkIALFy5AqVSiRYsW6NOnj92KGNUvjUbj0PYcCVVzjmZLNcdsxWK+4jBbsZhv7a1duxYZGRn46aefsGXLFqvXkpKSEBMTgzfffBNjxoxBZGQkPv30UyxYsACvvvoqbrjhBnzyySdo27ati3pve+1bNL72GTPjSp5VEYocw/eVOJ6U7XEPWpTczJPy9QShQf7o1TkCCX+dRm5BMf48fha9u7ju9743471ry+HnBYaFheG2224T0ReqA4VCYXdodVWC/LVQKhUwGiWuCVWF2mRLNcNsxWK+4jBbsZhv3UyYMAETJkyo9PWkpCSr7wcOHIiBAweK7laN2Lv2LcJD5K8vXOYT8mqL7ytxPClbSZJw8uwlAECgvy+ahdlOxXE3npSvJxkW3wkJf52BUqnAup2H0KtzhMMjWqhqvHftq3x8NnkU84J9jqxMr1Qq0SjI9KbIzudIqMrUJluqGWYrFvMVh9mKxXwbLnvX3vSEPJPzV3Jd0CvvwPeVOJ6U7aXsAuTpiwEAUa2aekTRwZPy9SRxHdugeXgQjEYj/jp1HrsPpbi6S16H9659LEJ5EcvV8WsqNNA0JS9PX4yKCqOzu+Q1apMt1QyzFYv5isNsxWK+Ddf1196yCHXhCkdC1QXfV+J4SraWU/Gi2njGVDzAc/L1JD4+SjwxdgAkyfQ34PINv6O4lDk7G+9dWyxCNXDmkVCSBOQUcEoeERERuZcgfy38daY1NViEIqqbEx64HhSJ06tzBHpEtQQAZOUW4vvtf7i4R9QQ1KkIderUKWRmZla/IbmtUIvFyXM4JY+IiIjcjEKhkNeFupxTgDJDuWs7ROTBLItQ7Vs1cWFPyF08MqIXVCofAMDanYdw/nKuaztEXs+hItSff/6JO+64AwCwevVq3H777RgyZAi2b98uom/kIK1W6/DPhAZdK0JlcyRUpWqTLdUMsxWL+YrDbMVivg2XvWtvnpInSUBmdn59d8lr8H0ljidkW15RgVPnLgMAmoYFIThA5+Ie1Zwn5OupIm5ogjsH9wAAVFQYsXTdbq5h5ES8d205VIR6//33cdNNN0GSJCxZsgTvvPMOFi1ahI8//tjhA2dlZWHSpEmIi4tDfHw85syZg/Jy+/+ytWvXLowePRo9evTAyJEjsWPHDqvXly5dikGDBqFHjx54+OGHcfr0afm1oqIizJw5E/Hx8YiNjcWLL76IwkLbET9paWmIj4/HuXPnnHbs+qRQKKDVah1eXNCqCJXHkVD21DZbqh6zFYv5isNsxWK+DVdl195yXagMTsmrFb6vxPGUbNMuZKPMUAHAs6bieUq+nsic7b1D4xAWYvq78I/ks0g8lurajnkJ3rv2OVSEOn36NKZNm4bTp0/jypUruO2223DTTTfZFG5qYvr06fDz88Pu3buxZs0a7Nu3D19++aXNdqmpqZgyZQqmTZuGgwcPYsqUKZg+fbo8DXDdunVYsWIFPv/8cyQkJKBLly6YOnWqXL2dPXs2Lly4gK1bt2Lbtm24cOECFixYYHWMX375Bffffz9yc3Odeuz6JEkS9Hq9w8cODbr2yEg+Ic++2mZL1WO2YjFfcZitWMy34ars2t/QOET++sJlFqFqg+8rcTwl25PpFouSt/acqXiekq8nMmfrq1Hh8TH95falP+zh1Gcn4L1rn0NFKB8fHxQWFuK3335Djx49oNFocP78eQQEBDh00LS0NCQmJuKFF16ATqdDq1atMGnSJHzzzTc2265btw5xcXEYOnQoVCoVbrvtNvTq1QvffvstAOC7777DAw88gA4dOsDX1xfPP/88MjIykJCQgOLiYmzYsAFTp05FSEgIwsLCMGPGDKxduxbFxaZHky5atAgffPABnnvuOace2xUqG0lWFauRUCxCVao22VLNMFuxmK84zFYs5ttw2bv2zcKD5K85Eqr2+L4SxxOyPZ7mmU/GAzwjX09lznZA9/a4sf0NAIDMrHz8sOuQC3vlPXjv2nKoCDV06FA89NBDWLx4Me6++26kpKRg4sSJGDVqlEMHPXnyJEJCQtC06bVffpGRkcjIyEB+vvU8/5SUFERFRVm1tW/fHsnJyXZfV6vViIiIQHJyMtLS0mAwGKxej4yMRElJCVJTUwEA99xzDzZu3Ii+ffva9LMux66MJElW/9lrE91u2dYo0A+ABEBCdn5RvfdFxDl5S7s79YXnxHPy9HZ36gvPiedk2U41Y16YHAAyruS6rB9Ensy8KLlSqUC7Gxq7uDfkbhQKBZ6+cyCUStPUsW9/PohLOQUu7hV5I5UjG7/22mv48ccfodVqcdtttyE1NRXjx4/HI4884tBBCwsLodNZL4Rn/r6oqAhBQUFVbqvValFUVFTt63q9HgDg53dtypl5W/O6UJaFsJr0s6bHroxer4fBYAAAaDQa+Pn5obi4GGVlZVb70Gq1KCwstKqc+vn5QaPRQK/Xo6KiQm739/eHSqWSj2uecxoYGAilUom8POt/MQwODobRaERBQQFglGA0GiFJpjWhysvLrdbM8vHxQWBgIAwGg9V5qVQqBAQEoLS0FCUlJXK7M89JrVYjPz/f6kN6jc7pKoVCgeDg4Dqfk1qtBgAUFxfL187Tz8ldrpMkSSgqKkJISIjXnBPgPtfJnK+/vz80Go1XnJO7XKfi4mL5NV9fX684J3e6Tvn5+XI/lUqlV5xTZdeJaibIXws/nQZFxWW4yJFQRA4rLi1DemYOAKBti3D4qh36M5AaiDbNwzBqwI1Y/9thlBkqsHz973j50RGu7hZ5GYd++/j4+GDs2LHw8TE9wjEtLQ2xsbHy9zVl/hBoyfy9v7+/VbtOp7P6EAkAJSUl8nZVvW4uPhUXF8vbm49TkymEdTl2ZQICAqyKYub9XF/MAmyzsNyHPeHh4VCr1VYLn5k/6FpSKBTyh3oACG8UiOy8QmTnF0GlUtlsD5gKMfbafX194evra9PurHOyLEg6ck6W6npOkiRBrVZDpVLZXDvAM8/JzNXXSZIkOVNvOSdLrj4nc74qlcprzul6rjonc0HD8neup5+Tu12n6/P1hnO6nvm9Sdbs/b9WoVDghvAQnEy/hMu5epQZyqHhH9EOs5ctOYe7Z3vy7CWY6+ZRHrQouZm75+vJrs/2/lt7Y9efJ5GnL8bv/zuFQyfS0SOqlYt65/l479pyaDrer7/+ioEDBwIAFi9ejClTpuDhhx/Gd99959BBO3TogNzcXFy5ckVuO3XqFJo1a4bAwECrbaOionDy5EmrtpSUFHTo0EHel+XrBoMBqampiIqKQtu2baFWq5GSkmJ1HPO0uerU5diVUSgUVv/Za6tNu0KhgK+vL5RKZbXbX98WFhQAQIE8fRGMRklYH0W2i9y3UqmERqOxydaTz8ldrpNSqYSvr6/dn3e3vnvidTLnq1Qqveac3OU6WWbrLefkDu3ulG99XSeyplAooNFo7GZjfkKe0SghM5tTRBxVVbZUN56Q7Yn0S/LXnrQoOeAZ+Xoqe9kG+Pni0duvLVWz9IfdKLcYEUw1x3vXPoeKUP/+978xffp0GI1GrFy5Ep988gm++eYbLF261KGDRkREIDY2FnPnzoVer0d6erq8ztT1xowZg8TERGzevBnl5eXYvHkzEhMTMXbsWADAuHHjsHLlSiQnJ6O0tBTvv/8+wsPDERcXB51Oh5EjR2LBggXIzs5GdnY2FixYgFGjRkGr1Vbbz7ocu75JkoSCgoJarS/R6OoT8iQJyCmofCphQ1WXbKlqzFYs5isOsxWL+TZcVV17cxEK4LpQtcH3lTiekO0Ji0XJoz1sUXJPyNdTVZbtkF7R8uL1Zy/mYNOeI67onsfjvWufQ0Wos2fP4t5770VycjKKi4vRv39/dO3a1WpEU00tXLgQ5eXlGDJkCO69914MHDgQkyZNAgDExMRg/fr1AEwLiX/66adYsmQJevXqhcWLF+OTTz5B27ZtAQB33303HnvsMUyePBl9+vTBsWPHsGTJEnkdn1mzZiEiIgKjR4/GiBEj0LJlS7z++us16mNdj13fKmpZobZ8Ql4On5BnV22zpeoxW7GYrzjMVizm23BVdu1vaBwif811oWqH7ytx3D3bE+mmIpROq7Z6L3kKd8/Xk9nLVqlU4pk7B8I8gOebrYnI5WCFWuG9a8uhyfQ6nQ5ZWVn49ddfERsbC5VKheTkZDRq1MjhA4eHh2PhwoV2X0tKSrL6fuDAgfI0wOspFApMnDgREydOtPt6QEAAZs+ejdmzZ1fZn5YtW+L48eM27XU5tqcIDb5WhMrmLxciIiJyQ83Cr63Ddf4yi1BENXUlV4+sXNM/NEe1bipP0yeqSofWTTEsvhO27f8bxSUGfLlpH6aPH+LqbpEXcOg30Lhx43DHHXdg6dKlePjhh/HXX3/hsccew/jx40X1j+pBo8Bri6VxJBQRERG5oxbhIfLXFzgdj6jGTpy9NhUvqpVnrQdFrvXIbX3hr9MAAH5JTMbfqRdc3CPyBg4VoaZMmYIFCxbgq6++wuDBgxEWFoa33noLTz31lKj+kQOqeipfVcIsRkJl5bEIZU9ts6XqMVuxmK84zFYs5ttwVXbtg/y18Lv6x9AFTserFb6vxHHnbE9aLErewQOfjAe4d76erqpsgwN0eGhkvPz9Z+t2w2g01ke3vAbvXVsOj8WMj4+HRqPBtm3bEBYWhtjYWBH9IgcpFAqrR1k7wrwwOQDk5HM63vXqki1VjdmKxXzFYbZiMd+Gq6prr1AocMPV0VCXc/UoM5TXc+88G99X4rh7tsc9eFFywP3z9WQ1yXZk365o0zwUAJCSfhnbEo7VV/c8Hu9d+xwqQmVlZWH8+PG499578dJLLyE9PR1Dhw61WcOJ6p8kScjLy6vVyvthQQHy11mcjmejLtlS1ZitWMxXHGYrFvNtuKq79uZ1oYxGCZnZBfXZNY/H95U47pyt0WhEytWRUGEh/lYPJPIU7pyvp6tJtj4+Sjxz1yD5+68370dBUUl9dM/j8d61z6Ei1Ny5cxEVFYUDBw5ApVIhMjISTz31FN59911R/SMH1PbmDg7QQqk0VWc5Eso+/uIQh9mKxXzFYbZiMd+Gq6prz3Wh6obvK3HcNdtzl3JRXGoAAER76FQ8wH3z9QY1ybZr5A0YFNMBAFBQWIqVPyWI7pbX4L1ry6Ei1P79+zFz5kzodDp5SNkTTzyBlJQUIZ2j+qFUKhFydXFyrglFRERE7qpF42D5a64LRVQ9y6l4UR5chCLXmzC6H7S+KgDAln1Hcfr8FRf3iDyVQ0UotVqNkhLT0DtzRa+wsJCLbXmB0KvrQuXpi1BRwcXmiIiIyP00D79WhDp/mUUoouqcSGcRipwjPCQA9w3rBcA0JXrJ2t84yodqxaEi1C233IIXXngBqampUCgUyMrKwptvvonBgweL6h85IDAwsNY/a54fLklArp5T8q5Xl2ypasxWLOYrDrMVi/k2XFVde07Hqxu+r8Rx12xPXB0JpVQq0L5VYxf3pvbcNV9v4Ei2YwZ2k0ekHjtzATv/PCGqW16D964th4pQzz//PPz8/DBixAjk5+djwIABKC4uxowZM0T1j2pIoVBAqVTWeuV9y0UKszklz0pds6XKMVuxmK84zFYs5ttwVXftg/y18NNpAHA6nqP4vhLHXbMtNZQj9UIWAKBlk0bQ+Wpc3KPacdd8vYGj2WrUKjx1x0D5+y827EVxaZmo7nk83rv2OVSE+vvvv/Hhhx9i7969+O6777Br1y783//9H6t7bqCuK+83ujodDwBOZ2TBaOSUPDM+1UAcZisW8xWH2YrFfBuu6q69QqFAi6tT8i7n6lFmKK/P7nk0vq/EcddsT527DKPR1KfoNp47Fc9d8/UGtck2tlMb9O4SAcD0UKvV2w4K6p3n471rn0NFqMmTJ6OsrAyhoaHo1q0bmjRpIqpfVM8sR0It+m4Hxv9rGV77vx+x8qcE/PF3Gh/DSURERG7BvC6U0SghM7vAxb0hcl8nz1quB8W/28h5nrxjANQqHwDAj7/9D+mZOS7uEXkSlSMbt2rVCkeOHEHv3r1F9YdcpGtkCyiVCvlfS4pLDDh04hwOnTgnb9OySQg6RjRDdJtm6BTRDK2aNoJS6VAdk4iIiKhOLBcnv3AlF62aNnJhb4jc13GrIlQzF/aEvE2zsGDcdXMMvv35ICoqjFj6w268+dRoTjujGnGoCBUcHIwJEyagZcuWaNKkidVN9vXXXzu9c1R/WjZphE9fvB+JR1NxPO0i/k69iJx86wXKz13KxblLudiemAwA0GnViG7dFB0jmqFjm2aIatMUgX5aV3SfiIiIGogbGofIX3NdKKLKmRcl16h90KZZqIt7Q97m7iE98evBZFzO0SPpeDr2/3UGfW9s5+pukQdwqAgVExODmJgYUX2hOlAoFAgODq5T9bllk0Zo2cT0r4mSJOFyTgGS0zLlotTp81dQUXFtraiqRktFtmyMyJaNEdE8zGMXQTRzRrZkH7MVi/mKw2zFYr4NV02uveVIqAwWoWqM7ytx3DHbPH2xPF21fcsm8PHx3NkL7pivt6hLtlqNGo+P6Y93vtoKAPj8xz2I7dgaGrVDJQavxnvXPofukMjISIwcOdKm/dtvv3Vah6h2JEmC0Wh02ur7CoUCTUKD0CQ0CINiOgAwPWHj1LnLSE69WOPRUgqF6cNiuxsao22LcETeEI52NzS2Wgjd3Tk7W7qG2YrFfMVhtmIx34arJte+RXiI/HXG5dz66ZgX4PtKHHfM9oTlVDwPXpQccM98vUVds+3XLRLdo1rifyfOITO7AHuPnMZNPaME9NQz8d61r9oiVHFxMXJyTAuNvfLKK+jRo4fV6u4FBQV45513cN9994nrJdVIQUEBgoODq9+wlnzVKnRu2xyd2zYHcHW0VK7eqih1/WgpSQIyLuch43Ie9hxKkdtDAv0QeUM42t4QjnZXC1PNw4Lcdo0p0dk2ZMxWLOYrDrMVi/k2XNVd+yB/LXRaNYpLDJyO5yC+r8Rxt2xPeNmi5O6WrzepS7YKhQLjh8Xhf1dnxmzZ+xeLUNfhvWur2iKUXq/H7bffjpIS09PRbrnlFvk1SZKgUCgwdOhQcT0kt6VQKNCkUSCaNAq0Gi11+vxlnDl/BWcysnDq3GWkXsiCobzC6mdzC4rwR/JZ/JF8Vm7T+qoQ0fxaUap9y8Zo2yLMbQtTREREVP8UCgVuaByClPTLuJyrh6G8Qn5KExGZnDx7Sf46urVnj4Qi99alXQu0atoI6Zk5OHr6As5ezEZrrkFGVai2CNW4cWNs374dxcXFGD16NDZu3Gj1uq+vL8LDw4V1kDyLr1qFThHN0SmiudxWUWHE+cu5OHX+Ms5kXMHp81dw6txl6ItKrX62pLQcyakXkZx6UW7TadXoHNEcXSJboGtkC7Rv2YQfNImIiBq45uHBSEm/DKNRQmZ2vrymJRGZBgqYn4wXEqhD40aBLu4ReTOFQoHhfbtg2Q97AABb9h3FU3cOdHGvyJ3VaE2osLAw/D97dx4XVb3/cfw1w8ywLyq44QIiKC4pguKWu5n73m62qJWp6U/bbrfF9m6at7LF9jJbTSu7ppaaWRq4WyoKKm6oCMg+wAwzvz+ICQQVdL7MMHyej4eP8juHcz7f9zkzHr5zzvcA7NixQ65KcWLOep+pm5uWFo3r06JxffpHtwFK/nFMy8rlyKk0jp4qGZg6cuqcbQLFUsYCU7krpgx6N9q0bEyHsKa0b9WUNi0b4WHQK++Ds2brCiRbtSRfdSRbtSTfuqsq+77c5OTnMmUQqorkfaWOM2V7Oi3L9mVvRItGTlXblXKFPjgre2Q7IKYNH/+wFZO5mPXbE7h9ePca+R2tNpBjt6IqT0y+atUqTCYT48aNIy0tjdmzZ7N//36GDBnC008/jV4vB5kjlc68X1toNBqCAnwJCvAltn2orT03v5Dk0yVXSh1IPsO+Iylk5hhtrxeZivkz6RR/Jp0CSga4wps3pH2rJnQICyYypDHenu52r7U2ZVubSLZqSb7qSLZqSb51V1X3fdnJyWVeqKqR95U6zpbtwXLzQdX+W/GcLV9XYq9sfb08uDaqNRu2HSTfWMRvu5MY1C3SDhXWbnLsVq5Kg1DffvstzzzzDPPmzQPghRdeICsri4ULF7J06VKWLFnCjBkzlBYqLs1qtWI2m9HpdLV6tNXHy50OYcF0CAtmdN+SfqWkZfHX4RT2HT7FvqOnSS1ztVRxscV2C983G3ah0UBo00DblVLtQpsQ4Ht1T+JzlWydkWSrluSrjmSrluRbd1V13zcNKnMllAxCVYm8r9RxtmzLzgcV7gKTkjtbvq7EntkO7dGBDdsOAvDj1n0yCIUcuxdTpUGoTz/9lEWLFtGnTx+Kior46aefePXVV+nfvz8hISHce++9MgjlBPLy8lxupLV08tHgoACGdG8HQGpGNvuOnGbfkRT+OpLCqdRM2/JWK3/f2pfG97/uRaOBUX06cfeoXlf1xnfFbJ2FZKuW5KuOZKuW5Ft3VWXfN2nwz+tyJVTVyftKHWfKtuyVUOHNa/8gFDhXvq7GXtm2admIkKYNSE5J59Cxsxw5lUarYJk7Wo7diqo0CJWcnMy115ZMLvbXX39hNpvp2rUrACEhIaSmpl7qx4Wwq4b1/WhY34/+MSXzS2Xm5JcMSB1OYd/RFJJT0rFaS5a1WuG7TXtw1+uYNKy7A6sWQgghhL34+3ji6aHHWGAi5Vymo8sRwmmYzMUcOXUOKLli0NfLw8EVibpCo9EwtEd73vrmVwB+3PoX90/o59iihFOq0iCUtfQ3emDPnj2EhYXh4+MDwPnz59Hpqjy1lBB2F+DrRa9OrenVqTUAOfkFHDh6mj2JJ1m1eS9WK3z18w4CfL0Yee01Dq5WCCGEEFdLo9HQNDCAwyfPcS4zF5O5WJ6eKwSQnJKG2WwBoI0LzAclapd+0W348IctFBSa2bTjEHeN7Imnu8HRZQknU6VH3UVERPD7778DsHbtWnr37m177bfffiM8PFxNdaJa3Nzk5AtKJsbr1j6UqWOuLfd40He/3czm3YlXtE7JVh3JVi3JVx3JVi3Jt+6q6r4vnRfKYrFyNiNbZUkuQ95X6jhLtuUmJW/pOoNQzpKvK7Jntl4eBvpGRQBgLDSxaechu627tpJjt6IqDUJNmTKFWbNmMXz4cBITE7n11lsB+M9//sP8+fO57bbblBYpLk+j0eDr6ysTnl1gRO9ruGFQNFBya94rn/3MnsST1VqHZKuOZKuW5KuOZKuW5Ft3VWffy7xQ1SPvK3WcKdtDZSYld4Un44Fz5etqVGR7fY/2tv//ccu+cndV1TVy7FauSoNQAwcO5J133mHs2LF89dVXNGvWDIC9e/fy8MMPM2zYMKVFisuzWq0UFRXV6Tf5xdw2NJZB3doCYDZbeO6D1Rw+ea7KPy/ZqiPZqiX5qiPZqiX52k9GRgaDBw8mLi7uost8/PHHDBgwgC5dujBy5EjWrl1bgxWWV5193zQowPb/Mi/U5cn7Sh1nybbIZGb/0dMA6HRaQpo0cGg99uIs+boiFdm2bt7QNiH+kVNpJJ2ou/NHy7FbuSoNQgHExMQwZcoUwsLCbG2ffvopN9xwg5LCRPXl5+c7ugSnpNFomDGxP93ahwAll4Y+9e4qzqRX/VtTyVYdyVYtyVcdyVYtyffq7dixgxtvvJHjx49fdJlNmzaxZMkS3nvvPXbu3MmMGTOYPXs2J09W76phe6rqvm8SWOZKqHS5Ha8q5H2ljqOzzTMW8uS7qzj793uhdbOGGPSuM2+vo/N1ZSqyHVLmaqjVW/6y+/prEzl2K6ryIJQQtZmbm5YHJ11H25DGAGTmGHliySoyc+RDQQghhOtZuXIl8+bNY86cOZdc7siRI1itVtsfNzc39Hp9rXjoTNMyg1ByJZSoyzKy83j0zZX8lZQCgLtBxx0jeji4KlGX9YlqjaeHHoDNu5PIzS90cEXCmcgglKgzPAx6npgynBaN6wEl80fMf+8HjIVFDq5MCCGEsK/evXvz008/XXbKhOHDhxMYGMiwYcNo3749DzzwAC+++CKNGzeuoUqvnL+PJ57uJb/kpMicUKKOOnUuk4de+4ajp9IB8PV257n7RtO+VVMHVybqMk93AwNiSqZDKSwy88vOgw6uSDgTGYRyIbXhW0tH8/Xy4KlpI2kQ4A1A0olzPP/hj5jMxZf8OclWHclWLclXHclWLcn36gQFBVUpQ5PJRNu2bfn666/ZvXs3Tz/9NI899hgHD176F4ayV0+VznVxYduVtru5uVVpeY1GQ5Mgf8BK6vlsikxmu9dS0+2qt6nT6VyuT86yn0qfgFWTtRw6fpaHXv+Gsxk5gJWgej68NGMcES0audx+Kv1ccKU+2evYu9r2sp+59lz/kO7tACtg5cct+7BYLHVyP6nK19mOveqQMzwXodFo8PHxcXQZtUJQgC9PTxvFw4tXkJtfyO5DJ/nv5+uZe+sgtNqK47KSrTqSrVqSrzqSrVqSb8155pln6NKlC9dccw0A48eP54cffmDlypU88sgjF/253NxcTCYTAAaDAS8vL4xGI0VF/1xd7OHhgYeHB3l5eZjNZlu7l5cXBoOB3Nxciov/+RLI29sbvV6PxWIhO/ufOZ58fX3RarVkZZW/2snf358mDfxIPHYGiuHw8RSaBvrj7++P2WwmLy/Ptqybmxu+vr6YTKZy83PodDp8fHwoLCykoKDA1m7vPmVnZ5c7Sb9UnywWCzk5ObY2jUZjtz75+PiQn5/vUn1ypv2k0WgwmUw10qe9SSks+vIXTOZiNBotwUH+PHTrQHzdS+p1xf1UXFyMVqt1qT45y37Kzs62e5/qe+tp1bQBiSdSOXoqlf1HUmgfFlwn91N2drbL9QnK76fqqNIg1KOPPnrZZV544YVqbVjYl9VqpbCwEHd3d3kEZBW0aFyfJ6YM57E3v8NkLubXXYnU8/Pi7lG9KuQn2aoj2aol+aoj2aol+daclJQUOnToUK5Np9Oh1+sv+XM+Pj54eXmVa/P09MTT07PCst7e3hddx4WsVisGg6HCvi890S1Lo9HQNDDAdgVKTkExfn5+tj5cuDyAXq+vtN3d3R13d/cK7fboE2Cr68L6K+uTVquttMar7ZPVaqWgoAAPDw+X6VMpR++n0s8sq9VaI33atPMQi774BYvFgkajoUNYU/5151B8PCuuxxX2U2m+pe91V+jThRzVJ4PBUOHfW3v2aVTfzvz38/UArI07QIfWzerUfvLz86uQb23v08X2U3XI7XgupOwIqbi8yJAmPDx5CFptyQfCd5v2sGLjrkqXlWzVkWzVknzVkWzVknxrxoABA/j000/Zt6/kVok1a9YQFxd32bmkSq/6KP1TWduVthcWFlZpeYCmQQGABtBwOj3b7rXUdLvqbRYUFLhcn5xlPxUWFtZILd//uoeFy37GYrECGnpeE8b8aSPx9fJw6f1U+rngSn1ylv1U9jPX3uvv3TkcHy8PQMPvew6TnVfxM8iZ8lWxn1Tm60zHXnVUachKrnISriq2fSj3T+zH619uBOCjH7ZSz8/LNpGeEEII4WqioqKYP38+o0aNYsaMGbi5uTFz5kyysrJo2bIlb7zxBpGRkY4us0pKBqFKnJbJyYULs1gsfPy/P8p9YXp9z/bcN65PpdNJCOEM3PU6BnZty3eb9mAyF7NxewKj+3Z2dFnCwap13VRRURGrVq3i7NmztonFTCYThw4d4q233lJSoBCqXRfbjsycfJaujgPg1S824O/tSXRkSwdXJoQQQly9CycZ37Xrn19idTodM2fOZObMmTVdll00DfzntoDTaZmOK0QIhczFxbz2xQY27jhka7vl+m7cNDim2lcgCFHThnRvz3eb9gCwestfjOrTSY7bOq5ag1D/+te/2Lx5M/Xq1cNkMuHl5UViYiJjxoxRVJ6oDoPB4OgSaq2JA6PJyM7nf7/9icVi5YWP1/DcfaNp07LkEdWSrTqSrVqSrzqSrVqSb91VnX3v7+OJp7seY6GJU+fkSqjLkfeVOqqyLSg08eLHa9iRcBwArVbDfeP7cH2PDpf5Sdcix646qrNt3qgeHVo35a+kFFLOZfFn0imuCW+mdJvORI7diqp17ebmzZv5/PPPefbZZ+ncuTOrVq3ioYceknkbnIBGo8HLy0tGla+QRqNh2pje9OoUBkBhkZn57/3AydTzkq1Ckq1akq86kq1akm/dVd19r9FoaPL31VDnzudgMhdf5ifqLnlfqaMq26xcI/9661vbAJRe58bDtw+pcwNQcuyqU1PZDi1zzK75Y5/SbTkTOXYrV61BKIvFQqtWrWjVqhUHDhwA4NZbb2X79u1KihNVZ7Vayc/PL/d4RVE9Wq2W/7tlEB1bBwOQk1fIk++sIi0zV7JVRI5btSRfdSRbtSTfuutK9n3pvFAWi5XU8zmXXrgOk/eVOiqyTc3I5uHFK0g8ngqAl6eBp+8ZSc9rwuy2jdpCjl11airb7h1C8fcpeZLblr1HyMzJV7o9ZyHHbuWqNQjVuHFjTpw4Qf369UlPT7cFmpeXp6o+UQ1FRUWOLqHWM+h1PHbnUEKDGwCQmpHDU++u4nyWnNSqIsetWpKvOpKtWpJv3VXdfV9+Xii5Je9S5H2ljj2zTT6dzoOvr+BUaiYA9f29eWnGODqEBdttG7WNHLvq1ES2hr8nKAcoLrbw87YE5dt0FnLsVlStQaiRI0dyyy23cPbsWfr168d9993HrFmz6NCh+peEpqenM336dGJiYoiNjeW5557DbDZXuuymTZsYOXIknTt3ZujQoWzcuLHc6++++y59+vShc+fOTJo0iSNHjthey8/P59FHHyU2Npbo6GgeeuihcoNmR48eZfLkyURFRdG7d2/efvtt22tTpkwhKiqq3J82bdrwxBNPACVXhkVFRdG5c+dyy+Tn142RXVfl7enO/KmjaFTfF4Bjp9NZ+PlGCotMDq5MCCGEEBdqUmYQ6tS5TMcVIoQd/HX4FA8vXkFGVsnvK8ENA3h55jhCmjRwcGVCXJ3re7S3/f+arftsDzoTdU+1BqGmTZvGv/71L3x9fXn88ccJDQ3F19eX559/vtobnj17Nl5eXmzevJnly5ezdetWPvroowrLJScnM3PmTB544AG2b9/OzJkzmT17NmfPngVg5cqVLF26lPfff5+4uDjat2/PrFmzbJe8PfPMM5w+fZq1a9eybt06Tp8+zYIFC4CSJ/vde++9dOzYkbi4ON555x2WLVvGjz/+CMB7773Hrl27bH8ee+wxmjRpwowZMwBISkrCZDIRHx9fbjkvL69q5yGcSz0/L+bfM8p22WjCsbM89e4P5BfISLYQQgjhTJrIlVDCRWzZe5gnlqwi31hyvhnRshEvzRhHw/p+Dq5MiKvXJNCfzhElE5KfTc9md+JJB1ckHKVag1AAfn5+FBYW4uPjQ//+/Rk/fjzNmzev1jqOHTtGfHw8Dz74IJ6enjRv3pzp06ezbNmyCsuuXLmSmJgYBg0ahE6nY9iwYXTt2pUvv/wSgK+++opbbrmF8PBw3N3dmTt3LikpKcTFxWE0Glm1ahWzZs0iICCABg0aMG/ePFasWIHRaGTbtm2kpqYya9YsDAYD7dq1Y9KkSZXWceTIEZ555hkWLFhAw4YNAfjzzz9p06aN08x47+Hh4egSXEpwUABPThmOh7sBjVbLviMp/Put78jOk4n47UmOW7UkX3UkW7Uk37qruvs++O85oQBOp2XatxgXI+8rda422193JfLSJ2ttk+tHt23Bc/eOtn0hWtfJsatOTWY7tGeZCcq31I0JyuXYrahag1DLli3jgQce4Ny5c0DJLXVz5szh22+/rdZGExMTCQgIoFGjRra2sLAwUlJSyM7OLrdsUlISERER5dpat25NQkJCpa/r9XpCQkJISEjg2LFjmEymcq+HhYVRUFBAcnIyiYmJhIaGlhtEKrvusubPn8+YMWOIiYmxtf35558UFhYyfvx4unfvzq233srOnTurlYW9aDQaPDw8ZOZ9Owtv0Yjn7ys9AdCQeCKVR99YQUa2zINmD3LcqiX5qiPZqiX51l1Xsu/9fTzxdNcDkCJXQl2UvK/Uudps9yaeZNHnP2OxlNzJ0T+mDf++exgefx/XdZ0cu+rUdLbd2ocQ4Fty11DcvqOkZ+XWyHYdRY7dylVrEOrDDz/k448/tg3qjBs3jvfff5+33nqrWhvNy8vD07P8qH7p3y+cT6myZT08PGzLXer13NySg7rs7XGly+bl5V20jgtr2L59O3v27LHdhld2O9dccw1vvvkmv/zyCwMGDODuu+/mxIkTF+271Wot96eytitpt1qt5OTkYLFYLru8vbbpTO0q1x3WLIjH7xxCPT8vwMrxMxk8/PoKzqRn1do+Oct+slgs5OTkVPrzzlZ7bdxPpfmW3nPvCn1ylv1UNltX6ZMztDtTvjW1n0R5VquV3NzcamWj0Whst+SlZuRgLi5WVV6tdiXZiqq5mmyTT6fz7IerMZtL/q0eHBvJnJsHonNzs3eZtZYcu+rUdLY6Nzeu6x4JlDzR9Ke4AzWyXUeRY7dyuuosnJ6eTmRkZLm2du3akZ6eXq2Nenl5YTQay7WV/t3b27tcu6enJwUF5W9/KigosC13qddLB5+MRqNt+dLt+Pj4XLSOC2v48ssvGTp0KEFBQeXaH3nkkXJ/v/vuu1mxYgWbNm3itttuq7Tvubm5mEwlE1wbDAZbDWVnzffw8MDDw4O8vLxyk7V7eXlhMBjIzc2luMwJlre3NzqdjuzsbMxms22k1dfXF61WS1ZW+W8F/f39bSf4pTQaDf7+/pjN5nITt7u5ueHr64vJZCo3OKfT6fDx8aGwsLBc/vbsk16vJzs7u9yb1hF90uv1NKnvy5N3Xc/T76/mXGYup1IzeOi1b3j+/rEEeOlrXZ+cZT9ZrSWPLfXx8XGZPoHz7KfSfN3d3TEYDC7RJ2fZT0ajkfz8fMxmM+7u7i7RJ2faT9nZ2bZ8tVqtS/TpYvtJVHQluTQJ9OfIqTQsFitnM3LK3aIn/iHHnDpXkm3q+RyefGcVxoKS3w1i2rXk/gn95KqJSsixq05NZzuke3uWr9+JxWJl7R/7uWFQNFpttWcJqjXk2K2oWoNQrVu35rvvvmPs2LG2tlWrVtGqVatqbTQ8PJzMzEzS0tIIDAwE4PDhwzRu3BhfX99yy0ZERLBvX/n7RZOSkmxP5AsPDycxMZH+/fsDJZONJycnExERQWhoKHq9nqSkJDp16mTbTukte+np6SQnJ2M2m9HpdLZ1h4eH27ZlNptZv349b7zxRoV+LFq0iCFDhtCuXTtbW1FREe7u7hfte+ngV1menp4VrsiCigNyZddRGS8vL/z9/cv9w1V6oluWRqOxndRfSKfTVdqu1+srbXd3d6+0v/bqk59fxYkYa7pPVqsVk8lEaLNGLJh9A48v+Z5Tqec5n5PPI4tXMn/aSFoFB9aqPpVy9H4q+wucq/SpLEf3qTTf0s83V+jThRzVp9LbuMt+5tb2Pjnjfiqbr6v0qazS96a4euXnhcqSQSjh9HLyC5j/7irbU/DCWzTk4UlDcHNz3V/GhQBoWM+XLm1bsH3/MdIyc9l+4Bjd2oc6uixRg6r1KTd79myefPJJbr75ZubOncstt9zCk08+ybx586q10ZCQEKKjo3n++efJzc3lxIkTvPnmm0yYMKHCsqNGjSI+Pp7Vq1djNptZvXo18fHxjB49GoDx48fz6aefkpCQQGFhIQsXLiQwMJCYmBg8PT0ZOnQoCxYsICMjg4yMDBYsWMCIESPw8PAgNjaWevXqsXDhQgoLC0lISGDp0qXl6jh48CCFhYV06dKlQm2HDh3iueee49y5cxQVFbF48WJyc3MZPHjwRfuu0WjK/amsTXW7I7bpKn0KqufLi/ePJTQ4ENCQlWvkX299S8KxM7WmT4VFZo6mpGOxWFx2P0mfpE/OsE3pk/SpKssK+2jc4J9BvpRzmY4rRIgqKDKZee6D1Rw/cx4ouZLvySkjZA4oUWcM7dHe9v8/bq0bE5SLf1RrEKpXr15899139OrVCx8fH/r06cOqVavo1q1btTf82muvYTabGThwIDfccAPXXnst06dPByAqKorvv/8eKJlI/I033mDJkiV07dqVN998k9dff53Q0JLR0gkTJnDHHXdw//330717d/bv38+SJUvQ60s+xJ988klCQkIYOXIk119/Pc2aNeOJJ54ASr6B/OCDDzh06BC9evVi2rRpTJo0iXHjxtnqPHHiBP7+/pV+m/rCCy/QokULRo8eTWxsLPHx8Xz44YcEBARUOw97uPAKK2E/ZbMN8PXi+fvG0jakMQD5xiKeWPI9uw9dfC4wRysutrDjwDEWLvuJ2578gAcWfsmT76yiyOT4y0PluFVL8lVHslVL8q27rmTfNw0qMwglk5NflLyv1KlqthaLhYWf/cy+I6cBCPD1ZP60kfIUvMuQY1cdR2QbE9mSwICSK413HDhGakb2ZX6i9pJjtyKNVWbJqhH5+fkcOHCAyMhIORBdREGhiWc/XM2eQycB0Om0PDL5emKd5HJSq9VK0olUftl5iF93JZKZY6ywTL/oCP7vlkHybbwQQjhAbTw3cNaaz2fnc/tTHwIlj7Z/atpIB1ckREVWq5V3Vm7mh9/+BMDDXcfz940hvEWjy/ykEK7ni3XbWLYmHoAbBkczaWh3B1ckrlR1zw2qdCXUyJEl/5APGDCAgQMHVvpHOJbVarU9ZUzY18Wy9XDX88Tdw4ntUDLoZDZbeP7DH/ll5yFHlGlzJj2LL9Zt494Xl/F//13O97/uLTcA5ePljl5X8sSVX3YcYunqPxxVqhy3ikm+6ki2akm+ddeV7vsAX088/76V6VRapoLKaj95X6lT1WxXbNxlG4DSajU8cvv1MgBVBXLsquPIbAfHRqLVlnwR/lPcAZd8sqkcu5Wr0oyY06ZNA2DGjBlyxYQTK3bBN66zuFi2Br2ORyYP4dUvNvDLjkNYLFZeWfYTBYVFXN+jQ43Vl5Vr5Lc9Sfyy4xAJyWcqvK7XudG1XUv6Rbchum0Ldh48zgsfrcFisfL1+p0E1fNlaM+aq7csOW7VknzVkWzVknzrrivZ9xqNhsaBfhw9lU5qRg7m4mJ5xH0l5H2lzuWy3bjjIB/9sNX295k39Cc6sqXqslyGHLvqOCrbBv4+xLYPZeufRzifnU/8vmR6XhPmkFpUkmO3oioNQpVeCVV2riQhRAmdmxtzbh6Ih0HPmq37sFrhja83kV9gYlz/KGXbLTSZid93lF92HGL7gWNYLBVH2Du0bkr/6Db07BiGj9c/85p179CKqWN6s2TFZgDeXvEr9f29neZWQiGEEKK6mgYGcPRUOhaLlbMZOfKEPOE0dh86watfbLD9fdKwWAZ1i3RgRUI4h+t7tmfrn0cAWLN1n0sOQomKqvVs4AEDBlR6JZRer6d+/fr079+fu+++G61WHi0q6hatVsv0CX3x8jCwYuMuAD5ctQVjYRG3DOlmtysILRYLe5NO8cvOQ2zZcxhjoanCMi0a16d/TBv6RIXTsJ7vRdc1ovc1nDufy4qNu7BYrLy8dB0vTK998xJYrVbO5+Tj5W6Qp8oIIUQd1jTwn8nJz6RlySCUcApHTqXx/Ic/UlxsAUp+6Z44MNrBVQnhHDqHN6NRAz/Opmez6+AJTqdl0aTMZ7lwTdUahLrhhhv46quvmDJlCs2bN+fUqVN88MEH9OzZk1atWvHZZ59RUFDAzJkzVdUrLsHb29vRJbisqmSr0Wi4Y0QPvD0NLF0dB8AX67ZjLDRx96he1RqIslgspGXmcuLs+ZI/qRkcP3OeE2czyDMWVVi+QYA3faMi6BcdQWjTwCpvZ/Lw7pzLzGHzriQKi8w8/f7/eHnW+HKPulatqsdtfkERp1LPc/JcJinnMjl1LpOTqec5nZZFQaEZX293nr13DK2Cq97/ukA+F9SRbNWSfOuuK933TQIDbP9/6lym3OpUCXlfqVNZtmfTs3nynVW2Lw1jO4Ry37g+Mr3JFZBjVx1HZqvVarm+R3s+/vtW1TVb93HnyJ4Oq0cFOXYrqtYg1Nq1a1myZAnh4eG2tm7dujFv3jyeeOIJ+vXrx6RJk2QQygE0Gg16vVwFokJ1stVoNNwwKAYPg553v/0NgO827SG/oIgZE/tVuEqwuNjCmYxsjp/J4MTZ85w8m8GJ1POcTD1PQaH5ktvy9NDT65ow+ke3oUNY0yu6AlGr1TL7poGcz87nr8MpZOYYefKdVbw8awJ+3h7VXl91XZitubiYsxk5nEo9z6lzmaScy+Jk6nlOpmaSmZN/yXXl5BXy3IereWX2RHnM8d/kc0EdyVYtybfuupp93yTony9QTqdl2asklyHvK3UqyzY7r4An311lO39pG9KYebcNljtGroAcu+o4Q7aDurbl0x/jKC62sH5bArde3w2DvlrDFE7LGfJ1RtXau8eOHSMkJKRcW/PmzTl69CgAzZo1Izs7227FiaqzWq1kZ2fj5+cn367Y2ZVkO6pPJzzdDSz+eiMWi5Wf4g5gLDTRo2MrTqaetw06paRlYjZbqlxLgwBvIpo3ok9UOF3bh+Buhw9og17Hv+4cysOvryip6VwWz7z/P569b7Rd1n8pR1PS+PH3PaRnF3DqXCan07IqndvqYrRaDY0b+GEyF3PufC6pGTm8+PEanr5nlO0JgHWZfC6oI9mqJfnWXVez74PLXAklg1AVyftKnQuzLTSZeeb9/3EqNROA4IYBPH73cDwM8svolZBjVx1nyDbA14ue17Ri864ksnKN/PHXUfpEhV/+B2sBZ8jXGVXrN8y2bduyZMkSZsyYYWv74IMPaN26NQC//vorwcHB9q1QVJk8+lGdK8l2cGwknu56Fiz7ieJiC7/tTuK33UmX/TmNBho18KNFo/o0a1iPFo3r07xRPZo1rIeXh+FKyr8sXy8Pnpo6grmvfkNmTj4JyWd4ZdlPPHz7ECXf2BUUmfh87TZWbtqF2WTGzc0NuPgHc4CvJ8FBAQQ3DKBpYADNGtUjOCiARvX90OvcSM/KZc6ir21XdL3z7Wbun9DP7nXXRvK5oI5kq5bkW3dd6b4P8PXE012PsdBEigxCVUreV+qUZmuxWFjw6Trb04oDfL2YP3VkjVxh7srk2FXHGbK9vnt7Nu8q+T3p+1/3cG3n1i4zaOMM+Tqbag1C/fvf/2bq1Kl8/fXXNGnShNOnT2OxWHjrrbfYuXMn999/P6+++qqqWoWodXp3bo2nu57nPvwRk7n84znd3LQEB/nTrFF9mv892NSsYT2CGwYovwKpMg3r+/HU1BE8+sZKjIUmtuw9wgertjBldG+7bmf3oRMs/voXzqZnA/98KBv0brZBpuCGATQLqmf7e9kn+1Wmgb8Pj905lEff+BaTuZg1W/YR2qQBw3p1tGvtQgghnJdGo6FxoB9HT6VzNiMbc3ExOje5KlbUHKvVypKVm/njz5K7RDzd9Tw1dQSNGvg5uDIhnFvH1sG0aFyf42cyOHjsLPuOnqZDq6aOLksoUq3fdNu1a8fatWvZuHEjp0+fJjg4mAEDBuDp6UlmZia//PILDRo0UFWrELVSdGRLXpoxlp+3JdDA39t2dVPjBn5Od3Ic1iyIhydfz9Pv/YDFYuW7TXsICvBhdN/OV73u7LwC3v/+NzZsO2hr0+ncGNunIyP6RBEY4HNVV121admYGTf0Y9Fn6wFYsnIzLRrXp0OYXJ0phBB1RZPAAI6eSsdisZKakUNTeUKeqEHLN+xk9e9/ASVTBjx6x1DCmgU5uCohnJ9Go2HCgC688tnPAKzYsEsGoVxYtS+30Gg0uLm5odFoMJlMmEwmPD09CQgIUFCeqA5fX19Hl+Cyrjbb8BaNCG/RyE7VqBXdtgUzJvbntS83APD+978TVM+XnteEXdH6rFYrv+5K5N1vfyMr12hr7xDWlOkT+tI00B+tVmuXS24HxLTlaEo63/6yG4vFygsfreGV2RPr9DeQ8rmgjmSrluRbd13Nvg8u82jvBZ/+RLtWTQhv3pDw5g1pEujvMrd3XCl5X6mz7WAKS1f/Qen0ArNvGkhUm+aOLcqFyLGrjrNke21Uaz5Z/Qdpmbls25/MsdPptGxS+y9wcZZ8nUm1Jya/4447MJlMNG3alJSUFP7zn//w8ccfl3tinqh5Go3Gbr/Ii/LqYraDYyNJPZ/NF+u2Y7WWnMg/e58X7UKbVGs9qRnZvLl8EzsSjtvavDwN3DmiB9fFtkOr1WK1Wu2a7Z0jenD8TDo7E06QnVfAsx+u5j8zx+HprmY+LWdWF4/dmiLZqiX51l1Xu+/L/sKSeCKVxBOptr97expo3bwhrZsFEdGiEa2bBRFUz7fOHGfyvlJnZ8JxFn/9C6UDUJNH9KB/TBuH1uRK5NhVx5my1bm5MaZvJ9777ncAVvyyizk3D3JwVVfHmfJ1JhprNWbKuvfeewkNDeXBBx9Eq9VisVh4+eWXOXToEO+//77KOmu9/Px8Dhw4QGRkJF5eXnZfv9VqJSsrC39/+ZbP3upqtlarlVe/3MD6+AQAfLzceXnWeJo1rHfZn7VYLKzavJdP18RRUGi2tfe8phX3jOtDfT9v2zZUZJuTX8C8V5eTci7Ltl1Vk6yXtfvQCd79djOe7gbuGXutw69+q6vHbk2QbNWqK/mqPjdQwdnPZ4pMZt7//ne2HzhGakbOZZf38/YgvHlDWjdvSHiLkgGqBv4+V1K606sr76uaZLVaWfnLbj763xbbg1aG976Ge8ZeKxnbkRy76jhbtsbCIu565hNy8wvRajW89+9JBAXU3iuJnC1fVap7blCtK6H27NnDa6+9ZvtFTqvV8sADD9C7t30nLhZCOJ5Go2HGxH6kZ+ay+9BJcvMLeeqdVSx4YAIBvhf/cDmaksbrX24s9+1zfX9v7h3Xhx4dW9VE6fh6efDvu4Yz99WvMRaUTLL+xU/buWVINyXbs1gsfPHTdr5Yt43SYf15r33DxIHR3Dg4Br3Oueb+EkIIV2XQ67hvfF8AsnKNJP19NVTpn/PZ+eWWz84rYEfC8XJX7Nbz87LdwtcqOJBWwUE08Pd26V8gRPXl5Bfw38/XE78vmdIHrfToGMa0Mb3lWBHiCnm6GxjWqwNf/bTDNj+tvR+SJByvWoNQbm5u5ObmUr9+fVtbbm4unp6edi9MCOF4Ojc3Hr1jKI+8seLvpw3l8PR7/+P56WPwcNeXW7bQZObLddv4ZuMuLJZ/LrAc2rMDk4d3x9vz0k+4s7fmjerx0KQhPP3eD1it8PnabYQ0aXDFc1tdTGZOPguW/cSeQyfLtVssVr78aTvb9ifzf7cMcol72oUQojbx9/EkOrIl0ZEtbW3pWbkknTxH4vFU2wBVdl5BuZ87n51P/L7kvwcXSvh5exAaHEir4EBCmwYSFhxEcFAAbm5qr7AVzinpRCovfryGs2Wuthvd5xruHt1H+VXXQri6kb2vYeXG3ZjMxaz7Yz83Do7B18vD0WUJO6rWIFT//v2ZO3cujz/+OM2aNePEiRM8++yz9O/fX1V9QggH8/Iw8MTdI5j32nLSM/NIPJHKS0vX8u87h9lOvvcmnmTx179wOi3L9nPNGgYw44b+tHfgky1iIlsyeXgPPvphKwCvfPYzTQL9CW0aaJf1/3X4FC99so7MnJJv1rVaDbde341ii5Uv1m3DYrFy5FQasxd9xW3XxzK2X2c5ORVCCAdq4O9DA38fYtuHAiW3Spw7n2O7Uirp5DkST6SSbywq93PZeQXsOXSy3BcOep0bIU0alAxM/T1AFdKkQZ2cg7CusFqtrN7yF+999xtmswUoma5g7q2DaN1EBiWFsIcAXy8GdYvkxy1/YSw08eOWv7hhUIyjyxJ2VK05oTIzM5k5cybbtm2zXWbap08fXn75Zfz86u7Tp6qiJuZ9sPcEz+Ifki0cO53OQ4tX2E7Mr+/RntuHd+fDVVv4Ke6AbTk3Ny03DIpmwoAuGPSXH+dWna3VauWVz37mlx2HAGhU35eFsyfi73PlV3BaLBaWb9jJsjXxtqu+6vl58dCk6+gQFgyUfEu66POfOX7mvO3nIkMbM/umgTX6yHA5dtWRbNWqC/nKnFCVc/S+t1gsnE7P5vDJcxw5dY6jKWkcOZVGZo7xsj+r0WD7sqNVcBBhwYE0b1yfQH9vp/gSwtHZ1mbGwiJe/2ojm3cl2dratGzEQ7cPoWE9X8lWMclXHWfM9nRaFve+uAyLxUqAryfv/ft23Kvwe4UzcsZ87a265wbVGoQqdeLECdLT0wkODiYoKOiKCq1ramIiT4vFIrPvKyDZ/mNv4kmeeGcVxcUl3/55uusxFppsr7cNaczMG/rTonH9i62inJrKttBk5tHFK23zVHUIa8oz945C51b9uZqyco288tlP7Ew4YWvrFNGMebcOrjBXVpHJzLI18az8ZZdtrih3g467RvZkaM8Oyo8nOXbVkWzVqiv5yiBURc68789n53Pk1DkOn0r7e2DqHKfTsqjKmbSbm5agej40qu9H4/p+NKzvR+MGfjSq70uj+n74+3jKvwlOLPl0Oi9+vIZTqZm2ttF9OzF5eA/0OjfJVjHJVx1nzvalT9by2+6SQd/pE/oytGcHB1dUfc6crz0pGYTatm3bZVfUtWvXqlVYRzn702TExUm25f2y8xALP/2pXJunu57JI3owtEf7an3TW5PZpmXmMmfR17Zb54b27MD0CX2rtY59R1J4+dN1pGfmASXfeN88pBs3Doq+ZL/3HUlh0efrOZuebWvrHNGMB24aSGCAuqcwybGrjmSrVl3JVwahKqpt+95YWETy6XSOnkrjaEo6h0+dIzklHZO5uFrrcTfo/h6Q8qdRfV8aNygZqGpUv2Sgyh7zKta2bJ3Fz/EHeOubTRSZSvapp4ee2TcNLDfHpGSrluSrjjNnm3QilTmLvgagcaAfSx651SmuKK0OZ87XnpQ8HW/SpEmXfF2j0XDgwIFLLiOEcA39ukRw7nwOn/zvDwC6tQ/hvvF9lQ6m2ENggA+P3TmUR99cidls4cctf9EquAHX97j8tyoWi4WVv+zmk9V/2G6/C/D1ZN5t19EpvNllf759q6a8Pu9GPli1hTVb9gGw+9BJ7n/5c+4d24d+0RF2/4fJarWSmZNvu2pNCCGE/Xm6G4gMaUJkSBNbW3GxhVPnMjmSksbRU2mkpGVyNiObM+nZGAtMla6nsMjM8TPny93CXZaPlzutggNp3awhrZsH0bpZQxo38HPpX2ocrdBk5u1vNvFzfIKtLTS4AY/cfn2N3lYvRF3VunlDOkU0Y8+hk5xJy2bL3iP07tza0WUJO6jSIFRCQsLlFxJC1BkTB0bTLrQJWq2Gti0b15qT4LYhjZkxsT///Xw9AG998yvNGtazzeNUmey8AhZ9/jPb9x+ztXVo3ZSHbhtCPb+qXwXg6W7g/gn96N6hFa9/tYH0zDzyjUW88tnPbPnzMPdP6Ffhdr7qsFgsHDuTwf6jp9l3JIX9R0+TnpmL3k3LnaN6MbxXx1r37ZEQQtRGbm5aWjSuT4vG9enXJcLWbrVayTUWcjY92zYodTYjh9SMbM5kZJOakXPRK6hy8wvZm3iKvYmnbG3engZaN29IWHAQ4c1LBqca1ZeBKXs4dS6TFz9eQ3JKuq1tSPd2TB17ba2dl0aI2mh8/y62B0J8s3EnvTqFyWecC6j2p+iZM2dYtWoVp06domHDhowYMYIWLVqoqE1Uk7wh1ZFsK7LXU+9qOtuBXdtyNCWN7zbtwWKx8sJHa3hl9kQaNaj4cIWE5DP8Z+lazp3P/btWuGFQDLcM6XrFAzrRbVvw+rybeHflZjb+PVn6H38e5cDRM0yf0Lfc5f2XUmQyk3TyHPuOpLDvSAoJyWfIu+BpTgAFRWbe+XtbMyb2p1WwfZ4MKORzQTXJt+5y1X2v0Wjw9fLA18uD1s0bVnjdYrFwPiefsxk5toGqs38PUJ1KzeR8dn655fOMRRWe2Ofj5U7rZkG0bt6Q8L8HqBrW97Vl6qrZ2tNvu5N47csNtjkv3Q067p/Qj/4xbS75c5KtWpKvOs6cbeeIZrQKDuTIqTSSTpxjb9KpKt2F4EycOV9HqdbE5H/++Sd33HEHrVq1olmzZhw/fpzDhw/z/vvvEx0drbLOWq82zvsghKsqLrYw/70f2HWwZHLxkKYNeHnmeDzc9UDJt9Xf/7qHD1Ztsd1+5+ftwdzbBtOljf0G3bfsPcybyzeRlfvPE5f6R0cwbWwffLzKz/+RZywkIfmM7UqnQ8dTLznniIe7juaN6pN4PNXWptVqGNO3MzcP6YqHQW+3fgghrkxtPDeojTW7iozsPJJOpJJ08hxJJ86ReCLVNs/hpfh6u5fcxtcsiC5tW1zy6t/awlhYxIqNu9h18AR+3p40DfKnWcMAmgYG0DQogAb+3tX+xa/IZOaDVVv4329/2tqaN6rHI5Ovr/IDV4QQ9vfrrkReXroOgC5tmzN/2igHVyQupPTpeLfffjuDBg3i9ttvt7V9/PHHrFmzhs8///zKKq4jamIiT7PZjE6nk9FWO5Ns1XFktjn5Bcx7dTkp57IA6NkpjEduH0KusZDXvtzAH38etS3bLrQJD066Tsm8V5k5+byx/Jdy22sQ4M20MddSbLGw78hp9h9NITkl/ZJPYPL38aR9qya0Cy350yo4CK1Ww55DJ1jy7W+cPPvPPCON6vty34R+RLeVq1ivlHwuqFVX8q2NAzpyPuNc0rNy/x6USrX9NzPHeJGlrVitMDg2knvG9rF98VKbWK1Wftl5iI9+2EpGVt5Fl/Nw19Ek0J/goHolA1R//7dpUAC+Xh4Vlj+bns1Ln6y1PUUXoF90BNMn9MXT3VCluuS4VUfyVac2ZFtcbOGeFz7lbEYOAK/OvbHWXNlfG/K1B6WDULGxsfz+++/odP/cxWcymejevTs7duy4sorrCHmaTO0l2arj6GxPnM1g7n+X2y65H9K9HbsPnbD9IwcwfkAUk4Z2x81N3XxKVquVX3Yc4u2Vv5JfyS11lWnUwI/2rZrQvlVT2oU2ITgooEKGpfl6enmz8pfdfPnzdszmfyYq7xMVztQxva9qLqq6ytHHrqurK/nKIFRFdWXfq2K1WknPyiPpZCpJJ/4ZnCq54tZKcXExbm5uNA0K4MFJ19G6WcXbAp1V4vGzvPPtbyQkn7mq9fh6u9sGp4KDAvB0N/DZ2nhy8wsB0Om03DP2WoZ0b1/lY1COW7UkX3VqS7Y//LaXJSs2A9C3SzjzbrvOwRVVTW3J92opeTpeKU9PT06fPk3z5s1tbadPn8bf37/6lQohhIM1b1SfByddxzPv/w+rFdb+sd/2mo+XO/93yyC6tgtRXodGo6F/TBs6tG7Ka19sYHeZ+T1KXi+5ZbBdaFPb1U4N/Kt+VZZBr+Om67pybVQ4i7/eyF9JKUDJ5c07Eo5x58heDO7WViYuF0KIWk6j0RAY4ENggA/dO7QCSn4JSsvKJX5fMu99uxlzsYWUc1nMe/Ub7hjeg1F9rnHqz//z2fl8snor67cllLsiuFv7EO4a1QuD3o1TqZmknMskJS2LU+dK/v9MerbtlvqycvIKScg7U+lgVqMGfjwyeUitGpwToi4Y1C2Sz9duIzuvgM27k5g0tHul87mK2qFag1DDhg1j5syZzJ071zYn1KJFixg2bJiq+oQQQqmu7UK4fXgPPv5hq62tTctGPHT7EBrW863RWoICfHn6nlGs/WM/8fuSCW3agHahTWgb0hhvT/fLr+AygoMCeP6+MazflsD73/9Obn4hecYiFn+1kY3bE7h/Yj+aN5J5L4QQwpVoNBqCAnwZ1rMDrRr78873f5B04hzFxRbe//53dh06zpybBzndVbEmczGrNu/li3XbbFcsAzRrGMCUMdeWu6U8KMCXzhHNy/28ubiYsxk5nEo9T8q5ksGpU+cySUnLJD2z4q18PTq2YtaNAyrMySiEcDwPg54R117DZ2visVisfLtpN/eM6+PossQVqtYg1AMPPEBGRgbTp0/HZDLh7u7O+PHjmTlzpqr6RDW4ubk5ugSXJdmq4wzZju8fRU5eARu2JzAgpi23DY1Fr3NMXRqNhut7tOf6Hu3tsr4L89VoNAzqFknXdiG8/91vtif07TtympkLvmTiwC5MHBiNQR5BfVnOcOy6MsnXPjIyMrjxxht59tlniY2NrXSZ+Ph4Xn75ZZKSkvDz8+OWW27hnnvuqeFK/yH7Xp1mDevx0oxxLFsTz4qNuwDYmXCCmQu+YPZNA4mObOngCkts25/Me9/9Zpu3EcDL08AtQ7oxvFcHdFU4RnRubgQHBRAcFFDhNWNhEafTsjl17jxn0rNp0bg+3dqFXNXtMnLcqiX5qlNbsh3eqyPfbNhJYZGZdXH7uem6rvj7eDq6rMuqLfnWpGrNCVWqqKiIrKwsAgMDXfreRnuqjfM+CCFc3+5DJ3hj+S+cScu2tQU3DGDGxH4u8QQlIZyZ6nODHTt28Mgjj3D8+HE++eSTSgehDh8+zPjx43nyyScZM2YMBw8eZPLkycyfP5/rr7++xmsWNWf3oRMsXPZzuSfsje7biduHdXfYFxEnzp7n/e9+Y0fCcVubRgNDurfntqGxteIXTiGEOu+s3MyqzXsBuHlIV24Z0s3BFQmo/rlBtW8A/+abb5gyZQq33347U6ZMYc2aNVdUqLAvq9VKUVERVzCmKC5DslVHslWrKvl2jmjO4gdvZuLALmi1JV8qnErN5NE3vuXVL9aTk19QU+XWKnLsqiX5Xr2VK1cyb9485syZc8nlPvvsMwYOHMjYsWPRaDS0bduWL774gujo6BqqtDzZ9+pcmG3J5/9N5eY+/G7THua9tpwTZZ6oWhPyjIW8991vzHj583IDUO1bNeG//3cj90/s59QDUHLcqiX5qlPbsh3dp5PtfPWH3/ZSUOZWXWdU2/KtKdUahHrrrbd4+eWXiYqKYvLkybRt25Ynn3ySL774QlV9ohry8/Mvv5C4IpKtOpKtWlXJ112v4/bhPXh17o20DWlsa/85PoH7XvyMjTsOYrFYLrGGukmOXbUk36vTu3dvfvrpp8vO27l3716aNWvG//3f/xEbG8vQoUOJj48nKCjokj9ntVrL/ams7Urb8/LyqrS8PbfpLO2qt5mfn1/u737eHvz7rqHcM+7av29Dt3L0VBpzFn3F2q37Kl2HPftUXFzM2q37mPbCp3y3abft35rAAG8enHQdz08fQ2jTBrViP+Xl5TlVjc527F1te+nngiv1yVn2U9nPXGfvU8P6vvSJCges5OQV8FP8AaffT47Ot6b2U3VU61rbzz77jPfee48OHTrY2oYMGcL//d//cdNNN1Vrw0IIIZxLSJMGvDRjLGu27uOj/23FWGAiK9fIK8t+5q1vNhEZ0ph2oU2JDGlMRMtGeBj0ji5ZCHERlxtEKpWVlcUnn3zCokWL+M9//sOuXbu455578Pf3r/R2vFK5ubmYTCXfQBsMBry8vDAajRQVFdmW8fDwwMPDg7y8PMxms63dy8sLg8FAbm4uxcXFtnZvb290Op1tALJ0ygdfX1+0Wi1ZWf/MDwTg7++PxWIhJyfH1qbRaPD398dsNtsGBaBkTg5fX19MJlO5AU6dToePjw+FhYUUFPxz5ac9+6TX68nOzi53ku6IPun1JZ/ZRqPRtu9K+zSi9zWENg7gv19s5NS5TPKNxbz+1QZ2HjrBHdfH4On+z68M9urTyfQ8lqz4laQTZ8vUqOPGwV0Z2as9xWYT2dnZtWI/Wa0lA3wBAQFy7CnoU2m+3t7eGAwGl+iTs+wno9Foe83d3b1W9Glsvyg2bk/AYrGw/Odt9GzfDHeDwSn3U3Z2ti1frVbr0sdedVRrTqjo6Gi2bt2KwWCwtZnNZnr37s0ff/xRrQ3XNarnULBarWRlZeHv7y/zdNmZZKuOZKvW1eSbnpXLO9/+xpY9hyt9XavVENYsiHahTWx/nO3JSirJsatWXcm3puZXatOmzUXnhBoxYgRt27ZlwYIFtrb58+eTkZHBq6++etGa27ZtW65mjUZT6Teh1W0HyMzMrLDvK1veXtt0pnaV67ZarWRnZ+Pn51fhfVW6fGGRife/38KarX+VvkJggDdzbx1M+1ZNr7hPxcXFpJ7P4diZDE6cOU/CsTPE70sG/lm2d+fW3DmiJw3r+znN/rhUn8q2lX5mBQQEVPh5Z6u9qn1ypvay/yZotVqX6NOVttt73Rf+e1tb+vTkO6vYmXAMgLm3DqZvlwin3E/OkK+9+1TZsnl5eSQkJFT5fKZaV0INHz6cRYsWMW/ePNss7x988AHXXXdddVYjFNHp5GlWqki26ki2al1pvg38fXh08vXsOHCMdXH72X/0TLnJay0WK4nHU0k8nsp3m/YA0CTQn3atSgakIkOa0KxhgEsPIMixq5bkWzPCwsLKfZMK2K48uJTSk+kL2y62bFXbrVYrer2+yuu3xzadrV3lunU6XaXZli7v4W7g/on96NK2Ba99uYHc/ELSMvN47K3vuGFQNDcN7oqbm/ai67dYLJzNyOH42QyOn8ngxJkMjp89z4mzGRSZiissDxpCgxswbcy15R6G4Uz742LtF7aVXmnmTDVWt92ZarmwvfRzobrrcYba7d1u73Vf+JlbG/o0YUAXdv49f9yKX3bRt0uE8hqr2+5M+dqrvbrruJhqneEdPHiQPXv28O233xIcHExqaiqpqak0bNiQgQMH2pZbv359tYoQV0+j0eDj4+PoMlySZKuOZKuWPfKNjmxJdGRLrFYrZzOy2X/0NPuPnOZA8mmOnzlfbtnTaVmcTstifXwCAH7eHkSGNim5ja9VE8KbN6zSY7VrAzl21ZJ8a85NN93ElClT+O677xg1ahTbt29n1apV5a6Mqkmy79WpTrY9OrYivHlDFn72E38lpWCxWPli3XZ2HzrJvFsHE1TPp5qDTRX5+3hy29BuXBfbDq222s9Kcipy3Kol+apTW7PtENaU8OYNSTyRytFT6ew8eILoti0cXVYFtTVf1ap1O97KlSurtNzYsWOvuCBXVRO34xUWFuLu7u7SVx44gmSrjmSrlup8s/MKOJB8mgNHT7P/6GkST6RiNl98AvOG9X15dPL1tG7e0O611DQ5dtWqK/k66na8qKgo5s+fz6hRowDYtGkTr732GkePHqV+/fpMmTLlonN9yvlM7XUl2VosFpZv2MmyNfFYLCW/MrgbdFit1ioPNmm1Gho38KNF4/q0aFSf5qX/bVTv78nQaz85btWSfNWpzdlu2XuYFz5aA8A14cE8d98YxxZUidqcb3VU99ygWldClQ4upaenc+rUKYKCgmjSpMmVVSrsrqCgAHd3d0eX4ZIkW3UkW7VU5uvn7UFs+1Bi24cCUGQyk3TyXLmrpXLzC23Lp2bk8PDiFcy9dTA9rwlTUlNNkmNXLcnXfg4ePFju77t27Sr39759+9K3b9+aLOmSZN+rU91stVotNwyK4ZrWzVjw6TrOZuRQWFT5BLQXG2xq1jAAg971b6+V41YtyVed2ppt9w6hNA3yJ+VcFnsTT5F4/CzhLRo5uqwKamu+KlXrX4Tc3FwefvhhNmzYgNVqRaPR0KNHD/773//i5+enqkYhhBC1gEGvs01SzoCSb9BPpmay/2gK6+IOkHg8lSJTMS98tIZJw7ozcWAXl/5WSAghXEXbkMa8Nu8m3vvuN7b+eQQ/b486O9gkhHAOWq2Wsf0688bXmwD4ZuMuHpl88ae6CudRrX8pFi5cSF5eHj/88APNmjXj2LFjPP/887z88ss888wzqmoUQghRC2m12pJfUhrXZ0BMWxZ/tZGNOw4BsHT1H5xMPc+Mif3klxYhhKgFvDwMzLpxALNuHODoUoQQAoABMW1ZtiaezBwjW/88Qsq5TJoGBTi6LHEZ1ZoFcOPGjSxcuJCwsDDc3d2JiIjg5Zdf5ueff1ZVn6gGg8Hg6BJclmSrjmSrlrPka9DrmHPLICYN++cR8Ru3H+Txt78nK9fowMqunLNk66ok37pL9r06kq06kq1akq86tTlbg17HyGs7ASVPbl65abdjC6pEbc5XlWoNQhmNRnx9fcu1+fn5YbFcfCJaUTM0Gg1eXl5ya4sCkq06kq1azpavRqPhhkExPDJ5CAZ9yWS0+4+eZu5/v+b4mQwHV1c9zpatq5F86y7Z9+pItupItmpJvuq4QrZDe7bH010PwPr4BDJz8h1c0T9cIV8VqjUI1alTJ1599VVKH6hntVp59dVX6dixY7U3nJ6ezvTp04mJiSE2NpbnnnsOs7nyiQ43bdrEyJEj6dy5M0OHDmXjxo3lXn/33Xfp06cPnTt3ZtKkSRw5csT2Wn5+Po8++iixsbFER0fz0EMPkZeXZ3v96NGjTJ48maioKHr37s3bb79dbt1TpkyhY8eOREVF2f78+uuvVdp2TbJareTn51ONhx2KKpJs1ZFs1XLWfHt1as2L94+lnl/J0zPOZuQw77Xl7Eg47uDKqs5Zs3UVkm/dJfteHclWHclWLclXHVfI1tfLgyE92gNgMhezavNeB1f0D1fIV4UqDULt2LEDgLlz57J8+XL69OnDTTfdRJ8+ffjhhx949NFHq73h2bNn4+XlxebNm1m+fDlbt27lo48+qrBccnIyM2fO5IEHHmD79u3MnDmT2bNnc/bsWQBWrlzJ0qVLef/994mLi6N9+/bMmjXLtqOfeeYZTp8+zdq1a1m3bh2nT59mwYIFAJhMJu699146duxIXFwc77zzDsuWLePHH3+0bf+vv/7i/fffZ9euXbY/ffr0qdK2a1pRUZFDtlsXSLbqSLZqOWu+4S0a8crsibQKDgTAWGDi6fd+4IffnOfE4XKcNVtXIfnWXbLv1ZFs1ZFs1ZJ81XGFbEf36YSbW8nQxv9+/xNjofP0yRXytbcqDUJNnToVgDZt2rB27Vpmz57NgAEDeOSRR1i9ejVhYdV71PaxY8eIj4/nwQcfxNPTk+bNmzN9+nSWLVtWYdmVK1cSExPDoEGD0Ol0DBs2jK5du/Lll18C8NVXX3HLLbcQHh6Ou7s7c+fOJSUlhbi4OIxGI6tWrWLWrFkEBATQoEED5s2bx4oVKzAajWzbto3U1FRmzZqFwWCgXbt2TJo0yVbHiRMnyMrKol27dpX241LbFkIIcWmBAT68OGMs3TuGAiX38i9ZsZm3V/xKcbHc5i2EEEIIIS4vMMCHftERAOQZi1j3x34HVyQupUqDUGWv7AkICGD8+PFMmzaN4cOH4+PjU+2NJiYmEhAQQKNGjWxtYWFhpKSkkJ2dXW7ZpKQkIiIiyrW1bt2ahISESl/X6/WEhISQkJDAsWPHMJlM5V4PCwujoKCA5ORkEhMTCQ0NLTdZWNl1//nnn3h7ezNnzhy6d+/OiBEjWL58+UVrK7vti7FareX+VNamut0R25Q+SZ+crRbpk3P0ycOg55HbhzCufxQlrPzvt7089d4qcvMLamWfXHE/SZ/U9UkIIYQQV29cvyjb/6/ctBuTudiB1YhLqdJzse09kVZeXh6enp7l2kr/np+fj5+f3yWX9fDwID8//7Kv5+bmAuDl5VVhO3l5eReto3TdRUVFdO7cmTlz5hAeHk5cXBwzZ87E29uboUOHXra2yuTm5mIymYCSmfK9vLwwGo3lLtPz8PDAw8ODvLy8cvNkeXl5YTAYyM3Npbj4nzeVt7c3Op0Os9lMVlaWbX/5+vqi1WrJysoqV4O/vz8Wi4WcnBxbm0ajwd/fH7PZXG7OLDc3N3x9fTGZTOX6pdPp8PHxobCwkIKCAlu7Pfuk1+vJzs4ud5LuiD7p9Xo8PDwwGo22fVfb++Qs+8lqtdq24yp9AufZT6X5ms1mDAaDU/fp5sFdaN6oHq99sR5zcTE79icz+5UveeLu4bRoEuh0+8loNNo+c93d3eXYs3OfsrOzbflqtVqX6NPF9pOoyMPDw9EluCzJVh3JVi3JVx1XybZF4/p0ax9C/L5k0jPz+G13Ev1j2ji6LJfJ156qNAhlNBoZOHDgJZdZv359lTdaehJ44Tag5OSuLE9Pz3InkQAFBQW25S71eungk9FotC1fuh0fH5+L1lG67JgxYxgzZozttd69ezNmzBh+/PFHhg4detnaKlO63Qv7eOFgVmVZlF3HhTQaDYGBgZW2+/v7V2grPam/kE6nq7Rdr9dX2u7u7o67u3uFdnv0CSg3IFm2/pruk0ajueg31rW1T+A8+0mj0bhcn8D19hOo7dOgbpE0qu/L8x+tITe/gLMZOTzyxrf8665htA9tUiv7BK63n0B9nwICAiosX9v7dLH9JMrTaDRywq6IZKuOZKuW5KuOq2U7tn8U8fuSAVjxyy76RUc49Ml0rpavvVTp7Eev1zNjxgy7bTQ8PJzMzEzS0tJsAyeHDx+mcePG+Pr6lls2IiKCffv2lWtLSkqiQ4cOtnUlJibSv39/oGSy8eTkZCIiIggNDUWv15OUlESnTp1s2ym9bS49PZ3k5GTMZrPtRDApKYnw8HAAli9fbrvqqVRRUZHtpPZS274YjUZT4Y1wsTdGddqtVit5eXl4e3tXaf322Kaztatat9VqJTc3t9JsVW5X9bod1V627VLHrSNrrG67M9VStr1svtVdj6Nq79i6Ga/MnsjT7/3AydRMcvIL+fdb3zHzhv4M7Nr2qtZvz9orO3bl2LNfn5wlX3u1V3cddVlV/l0QV0ayVUeyVUvyVcfVsm0f2oSIlo04dOwsySnp7D50kqg2zR1Wj6vlay9VmhNKp9MxduzYS/6pjpCQEKKjo3n++efJzc3lxIkTvPnmm0yYMKHCsqNGjSI+Pp7Vq1djNptZvXo18fHxjB49GoDx48fz6aefkpCQQGFhIQsXLiQwMJCYmBg8PT0ZOnQoCxYsICMjg4yMDBYsWMCIESPw8PAgNjaWevXqsXDhQgoLC0lISGDp0qW2OnJzc3nmmWfYv38/FouFX375hR9++IEbb7zxstt2BLmsXx3JVh3JVq3amG+TQH9enjWBzhHNACgutvDfz9fzyf+2YrE4z4TltTHb2kTyrbtk36sj2aoj2aol+arjStlqNBrG9ets+/uKjTsdV8zfXClfe6nSlVAqJs587bXXePrppxk4cCBarZYxY8Ywffp0AKKiopg/fz6jRo0iLCyMN954gwULFvDYY48RHBzM66+/TmhoydOUJkyYQE5ODvfffz8ZGRl07NiRJUuWoNfrAXjyySd56aWXGDlyJCaTiYEDB/L444+XdF6n44MPPuDpp5+mV69eeHl5MWnSJMaNGwfA5MmTyc/PZ8aMGaSnp9O8eXNeeukl2yDT5bYthBDiyvh4ufPk1BEsWbmZNVtKrob9ev1OTp3LZNrYa2ngX/2HYgghhBBCCNfWo2MrGtX35WxGDrsPneTIqTRaBVectkY4jsZahRGmJ598kvnz59dEPS4rPz+fAwcOEBkZWWFOKHuwWq1kZWXh7+8vl/rZmWSrjmSrlivka7VaWbV5L+9//zsWyz//XLUKDiQmsiUx7VrSpkUjtNoqXdhr17pqe7bOrK7kq/rcQAU5n6m9JFt1JFu1JF91XDXbH37by5IVmwHoHx3B/9062CF1uGq+F6ruuUGVroSSAajaobacwNZGkq06kq1atT1fjUbDqD6dCA4K4KVP1mIsLHlC5ZFTaRw5lcZXP+/Ax8udLm1aENOuJV3atMDfp+Kk0yrU9mydneRbd8m+V0eyVUeyVUvyVccVsx3UNZJla+LJzS9k065Ebh/eg8AAx1xF74r5Xi15LIuL0Gg0GAwGR5fhkiRbdSRbtVwp3+jIlrw+7yZ+ij/AjoRjJJ04Z3stN7+QX3cl8uuuRDQaiGjRqOQqqciWtAoOVHKVlCtl64wk37pL9r06kq06kq1akq86rpqth7ueYb068NVPO7BYrHy/eQ93jexV43W4ar5XSwahXETpE9x8fHxc+lI/R5Bs1ZFs1XK1fBs18OO2obHcNjSW89n5bD9wjO0Jx9h18DjGgpIrpKxWOHjsLAePnWXZmngCfD2JjmxJTNuWdI5ojo+Xu11qcbVsnY3kW3fJvldHslVHslVL8lXHlbMd2fsaVm7cjclczJot+7hxUAzenvY5D6wqV873asgglAspLi52dAkuS7JVR7JVy1XzrefnxeDYSAbHRmIuLiYh+Qzb9h9j+4FjHD+TYVsuM8fI+vgE1scnoNVqaBfahOjIlvTtEk5QgO9V1eCq2ToLybfukn2vjmSrjmSrluSrjqtmG+DrxYCYNqz9Yz/GQhPr4vYztl9UjdfhqvleDRmEEkIIUavp3NzoEBZMh7Bg7hzZk9TzOew4UDIgtSfxJIVFJY/GtVis/HU4hb8Op/DVz9v5z8zxhDRp4ODqhRBCCCGECqP7dmbtH/sB+O7XPYy89hp0bm4OrkrU7OOEhBBCCMUa1vNlaM8OPH73cD575m7m3zOSUX2uoWmQv20ZY4GJBZ+uo8hkdmClQgghhBBCleaN6tGtfQgA6Zl5bN6V5NiCBCCDUC7F29vb0SW4LMlWHclWrbqer0Gvo0ubFkwdcy1LHr2Ntx+9lZZN6gNw7HQGn6z+44rXXdezVU3yrbtk36sj2aoj2aol+arj6tmO7dfZ9v8rN+3CarXW6PZdPd8rIYNQLkKj0aDX62XCMwUkW3UkW7Uk34qCgwKYd9t16HQl//x9t2kPuw+dqPZ6JFu1JN+6S/a9OpKtOpKtWpKvOnUh2/atmhLeoiEAR0+lsyfxZI1tuy7keyVkEMpFWK1WsrKyanxkty6QbNWRbNWSfCsX0qQBdwzvYfv7f79YT05+QbXWIdmqJfnWXbLv1ZFs1ZFs1ZJ81akL2Wo0GsaVmZB8xcZdNbbtupDvlZBBKBciB7c6kq06kq1akm/lRl57DZ0imgElcwS88fUv1c5KslVL8q27ZN+rI9mqI9mqJfmqUxey7dGxFY3qlzwVedfBExxNSauxbdeFfKtLBqGEEELUOVqtltk3DcTHyx2A3/ccZuOOgw6uSgghhBBC2Jubm5YxZeeG+mW3w2oRMgglhBCijgoM8GHGxH62v7/9za+cTc92XEFCCCGEEEKJQV0jbV8+btp5iLTMXAdXVHfJIJQL8fX1dXQJLkuyVUeyVUvyvbRenVozsFtbAIyFJhZ+9hPFxZYq/axkq5bkW3fJvldHslVHslVL8lWnrmTr4a5nWM8OAFgsVlZt3lsj260r+VaHDEK5CI1Gg1arlZn3FZBs1ZFs1ZJ8q2bamGtt8wQcOHqG5Rt2XvZnJFu1JN+6S/a9OpKtOpKtWpKvOnUt2xG9r7E9IfnHrX+RX1CkdHt1Ld+qkkEoFyEz76sj2aoj2aol+VaNl4eBubcNRqstOUH4bG08h46fveTPSLZqSb51l+x7dSRbdSRbtSRfdepatvX8vBgQ0wYAY4GJdX/sV7q9upZvVckglBBCiDovMqQJEwdGAyWXaC9c9hMFhSYHVyWEEEIIIexpTN/Otv//7tc9mIuLHVdMHSWDUEIIIQRw03UxhLdoCEDKuSze//53B1ckhBBCCCHsqXmj+nRtFwJAWmYum3cnObagOkgGoYQQQghA5+bG3FsH427QAbBm6z7i9h11cFVCCCGEEMKexvXvbPv/lb/sktvlapgMQrkIjUaDv7+/THqmgGSrjmSrluRbfcFBAUwZ3dv299e+2MD57PwKy0m2akm+dZfse3UkW3UkW7UkX3XqarbtWzUlvHnJ1e9HT6WzJ/Gkku3U1XwvRwahXITVasViscgorgKSrTqSrVqS75UZ0r0dsR1CAcjOK+DVL9ZXyFCyVUvyrbtk36sj2aoj2aol+apTV7PVaDSMveBqKBXqar6XI4NQLiQnJ8fRJbgsyVYdyVYtybf6NBoNM2/oT4CvJwA7Eo7zv9//rLCcZKuW5Ft3yb5XR7JVR7JVS/JVp65m27NjGI3q+wKwM+EER1PSlGynruZ7KTIIJYQQQlzA38eT2TcNtP39g++3cOJshgMrEkIIIYQQ9uLmpmV0mSflfbtpt8NqqWtkEEoIIYSoRHRkS4b37giAyVzMgk9/wmSWx/gKIYQQQriCQd3a4uPlDsCmnYmkZeY6uKK6QQahXIhMeKaOZKuOZKuW5Ht17hzZk+aN6gFw5FQay9bE2V6TbNWSfOsu2ffqSLbqSLZqSb7q1OVsPd0NDOvZAYDiYgurNu+1+zbqcr4XI4NQLkJm3ldHslVHslVL8r167nodc28djJtbyT+XKzbu4q/DpyRbxSTfukv2vTqSrTqSrVqSrzqSLYzofQ06Xcl53o9b/yK/oMhu65Z8KyeDUC7CarViMplk5n0FJFt1JFu1JF/7CGsWxKShsQBYrbBw2c/k5BdItgrJsVt3yb5XR7JVR7JVS/JVR7KFen5e9I9uA4CxwMS6P/bbbd2Sb+VkEMqF5OXlOboElyXZqiPZqiX52sfYfp3pENYUgLTMXN5e8atkq5jkax8ZGRkMHjyYuLi4yy576NAhOnXqVKVlVZJ9r45kq45kq5bkq45kC2PKTFD+3a97MBfbbw5QybciGYQSQgghLkOr1TLnlkF4eRoA+HXnIX7fe8TBVQlxaTt27ODGG2/k+PHjl13WaDQyd+5cCgoKaqAyIYQQwnm0aFyfmHYtgZIvG3/bc9jBFbk2GYQSQgghqqBhPV+mj+9r+/sHP/zB2YxsB1YkxMWtXLmSefPmMWfOnCotP3/+fAYNGqS4KiGEEMI5je8fZfv/lRt3yS10CskglAtxc3NzdAkuS7JVR7JVS/K1r75dIugXHQGAscjMnEVfs/aP/VgsFgdX5nrk2L06vXv35qeffmLYsGGXXfbbb7/l2LFjzJgxowYquzzZ9+pItupItmpJvupItiXat2pKePOGQMkTkfcknrTLeiXfinSOLkDYh0ajwdfX19FluCTJVh3JVi3JV417xvbhQPIZzqZnk5tfyOKvNrJxewLTJ/SjReP6ji7PJcixe/WCgoKqtNzhw4dZtGgRn3/+ebVOlK1Wa7lviTUaTaXfGl9Ju4+Pj20bl1rentt0lnbV2/T19a2w72p7n5xlP/n4+DhVjfbokzO1l34uAC7TpytpV7Husp+5rtKnK20f3bcTCz79CbCy8pfddApvdtV9cnS+9mqv7jouRQahXETpzPt6vV4eAWlnkq06kq1akq8aPl7uvDxzHO9+u5nNu5MADfuOnGbWwi8ZPyCKGwfFYNDLP69XQ47dmlFYWMicOXP417/+RdOmTav1s7m5uZhMJgAMBgNeXl4YjUaKiv55tLWHhwceHh7k5eVhNptt7V5eXhgMBnJzcykuM/mrt7c3Op2OjIwMtFqtbd/7+vqi1WrJysoqV4O/vz8Wi4WcnBxbW+njsM1mc7nJYN3c3PD19cVkMpGfn29r1+l0+Pj4UFhYWG4+LHv2Sa/Xk52dXe4k3RF90uv16PV6TCaTbd/V9j45y36yWq1YLBbq16/vMn0C59lPVquV4uJi/Pz8MBgMLtEnZ9lPRqOR4uJi3NzccHd3d4k+Xc1+6hbZgkb1fUlJy2TbviPsSThCSJMGV9yn7OxsW75ardalj73q0FjlZscakZ+fz4EDB4iMjMTLy8vu67darWRlZeHv7y8n7HYm2aoj2aol+apTmu2Rs9m89c0mzqT9MzdUk0B/pk/oS+eI5g6ssHarK8eu6nODUm3atOGTTz4hNja2XPv27du56667MBgMtracnBy8vLwYPXo0Tz311EVrbtu2bbma7fXtK0BmZmaFfe/s357bq13luq1WK9nZ2fj5+VV4X9XWPjmq/cK20s+sgICACj/vbLVXtU/O1F723wStVusSfbrSdnuv+8J/b12hT1fbvmrzXt79djMAep2O4b06MH5AFwJ8vardJ2fI117tl1o2Ly+PhISEKp/PyFe1QgghxBWKimjO4gdv5quftrN8w04sFiun07J4/O3v6R8dwd2je+Pv4+noMoWoVExMDHv37i3X1qZNG95+++0KA1YXKj2ZvrDtYstWtb30doWqrt8e23S29prYZk1v15nytVf7xY5PZ6qxuu3OVMuF7WWPW1fp05W223vdF37mukKfrqZ9cGwk//v9T1LOZWEyF/Ptpj2s/WM/I/tcw9i+Ufh4uVdpPc6Ur73aq7uOi5GJyYUQQoir4K7XMWlYd16fdxORoY1t7Rt3HOLeF5fxU9yBat8rL4RKUVFRfP/9944uQwghhHA6nu4G/jNzPGP6dUavK5kr0Vho4qufdjDluU/46uftGAuLLrMWcSlyJZQL0elkd6oi2aoj2aol+apzYbYtGtfnxfvHsi7uAB/9sIU8YxG5+YW89uUGNvw9cXnzRvUcVG3tI8eu/Rw8eLDc33ft2lXlZR1B9r06kq06kq1akq86km1F/j6e3D2qF6P7dOKrn3ewLm4/xcUW8oxFLF0dx/e/7mXiwC4M7dnhsvOASr4VyZxQNaSm5n0QQgjheJk5+bz33W9s2ploa3Nz0zJxYBcmDoyWicsFUDvPDWpjzUIIIcTVOJuezec/bWPj9oNYLP8MnzQI8ObGQTEM6hZpu2qqLqruuYHcjucirFYrBQUFcsuHApKtOpKtWpKvOpfLNsDXi3m3XcdT00bSqL4vAMXFFr5Yt52ZC75gb+LJmiy31pFjt+6Sfa+OZKuOZKuW5KuOZFs1jRr4Mfumgbzx0M1cG9Xa1p6emcebyzdx34vLWL8tAYvFUu7nJN/KySCUCyn76EZhX5KtOpKtWpKvOlXJNrptCxY/dDMTB3ZBqy2ZtDHlXBaPvfUdiz7/maxco+oyay05dusu2ffqSLbqSLZqSb7qSLZV16xhPR6aNITX5t1It/YhtvazGTn89/P13P+fz/ltd1K5wSjJtyIZhBJCCCEU8jDouX14D16beyNtQ/6ZuHzDtoPc95JMXF6T2rRpw80333xFP7t+/XpGjhxJp06dGDFiBOvXrwfg9ddfp02bNhX+rFixwp6lCyGEEMJJhDYN5PG7h7Nw9gQ6RzSztZ9MzeSlT9Yye9FXxO87quz8rrafzzhsECo9PZ3p06cTExNDbGwszz33HGazudJlN23axMiRI+ncuTNDhw5l48aN5V5/99136dOnD507d2bSpEkcOXLE9lp+fj6PPvoosbGxREdH89BDD5GXl2d7/ejRo0yePJmoqCh69+7N22+/XW7dn3/+OUOGDCEqKoohQ4awbNky22sWi4WoqCg6d+5MVFSU7U9+fr49IhJCCOFCWjZpwEszxnL/xL54eRoAyMkrmbj8kcUrOXY63cEVios5fvw4DzzwAEVFRUyePJns7GweeOABjh8/Tvfu3XnggQdsf5o1a4aPjw9dunRxdNlCCCGEUCiiRSOeuXc0L9w/ptwTko+eSueZ91fz0OsrOHg81YEVlucs5zMOmxl19uzZNGrUiM2bN5OWlsZ9993HRx99xJQpU8otl5yczMyZM3nllVfo168f69atY/bs2axbt45GjRqxcuVKli5dyvvvv0+LFi1YtGgRs2bNYtWqVWg0Gp555hlOnz7N2rVrKS4uZvbs2SxYsIAnn3wSk8nEvffey+DBg3n33XdJSkrinnvuoWXLlgwdOpSff/6ZV155hXfffZdOnTqxe/dupk2bRmBgIEOGDCEpKQmTycTOnTsxGAwOSvIfzlCDq5Js1ZFs1ZJ81bmSbLVaLdf36EBs+1a8+91mNu9KAmD/0dPMWvglY/p25qbrYvB0l/1W08fupEmTiI+Pr9A+Y8YMOnbsSKtWrXjwwQe59tprCQoK4tlnn+Wvv/5i2LBhdO3aFYBt27bx2muv8dxzzxESElKj9bsS+dxSR7JVR7JVS/JVR7K9eh3Cgnlpxjh2Jhzn0zVxJJ04B8DBY2d49qMzPDnVQExkSI3UUhvOZxwyCHXs2DHi4+P59ddf8fT0pHnz5kyfPp2XX365wiDUypUriYmJYdCgQQAMGzaMFStW8OWXXzJr1iy++uorbrnlFsLDwwGYO3cuX331FXFxcXTq1IlVq1bxySefEBAQAMC8efO4/fbbeeihh9i1axepqanMmjULg8FAu3btmDRpEsuWLWPo0KGcPXuWqVOn0rlzZwCioqKIjY1l27ZtDBkyhD///JM2bdo4xRtXo9HIU2oUkWzVkWzVknzVudps6/l58dCkIQzqFsnb3/zK6bQsLBYrKzbu4tddiUwd05seHVuh0WjsWHXt4Yhjd/z48fTo0aNCe9euXenatSv9+vWzte3evRuAVq1alVv2mWeeITIyknHjxqks1aXJ55Y6kq06kq1akq86kq39aDQaoiNb0qVtC7b+eYRla+I4fuY8AC98tIZn7h1Nu9AmyuuoDeczDhmESkxMJCAggEaNGtnawsLCSElJITs7Gz8/P1t7UlISERER5X6+devWJCQk2F6fOnWq7TW9Xk9ISAgJCQkEBARgMpnK/XxYWBgFBQUkJyeTmJhIaGhouUGk1q1b88477wBw6623lttueno627Zt49FHHwXgzz//pLCwkPHjx3Pq1CnCwsKYO3euQy7Bt1qtGI1GPD096+wvLapItupItmpJvurYK9subVqw+MGbWL5hJ8vX78RkLiYtM5cXPlpDdGQL7h3Xh8YN/O1Yee3giGO3R48elU4e6u9fPv933nmHH374gYEDB9K2bVtb+2+//cbBgwd55ZVX5P12FeRzSx3JVh3JVi3JVx3J1v40Gg09rwmje4dQ/rN0HZt3JVJksvL0ez/wwv1jCW0aqHT7teF8xiGDUHl5eXh6epZrK/17fn5+uUGoypb18PCwzbt0qddzc3MByo3uli6bl5d30Toqm9Pp3Llz3HPPPXTo0IERI0bYtnPNNdfwwAMP4O/vz7Jly7j77rv5/vvvad68eaV9t1qt5SYo02g0lU5YVt12gMLCQjw8PC67vL226UztKtdttVopKiqqkG1t7pOj2i9ss1qtFBYW4unp6TQ1Xm2fnKm9NF8PDw+X6dOVttt73WWzvdr163Vu3HxdV/pGhfPOt5vZmXAcgB0HjjH9pc+5YVA04/pHode5Ke2TM7SryLcylW137ty5bNu2rcKy999/PzNnzgRg8eLFLF68mMjISJ577rly61m5ciVeXl4MGDDgqv6dF1BUVFTh/EzYh2SrjmSrluSrjmSrhlarZc7NA8nIyuFA8lnyjEU8seR7/jNzPE0C1X3BOG/evIvejlf2fOb1118nMjKS559/vtxypeczAwcOVFajQwahvLy8MBrLP5a69O/e3t7l2j09PSuM5BUUFNiWu9TrpYNPRqPRtnzpdnx8fC5ax4U17N69mwceeICYmBheeOEFdLqS2B555JFyy919992sWLGCTZs2cdttt1Xa99zcXEwmE1By/21pDUVFRbZlPDw88PDwIC8vr9xk7V5eXhgMBnJzcykuLra1e3t7o9PpbINnpSOWvr6+aLVasrKyytXg7++PxWIhJyfH1qbRaPD398dsNpebuN3NzQ1fX19MJlO5wTmdToePjw+FhYXl8rdnn/R6PdnZ2eVO0h3RJ71eD5QcG6X7rrb3yVn2k9VqJT8/n4CAAJfpEzjPfirN19vbG4PB4BJ9cpb9ZDQaba+5u7vbpU8N6/nw1NSR/LR1Lx//uI3z2XkUFxfz6Zo4Nu44yG3XRdMx7J/LuF15P2VnZ9vq1Gq1dusTlDxUpOx2S/s0depUxo4da2t3d3dHp9PRpEkTsrKyWLp0KYsXL6ZTp068++67ALb1WK1Wtm7dSkxMDIWFhRQWFlZ5PwkhhBDCNRn0Oubc2I+FX2zi4LGzZOYYefzt73hp5jga+Pso2ebMmTPJyMio0N66dWsA3nvvPV5//XWioqJYsmRJuSukyp7PVHYBhr04ZBAqPDyczMxM0tLSCAwsuRzt8OHDNG7cGF9f33LLRkREsG/fvnJtSUlJdOjQwbauxMRE+vfvD4DJZCI5OZmIiAhCQ0PR6/UkJSXRqVMn23ZKb9lLT08nOTkZs9lsG1hKSkqyzS8FsHz5cp599llmzZrFXXfdVa6ORYsWMWTIENq1a2drKyoqwt3d/aJ9Lx38KsvT07PS0ecLB8PKrqMyXl5e+Pv7l7tsrvREtyyNRmM7qb+QTqertF2v11fa7u7uXml/7dWnslfFla2/JvtktVoxmUx4enpWes90bexTKUfvp7K/lLpKn8pydJ9K8y39fHOFPl3IUX0qHdAo+5lrjz5pNBoG97iG3l3a8tnabXy/eQ9Wi5WUc1n8Z9l6ro0K5+5Rvajv510n9lPZfO3Vp1OnTvHKK6+Ua7vlllvo06dPpTVarVb279/Pm2++iUajoUuXLrYn5fbr14/IyEjOnDlDeno67dq1q/axJ4QQQgjX5emu54kpw3n0jZUcP3Oesxk5PLHke16cMQ5frysf6Dlx4gRPPPFEubZbbrmFbt26XfRn9u/fz8KFC9FoNERFRdnOZ/r3709kZCRnz561nc+o5JCzn5CQEKKjo3n++ed5+umnOX/+PG+++SYTJkyosOyoUaP48MMPWb16Nddddx3r1q0jPj6exx57DCiZeOv111+nT58+hIaGsmjRIgIDA4mJiUGv1zN06FAWLFjAq6++CsCCBQsYMWIEHh4exMbGUq9ePRYuXMjs2bM5evQoS5cuZc6cOQCsXbuWp556irfeeotrr722Qm2HDh1i+/bt/Pe//8Xf35933nmH3NxcBg8efNG+azSaCvdWXuxey+q0W61W2728VVm/PbbpbO0q1116O1NNb9eZ8rVX+4Vtpb/gOlON1W13ploubC97j7+r9OlK2+297gs/c+25fi8Pd6aM7s3Arm15c/kmEpLPABo270pi+4FjTBranWE9O+DmprVrn5yhXXW+586d48svvyzX1qdPn3LzIVy4nhUrVmCxWAD48MMPba8FBQXRrl070tLSAGjUqJFdjr26TuW3r3WdZKuOZKuW5KuOZKuWh4cH7u7uPH3PKB5+fQVnM3I4fuY889/9gWfvHY2Hu/6K1lvd8xmAb775xnY+88EHH9jag4KCiIyM5Ny5kqf6lZ27WwWN1UETEqSlpfH0008TFxeHVqtlzJgxzJs3Dzc3N6Kiopg/fz6jRo0CYPPmzSxYsIDjx48THBzMgw8+SN++fYGSwZcPP/yQZcuWkZGRQceOHZk/fz6hoaFAye1vL730Ehs2bMBkMjFw4EAef/xx2xUtx44d4+mnn2bPnj14eXlx2223MW3aNABGjhxJUlJShTfmyJEjefrpp8nMzOSll15i06ZNGI1GOnbsyL/+9a9Kd3x+fj4HDhwgMjJSnkAghBCiyiwWCz9vS+DDVVvIzf/nNq9WwYFMn9CXNi0bO7A6cTVq47lBbaxZCCGEcAYp5zJ5ePEKMnNKpgTqHNGMx+8ejkFfu6+Mru65gcMGoeoa1SdtVquVvLw8vL295ZtVO5Ns1ZFs1ZJ81XFEtlm5Rj7+31Z+ijtga9NoYEj39tw+vPtVXdLtbOrKsVsbB3TkfKb2kmzVkWzVknzVkWzVqizfoylpPPLGSvKNJXNu9uoUxkOTrkOrrfzq9tqguucGtbenogKZ4FQdyVYdyVYtyVedms7W38eTWTcO4D+zxhHStAEAVius2bqP+178jFWb91Jkcp39Lcdu3SX7Xh3JVh3JVi3JVx3JVq0L8w1tGsiTU0Zg0Jc89fj3PYd5c/mmOvXEXBmEEkIIIWqRyJAm/HfODUwZ3QvPv+cRyMo18s7KzUx9/lN++M21BqOEEEIIIVxJu9Am/OuOoWi1JVdHrf1jP5+s/sPBVdUcGYQSQgghahk3Ny2j+3bmzYdv4dqo1rb2jKw8lqzYzLQXPuV/v/8pg1FCCCGEEE4oOrIlc28dTOldkMvX7+SbDTsdW1QNkUEoF1Jb5pOojSRbdSRbtSRfdZwh28AAHx6aNIRX595IbIdQW3t6Zh5vf/Mr97ywjB+3/IXJXOzAKq9MdfM9ffo09957L506daJPnz4VnhhT1rp162jTpg1ff/11ufYzZ87QqVMn5s2bd0U1A+zZs4exY8cSHR3NPffcY3tyXmU1fvPNN1e8HVfmDO8tVyXZqiPZqiX5qiPZqnWpfPtEhXPvuD62v3/0w1bWxe3HbDYzduxY+vTpc9GfhcrPZ06cOMG0adOIioqif//+Fc51quPrr79mwIABdO/enWeffRaTyWR7bdu2bYwbN46OHTtyww03cPDgwSqvVwahXIRGo8FgMMiEcgpItupItmpJvuo4W7atggP5913DWPR/E+nWPsTWnpaZy5vLN3HPC5+yZmvtGYyqbr7FxcVMnTqVHTt2cMstt+Dr68uTTz7JoUOHKiy7YcMGHnnkkQrthw8f5q677qKgoOCK6zYajdx7771kZGQwZswYfv/9dx599NGL1vjcc89x4sSJK96eK3K295YrkWzVkWzVknzVkWzVqkq+w3p1ZNKwWNvfX/tsLZPunMr+/fsvue6Lnc/MmjWLP/74gxtvvJEmTZrw73//m99//73ate/du5d///vfBAYG0qNHD5YuXcpHH30ElAx0TZ06lcLCQm699VbOnj3Lq6++SnFx1c4za/ezAIWN1WolNzcXHx8f+RCxM8lWHclWLclXHWfNtnWzhjx+93CSTqTy2dptbNufDMC587m88fUmvl6/k4kDoxnUrS06NzfHFnsJ1c13+/btJCYm8uijjzJhwgRuu+02cnJyaN68ebnl3nzzTV599VVCQkLIy8uztcfFxXHXXXfRrFmzStf/4YcfsnTpUs6fP0///v2ZP38+vr6+FZaLi4sjIyODf//730yaNIm0tDTWrVtHVlYWCQkJFWpMTU3FaDRWMx3X5qzvLVcg2aoj2aol+aoj2apV1XwnDowmO6+Aleu3c2T9B2jcdHh6XvwKqoudz2RmZrJ//34GDx7MI488Qm5uLtHR0XzzzTf06tWLvLw8XnzxRdatW4ebmxs333wzM2bMqLS2H3/8EYBnnnmGiIgI4uLiWL16NVOnTuXbb7/FaDSyYMECGjduzMiRIzl8+HCVjyG5EsqFVHXkUVSfZKuOZKuW5KuOM2fbunlDnpgynFdmTyA6soWtPTUjhze+/oV7X1hWcrm3E/ehOvkmJCQA8Ouvv9KtWzeGDBlCfHw8np6e5ZYLCgritdde45577inX7ubmxpw5c3j77bcrrHvVqlW8+OKLtGzZkgkTJvDzzz/zxBNPVFrH0aNHAWyDWc2bN8disXDs2LFKa9yxYwfu7u5V7mdd4czvrdpOslVHslVL8lVHslWrKvlqNBruGtmTPl1a4xfclpa9b8as0WEutlS6/MXOZ3x9OeMiiAAAKMtJREFUffHy8mLv3r3s3LnTNpCUlJQEwIsvvshXX33FoEGDiI2NZfHixXzxxReVbqPsOY1Go6FZs2a2ttJzmoULF9K9e3emTp1KdnY2Wm3VhpdkEEoIIYRwUeEtGvHU1JEseGA80W3/GYw6m5HD619u5N4XP+OnuANOPRhVFaXfAp44cYKnn36a6OhoXnjhBdtJUqmJEycyZMiQCj8fExPDlClT0Ov1FV5buXIler2exx57jNtuu43Bgwezbt06cnNzycjI4PDhwxw+fJjjx4+Tn58PgMFgALCtz2g0VlrjwoULOXbsmP2CEEIIIUStpNVqmTdpGCNvuhOdhzdWq5XsPCPHz2RUWPZi5zNubm48/vjjZGRkcPPNN/P666/j4+OD0WjEYrHw3Xff0alTJ6ZNm8bs2bNp0aIFK1asACAlJcV2TlP2Su2y5zRGoxGr1Wo7p7FarTz33HPo9XoWL15MUVFRlfoqt+MJIYQQLq5Ny8Y8NW0kCcln+HxdPDsTSuYhOpuezWtfbuCr9du5cVAM/aPb4OZW+76fKr3i6c4772TChAm0a9eOsWPHEhcXR9u2ba9q3adPn8ZkMjF8+PBy7cnJyWzcuJHFixcDEBwczK233gpgOwkr/a+np+dFa9y/fz/XX3/9VdUohBBCiNrPzU3LQ5Ou46l3f+DIhpJBnseXfM9/ZoyjUQO/Kq1j3Lhx9OjRg8OHD9OxY0euu+46fHx8yMjIoLCwkD179nDdddfZli99gMrDDz9MfHw8AGPHjsXDwwMoOZfR6/UUFRXh4eGBRqOxvfavf/2LsLAwMjIyWLhwIUePHiUqKuqyNcoglAvx9vZ2dAkuS7JVR7JVS/JVpzZm2zakMfOnjeJA8mk+WxPP7kMnATiTls2rX2zg83XbGNK9HYO6RVLfz7H9q06+rVq1AiAjo+TbQrPZDGA7SboagYGBnDx5kpdeegmtVsvx48cJCAigadOmDB06lPDwcKBkoKn0qTGlk42fPHkSjUZDy5YtOX/+fKU1ln7DKP5RG99btYVkq45kq5bkq45kq1Z18zXodfz7rmGsfv8FTGYLGVl5JQNRM8cR4Hv5Jxm+++67nD59mieeeIIzZ86QmZlJv379CAgIQKfTER4ezr333gvAoUOHaNmyJQAzZ860naMEBwfz/fffAyXnNG3atCElJYXQ0JInMYeFhbFx40bS09MJCwur9nmXDEK5CI1GU+ltBOLqSbbqSLZqSb7q1PZsI0Oa8My9o9l/9DSfrY1nz9+DUakZOSxdHceyNfF07xDKkB7t6RzerMr3+NtLdfPt0aMHwcHBvPfee+Tk5PDHH3/g4eFB7969efPNN4mIiGDQoEFXVMvIkSOJj49nxYoVhIeH89VXX+Hn58fYsWOpX78+rVu3ti2bm5uLv78/7733HsePH2f9+vX06tULf3//i9bYqVOnK6rLVdX295Yzk2zVkWzVknzVkWzVutJ8vTwMBPh6kpFVMl3CydOpTL7/QabfNpohZa5iqsyZM2dYtmwZJpOJv/76C41Gw8SJE9HpdFx//fWsXr2a33//neLiYr755htGjBjB6NGj6datW7n1FBYW8sknn/DEE08QHBxMWloakydPBmD06NF89NFHPPLIIwwdOpTly5fTvHnzCg+EuZjad829qJTVaiUrKwur1eroUlyOZKuOZKuW5KuOq2TbLrQJz947mhdmjCW6bQtKH2pisVjZsvcITy5ZxbTnP+Wrn3eQmZNfY3VVN1+DwcA777xDZGQkn332GRaLhcWLF2O1Wnn11VdZs2bNFdcyceJE7r//fhISEli2bBlt2rRhyZIllZ5U+vj48N577xEYGMiKFSvo0aMHL7zwwkVrXLhwIYGBgVdcmytylfeWM5Js1ZFs1ZJ81ZFs1bqafLVaLX7enjQI8MZiKiQh7mfeeO/Ty/7czJkz6devH99//z05OTm8/PLLxMTEAPDEE08wbNgw/ve//7F27VqGDx/OU089Vel6YmJieP7550lPT2fLli3cdttt3HHHHQBERETw6quvotPpWLZsGREREcyZM6fKX1pqrHLE1Yj8/HwOHDhAZGQkXl6Xv4yuukoPcH9/f3m8pp1JtupItmpJvuq4arZn07NZF7efdXEHKgw6abUaundsxdAe7bmmdbDSq6NcNd8LqT43UEHOZ2ovyVYdyVYtyVcdyVYte+SbePwsDy9eiclcclXUrBsHMDg20p5lXrXqnhvI7XhCCCGEAKBRAz8mDevOzUO6Er8vmTVb97HrYMn8RhaLlS17DrNlz2EaB/pxfff2DOzatkrzEwghhBBCiOoLb9GI+8b35bUvNwDw5je/ENKkPuEtGjm4sisnt+MJIYQQohydmxs9rwnj6XtG8e5jtzFhYBcCfD1tr59Jy+ajH7Zyx9Mf89Ina9mTeFIu5RdCCCGEUGBwbCTDenUAwGy28NxHP9boNAn2Jrfj1ZCauHzdYrGg1WrlUko7k2zVkWzVknzVqYvZmszFxO87yo9b99kmMi+raZA/Q/6+Osrfx7OSNVRdXclXbserqK7se0eQbNWRbNWSfNWRbNWyZ74mczGPvfUtB46eAaBDWFOeuXcUOjc3e5R6Vap7biBXQrkIjUYjHx6KSLbqSLZqSb7q1MVs9To3enVqzbP3jmbJo7cyrn9UucGmlHNZfLhqC5Pnf8ST73zP/37/k3OZOVe0rbqYrygh+14dyVYdyVYtyVcdyVYte+ar17nxyOTrqedXMsjz1+EUPvph61Wv1xFkEMpFyJMN1JFs1ZFs1ZJ81anr2TYNCuDOkT358InJPHT7dVwTHmx7rbjYws6EE7z9za/c9fQnPLDwSz5bG0/SydQq51XX863LZN+rI9mqI9mqJfmqI9mqZe986/t58+gd1+PmVjKM892mPWzcftAu665JMjG5EEIIIa6IXufGtZ3DubZzOKfOZbL2j338tjuJc+dzbcscOZXGkVNpfL52Gw0CvIltH0q39qF0DGuKQS+nIUIIIYQQVRUZ0oR7x13LG19vAuD1rzbSonF9wpoFObiyqpOzPyGEEEJcteCgAO4a2Ys7R/TkaEo68fuOEr8vmcQTqbZl0jPzWP37X6z+/S883fVEtW1B9/ahREe2xM/bw4HVX5zRaMRgMODmBHMuCCGEEEIM6d6exBOprPvjACZzMc9/9COvzJ54yTk5jUYj7u7uaLWOvxlOBqGEEEIIYTcajYZWwYG0Cg7kpuu6kpaZy/YDyfzx11H2Jp7CZC4GwFhoYsuew2zZcxitVkO70CZ0ax9Ct/ahNA30d3Av/vHee+/Rs2dPoqOjHV2KEEIIIQQajYZ7xvbhaEo6icdTSc3I4eVP1zF/6kjbrXoXeuuttxg8eDAdO3as4WorkkEoF6HRaPD395dJ5RSQbNWRbNWSfNWRbKsuMMCH63t04PoeHTAWFrHr4Ani9h1l2/5kcvIKAbBYrPx1OIW/DqfwwfdbaNYwgO4dW9G3SwQhTRo4tH6j0YjRaHRoDXWJvLfUkWzVkWzVknzVkWzVUpmvQa/jX3cMZc6ir8jMMbLn0Ek+Wf0Hd47sWenyznQ+I4NQLkIer6mOZKuOZKuW5KuOZHtlPN0N9LwmjJ7XhGGxWDiQfIb4fcnE7TvKqdRM23InU8/z9fodLF+/gxaN69MnKoI+UeE0qaErpCwWC7m5ufj5+dnarFYrOTk55dqE/cl7Sx3JVh3JVi3JVx3JVi3V+QYG+PDw7UN47K3vsFisrNi4i9bNg7i2czhQcj6Tl5eHr6+v7Wcqa6tpjr8hUNhNTs6VPQpbXJ5kq45kq5bkq45ke3W0Wi3tWzXlzpE9efuRW3nrkVu4c2RP2rdqgkarwVJcctve8TPn+fTHOKY9/yn/t+hrvtu0m7TM3Mus/eocPHiQ119/nezsbFvbhg0b+Oijj5RuV5SQ95Y6kq06kq1akq86kq1aqvPtEBbMlNG9bX9/9YsNJJ9OB+Cvv/5i8eLF5OXl2V5fu3Yty5YtU1rT5ciVUEIIIYRwuGYN69GsYT3G9Y/ifHYe67b+yY6DpziQfMa2TOKJVBJPpPL+97/TvlVT+nYJp+c1re0+qXmrVq3Q6/X89ttvABQVFREXF0dsbKxdtyOEEEIIcbVG9O5I4olUNm4/SGGRmec+XM0rsyfSunVrALZs2QJAQUEBO3bsoG/fvo4sVwahhBBCCOFcAny9GBLblhuui+Xc+Rw2707i112JHDmVBoDVim0Oqbe++ZWoNs3pGxVB946heLobrnr77u7u9OjRg02bNqHX6zl+/DgajYYePXpc9bqFEEIIIexJo9Fw/8R+HD+TweGT5ziTls3CT3/iiSnD6datG3FxcVitVo4cOYLBYKBbt24OrVcGoVyI3MerjmSrjmSrluSrjmSrVmm+Dev7MX5AF8YP6MKJs+fZvDuRTTsPkXIuCyiZ1HzHgePsOHAcvc6Nru1D6BsVTkxkSwz6Kz/N6datGzs2bCAgORnd/gP0ad8OQ34+eHnZpX/i4uS9pY5kq45kq5bkq45kq1ZN5euu1/GvO65n9qKvyMkrZEfCcZatjWd83x7s2bSJgFOncDuUSN/OnXDLyQF39xqpqzIaq9VqddjW65D8/HwOHDhAZGQkXnICK4QQQlwxq9XK4VPn2Lyr5AqpyuaI8nTX071DKB1bN6NdqyY0Daz602kKDh4ifckSstasQWOx/POCmxt+Q4bQ4J578GgTcdX9qIlzg4yMDG688UaeffbZi95O+Pnnn/PRRx+RmppKw4YNuf3227n11lsdVrMQQgghrsyexJM8seR7LBYrjbPTmGFJQf/Hb051PiNXQrkIq9WK2WxGp9PJaLadSbbqSLZqSb7qSLZqXS5fjUZD62YNad2sIZOHd+dA8hl+3ZXI73sOk5Vb8vhhY6GJjTsOsXHHIQD8vD1oG9KYdqFNaBvSmPDmDSu9Uip382+cvP9+rMXF5U/YAIqLyV67lpyff6bZG2/gc23vCj/vTHbs2MEjjzzC8ePHL7rMzz//zCuvvMK7775Lp06d2L17N9OmTSMwMJAhQ4bUYLUl5L2ljmSrjmSrluSrjmSrliPy7RTejDtG9OC39z9n8o7VaK0WNBded+Tg8xl5Op4LKTvrvbAvyVYdyVYtyVcdyVatquZb+pS9+8b35eMn72D+PSMZ2K0tnh76cstl5xUQvy+Zj37YyiOLV3LjY+/y4Gvf8P73v7Nl72Eyc/IpOHioZADKZIK/n85XQXExVpOJk/ffT8HBQ1fbTWVWrlzJvHnzmDNnziWXO3v2LFOnTqVz585oNBqioqKIjY1l27ZtNVRpRfLeUkeyVUeyVUvyVUeyVcsR+V7fxIs7d/6Im6UYt4vd+ObA8xm5EkoIIYQQLsHNTUuXNi3o0qYF08f35dDxs+w/eoYDyadJSD5Dbn6hbVmz2UJC8hkSks/w7d9td+3fQITJjPZyMxVYrViLi0l/5x2CFy5Q1p+r0bt3b0aOHIlOp7vkQNSFt92lp6ezbds2Hn300Uuu32q1UnZGB41GQ2UzPFS3vbJ1X2x5e23TmdpVrrv0767UJ0e1X9hW9ph1lhqvtk/O1F6ar9VqdZk+XWm7vddd9o+r9MkZ2h2db/qSJbhRhVmX/j6fSXtnCcEL/jmfscd+uhQZhBJCCCGEyzHodXQIC6ZDWDAAFouFU+cy2X/0DAnJp9l/9LRtcnMAn8J8wo8duPwAVKniYrLXrKHRvx5F16CBii5claCgoGr/zLlz57jnnnvo0KEDI0aMuOSyubm5mEwmAAwGA15eXhiNRoqKimzLeHh44OHhQV5eHmaz2dbu5eWFwWAgNzeX4jJXnHl7e6PT6cjPzwf+mczV19cXrVZLVtY/+wvA398fi8VCTk6OrU2j0eDv74/ZbC737bObmxu+vr6YTCbb+gF0Oh0+Pj4UFhZSUFBga7dnn/R6PdnZ2eVO0h3RJ72+5OpAo9Fo23e1vU/Osp+sViv5+fkEBAS4TJ/AefZTab7e3t4YDAaX6JOz7Cej0Wh7zd3d3SX65Ez7KTs721anVqutkT5lHz9Oztp1F7+i+0LFxeSsWUvG/ffjVr/+Fe+n6pCJyWuI6ok8rVYrubm5+Pj4yP28dibZqiPZqiX5qiPZqlVT+WblGkk4dob9R05T8NM6+q37strrCF70Cn5Dh17R9mtqku82bdrwySefXHRicoDdu3fzwAMPEBMTwwsvvIDBYKh0udKa27ZtW65me14JlZOTU2Hf17Vvz1Ws22q1kpeXh7e3d4X3VW3tk6PaK7sSKjc3F19f3wo/72y1V7VPztRe9t8ErVbrEn260nYVV0KV/ffWFfrkDO2OzDfrf6tJmTu3wmuX0/SVhbbzmerup7y8PBISEmRi8rpGo9Fc9B8+cXUkW3UkW7UkX3UkW7VqKl9/H09i24cS2z6U8wWnOLOu+usozq34ZL7aZvny5Tz77LPMmjWLu+66q0o/U3oyfWHbxZatTrufn1+Vl7fXNp2pXdW6L/e+qo19cmT7hYOkFztuHVljddudqZay7Rfm6wp9upp2e667smO3tvfJWdodla8l/8rmoLLk5VX4XLvaWi5GJiZ3EVarlaKiomrfjykuT7JVR7JVS/JVR7JVyxH5unl7X9nP+fjYuZKatXbtWp566ilef/31Kg9AqSTvLXUkW3UkW7UkX3UkW7XkfKZyMgjlQsreQyrsS7JVR7JVS/JVR7JVq6bz9erWDdzcqvdDbm4lP1fLREVF8f333wOwePFiiouLmTVrFlFRUbY/TzzxhMPqk/eWOpKtOpKtWpKvOpKtWnI+U5HcjieEEEKIOk8XGIjfkCFkr11btck83dzwu/56p5yU/EIHDx4s9/ddu3bZ/n/VqlU1XY4QQgghFKkN5zNyJZQQQgghBNDgnnvQuLnB5eY20GjQuLnRYNq0milMCCGEEKKKnP18RgahXIhOJxe2qSLZqiPZqiX5qiPZquWIfD3aRNDsjTfQ6PUXv5TdzQ2NXk+zN97Ao01EzRZYR8h7Sx3JVh3JVi3JVx3JVi05n6lIBqFchEajkUeFKyLZqiPZqiX5qiPZquXIfH2u7U3I11/jd/31FU/c/r5kPeTrr/G5tneN11YXyHtLHclWHclWLclXHclWLTmfqZwMe7oIq9VKYWEh7u7u8iFiZ5KtOpKtWpKvOpKtWo7O16NNBMELF9DoX4+SHx9PcW4ubj4+eHXrVivmgKrNHL3vXZlkq45kq5bkq45kq5aj83XW8xmHXQmVnp7O9OnTiYmJITY2lueeew6z2Vzpsps2bWLkyJF07tyZoUOHsnHjxnKvv/vuu/Tp04fOnTszadIkjhw5YnstPz+fRx99lNjYWKKjo3nooYfIy8uzvX706FEmT55MVFQUvXv35u2337bbtmtaQUGBw7bt6iRbdSRbtSRfdSRbtZwhX12DBvgNHUq9iRPxGzpUBqBqiDPse1cl2aoj2aol+aoj2arlDPk62/mMwwahZs+ejZeXF5s3b2b58uVs3bqVjz76qMJyycnJzJw5kwceeIDt27czc+ZMZs+ezdmzZwFYuXIlS5cu5f333ycuLo727dsza9YsrFYrAM888wynT59m7dq1rFu3jtOnT7NgwQIATCYT9957Lx07diQuLo533nmHZcuW8eOPP9pl20IIIYQQQgghhBCihEMGoY4dO0Z8fDwPPvggnp6eNG/enOnTp7Ns2bIKy65cuZKYmBgGDRqETqdj2LBhdO3alS+//BKAr776iltuuYXw8HDc3d2ZO3cuKSkpxMXFYTQaWbVqFbNmzSIgIIAGDRowb948VqxYgdFoZNu2baSmpjJr1iwMBgPt2rVj0qRJtjquZttCCCGEEEIIIYQQ4h8OGYRKTEwkICCARo0a2drCwsJISUkhOzu73LJJSUlERJSfrb1169YkJCRU+rperyckJISEhASOHTuGyWQq93pYWBgFBQUkJyeTmJhIaGgoBoOhSuuuzrYvxmq1lvtTWduVtuv1+iotb89tOku76m0aDAaX65Oz7Ce9Xu9UNbrafir9XHClPjnLfir7mesqfXJ0uzPlW1P7SVRU9rxM2Jdkq45kq5bkq45kq5bkW5FDJibPy8vD09OzXFvp3/Pz8/Hz87vksh4eHuTn51/29dzcXAC8vLwqbCcvL++idVRl3VV5vTK5ubmYTCag5ID08vLCaDRSVFRUbh0eHh7k5eWVmyfLy8sLg8FAbm4uxcXFtnZvb2/0ej1ms7ncIJ6vry9arZasrKxyNfj7+2OxWMjJybG1aTQa/P39MZvN5ebMcnNzw9fXF5PJVK5fOp0OHx8fCgsLy93nau8+ZWdnlztJd2Sf8vPzXa5PzrKfNBoNJpPJpfrkTPvJYDCg1Wpdqk/Osp9MJpPL9QmcZz+ZTCaX6xOU30+iPI1GU+68TdiPZKuOZKuW5KuOZKuW5Fs5hwxClZ4EllX6d29v73Ltnp6eFSbzKigosC13qddLd7jRaLQtX7odHx+fi9ZRlXVX5fXKlG73wj5eOJgFFbMou44LWa1WdDodnp6e5WbeLz3RLUuj0aDVaiu0Q8nJe2Xter2+0nZ3d3fc3d0rtNujT0C5Acmy9ddkn6xWK/n5+Xh4eLhMn0o5ej9ZrVaMRqPt+HWFPpXl6D6V5uv292NZXaFPF3JUnwwGA0ajsdxnbm3vkzPtJz8/vwr51vY+XWw/ifJKP7cuPJ8RV0+yVUeyVUvyVUeyVUvyrZxDzn7Cw8PJzMwkLS2NwMBAAA4fPkzjxo3x9fUtt2xERAT79u0r15aUlESHDh1s60pMTKR///5AybemycnJREREEBoail6vJykpiU6dOtm2U3rbXHp6OsnJyZjNZtuJYFJSEuHh4Ve97YspverjwraLLVvVdqvVislkwsvLq0rrt8c2na1d5bqLioou+uFRW/vkqPYL20qvDHSmGqvb7ky1XNhe+rlQ3fU4Q+32brf3ui/8zHWFPjlDuzPla6/26q6jriv9N1fYn2SrjmSrluSrjmSrluRbkUPmhAoJCSE6Oprnn3+e3NxcTpw4wZtvvsmECRMqLDtq1Cji4+NZvXo1ZrOZ1atXEx8fz+jRowEYP348n376KQkJCRQWFrJw4UICAwOJiYnB09OToUOHsmDBAjIyMsjIyGDBggWMGDECDw8PYmNjqVevHgsXLqSwsJCEhASWLl1qq+Nqti2EEEIIIYQQQggh/qGxOmhWzLS0NJ5++mni4uLQarWMGTOGefPm4ebmRlRUFPPnz2fUqFEAbN68mQULFnD8+HGCg4N58MEH6du3L1ByBdCHH37IsmXLyMjIoGPHjsyfP5/Q0FCgZA6ml156iQ0bNmAymRg4cCCPP/647cqAY8eO8fTTT7Nnzx68vLy47bbbmDZtmq3Oq9l2Wbm5uRw8eJCQkBAlI6FWq5Xc3Fx8fHzkm1U7k2zVkWzVknzVkWzVqiv5Go1GkpOTadOmzUVv+3M2cj5Te0m26ki2akm+6ki2atWVfKt7PuOwQai6pvTWPyGEEEKIskJCQmjQoIGjy6gSOZ8RQgghRGWqej4jg1A1xGw2k5WVhbu7O1qtQ+6CFEIIIYQTsVgsFBYW4u/vX2smKZfzGSGEEEKUVd3zGRmEEkIIIYQQQgghhBDKyVdYQgghhBBCCCGEEEI5GYQSQgghhBBCCCGEEMrJIFQtl56ezvTp04mJiSE2NpbnnnsOs9ns6LJcxurVq2nXrh1RUVG2Pw8++KCjy6rVMjIyGDx4MHFxcba2PXv2MHHiRKKiohgwYABff/21Ayus3SrL98knn6RDhw7ljuMvv/zSgVXWLgkJCdx5551069aNXr168dBDD5GRkQHIsXu1LpWtHLd1i5zPqCXnM/Yn5zNqyfmM/cn5jDpyPlNNVlGr3Xbbbda5c+da8/PzrcePH7cOHz7c+u677zq6LJfx4osvWh955BFHl+Eytm/fbh00aJA1IiLC+scff1itVqs1MzPT2q1bN+unn35qNZlM1i1btlijoqKse/bscXC1tU9l+VqtVuvYsWOtK1ascGBltZfRaLT26tXL+uqrr1oLCwutGRkZ1qlTp1rvueceOXav0qWytVrluK1r5HxGLTmfsS85n1FLzmfsT85n1JHzmeqTK6FqsWPHjhEfH8+DDz6Ip6cnzZs3Z/r06SxbtszRpbmMP//8kw4dOji6DJewcuVK5s2bx5w5c8q1r1u3joCAAG699VZ0Oh09evRg5MiRchxX08XyLSoq4tChQ3IcX6GUlBTatm3L/fffj8FgoF69etx4441s27ZNjt2rdKls5bitW+R8Rj05n7EfOZ9RS85n1JDzGXXkfKb6ZBCqFktMTCQgIIBGjRrZ2sLCwkhJSSE7O9uBlbkGi8XCvn37+OWXX+jfvz99+vTh8ccfJysry9Gl1Uq9e/fmp59+YtiwYeXaExMTiYiIKNfWunVrEhISarK8Wu9i+SYkJGA2m3nttdfo2bMnQ4YM4Z133sFisTio0tqlVatWvPfee7i5udna1q5dS/v27eXYvUqXylaO27pFzmfUkvMZ+5LzGbXkfEYNOZ9RR85nqk8GoWqxvLw8PD09y7WV/j0/P98RJbmUjIwM2rVrx5AhQ1i9ejVffPEFycnJMofCFQoKCkKn01Vor+w49vDwkGO4mi6Wb05ODt26dWPSpEls2rSJl19+maVLl/LBBx84oMrazWq1smjRIjZu3Mhjjz0mx64dXZitHLd1i5zPqCXnM/Yl5zNqyfmMenI+o46cz1RNxXe4qDW8vLwwGo3l2kr/7u3t7YiSXEpgYGC5y1A9PT158MEHueGGG8jNzcXHx8eB1bkOT09PcnJyyrUVFBTIMWwnvXr1olevXra/X3PNNUyePJnVq1czZcoUB1ZWu+T+f3v3H1JX/cdx/OXd8k7Mubm1XOCQyF0aY17NHxQsbD9xCvmLtV9sK1izEjQpWrXWcD+yrRwVSWtr/mCDWwyU/fxjwhyK89bgFrUQTWvuGv7KHF364VXv94/4nm9W23c6z65Xnw8QPOd8zrlvDh84L97n3HM9Hr366qu6evWqjh8/LpvNxtwdJ/92bm02G/N2CiHPmIs8c3dwTTAXeWZ8kGfMQ565fTwJFcBiYmLU39+v3t5eY11ra6siIyMVFhbmx8omh6amJr3zzjvy+XzGuoGBAVksFgUHB/uxssll4cKFamlpGbHuu+++U0xMjJ8qmlxqamrkcDhGrBsYGNCMGTP8VFHgaW9vV3Z2tjwej06ePCmbzSaJuTsebnZumbdTC3nGXOSZu4Nrgrm4Ltw58ox5yDOjQxMqgEVHR+uRRx7R/v375fF4dP36dZWWlionJ8ffpU0Ks2bN0okTJ3T06FENDg7qxx9/1MGDB5WZmUloG0crV65Ub2+vysvL5fV61djYqNOnTys7O9vfpU0KPp9Pb731li5fviyfzyeXy6XKyko99dRT/i4tINy4cUNbtmxRfHy8PvnkE0VERBjbmLt35lbnlnk7tZBnzEWeuTu4JpiL68KdIc+YhzwzekG+v94WQcDp7e1VUVGRnE6nLBaLMjIy9NJLL414MRrG7vPPP1dJSYmam5tltVqVlpaml19+WVar1d+lBTSbzabKykolJydL+vNXe/bt26fm5mZFRETo+eefV1ZWlp+rDFx/P78Oh0NlZWXq6urS3Llz9fTTT2vjxo1+rjIwlJWVqbi4WCEhIQoKChqxzeVyMXfvwP87t8zbqYU8Yy7yjDnIM+Yiz4wf8ox5yDOjRxMKAAAAAAAApuPreAAAAAAAADAdTSgAAAAAAACYjiYUAAAAAAAATEcTCgAAAAAAAKajCQUAAAAAAADT0YQCAAAAAACA6WhCAQAAAAAAwHQ0oQBgkhsaGtL169f9XQYAAMCYkWeAyYEmFIBJz2azyel0+ruMf3A6nbLZbIqLi1NcXJxiY2OVlJSk3NxctbS0jNvnvPjii6qurpYkud1u2Ww2ud3ucTs+AAAwH3mGPANMBjShAMDPXC6XXC6XvvrqK50/f14PPPCA1q9fr7a2tnE5/s8//zwuxwEAALgZ8gyA20ETCsCUNjAwoLffflupqamKi4vTo48+qj179sjn8+nLL7/Uww8/rM7OTmP8119/LbvdLo/Ho4GBAb333ntavny5kpKStG3bNl27ds0Ya7PZtHfvXiUnJys3N/e26pkzZ4527dqlhx56SB9++KGxvqGhQTk5OUpISFBaWppOnTplbNuxY4dee+01bd68WXa7XampqaqpqZEkvf7667py5YoOHz48oobTp08rNTVVdrtdW7duVVdX15jPIQAA8C/yDHkGCBQ0oQBMaRUVFaqrq1NFRYVcLpdKS0vlcDjU2Ngou92uBx98cERAqq6u1urVq3Xvvffq0KFDqq2tVXl5uerq6hQbG6tnnnlGf/zxhzG+vb1dtbW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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Fixed: Removed sharey=True as the two datasets have different stress scales\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5), dpi=100) \n", - "\n", - "for ax, dataset_name in zip(axes, sorted(stress_tables.keys())):\n", - " stress_df = stress_tables[dataset_name]\n", - " row = optimal_layers_df[optimal_layers_df[\"dataset\"] == dataset_name].iloc[0]\n", - " opt_layer = int(row[\"optimal_layer\"])\n", - " opt_stress = float(row[\"min_stress\"])\n", - "\n", - " ax.plot(stress_df[\"layer\"], stress_df[\"stress\"], color=\"#1f4e79\", linewidth=2, alpha=0.8)\n", - " ax.scatter([opt_layer], [opt_stress], color=\"#d62728\", s=80, zorder=5)\n", - " \n", - " # Using 'offset points' prevents the Y-axis from stretching to infinity\n", - " ax.annotate(\n", - " f\"L={opt_layer}\\n{opt_stress:.2e}\",\n", - " xy=(opt_layer, opt_stress),\n", - " xytext=(0, 15), \n", - " textcoords=\"offset points\",\n", - " ha='center',\n", - " arrowprops={\"arrowstyle\": \"->\", \"color\": \"gray\"},\n", - " fontsize=9,\n", - " fontweight='bold'\n", - " )\n", - " \n", - " ax.set_title(dataset_name.replace(\"_\", \" \").title(), fontsize=11)\n", - " ax.set_xlabel(\"Layer Depth\")\n", - " ax.set_xticks(np.arange(0, 28, 5)) # Cleaner X-axis\n", - " ax.grid(True, linestyle='--', alpha=0.3)\n", - "\n", - "axes[0].set_ylabel(\"Topological Stress (1 - score)\")\n", - "plt.suptitle(\"Layer-wise Circular Stress Scan\", fontsize=14, weight=\"bold\", y=1.05)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0602e4f3", - "metadata": {}, - "source": [ - "## 6) Shape Discovery with Train/Test Split at Each Optimal Layer\n", - "\n", - "For each dataset domain:\n", - "- Build a deterministic hue-aware split (train/test)\n", - "- Run `discover_manifolds` on **train only**\n", - "- Refit each candidate shape on train and evaluate on held-out **test**\n", - "\n", - "This prevents manifold selection from using the same samples for fitting and final evaluation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "96de6338", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WARNING] Less than 100 datapoints in X might lead to noisy results\n", - "Computed and cached CircularShape\n", - "Computed and cached SemicircularShape\n", - "Computed and cached EuclideanShape\n", - "Cache cleared\n", - "Computed and cached CircularShape\n", - "Computed and cached SemicircularShape\n", - "Computed and cached EuclideanShape\n", - "Cache cleared\n" - ] - }, - { - "data": { - "text/html": [ - "
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datasetoptimal_layern_totaln_trainn_test
0full_simple27604812
1wild_textured2736028872
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" - ], - "text/plain": [ - " dataset optimal_layer n_total n_train n_test\n", - "0 full_simple 27 60 48 12\n", - "1 wild_textured 27 360 288 72" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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datasetshapetrain_cv_scoretrain_cv_stdtest_scoretest_closure_ratioperiodicity_scorecoverage_ratio
2full_simpleCircularShape0.5050900.1279040.9186730.8613930.4225731.0
0full_simpleSemicircularShape0.6878030.1396440.8002213.9902360.0184951.0
1full_simpleEuclideanShape0.6838990.1966020.6460895.5181910.0040131.0
4wild_texturedCircularShape0.5258990.0670580.9414831.7270270.1778121.0
3wild_texturedSemicircularShape0.5961310.1591320.7969234.7916120.0082991.0
5wild_texturedEuclideanShape0.5219480.2338240.6920435.0680190.0062951.0
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" - ], - "text/plain": [ - " dataset shape train_cv_score train_cv_std test_score \\\n", - "2 full_simple CircularShape 0.505090 0.127904 0.918673 \n", - "0 full_simple SemicircularShape 0.687803 0.139644 0.800221 \n", - "1 full_simple EuclideanShape 0.683899 0.196602 0.646089 \n", - "4 wild_textured CircularShape 0.525899 0.067058 0.941483 \n", - "3 wild_textured SemicircularShape 0.596131 0.159132 0.796923 \n", - "5 wild_textured EuclideanShape 0.521948 0.233824 0.692043 \n", - "\n", - " test_closure_ratio periodicity_score coverage_ratio \n", - "2 0.861393 0.422573 1.0 \n", - "0 3.990236 0.018495 1.0 \n", - "1 5.518191 0.004013 1.0 \n", - "4 1.727027 0.177812 1.0 \n", - "3 4.791612 0.008299 1.0 \n", - "5 5.068019 0.006295 1.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "split_rows = []\n", - "discovery_tables = {}\n", - "discovery_long_rows = []\n", - "\n", - "for _, row in optimal_layers_df.iterrows():\n", - " dataset_name = row[\"dataset\"]\n", - " optimal_layer = int(row[\"optimal_layer\"])\n", - " bundle = scans[dataset_name]\n", - "\n", - " X_opt = np.asarray(bundle[\"layer_data\"][optimal_layer], dtype=np.float64)\n", - " y_opt = np.asarray(bundle[\"hue_norm\"], dtype=np.float64)\n", - " angles_opt = np.asarray(bundle[\"angles_deg\"], dtype=np.float64)\n", - "\n", - " train_idx, test_idx = periodic_train_test_split(angles_opt, test_fraction=0.2, offset=0)\n", - " X_train, X_test = X_opt[train_idx], X_opt[test_idx]\n", - " y_train, y_test = y_opt[train_idx], y_opt[test_idx]\n", - " angles_test = angles_opt[test_idx]\n", - "\n", - " split_rows.append(\n", - " {\n", - " \"dataset\": dataset_name,\n", - " \"optimal_layer\": optimal_layer,\n", - " \"n_total\": int(X_opt.shape[0]),\n", - " \"n_train\": int(X_train.shape[0]),\n", - " \"n_test\": int(X_test.shape[0]),\n", - " }\n", - " )\n", - "\n", - " result_df = discover_on_train(X_train, y_train, label=dataset_name)\n", - " discovery_tables[dataset_name] = result_df\n", - "\n", - " for _, r in result_df.iterrows():\n", - " shape_name = str(r[\"shape\"])\n", - " if shape_name not in SHAPE_FACTORIES:\n", - " continue\n", - "\n", - " model = fit_shape_smds(X_train, y_train, SHAPE_FACTORIES[shape_name]())\n", - " test_score = float(model.score(X_test, y_test))\n", - " test_coords = model.transform(X_test)\n", - " closure = seam_closure_ratio(test_coords, angles_test)\n", - "\n", - " discovery_long_rows.append(\n", - " {\n", - " \"dataset\": dataset_name,\n", - " \"shape\": shape_name,\n", - " \"train_cv_score\": float(r[METRIC_COL]),\n", - " \"train_cv_std\": float(r.get(\"std_scale_normalized_stress\", np.nan)),\n", - " \"test_score\": test_score,\n", - " \"test_closure_ratio\": float(closure),\n", - " \"periodicity_score\": periodicity_score(float(closure)),\n", - " \"coverage_ratio\": float(hue_coverage_ratio(angles_opt)),\n", - " }\n", - " )\n", - "\n", - "split_df = pd.DataFrame(split_rows).sort_values(\"dataset\").reset_index(drop=True)\n", - "display(split_df)\n", - "\n", - "discovery_long_df = pd.DataFrame(discovery_long_rows)\n", - "display(\n", - " discovery_long_df.sort_values([\"dataset\", \"test_score\"], ascending=[True, False])\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "206a56d4", - "metadata": {}, - "source": [ - "## 7) Periodicity-Aware Topology Arbitration \n", - "\n", - "- Primary term: **held-out test score**\n", - "- Geometry term: **periodicity score** from seam closure\n", - "\n", - "Composite:\n", - "`composite_score = 0.7 * test_score + 0.3 * periodicity_score`\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "129f4e56", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datasetshapetrain_cv_scoretrain_cv_stdtest_scoretest_closure_ratioperiodicity_scorecoverage_ratiocomposite_score
2full_simpleCircularShape0.5050900.1279040.9186730.8613930.4225731.00.769843
0full_simpleSemicircularShape0.6878030.1396440.8002213.9902360.0184951.00.565704
1full_simpleEuclideanShape0.6838990.1966020.6460895.5181910.0040131.00.453466
4wild_texturedCircularShape0.5258990.0670580.9414831.7270270.1778121.00.712382
3wild_texturedSemicircularShape0.5961310.1591320.7969234.7916120.0082991.00.560336
5wild_texturedEuclideanShape0.5219480.2338240.6920435.0680190.0062951.00.486318
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" - ], - "text/plain": [ - " dataset shape train_cv_score train_cv_std test_score \\\n", - "2 full_simple CircularShape 0.505090 0.127904 0.918673 \n", - "0 full_simple SemicircularShape 0.687803 0.139644 0.800221 \n", - "1 full_simple EuclideanShape 0.683899 0.196602 0.646089 \n", - "4 wild_textured CircularShape 0.525899 0.067058 0.941483 \n", - "3 wild_textured SemicircularShape 0.596131 0.159132 0.796923 \n", - "5 wild_textured EuclideanShape 0.521948 0.233824 0.692043 \n", - "\n", - " test_closure_ratio periodicity_score coverage_ratio composite_score \n", - "2 0.861393 0.422573 1.0 0.769843 \n", - "0 3.990236 0.018495 1.0 0.565704 \n", - "1 5.518191 0.004013 1.0 0.453466 \n", - "4 1.727027 0.177812 1.0 0.712382 \n", - "3 4.791612 0.008299 1.0 0.560336 \n", - "5 5.068019 0.006295 1.0 0.486318 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "arb_df = discovery_long_df.copy()\n", - "arb_df[\"composite_score\"] = 0.7 * arb_df[\"test_score\"] + 0.3 * arb_df[\"periodicity_score\"]\n", - "\n", - "display(arb_df.sort_values([\"dataset\", \"composite_score\"], ascending=[True, False]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "1866b549", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datasetwinner_shapewinner_test_scorewinner_train_cv_scorewinner_closure_ratiowinner_composite
0full_simpleCircularShape0.9186730.5050900.8613930.769843
1wild_texturedCircularShape0.9414830.5258991.7270270.712382
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" - ], - "text/plain": [ - " dataset winner_shape winner_test_score winner_train_cv_score \\\n", - "0 full_simple CircularShape 0.918673 0.505090 \n", - "1 wild_textured CircularShape 0.941483 0.525899 \n", - "\n", - " winner_closure_ratio winner_composite \n", - "0 0.861393 0.769843 \n", - "1 1.727027 0.712382 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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shapemean_train_cv_scoremean_test_scoremean_periodicitymean_compositestd_compositen_domains
0CircularShape0.5154950.9300780.3001930.7411120.0406312
2SemicircularShape0.6419670.7985720.0133970.5630200.0037952
1EuclideanShape0.6029240.6690660.0051540.4698920.0232302
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" - ], - "text/plain": [ - " shape mean_train_cv_score mean_test_score mean_periodicity \\\n", - "0 CircularShape 0.515495 0.930078 0.300193 \n", - "2 SemicircularShape 0.641967 0.798572 0.013397 \n", - "1 EuclideanShape 0.602924 0.669066 0.005154 \n", - "\n", - " mean_composite std_composite n_domains \n", - "0 0.741112 0.040631 2 \n", - "2 0.563020 0.003795 2 \n", - "1 0.469892 0.023230 2 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Global topology winner across full+wild domains: CircularShape\n" - ] - } - ], - "source": [ - "dataset_winners = []\n", - "for dataset_name, group in arb_df.groupby(\"dataset\"):\n", - " winner_row = group.sort_values(\"composite_score\", ascending=False).iloc[0]\n", - " dataset_winners.append(\n", - " {\n", - " \"dataset\": dataset_name,\n", - " \"winner_shape\": winner_row[\"shape\"],\n", - " \"winner_test_score\": float(winner_row[\"test_score\"]),\n", - " \"winner_train_cv_score\": float(winner_row[\"train_cv_score\"]),\n", - " \"winner_closure_ratio\": float(winner_row[\"test_closure_ratio\"]),\n", - " \"winner_composite\": float(winner_row[\"composite_score\"]),\n", - " }\n", - " )\n", - "\n", - "dataset_winners_df = pd.DataFrame(dataset_winners).sort_values(\"dataset\").reset_index(drop=True)\n", - "display(dataset_winners_df)\n", - "\n", - "global_shape_df = (\n", - " arb_df.groupby(\"shape\")\n", - " .agg(\n", - " mean_train_cv_score=(\"train_cv_score\", \"mean\"),\n", - " mean_test_score=(\"test_score\", \"mean\"),\n", - " mean_periodicity=(\"periodicity_score\", \"mean\"),\n", - " mean_composite=(\"composite_score\", \"mean\"),\n", - " std_composite=(\"composite_score\", \"std\"),\n", - " n_domains=(\"dataset\", \"count\"),\n", - " )\n", - " .reset_index()\n", - " .sort_values(\"mean_composite\", ascending=False)\n", - ")\n", - "\n", - "global_winner_shape = str(global_shape_df.iloc[0][\"shape\"])\n", - "display(global_shape_df)\n", - "print(f\"Global topology winner across full+wild domains: {global_winner_shape}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "52e90df2", - "metadata": {}, - "source": [ - "## 8) Manifold Visualization\n", - "\n", - "We project each domain with SMDS using the selected topology and color points by hue (`c=angles`, `cmap=\"hsv\"`)." - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "c36719ec", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Kli0bXrd69eo8dSU/7rjjwo8jX6+ff/45PElZ6HihCjzIe3havHhxjjrqqKhgsGLFihx11FEcfvjhMfd55ZVXKFasGC1atIjab/369bz55pt7vKa/InLCtVKlSkWdf3+bMmUKJUqUoHnz5pQsWZKTTz4ZCI5fHBmclixZkqZNm0a15cMPP8wWzO1vd9xxRzhoLFWqFK1ataJhw4bh92dGRgbPP/98jvtPmTKFsmXL0qxZM5KTk8PXd+mll0ZV+GdkZPDdd9/x4IMP0rVrVzp06MDjjz/O6tWr99jGvXmv/NXrefrpp9m5cye1a9eOCj1TUlK46aab2LJlS3jZoEGDor4/tWrVomnTpuHrXrVqFddff32ehpiRJEn/LIankqQ8i/zDOCEhge7du1O6dOl9Pl6JEiXCjyP/iM3N1VdfnWP31czMTKZNm0a/fv0455xzWLFiRa7HigzMQt1+s3bZ3x9yu84BAwbkGEilpKQwbtw4LrnkEm688UZSU1P36fyPPPIIF198Mb1796Z79+4cffTRfPrpp+H19erV4+ijj85x/1deeYVhw4bx1VdfcdpppwHBQHLdunUAlClThjfffJOxY8cyduxY3nzzzXDX9TVr1vDss8/usY1//PFHODiMi4vjmWeeYcyYMQwfPpwPP/yQUqVKAcEJykLhPcBzzz0Xfty+fXu++uqrcFsjKzJDQzm88cYbnHnmmeF96tatyxtvvMHjjz++xzbuj2tOT0+nSZMmjB8/nqFDh/Lpp59GBUjz5s3bYzvatm1LyZIlgeCwA6FxgLOO3Qu7q0/XrFkTrhwsXbo0RxxxRI7Hr1ixIm+88UbUuMRt2rThjTfe4Pbbb4+5z8EHH8xnn33G0KFDGT9+fFRVdWhStLx46623WLhwIQsXLsxTl30g3FUc+Eu/j/IiOTmZDz/8kGHDhjF+/Phwlevzzz8fDk4bN27M559/zvvvv89XX30VNXHSoEGD8q1tf/zxBzt37iQxMZEKFSowbtw4hgwZwsiRI3nkkUfC20WGzVlVqFCBsWPH8t577zF+/PjwRHq1a9fmqaeeCr/vsvrtt9945ZVXOPnkkxk5cmSu7czre2V/XE8gEODpp5/m008/5dNPP+XRRx8Nr1u7di3Dhw8HYMeOHVGT+fXr148vvviC999/n7Fjx4YrcBcuXMhXX32V6/VJkqR/HsNTSVKeZWZm0qxZM0qWLElGRgb9+/end+/ebN26dZ+OF1nBk9dqnvj4ePr3789rr73GscceGzUWa6T58+dz1VVXsWPHjhyP1bJly/CkSaHgKT/C08huvJHdRwHKly/Pe++9x/3330+jRo1yPMaYMWO4//779+n88+bNY/LkyUyZMoW5c+dGdYGtUKECTz31VI6v42GHHcZRRx0FBKvpSpcuTVpaGp988kl4m759+0ZV0B555JH07ds3/Hz06NHZrjurb7/9NrxNs2bNwrOgh9oQ+fybb74BghOIRU4Idscdd4TDs5IlS3LLLbdw7bXXMnDgQO68885cz78n+/Oab7311nBlbIUKFaIqSSODwJwkJyfTtm1bIBjGZn3vJiUlhSvzpk+fDkRXnR5zzDHhyd/2l8svvzzc3To5OTkqMIwcHiI/RP7uiPxZyw/t27cPB4oHHnggiYmJpKSk8P3334e3ufHGG8OTVSUlJXHDDTfQt29fBgwYwIABA/KtbVWqVGHUqFH88MMPDB8+PBzKBwKBqArnrF3gI5166qnhtmet4O3YsSNjx46lV69eOQ7zkZKSQr9+/bKNIx0pr++V/XE9J554IieddFL4+RlnnBH1oUCoqn/mzJnhqu1KlSpx8cUXh7epUaNG1MR93377bY7nkyRJ/0yOeSpJyrPPPvsMCHZf7NmzJ6tXr2bWrFm8/PLLUbPc51VKSkr48d5WjB1zzDEcc8wxbNq0ie+++46JEyfy7bffRnWX/vnnnxk7dmxUpWGkMmXKcOihh7Jw4UJ+//13lixZEp4l+oADDuDQQw9l3Lhxe31dWYUqA0PnzCoxMZFzzjmHc845h5UrVzJhwgQmTpzIpEmTovYdPnw4V111VVS3872VmJhI6dKlqVKlCscccwx9+vShfPnyOW4fa+zO3377LSrkO/7447Ntc/zxx4eHdNi2bRvLly+POUFLSGT12KxZs7JN7BQpcuzFkJIlS2Ybi7B169bZxtbdV/vzmrNeW+QYrqEhI/bk+OOPD09wNWnSJI477rhwUNq8eXM2bdrEokWLwuOB5ud4p0C24D9ySI68DKHxV0T+TO3rBzl5Fevn4ZdffokKyrO+FnXr1uXGG2/M13ZFSkpK4o8//uDjjz9mzpw5zJw5M1wxDbl/ULWn8YmrVq3KXXfdRb9+/fjhhx+YOHEiEyZMYN68eeExTDMyMnjuuecYPHhwzGPs7Xvlr1xPrOE4WrVqFZ6sMDT54eLFi8Pr16xZk6ffP5Ik6d/D8FSStNeqV6/OxRdfTP/+/QH49NNP9zo83bp1a1TQkZdZxkNCFULlypXjgAMOoGvXrnTt2pXMzExGjhzJ//3f/4VDqMmTJ+cYnkLwD+nQzNqvvvpquGt8ixYt9tvs9pHjnGa9zoyMDNatW0eZMmUoUaIENWvW5Pzzz+f8889n27ZtDBgwIBxCZGZm8v333+91eBprxvi8ilVhtmnTpqjnlStXzrZN1mV7qqjcm9ArdP7IfSLHY8wP+/Oaswbo+1IFGhneTpo0iWXLloU/OGjdujVbt25l0aJFLF68mA0bNoTHRo2Pj89xcp2/Ius1hcapBaJmas8PkRPWpaSk8Mcff+Q4QdXcuXNZt24d7dq126fXPdbPQ9b3bvHixff6uLHsqVo7lilTpnDPPfdEVWQnJyfTuHHj8KR1ucmpohSCE5tt3LiRqlWrkpCQEP5w4oYbbmDJkiXccMMN4WBx2rRp4S73We3Ne+WvXs+BBx6YbVmoshZ2f7C1L79/JEnSv4fhqSQpV1u3bmXVqlXUr18/anmNGjXCj/MySUhWWcd2bNq06R73ufnmm/nss89IS0vj5JNPztYFNj4+nrPOOovPPvssXJW3du3aXI/ZsmVLhgwZAsDHH38ctXx/SE1NjaqqDF1nZmYmnTp14vfffycjI4N77rmHnj17Ru1bqlQp+vXrx4gRI8JBXGTFVUGIFUpmrVT9888/o7rRAtlmo9/TDPKRgVO7du2ixjLNKhTIhMZBheD7NDMzMzwMAwSDmP0VgO/Pa44Mi/ZVlSpVOOyww5g/f364wjqkTZs2bN26lbfeeotAIMC7774bDlabNWuWa6Xxvop83Qta1uE1cvvA5J133mH48OEccMABnHnmmdxxxx17da5YwWjWqvktW7ZkGxt0T+/FWBXHkZX5efHrr7/St29fUlJSiIuL46KLLuLEE0+kadOm/PHHH3Tq1GmPx4h1fV988QX9+vVj8+bNJCUlMWXKlGzXXLduXa677jquueYaIPj7bcOGDTEn78rre2V/XE+sISMig9LQ75DIcakPOeSQ8FiosRTme12SJBUO//eXJMWUkpLCkUceSatWrTjttNOiujVCdJfpfQljRowYEX5cvXp1mjRpssd9qlatGu7WOWHChByD0ciu+7EqBCNFduuOrPTaX+Odjh07NjzualxcHJ07dwaCf4BXqFAh3OV05MiRMcdr3LZtW1TX/T1dz/4Wq3KsatWqUeHQ119/nW2byElVypQpwyGHHJLreSK73P/8888kJSVRqlSp8NeIESP47LPPoirQ6tSpE36cnp7O/Pnzo445ceJEjjjiCHr27MkDDzwQ87x5rYrMj2v+q0LVp4FAgDfeeAMIVrE2b96c1q1bh8O60DqADh067NO58rt69K+oV69e1KRrgwYNihlGrlq1ijFjxgDB6sG8TlIXKVbwfdBBB0X9nISG/ghZunQpLVu25Oyzz+bOO+8M/zwnJyeHt8nali1btuRp/NtII0eODAeuxx13HHfccQetW7cmOTk56ndibmJdX+3atcNtSU9Pj/qQKdKff/4ZfpyYmPiXQ/r9cT2hMU0jRU5KFfq9E/mz+ttvv7Fjx46o3z9ffvklY8aM4eeff873cXUlSVLRY3gqSYqpRIkSVK9ePfz80UcfDXdpX7ZsGW+++WZ43bHHHpvn42ZkZPDOO+/w0UcfhZf17NkzT9V4p556avjx1q1bueGGG6KqXtPS0njmmWf48ccf89y2atWqUa1atahlSUlJeQpz92TKlClRs0Ife+yxUSFh5PXMmjWLBx54IKrabP369dx6663hUDcxMTE8UVBhSk5ODofAAC+++GLUhDnff/89L7/8cvj56aefvsfvb/v27cNh35o1a3j00UfDIcWMGTN48MEHuf322+nWrVt4vMI6depEhR4PPfRQuEttamoqL730Eps3b2bGjBlRYX9kd+1QKLSncDA/rvmviuy6H7ruww8/nJIlS1K+fHkOPfTQqHWwd+OdRr5OoWMU1RD1uuuuCz9euHAh119/PevXrw8vW7x4MX379g3/DouLi6N379775dwlS5akTZs24ecDBgxg1apVQPADmeeee47t27czZ84cpk+fHg7hI7uUz549OypAza3yOieRgeLixYvDFZYbNmzgqaeeitp2b4YEqFu3Locddlj4+WOPPZZtxvkZM2bwzDPPhJ8fddRRf/n9vz+u55tvvgn3LIBgFe0XX3wRfn700UcDcMQRR4S/L+np6dx7773hD72WLVvGvffey1133cW5557Lhx9++JeuS5Ik/f3YbV+SlKPLL788PNHJN998Q4cOHahRowYLFy4M/2FZsmRJrrzyyhyPETlrcWpqKsuWLQuPWQrBsCevIcZhhx3GGWecwciRI4HguHqdO3emdu3alChRgl9++SXq2A0bNoyayTknrVq1YvTo0eHnjRs33usxNB955JFwN+60tDR+//13fvvtt/D60qVLc/fdd0ftc9555zFkyJBwNeU777zDiBEjqFOnDunp6Sxfvjwc9gCce+65ezU2bH669tprGT9+PFu2bGHLli307t07XAm6dOnScMhWpUoV/vOf/+zxeLVq1eKUU04Jfx/eeustxo0bR7Vq1Zg3b164Qrd+/fpR1ZPXXHMNt9xyCxCcWf6EE06gXr16rFixIio8u+yyy8KPIyvili9fzhlnnEF6enq4KrGgrvmvatq0KeXLl4+6ziOOOCL8uE2bNlGT29SoUSMcqOZF5Os0efJkunfvTunSpaM+ONnfevXqFZ6p/ZprruHaa6/N037HH388vXv3Drftyy+/5Ntvv+Wwww4LD50RObHQJZdcssfJkfbGf/7zH6ZMmUJmZiZLlizhpJNOon79+qxZsyZqzOM+ffqEH7do0SL8eOPGjXTr1o2jjjqKBQsWMHv2bOLj4/eqyjGyEnvlypV07NiR2rVrs3DhwqjqdQj2LIg1eV1ObrvtNi677DIyMzPZvn07ffv2DX/wtHbt2vDESxD8kGd/vP/31/Xcd999DBo0iOLFi7N48eLwz2nlypU5/fTTgWD3/YsuuogXX3wRgHHjxjF16lQOPvhgFixYEP49XLFiRXr06PGXr02SJP29WHkqScpR165do0KndevWMWfOnKjg9JlnnqFWrVo5HmPy5Mnhrx9++CEq3GzUqBEDBw6M6r66J//73/+iqufS09NZtGgRs2fPjjp2vXr1eOGFF2J2O88q6/im+9Jlf968eeHrnDFjRlRweuCBBzJw4MBsr1OxYsV45ZVXoqpRt23bxo8//hj1BzvAiSeeSL9+/fa6XfmlVq1aPPfcc+HJVwKBAEuWLGHJkiXhcKJq1aq88sorUbPJ5+a+++6Lqvj9888/mT17driqrFKlSjz//PNRY0eedtppXH755eHn27ZtY/bs2VGBYt++fcMVZgBt27aNGrdwwYIFLFmyJPy+Lshr/itiTf4UGZ5GPoa977Lfrl278ONAIMDcuXOjxu8tau644w6uvvrq8M98eno6c+bMYdGiRVHBaa9evbj11lv367lbt27NXXfdFX5f7dixgx9//DEqOD399NOjgrf27dtHBai//vorw4YNY/bs2bRq1Sqq0jkvevToEfU7ZuPGjfzwww9s376dkiVLRo3B+8svv+zVsdu1a8f//ve/qN/Vq1evZubMmVHBafHixXnwwQejrmtf7Y/rOeOMMyhWrBgrVqzg559/Dv+clixZkieffDJq7NZrrrkm6v+WDRs2MGvWrPDv4ZIlS/LCCy9kG89WkiT98xmeSpJyddttt/HKK6/Qvn17ypUrR1JSEtWqVeOcc87h448/3qsu+8nJyVSqVIn27dvzyCOP8N5772XrMr8nxYsX58UXX+T555+nS5cu1KhRg2LFilGsWDGqVq1K+/btue+++/joo4+ihh3ITdaw9K+Od5qYmEi5cuVo0aIFN998M59++mmO3e0POuggRowYwT333MPRRx9NhQoVwuN9HnTQQZx22mm89NJLPPvss/s0O3h+atu2LaNGjeL666/n8MMPp3Tp0pQoUYIGDRpw7bXX8sknn9CgQYM8H6906dK8++673HnnnTRv3pyyZcuSlJREjRo16NmzJx9++GHMoP6WW25h0KBBnHDCCRx44IHh8RaPP/54XnnllXD1dMhBBx3EU089xaGHHkpSUlJ429B4ugV5zX/VcccdF36ckJAQ9d5t06ZNVNAc2c0/L4488kjuvvtuDjroIJKSkqhUqVK2QLaouf766/n444+58MILqV27NiVLliQ5OZlatWpx1llnMXTo0KiQc3+64IILGDZsGCeffDKVKlUiMTGRAw44gLZt2/L444/z2GOPRX0/4uPjee211+jduzdVq1YlOTmZevXqccstt/Dmm2/u1YdKEPz5GTJkCGeddRbly5cnMTGRSpUq0bVrV95///2oMPbzzz/f6+s7++yzGT16NJdccgmHHXYYpUuXJjExkQMPPJDDDz+cyy+/nNGjR+c4Wdfe2h/X07ZtW4YOHUq7du0oWbIkZcuWpUuXLnzwwQfZ3stJSUm88MILPPzwwxx55JGUK1eOxMREKleuzJlnnsnw4cPzNLGhJEn654kLFNXBqyRJ0j/OpEmTuOSSS4BgWDF37txCbpGkf5IOHTqEK//79+9Pt27dCrlFkiTp787KU0mSVGAWL14cfhwaI1aSJEmSiionjJIkSfnuww8/ZOjQofz000/hZYccckjhNUiSJEmS8sDwVJIk5btvv/2WOXPmRC07//zzC6k1kiRJkpQ3hqeSJKlAlC1bltTUVA4++GAuvvhiTjvttMJukiRJkiTlygmjJEmSJEmSJCkGJ4ySJEmSJEmSpBgMTyVJkiRJkiQpBsNTSZIkSZIkSYrB8FSSJEmSJEmSYjA8lSRJkiRJkqQYDE8lSZIkSZIkKQbDU0mSJEmSJEmKwfBUkiRJkiRJkmIwPJUkSZIkSZKkGAxPJUmSJEmSJCkGw1NJkiRJkiRJisHwVJIkSZIkSZJiMDyVJEmSJEmSpBgMTyVJkiRJkiQpBsNTSZIkSZIkSYrB8FSSJEmSJEmSYjA8lSRJkiRJkqQYDE8lSZIkSZIkKQbDU0mSJEmSJEmKwfBUkiRJkiRJkmIwPJUkSZIkSZKkGAxPJUmSJEmSJCkGw1NJkiRJkiRJisHwVJIkSZIkSZJiMDyVJEmSJEmSpBgMTyVJkiRJkiQpBsNTSZIkSZIkSYrB8FSSJEmSJEmSYjA8lSRJkiRJkqQYDE8lSZIkSZIkKQbDU0mSJEmSJEmKwfBUkiRJkiRJkmIwPJUkSZIkSZKkGAxPJUmSJEmSJCkGw1NJkiRJkiRJisHwVJIkSZIkSZJiMDyVJEmSJEmSpBgMTyVJkiRJkiQpBsNTSZIkSZIkSYrB8FSSJEmSJEmSYjA8lSRJkiRJkqQYDE8lSZIkSZIkKQbDU0mSJEmSJEmKwfBUkiRJkiRJkmIwPJUkSZIkSZKkGAxPJUmSJEmSJCkGw1NJkiRJkiRJisHwVJIkSZIkSZJiMDyVJEmSJEmSpBgMT6Us3nnnHU444QQaN25Mt27d9mrfZ599lgYNGvD9998DsHLlSho0aMDtt9+eH00tsHPkplevXjRo0GC/HvP222+nQ4cOhXLugvbFF19w4YUX0qJFC5o0acJpp53G4MGDyczMDG8Tel/l9vXss88W4lVIkqT9Kes9zvDhw2nQoAHDhw/f47633347DRo0YOXKlft8/uXLl+/zvkXxPHsrr6936D58T9t9//33f/v7tQ0bNnD//ffToUMHGjduTNu2bbn++utZsmRJ1HZ7umf9u9+7S/p3SizsBkhFycKFC/nf//7HQQcdxF133UX58uULu0l7VL58eR599FEOOuigwm6K9tLIkSO57bbbqF27NldddRUlSpTg888/58EHH2T+/Pn0798fgM6dO8f8/u7cuZP+/fuzY8cOjjvuuIJuviRJyid9+/alR48ehXLuq666iq1bt/LWW2/9I86jv27Hjh1cdNFFLF68mG7dutGkSRNWrlzJkCFDmDhxIkOHDuXQQw8F4NFHH415jIkTJ/Lxxx9z4oknFmTTJWm/MDyVIixatAiACy+8kPPOO6+QW5M3JUuW5IwzzijsZmgvpaWlhYP6jz76iBIlSgDBSpNrr72W4cOHc/7559O0aVMaNmxIw4YNsx3jscceY8uWLdx33300bdq0oC9BkiTlk6OPPrrQzj1+/HjatGnzjzmP/ro33niDRYsW8b///Y9zzz03vPzkk0/mnHPO4bHHHuPll18GiPl3ye+//85DDz3EIYccEi4OkKS/E7vtSxHS09MBKFOmTCG3RP90s2fPZuvWrZx55pnh4DQkdNM5bdq0HPf/8ccfee2112jbtu3fJuiXJEnS3893331HUlJStmroww8/nHr16uV6zwpw7733snHjRh544AFKly6dn02VpHxheCrt0qFDB/r16wdAv379wmOX5jbmUYcOHfI0Nue+Wr9+PXfeeSedOnWicePGHH300dxwww38/PPP4W1ijXnaq1cvTj31VObOncsll1xCixYtaNOmDf/973/ZvHkzCxYs4NJLL6VFixYcc8wx3H333WzdujW8f2iMzXnz5nHTTTfRqlUrjjjiCPr27cuCBQv22O7MzEzeeustzjjjDJo2bUrr1q3p06cPM2bM2L8vUAxr167loYceokuXLjRt2pSmTZvStWtXBg4cyM6dO4Hd407dd9992fb/448/OOyww7jrrruilv3f//0f7du3p3Hjxpxwwgk88MADbNiwIWrfDh06cOWVV/LCCy/QunVrWrZsyTvvvBOznc2aNWPMmDFRn95HXgNAQkJCjtfZv39/EhMTufvuu/f8okiSpAJ39tln06pVq/D9BwR7nrRo0YLDDz886t4rEAhw9NFHc8kllwB5H9d92LBhnHbaaTRt2pROnToxePDgfW5v6P4IYOrUqdnuf+fMmUPfvn1p06YNTZo04dRTT2XQoEFkZGSEt/m///s/GjRowIABA6KO/eqrr9KgQQMefPDBXM/ToEEDevXqla1tOc0rMHDgQG688UaaNGlCu3bt+PHHHwHYvHkzjzzyCB07dqRx48Ycc8wx9OvXj1WrVsW87osuuoiWLVvStm1bHnjgAbZv377Pr+Pe+Pzzz7n00ks58sgjOfzwwznyyCPp27cvc+fODW/Tq1cvGjVqxJ9//plt/7vvvpvDDjuM1atXh5eNGjWKc845h+bNm9OiRQsuuOACxo8fH7Vf6O+bTz75hB49etC4cWNOOukkduzYEbOdTzzxBB988EG2e9NAIMC6deuIj885Vpg0aRJfffUVp59+OkcccUSeXhdJKmrsti/tcscdd/Dtt98ybNgwzj33XFq1akXdunX57bffCqU9GRkZ9OnTh5UrV3LBBRdQo0YNVqxYwdtvv83EiRMZO3YslSpVynH/NWvW0Lt3b0455RROOukkvv76a0aMGMGqVatYuHAhJ510Unj5sGHDiIuLyxYmXnvttZQqVYprrrmGzZs3M3jwYM4//3zeeecdGjVqlOO5b7nlFj755BO6dOnCOeecw6ZNmxg+fDi9evXiySef5KSTTtpvr1OkLVu2hM93/vnnc/DBB7NhwwZGjhzJM888Q0JCQvim/6CDDmLMmDHccccdJCUlhY/x8ccfk5mZGZ4sbMWKFZx//vmkpaVx7rnnUqNGDRYsWMDQoUP59ttvGTp0aNTYuFOnTmXevHlcd911bNiwgaOOOipmW5OTk6lbt2625Tt37gz/4ZNTV7ZvvvmGGTNmcO6558Y8hiRJKnwdO3Zkzpw5zJo1i9atWwMwY8aMcDA3ffp0jj/+eCAYTK5du5arr746z8d/4oknePnll2nZsiW33HILa9euzRZa7o26devy6KOPctttt1GnTh369u1Ly5YtAfjyyy+5/vrrqVmzJn369KFkyZJ89913PPLII8ycOZNnn32WuLg47rjjDqZNm8Yrr7zCiSeeSKNGjZg7dy4DBgzg8MMP59Zbb2Xz5s05nmdvvfrqq9SvX5+77rqL5cuX06hRIzZt2sR5553HqlWrOPvss6lXrx6//PILQ4cO5auvvmLYsGEcfPDBAHz99df85z//oXLlylx55ZXEx8fzwQcf5Glirr/qzTff5KGHHqJNmzZcc801JCUlMXfuXEaMGMEPP/zA+PHjKVWqFN27d2fq1Kl88skn4XAdgkH8p59+ylFHHUW1atWA4JBOr776KkcffTQ33XQTO3bs4JNPPuGqq66iX79+XHzxxVFtuOuuu+jUqRM9evRg+/btFCtWLGZbK1WqFPPvjlGjRrFmzRo6duyY43U++eSTJCUlceONN+7DqyRJRYPhqbRLp06d2Lx5M8OGDaN58+aFPo7oTz/9xLx587jlllu4/PLLw8sPO+wwnnnmGebNmxe+4Y5l48aNUft269aN9u3bM3XqVG6//fbwzVf37t05/vjj+frrr7Mdo3Tp0gwbNizcrbxz5850796d/v375zi4/9ixY/nkk0+49dZb6dOnT3h579696dGjB/feey/HHXdctq7q+8NHH33Eb7/9xrPPPhs1GP15551Hu3bt+OSTT+jbty9xcXGcddZZPP3003zzzTd06tQpvO2IESM45JBDwjfx999/PykpKXz00UdRkzadeOKJXHLJJTzzzDPce++94eXbt29nwIAB+zyB07333suSJUvo3LlzjgH1G2+8QUJCQtT7QpIkFS0dO3bkqaeeYsKECeHw9LvvvqN8+fJs27aNyZMnh+/lxo8fT1xcXNQ9SW5+/fVXXnvtNVq3bs3gwYPDFYEnn3wyZ5999j61t2LFipxxxhncdttt4ccAKSkp3HnnndSvX5+hQ4eSnJwMBOcIGDBgAC+88AJjx46la9eulChRgscff5zzzjuPfv368c4773DzzTeTnJzMgAEDSE5OzvE8+2rgwIFUrFgx/HzAgAH8+uuvvP3227Ro0SK8vFu3bnTr1o0HHniAV155hUAgwP3330/JkiX54IMPqFChAhC8b+zRowfbtm37S+3KTUZGBi+88AKNGjUK39eFlC1bltdee43vvvuOE088kS5dunD//fczcuTIqPD0yy+/ZNOmTZx11llAMIB/9dVX6dmzJ/fcc094u4svvpjLLruMxx9/nJNOOomqVauG19WrV49HH32UuLi4vb6GRYsWcf/995OYmJhj6D9jxgx+/PFHunXrRvXq1ff6HJJUVNhtXyqiKleuTEJCAu+++y6ffPIJmzZtAuCkk05izJgxuQanIaeeemr4cVJSUvhT9q5du4aXJyQkULNmTf74449s+1955ZVRIWejRo045phjmD59OuvXr495zk8++QSALl26sH79+vDXjh07OPHEE9mwYcMex0XaVxdddBGTJk3K9ofHhg0bKFOmTFQXrG7duhEfH8/HH38cXvbjjz+GZxGFYJev0B88pUuXjrqehg0bUqtWLT7//POocyUnJ+/TJA+ZmZncfffdvP/++9SuXZsHHngg5nZLly5l0qRJdOnShVq1au31eSRJUsE49NBDOeigg/juu+/Cy7777jvatWtHkyZNmDx5cnj5V199RdOmTalSpUqejj1+/HgyMjLo3bt3VPB22GGHceyxx+6/iyDY7XrDhg106dKFrVu3Rt0Phe4pI++HGjduzHXXXceCBQvo3r07y5cv5/7774/6EHp/adasWVRwGggEGDNmDHXq1OHggw+OamuFChVo3rw53333Hdu2bWP+/PmsXLmSM844IxycQnDug/PPP3+/tzVSQkIC3377bbbgdPv27eEeUaH71hIlStC1a1fmz58fNXTXiBEjKFOmDJ07dwZ234N37do16rq3bNlC165dSU9P56uvvopqxwknnLBPwelPP/1E79692bx5M3fccQeNGzeOud2QIUMAogoqJOnvyMpTqYiqUqUKd911F4888gg33XQT8fHxNGrUiGOPPZYzzzyTQw45ZI/HiLyZBMI3Y5UrV45anpiYSCAQyLZ//fr1sy2rU6cO3377LStXrozqrh6ybNkygFwrJ/JzKIT4+Hhee+01fvzxR1auXMkvv/wSHlMsMgiuWrUq7dq146uvvmLz5s2ULVuWESNGEB8fz5lnngnA8uXLyczM5Ouvv86x+z1AamoqxYsXB+DAAw8kMXHvfrVu376dm2++mfHjx3PooYcyaNAgypUrF3Pb0B8nhV0ZLUmS9qxDhw4MHjyYDRs2kJmZyfz587ngggv47bffeOGFF1i3bh07duxg4cKF3HzzzXk+7q+//goQM5A89NBDs41x+VeE7u2efPJJnnzyyZjbZL2369OnD19++SWzZs3ipJNO4pRTTtlv7YmU9V53w4YNbNy4kY0bN+Z67/b777/n+hrWq1dv/zY0huTkZGbMmMHYsWP59ddfWbFiBatWrQrfk2dmZoa37d69O++99x4jR44MD9EwceJEunfvHr4HDX2fLrzwwhzPmfX7lNsQYDn55ptvuPHGG9m+fTt33nknF1xwQczt0tLS+Oqrr2jcuLHDTEn62zM8lf6CnTt37nVQtjd69uzJKaecwjfffMN3333H999/zwsvvMArr7zCgAEDwp805yRyLM9Ief2EOdQtK1JoUoCcrjsjI4NSpUrx3HPP5Xjc2rVr5+n8e2vRokX06tWLHTt2cOSRR9KuXTt69+5Ny5YtY04+0L17dyZOnMinn37KWWedxejRo2nXrl246iN009qlS5dcZ7SPfC329v2wZs0arrzySubNm8cRRxzBwIEDOeCAA3Lc/ssvv6RcuXL7VN0qSZIKVseOHXnjjTeYPHkyGRkZBAIB2rVrx6pVq3j++ef5/vvvwxNQ5rXL/p5Ehm7783jXXXddVDf4SKVKlYp6/ueff4bDvO+//541a9bsU1AXEjnpVqSs912h+9RWrVpxzTXX5Hi8qlWrhqs4YxUQ7O/XMJa7776bYcOGUa9ePZo1a8Zxxx1Hw4YNWbZsWbZ5CJo3b069evUYNWoUN998M6NGjWLnzp3h3lKw+9qff/75HIfHCo2NGpLb5KSxvPvuu9x///3Ex8fz+OOPR/Vyy2rKlCls27Yt120k6e/C8FTag9BNRWpqatTy9PR0Nm7cmO0T7/1lw4YN/PzzzzRs2JDTTz+d008/HQjeiFx22WW88MILewxP/6rly5eHu/qHLF26lMTExBy7jNesWZNly5bRsGHDbJWp8+fP588//8yX8U4BHnroITZv3szo0aOjPuFOT09nw4YNUV2yIPhHSrly5fj000+pXr06GzdupHv37lHXArBjxw7atWuX7XxffPEF5cqV2+cAfe3atfTq1Ytly5Zx6qmn0r9//5iBdUhqaipz586lU6dOOQbjkiSp6GjVqhXlypVjwoQJABxyyCFUr16dSpUqUbJkSSZPnszq1aupW7cuderUyfNxQ/dnixcvpmHDhlHrfvnll/13Aey+HypevHi2+6GtW7cyceLEqGA0MzOTW2+9lW3btnHXXXfx0EMPcfvtt/Pqq6/u8QP8hISEbPfcELxnyovy5ctTsmRJNm/eHPPe7bvvviM+Pp5ixYqFe3EtWbIk23b7+zXMavr06QwbNoxTTz2Vxx9/POp1mTVrVsx9unXrxqOPPsoPP/zAuHHjqFOnDs2bNw+vD32fKleuTJMmTaL2/eWXX1i6dCklS5bc5za/8cYb9O/fnwMOOICBAwdyxBFH5Lr99OnTATjmmGP2+ZySVFQ45qm0B6Eu7nPnzo1aPnbsWHbs2JFv5504cSK9evVi6NChUcubNGlCcnJyvla8hrz++utRn7zPmjWLSZMmceyxx1KmTJmY+3Tp0gUg22yvW7du5YYbbuA///lPvr1uGzZsoESJEtmC3bfeeovU1NRsVQvJycmcdtppTJ06lY8++ogDDjggquqjYsWKtGrVim+//ZYZM2ZE7fvtt9/yn//8h5dffnmf2pqRkcF1113HsmXLOO+883j88cdzDU4hOL5URkZGjuNKSZKkoiUhIYHjjz+eCRMmMHXq1HBX8qSkJNq0acOECRP4/vvv9/oD8c6dO5OUlMRrr71GWlpaePnSpUuzjWu5t+Lj46Pu/4455hhKlSrFG2+8Ea6SDXnxxRe5/vrr+eabb8LLXn31VaZOncrll19Or169uPDCC5k4cWK2yUazngeC991Lly6Nmqxp48aNMSc2jSUhIYFOnTrx888/h8cADVmwYAFXXnklDz74IImJiTRs2JDatWvz8ccfs3LlyvB2qampvP3223k6377auHEjEBwiKzI4Xb9+PR988AGwu5I05MwzzyQpKYmhQ4cye/bsqKpTCM6LAPDss89G7Zuenk6/fv3o27dvzDkO8mLChAk8/PDDlCtXjrfffnuPwSkE/3YqWbKkXfYl/SNYeSrtwZFHHkmtWrUYPnw4ycnJHH744fz000+MGDGCmjVrxuzqsz907tyZhg0b8vTTT7NixQqaNGnC9u3bGTFiBCkpKVx66aX5ct5IM2fOpFevXpx88sn88ccfvP3225QrV4477rgjx326devGp59+yrBhw/j111/p0KEDO3fu5P3332f58uXceuuteZ4MYW917NiRgQMHcumll9K1a1cCgQDffvstX3/9NcWLF2fLli3Z9unRowdvvfUWo0eP5vzzz88WYN5zzz1ceOGFXHzxxZx77rkceuihLF26lKFDh1KuXDn++9//7lNbx44dy4wZMyhXrhzNmjWLmrgqpEGDBlHVJKHub6HKAkmSVPR17NiRESNGAERVQ7Zr1y4cCu5tl/3q1atz44038uijj3LOOedw5plnsnXrVt566y3Kli3LunXr9rm9FSpUYMGCBQwZMoTWrVtTv3597r77bvr168dpp53GueeeS+XKlZkyZQpjxoyhadOm9OzZEwh+0PvMM89Qv3798AzsN910E9988w2PP/44bdu2DY+pH+s83bt357nnnuPiiy+mW7dubNmyhWHDhnHAAQfkufr0lltuYdq0adxyyy1MnDiRZs2asXr1aoYOHUpCQkLUTPQPPPAAl156KWeffTYXXHABpUuX5sMPP4x5z7g/tWzZknLlyvHiiy+yfft2atasycqVK6POvXnz5qh9KlSoQPv27Rk5ciQJCQnZxr8/6qij6NGjBx988AHnnHMOXbt2JTk5mY8//pg5c+bQs2dPmjZtuk/tffDBBwkEAnTo0IH58+czf/78bNtkbc+yZcuoWrUq8fHWa0n6+zM8lfYgNAHR448/zujRoxkxYgRNmzbl9ddf5+WXX2bBggX5ct7ixYvz+uuv8+KLL/L111/z8ccfk5SURJMmTXjllVf2+0yqsTzyyCOMGTOGJ554gmLFitG5c2euv/56atSokeM+CQkJvPjii7z55puMHDmSxx9/nBIlSlC3bl2effZZTjzxxHxr79VXX01CQgIjRowIdyuqXbs2AwcO5Mcff+TFF19k+vTptG7dOrxPw4YNOfzww5k3b162T/AhGGAOHz6c559/nk8//ZShQ4dSqVIlTjrpJK6++upswxrkVWjm3Y0bN9KvX7+Y21xzzTVR4en69esBcqz6lSRJRc8xxxxDsWLFSE9Pp23btuHlofHLq1atmq2bdV5cdtllVKtWjddee42nnnqKcuXK0bt3b3bs2MGLL764z+297bbbePzxx3nooYfo27cv9evX58wzz6RatWq8+uqrDB48mB07dlC9enWuuuoqLrvsMkqWLElqaio333wzgUCAhx9+OPyBdIkSJXjooYfo1asXt9xyCx988AHJyckxz3PVVVcBwZnkH3zwQapXr84FF1xAzZo1ufbaa/PU/ipVqvDhhx/ywgsvMH78eEaNGsWBBx5ImzZtuOqqq2jUqFF429atWzNkyBCeeeYZ3njjDSAYZB9//PFcf/31+/wa7kn58uUZNGgQTz75JEOHDiUtLY0qVarQpUsXLrnkEk466SQmTJjAZZddFrVfjx49+PLLLznmmGOyTQALwTC4efPmDBs2jGeffZaEhAQOOeQQHnjgAXr06LFPbV21alX4A/zhw4czfPjwmNtlDU/Xr1+fbUgJSfq7igvkV9mcpL+tZ599lueee47Bgwdz5JFHFvj5b7/9dqZOnbpfZ4rNTbdu3UhPT2fUqFEFcj5JkiT9/a1cuZKOHTvSv3//mB/C729fffUVffv2zfeCBElSNGvoJf2rzZgxg3nz5nHeeecVdlMkSZKkmAKBAG+//TaVK1emQ4cOhd0cSfpXsdu+VEDS0tLYtGlTnrcvX748CQkJ+diif7fnn3+eBQsW8N1331G9evUCqRaQJEkqSBs3biQ9PT1P2xYvXtyhgYqgX3/9laeeeopffvmFefPmcffddxfIxLGSpN38rSsVkB9++IGLLrooz9t/+eWXTgyUjzIyMvj222+pU6cO/fv3p0SJEoXdJEmSpP3q2muvZerUqXna9qyzzuLhhx/O5xZpb5UpU4Zp06axY8cOrrzyyvDkXJKkguOYp1IB2bRpE/Pmzcvz9q1ataJYsWL52CJJkiT9k82dOzfbrO05qVy5MvXq1cvnFkmS9PdjeCpJkiRJkiRJMThhlCRJkiRJkiTFYHgqSZIkSZIkSTEYnkqSJEmSJElSDIankiRJkiRJkhRDYmE3QJIkSZIkSfq3+eGHHyjIedzj4uJo0aJFgZ3vn8LwVJIkSZIkSSpggUCAQCBAXFp6/p8rOSnfz/FPZXgqSZIkSZIkFYK4tHRadumV7+eZOe4tKJac7+f5JzI8lSRJkiRJkgqNoWZR5oRRkiRJkiRJkhSDlaeSJEmSJElSoXE80qLMylNJkiRJkiRJisHKU0mSJEmSJKnQOOZpUWZ4KkmSJEmSJIkVK1YwYMAApk2bxubNm2nQoAG9e/ema9euUdtdfPHFTJ48OeYxBg8ezJFHHhl+/vPPP/PUU0/xww8/kJqaStOmTbnuuuto1apVvl7L/mJ4KkmSJEmSJBWKOAqm8jRuj1usXr2ac889l/T0dHr16kWFChUYM2YMN954I6tWraJPnz7hbRcuXEjjxo256KKLsh2nbt264cdLliyhZ8+eFCtWjF69elGqVCmGDBlC7969GTRoEG3atNk/l5ePDE8lSZIkSZKkf7kBAwawfv16hg0bRrNmzQA477zz6NGjB8899xznnnsuZcqU4c8//2T9+vV069aNM844I9dj9u/fn7S0NIYPH06tWrUAOP300zn99NO57777GD16NHFxew52C5MTRkmSJEmSJEn/cnFxcRx33HHh4BQgISGBtm3bkpKSwrJly4Bg1SnAoYcemuvx1q5dy4QJE+jUqVM4OAU48MADOfvss1m8eDFz5szJhyvZv6w8lSRJkiRJkgpNUmE3AICHH3445vKffvqJ+Ph4qlatCsCCBQsAqF+/PgDbt2+nePHixMdH12jOnj0bgObNm2c7ZiignT17dlRYWxRZeSpJkiRJkiQpbOvWrfz444/897//ZcqUKfTq1YvKlSsDu8PTjz76iKOPPpoWLVrQsmVLbrvtNtavXx8+xu+//w5AtWrVsh2/SpUqAKxcuTK/L+Uvs/JUkiRJkiRJKjRFo/I00u23387nn38OBCtH+/btG163aNEiIFiRetttt1GsWDEmTZrEe++9x+zZs3n//fcpW7YsW7duBaBUqVLZjl+iRAkAUlJS8vtS/jLDU0mSJEmSJElh3bt358wzz2Tu3Lm88cYbnHnmmbzzzjvUqlWL8847j23bttGnT59wV/2TTjqJ2rVr8/DDD/Paa69x4403EggEAML/Rgoty9rVvygq+i2UJEmSJEmS/rGSC+Br75xwwgl06tSJG264gSeffJI//viD559/HoALLriAK664IlvwecEFF5CQkMCECROA3RWnsapLU1NTAShTpsxet62gGZ5KkiRJkiRJiqlDhw6ULl2auXPn5rpdcnIyZcuWZdu2bQDUrFkT2D32aaTcxkMtagxPJUmSJEmSpEJT+JWn69evp0uXLtxwww3Z1qWlpbFjxw6KFSvGwoULOeWUU3jggQeybbdu3To2bNjAwQcfDECTJk2Ij49nzpw52badPXs2AC1atNhj2wqb4akkSZIkSZL0L1a+fHmSkpL44osvwhNChQwaNIj09HQ6d+7MwQcfzJo1axgxYgSrVq2K2u6JJ54A4KyzzgKgYsWKtGvXjnHjxrFixYrwdhs2bOD999+nYcOGNGrUKJ+v7K+LC8QatVWSJEmSJElSvpk5cybsSKdllyfz/1zjboJiSbRs2TLHbaZPn86ll15KyZIl6dmzJxUrVmTKlCmMGzeOVq1a8frrr1OsWDFGjRrFrbfeSsWKFenZsydlypThyy+/ZPLkyZx22mk8/vjj4WMuWrSIc889l1KlSnHxxReTnJzMO++8w6pVq3j99ddp3bp1vl/7X2V4KkmSJEmSJBWwohaeAsyfP59nnnmG6dOnk5KSQq1atTjttNPo06cPycm7u/9PnjyZl156idmzZ5ORkUHt2rU555xzOP/887NNJDV//nyefPJJZsyYQXx8PI0bN+aGG26gefPm+XGp+53hqSRJkiRJklTAdoenz+X/ucZdk6fwVNk55qkkSZIkSZIkxZBY2A2QJEmSJEmS/p3igOQ9brV/zqN9YeWpJEmSJEmSJMVg5akkSZIkSZJUaJIKuwHKhZWnkiRJkiRJkhSDlaeSJEmSJElSoSmIMU+1r6w8lSRJkiRJkqQYrDyVJEmSJEmSCo2Vp0WZlaeSJEmSJEmSFIPhqSRJkiRJkiTFYLd9SZIkSZIkqdAkFXYDlAsrTyVJkiRJkiQpBitPJUmSJEmSpEJj5WlRZuWpJEmSJEmSJMVg5akkSZIkSZJUKOKA5AI6j/aFlaeSJEmSJEmSFIOVp5IkSZIkSVKhcczToszKU0mSJEmSJEmKwcpTSZIkSZIkqdAUxJin2ldWnkqSJEmSJElSDFaeSpIkSZIkSYXGytOizMpTSZIkSZIkSYrBylNJkiRJkiSp0CQVdgOUCytPJUmSJEmSJCkGK08lSZIkSZKkQuOYp0WZlaeSJEmSJEmSFIOVp5IkSZIkSVKhsfK0KLPyVJIkSZIkSZJiMDyVJEmSJEmSpBjsti9JkiRJkiQVijggqYDOo31h5akkSZIkSZIkxWDlqSRJkiRJklRonDCqKLPyVJIkSZIkSZJisPJUkiRJkiRJKjQFMeap9pWVp5IkSZIkSZIUg5WnkiRJkiRJUqGx8rQos/JUkiRJkiRJkmKw8lSSJEmSJEkqNMmF3QDlwspTSZIkSZIkSYrBylNJkiRJkiSp0Fh5WpRZeSpJkiRJkiRJMVh5KkmSJEmSJBWapMJugHJh5akkSZIkSZIkxWDlqSRJkiRJklQo4iiYytP0AjjHP5OVp5IkSZIkSZIUg5WnkiRJkiRJUqFJLoBzWHm6r6w8lSRJkiRJkqQYrDyVJEmSJEmSCk1BjHmqfWXlqSRJkiRJkiTFYHgqSZIkSZIkSTHYbV+SJEmSJEkqNAUxYZT2lZWnkiRJkiRJkhSDlaeSJEmSJElSobHytCiz8lSSJEmSJEmSYrDyVJIkSZIkSSo0SYXdAOXCylNJkiRJkiRJisHKU0mSJEmSJKlQxBEokHgurgDO8c9k5akkSZIkSZIkxWDlqSRJkiRJklRIMo3nijQrTyVJkiRJkiQpBqNtSZIkSZIkqZAUpcrTFStWMGDAAKZNm8bmzZtp0KABvXv3pmvXrlHbrVq1igEDBjBp0iS2bt1K/fr1ufLKK+nYsWO2Y/7888889dRT/PDDD6SmptK0aVOuu+46WrVqVVCX9ZdYeSpJkiRJkiT9y61evZpzzz2Xb7/9lh49enDrrbeSmJjIjTfeyKuvvhrebs2aNVx44YV88cUXdO/endtuu42dO3dy9dVXM2rUqKhjLlmyhJ49ezJnzhx69erFDTfcwO+//07v3r2ZOnVqQV/iPokLBAKBwm6EJEmSJEmS9G8yc+ZM2AGHd2me7+eaN24WFIOWLVvmuM1///tfRo4cybBhw2jWrBkAGRkZ9OjRg2XLljFhwgTKlCnDPffcw7BhwxgyZEj4eDt27OCcc87hjz/+YPz48ZQsWRKAPn36MG3aNEaPHk2tWrUA2LBhA6effjply5Zl9OjRxMXF5e/F/0VWnkqSJEmSJEn/cnFxcRx33HHh4BQgISGBtm3bkpKSwrJly8jIyODjjz+mefPmUUFssWLFuOiii9iwYQNff/01AGvXrmXChAl06tQpHJwCHHjggZx99tksXryYOXPmFNj17auiM6iCJEmSJEmS9C+zs4jUNj788MMxl//000/Ex8dTtWpVfv75Z7Zv307z5s2zbRcKXWfPnk3Xrl2ZPXs2wB63jQxriyLDU0mSJEmSJElhW7duZdmyZbz99ttMmTKF3r17U7lyZebPnw9AtWrVsu1TtWpVAFauXAnA77//nuO2VapUidq2KDM8lSRJkiRJkgpBAEgroPPszciit99+O59//jkQrBzt27cvAFu2bAEIj2kaqXjx4gCkpKQAwQAWoFSpUtm2LVGiRNS2RVnRqAuWJEmSJEmSVCR0796dgQMHctVVV7Fw4ULOPPNMVqxYQW7zzofWxcfHRz2PtU/WbYsyK08lSZIkSZIkhZ1wwgkAdOrUiaZNm3LVVVfx/PPP07lzZyB2xWhqaioAZcqUAXZXnOZl26Ks6Me7kiRJkiRJ0j9UegF8/RUdOnSgdOnSzJ07l5o1awK7xzONFFoWGvs0L9vGGg+1qDE8lSRJkiRJkv7F1q9fT5cuXbjhhhuyrUtLS2PHjh0UK1aMOnXqUKZMGebMmZNtu9mzZwPQsmVLAJo0aUJ8fHyu27Zo0WI/XkX+MDyVJEmSJEmSCklaAXztSfny5UlKSuKLL75g0aJFUesGDRpEeno6nTt3JjExka5duzJ9+nRmzpwZ3mbHjh0MHjyYihUr0r59ewAqVqxIu3btGDduHCtWrAhvu2HDBt5//30aNmxIo0aN9uq1KgxxgdxGepUkSZIkSZK0382cOZPADqjSpWW+n+uPcTOJK7a7KjSW6dOnc+mll1KyZEl69uxJxYoVmTJlCuPGjaNVq1a8/vrrFCtWjDVr1nDWWWeRkpLCJZdcQoUKFfjggw+YN28eTz75JF27dg0fc9GiRZx77rmUKlWKiy++mOTkZN555x1WrVrF66+/TuvWrfP92v8qw1NJkiRJkiSpgIXC0wMLIDzdkIfwFGD+/Pk888wzTJ8+nZSUFGrVqsVpp51Gnz59SE5ODm+3YsUKnnjiCSZNmkR6ejoNGjTgqquu4rjjjot5zCeffJIZM2YQHx9P48aNueGGG2jevPn+vsx8YXgqSZIkSZIkFbCiGJ4qu8TCboAkSZIkSZL0b5Ve2A1QrpwwSpIkSZIkSZJisPJUkiRJkiRJKiRphd0A5crKU0mSJEmSJEmKwcpTSZIkSZIkqZA45mnRZuWpJEmSJEmSJMVg5akkSZIkSZJUCALAzgI4TwIQVwDn+Sey8lSSJEmSJEmSYrDyVJIkSZIkSSokaQVwjhIFcI5/KitPJUmSJEmSJCkGK08lSZIkSZKkQmLladFm5akkSZIkSZIkxWDlqSRJkiRJklRI0gu7AcqVlaeSJEmSJEmSFIPhqSRJkiRJkiTFYLd9SZIkSZIkqZAUxIRR2ndWnkqSJEmSJElSDFaeSpIkSZIkSYXEytOizcpTSZIkSZIkSYrBylNJkiRJkiSpEASA9AI6j/aNlaeSJEmSJEmSFIOVp5IkSZIkSVIhcczTos3KU0mSJEmSJEmKwcpTSZIkSZIkqZBYeVq0WXkqSZIkSZIkSTFYeSpJkiRJkiQVkp2F3QDlyspTSZIkSZIkSYrBylNJkiRJkiSpkDjmadFm5akkSZIkSZIkxWDlqSRJkiRJklRIrDwt2qw8lSRJkiRJkqQYrDyVJEmSJEmSCkl6YTdAubLyVJIkSZIkSZJiMDyVJEmSJEmSpBjsti9JkiRJkiQVggAFM2FUoADO8U9l5akkSZIkSZIkxWDlqSRJkiRJklRICqLyVPvOylNJkiRJkiRJisHKU0mSJEmSJKmQpBd2A5QrK08lSZIkSZIkKQYrTyVJkiRJkqRC4pinRZuVp5IkSZIkSZIUg5WnkiRJkiRJUiGx8rRos/JUkiRJkiRJkmKw8lSSJEmSJEkqJFaeFm1WnkqSJEmSJElSDFaeSpIkSZIkSYUgAKQX0Hm0b6w8lSRJkiRJkqQYrDyVJEmSJEmSCklBVJ5q31l5KkmSJEmSJEkxWHkqSZIkSZIkFZK0wm6AcmXlqSRJkiRJkiTFYOWpJEmSJEmSVEgc87Ros/JUkiRJkiRJkmIwPJUkSZIkSZKkGOy2L0mSJEmSJBUSJ4wq2qw8lSRJkiRJkqQYrDyVJEmSJEmSComVp0WblaeSJEmSJEmSFIOVp5IkSZIkSVIhSS/sBihXhqeSJEmSJEmSWLhwIQMHDmTq1Kls3bqVypUr07FjR6677jrKlCkT3u7iiy9m8uTJMY8xePBgjjzyyPDzn3/+maeeeooffviB1NRUmjZtynXXXUerVq3y/Xr2B8NTSZIkSZIkqRAEKJgxTwN52Gbp0qWcd955JCQkcMEFF1CtWjVmzZrF22+/zZQpUxg2bBglS5YEgiFr48aNueiii7Idp27duuHHS5YsoWfPnhQrVoxevXpRqlQphgwZQu/evRk0aBBt2rTZX5eYbwxPJUmSJEmSpH+5Bx98kPT0dIYNG0b9+vUBOO+882jUqBEPPvggQ4YMoU+fPvz555+sX7+ebt26ccYZZ+R6zP79+5OWlsbw4cOpVasWAKeffjqnn3469913H6NHjyYuLi7fr+2vcMIoSZIkSZIkqZCkFcDXHtuQlsb06dNp1apVODgNOfPMMwGYNm0aEKw6BTj00ENzPebatWuZMGECnTp1CgenAAceeCBnn302ixcvZs6cOXloXeGy8lSSJEmSJEn6F0tMTGT06NEEAtk7+K9duxaA+PhgDeaCBQsAwiHr9u3bKV68eHh9yOzZswFo3rx5tmM2a9YsvE3ocVFleCpJkiRJkiQVkvTCbgDBYDSyOjTSoEGDAMKTQIXC048++ojLL7+ctWvXUqJECU488URuv/12ypcvD8Dvv/8OQLVq1bIds0qVKgCsXLly/15IPjA8lSRJkiRJkpTNiBEjeP/996lWrRpnn302AIsWLQLgp59+4rbbbqNYsWJMmjSJ9957j9mzZ/P+++9TtmxZtm7dCkCpUqWyHbdEiRIApKSkFNCV7DvDU0mSJEmSJKmQ5GVM0sLw0Ucfceedd1KyZEmeeeaZcAh63nnnsW3bNvr06RPuqn/SSSdRu3ZtHn74YV577TVuvPHG8BAAsYYCCC3L2tW/KCr6LZQkSZIkSZJUYAYOHMjtt99OyZIleeWVV2jatGl43QUXXMAVV1yRLfi84IILSEhIYMKECcDuitNY1aWpqakAlClTJr8uYb+x8lSSJEmSJEkqJEWp8jQ9PZ27776b4cOHU6VKFV5++WUaNmyYp32Tk5MpW7Ys27ZtA6BmzZrA7rFPI+U2HmpRY+WpJEmSJEmS9C+XkZHBzTffzPDhw2nQoAHvv/9+tuB04cKFnHLKKTzwwAPZ9l+3bh0bNmzg4IMPBqBJkybEx8czZ86cbNvOnj0bgBYtWuTDlexfhqeSJEmSJElSIdlZAF958fTTTzNu3DiaNm3KO++8Q5UqVbJtc/DBB7NmzRpGjBjBqlWrotY98cQTAJx11lkAVKxYkXbt2jFu3DhWrFgR3m7Dhg3hYLZRo0Z5bF3hsdu+JEmSJEmS9C+2atUqXnvtNeLi4ujcuTPjx4/Ptk3FihU5+uij+b//+z9uvfVWzjnnHHr27EmZMmX48ssvmTx5Mqeddhonn3xyeJ///ve/nHvuuZx//vlcfPHFJCcn884777Bp0yYGDBhQgFe47wxPJUmSJEmSpH+xqVOnsnNnsEY1VEGaVZs2bTj66KM57bTTqFixIi+99BKvvPIKGRkZ1K5dm7vvvpvzzz8/ap/69eszZMgQnnzySZ5//nni4+Np3LgxjzzyCM2bN8/vy9ov4gKBQKCwGyFJkiRJkiT9m8ycOZO0HXB6l5b5fq6Px80kuRi0bJn/5/qnccxTSZIkSZIkSYrBbvuSJEmSJElSIQgAmQWQztntfN9ZeSpJkiRJkiRJMVh5KkmSJEmSJBWGuIKpPCWuAM7xD2XlqSRJkiRJkiTFYOWpJEmSJEmSVEgKpPJU+8zKU0mSJEmSJEmKwWxbkiRJkiRJKiRWnhZtVp5KkiRJkiRJUgxm25IkSZIkSVJhSSrsBig3Vp5KkiRJkiRJUgxWnkqSJEmSJEmFJbmwG6DcWHkqSZIkSZIkSTFYeSpJkiRJkiQVFitPizQrTyVJkiRJkiQpBitPJUmSJEmSpMKSVNgNUG6sPJUkSZIkSZKkGKw8lSRJkiRJkgqLladFmpWnkiRJkiRJkhSD4akkSZIkSZIkxWC3fUmSJEmSJKkwxAHJBXQe7RMrTyVJkiRJkiQpBitPJUmSJEmSpMLihFFFmuGppP1qeyoMmwCf/QCZATi2EVx4ApQrnbf912+FNyfBqDmQthOOOASuaA+HVc97G3ZmwvRVkJIOjStDpVL7dCmSJEkqZJu3waZtULoE/LgseH+5MxOW/wmBAGxKhc0psDUFtqfBth3Be8CdwNYdkJ4JmXGwYyekA5lAZmbwOBkBiIsHEiAuAQJxEB8HCfEQFwfx8ZCUDMUSISERkpKgdDIkJkLJYlAyCUrsWlauOJQpHtyn3oHB8xRLgMMrwKY0qFACShmOSNLfkuGppCg/LoZnh8FnU2BnBhzbHK45B45uvud9l6yG0++HFWt2Lxs7HR4bDh/eAa0PzX3/xX/Aqc/Cqo27l81ZGQxTX+wF5xyx5za8MQsengirtgSfJydAt8PgiROhTLE97x+yaQcEgHJ7sY8kSZJytm07pKYFP2yfuSD4eMUfsHotrFoHG7fBHxtgS2rwA/X0AKRnBMPPnYFgsJmZGbxHi0+EDHaHn4H44HB+gdDJ4gj+tRuX5XE80X8FxwHFyT6gXcKuZaF9Ix9nGTcwLmFXGwiGrJm72hIfHwx4E+KD7U1KDN6bli8JBxSHiiXgwBJQozTUKAnVS0KJBGhVEZLjoVQilPAvdunfoSDGPNU+81ex9A/060p472PYuAka1IMep0KJEnve7/Mp0PMu2JG+e9kn38GY7+DpW+Di03LeNxCACx6PDk5DNmyFcx+BuQOhRC5h5GVvRgenITsz4aq3oV1dqFk+5/1fmQE3fxa9LC0Dhs6FJRtg3IWQuIeRnkcthyd/hBm7rqNZBbi+CfSok/t+kiRJ/3YLl8Kf6+D3tTBvCSz5FVavgT/WwdqNsGl78N4sQDBwDFd+EryXBIKhZcKux6HQEiApGKKGZKQH1wXiCJaZJkAgYfd6AruWh/7izdj1OHPXV+i4yexOXUPHj49YnxixPlQ5mmXbQPzu9Rm7tg3sCnqJg4z44DlTdwZ3WbcF2L77OuPidh82cVf4Gh8HyUlwQDGoWAyqlYDqxaFOaWh8QHBZzRJQ2x5WkpTvDE+lf5BAAPo9AC8O3nWztsud/WHQU9DpuJz3TUuHvv2jg9PwcYGbB8DJ7aBKhdj7f/Mj/PRrzsdfswk+nBTswh/LjOXwQy77p2fA4Mlwxymx16fuhAcn5Lz/tN9g9CI4s2HO27z8E9wyJXrZ7HVw6dewfAvc0iznfSMt3QYrUqBGcaiXx+EKJEmSiro//oSFS2D1nzB7LsxfDL/8Biv/gK3bg9WhGQS7xMftqgbNNrtzHMHQMINg8EiWbTLYXSG6k2BgGRfxOFJkELrreFHHCrA76MyMeBzaNnSMyNAVoqtQ42Isi5QQsV2smawjjx366zs+enkg1K5E2LkrfM1MDL6e21Nh9Q74cfOu0yTuqnDdVc2amAAHJEKNUlC7JDQqBU3KQJVkOLw0lHeoAOnvwcrTIs3wVCqipk2Fb74JPu7QAVq22vM+T78Mz7+RffnGTdDzKvhuNByaQwXlJxNhzcacj52+E94eCzdfGHv99MV7bt/0n3MOT+f+tuf9f8xlm2+Ww/qU3Pf/cH7O4emGHfB/03Pe96EfoGc9qJ7Lp/vzNsOt82Diut3L2h4IjzWGZgfk3jZJkqSiYMMG2LwFvp8Os2bDj/Nh8S+w+g9ISQ3mjhmBYLVkuFoUoqtEdwV8ZLA7/AwJsDuwDAWfWUPHWOFmZBAaKbKKNK/rI9ud9XFkCBoZmMYKTyPPlTWADa2Pi7F+T9tmHRogIlwNhc0Zu4YtSA9ASgB+3woztu76vhDsbRWfACUToVZxOKQkNC0FR5SGVqWgbAKUNQ2QpDzx16VUxKxdCxf3himTdy97uD8cfQy8/gaUz6Hbeno6DHw95+Om7oCXBsPj98ZevzQP4eWyXLYpnodPtYvn8mlameJ73r9sLttsSdvz/pt35Lzug6WQsjPn9TszYegSuKlp7PVLt8HJk2FjlsrdKRug62T48mhoWGbPbZQkSSoImZkw6wdYugxmzoJpM2DxUvhjTXDSTogORzMju7Jn7SIP0VWikeFnqLt81MnZHZpGhpshewo3/y7i8vA4ZE9BbeSy0OsZR7bXNhAILtu5a/u0AGxMgx/TYPSm3dsVS4AqxaBhiWCY2roU1EmGZnkY6ktSPrBKvEgzPJWKmAsvgOnTsi//biL0vghGjY6930+Lgje7ufkyl27tlQ/cc9sq5bLNaUfCXW9HDxeQ1elH5ryuS+NggLolNedteuRSfdu4cs7rwttUynndH3uoWgVYvS3ndY8vzh6chmzZCY/+DINa5rx/IABjN8Pg9fBbGtRIhl7l4eSywTGvJEmS9lV6Onw3IRiWzpgJs2bBb6tgRyrs3AnEBcfoDH0RF93lPgDE7ZqoKccu8rA7KA1Vl+4a6zNXgT2s35O9CSVjVZQmxFgfq+17E/LmtiwncVn+zSo+4t9Y1ayRx4kIuXMaHiA1Dn5Jh18CMC4VWBec0Kp4AtRKgpbFoU1xaFYMjinh/aikoiUjI4M5c+bw/fff88svv7Bhwwbi4+OpVKkSNWvWpH379jRo0GC/nc/wVMonG9bD0EEwbiSkbIfGLaDXldA8lxnjv5sYOzgNmTwJpn4PbXIJIffVGcfBrc8Ex1WKJQ644OSc9z+4MvQ8Dt7+Kvb645tAu8Ny3r9UMbj9ZLjzo9jr29eHTo1y3r9hRTjmIJiYw7ipCfFwWS7hZa08DLZ/cA6Vo4EAfLgq930//h3SMoMzp2aVEYA+v8CHG3cvm50CYzbBWeVg0MGQ4A2rJEnKg0AApk6CaVNg2mSYORN+Xx2sJg1XlGYNR3d1iY8LLWdXWJolnIvL3B2uxqwo3VOYGEuse5zIZfERy3KryMxpfNXIoDFW6JhB9hAyMigNrY8cMzXWtrGOE4ixXWj5vt7b7Sks3tPwAJGvQ1L04/S44DAAP6XBT+nw9hZISICkBKiRCK2Lw9HFoW0yHOH4jNL+5c9UnmzdupWhQ4cyePBg1qxZQyAQ+z+bJ598kurVq3PhhRdy3nnnUSIvM2jnwvBUygcrlsOFXWH1yt3Llv0Mo9+HOx+Bi/rG3m/8+D0f+8svY4enjepDlUq5V592PDbndWVLw/+uhFuejr3+qh5Qr1bubXv6CiiRDG+OD05ABcHQ8syj4Nkrc98X4NqOwe7/T3wGqzYGl5VIgvPaQP9uu2cizclLp0LXIfDLxujlCfHwzElQJ5fK2e51oN9U2JpD9WixBDivbux16QFIyci9bWmZsCMjdnj6ytro4DTSRxuhTSn4Ty5Vs5Ik6d/rtxUwdSJ89zVMmQS/LIW0NMjYFZSG5lQKTRafTvBJXMaucDSnCZZyGmM0ctvc5FZJmdO4oiGRkzDFZ1kWadfYoHHxwWuJI3i/mBkIhn6Zu/7aTUiCjDiIjw9OuJQBJO7aLpAJ8YHgsARxccGvjAAk7AqPMzOD95IZ8cHXjETIjAs2KwAEMiJC551kH680tCzrhFVZ/xKPrNrNr9cdYofNWcelDe2/67XKAJYAS3bAsB3B165YHNRPgKOKQYdicHQyVM1pUi1J2g9GjBhB//792bp1K61bt+aKK66gUaNGHHzwwZQpU4bMzEzWr1/PqlWrmD59OlOnTuWxxx7j5Zdf5qabbuLss8/e53PHBXKKaSXts54nwfRJsdfFxcHoKXBojCrM++6FZ5/J/dg33gR33hV73dMvw/89EntdieIwcVTOE0aFDB8PT7wNPy4JPj+kGlxzDlzRLff9Iq3dBF//CBmZcHQjqFkx7/sC7MyAGb/Ajp3QpAYcmIeq0JDNO+DtOTByIWxPh9bVoE9LODwP3frfXwJXfBu8YY4UFwdPHQWX5jDZFECrr+DnXLr11yoBczvEDoBbzofFuYzHWrsYzM6landlBry+HaalQXIcnFoczikOJb2BlSTpHyUQgKWL4PtvYcJnMP07WLcWdqTvztwy2N3bPPKWJj1iWfjxrnFMowpG43ZXnIbC1aj1ocA1nuwBYGQ38VAgl6XreHi70Biqibuz2rg4iE8OtiE+MfiVXCwYfh5QCooXgwNLQ8kSUK4MlC0VvMctVwbKlgh+iJ+UCFXKQXomlC4e3G9LKtQ8ELbvhPQMOKRC8IP6ksnB/f7YAtUPgHXbg/ef1Q6A5euCH+onJsDqLVCuOKxJgdSdwTD1j+3BY6XshM2psCkdtmfA5jTYtBO27YSNO4L3o1t2BsPXtF0hbWZccDKnjPjgtYbnsAq9LnFEj38YF/F6we4KtVivbWjirqzHIMuy0Pcn1nZ5WR+/+9xxccHq1MoJ0D4JTkyCYxOgjvei0h7NnDmTHTuhywu5dJPcT8ZdNZNiidCyZf6fa3+7/PLLmTt3Ln369OHMM8+kQoUKedpv9erVvPfee7z99ts0b96cV155ZZ/Ob3gq5cGMr2DS2OAn+E3bwbGnQ1IOZfU/z4dT9tCt/oIr4J7Hsy8fPx7O6ZH7vh+NhGNzqCANBOCOB+GFN6PHHj2wHAx6Cjq2z/3YkVatgYwMqFllzxWf/ySTfoenf4QvfgvexB5XDa5rDCfUyH2/F5fBbfNyXn9fQ7ixXvblqZlQec6e2/V709hh6KepcNFGSM3ym7xuAoyqADVjVWlIkqS/jZTtMPY9GDUU5v0A69cFP2gOCd0ChCoEIVjsGIBsvcZD69Mj1gUSsxQzxkFmXsLTUEgXKSl6XVxCMKCNTwiOmRkXD4nFoFIFKF8OalSFCgdAzcpQozJUrQjJydD80OCH8KVLBNf/U/y5HVIzghWvczdAWgb8lgqrtsNv22DtDliZCht3wtr03ZM+hQLxuIRdwyaEgutYwWZkoJr175VY4WmsEDxyWazvc0RwGnW+iMcJcVAFaB0P5yfAWQnBalVJ0QxP8+aRRx7hP//5D6VLl96n/Tds2MDzzz/PnXfeuU/7G55Kudi0Hu46F+Z9H728ck3o/wHUjjEG55jhcMPFuR+37XEweFT25YEAHN8e5uUQwjVrBl/mMKZopBW/wQejYMMmaFgPzuoKf3GID+1BRgAumQkjVmdfd3IVeLsVJMUIPzMDUOVH2JHLZArJccHwNDHLDecfGdB0DaTk8Fu8XTJ8mrcP5CRJUhGRsh2+HgnjP4LpE2Ht78Hwcmdgd8/vyCA0a3VpKDTdmWVdaD271oWrUxOjjxOr8jS0PlSpGgpI43ZVrcbHQ2JxKF0aKlaEmtWhelU4tDYcXg8qHgiVykOjGB8kK3ezN8LGNPg9FeZthsXbYdUOWLkD1mfCtkxIz/J9CgersSpTQ4FqKBTdUwVxrIKRUAC7p5B1Vxvi4oMPa8QFODYuQI+4OLrGQbF9HvhV+ucIh6evFkB42ufvG54WNsc8lXLx0GXZg1OAP1fC7d1h8A9QrHj0urLl9nzcsjl8gh4XB++8Cz3Pg59+il7XuDG89U6emk2tGnBjDuOqKn8kxMEbLWH07zB4BaxIgerFoVctOKNazhM+xcfBmQfAsA05H/v0ctmDU4A3t+ccnAJMSoMf06FJrK5Wu2wPwAagIlYDSJJUGDIz4ZeFwcB0zDvw68+wMy34oXqAYIaVzu4e3elEz2UUKYFgMBrqbb3HKplY//fvOnhoQqm4uGBbEpODVaMHHgh168JBNaFRA2jWCBrUC3afr1Ft769fuWtWLvf1K1KCY+/P2w5zt8JP2+CXVFiWBhsDwdB9Z+j7CLsntsog+L2O9QF+bm+cyPdMrLFpI+0KcQMECCRksgIYAgwlQLEANIyDC4EziaM2ccQZpkoqogxPpRws+wmmfZnz+jW/wVcfwkkXRC8/8lioVAXW/JHzvqfk0jW/Zk34+tvgxFBf76oy7dAROuQwXqaKjvg4OL1a8Gtv3FIFxmyGLTEmnSqdALfmMF7rrJ17PvYPOYSnSwPwQAA+DkAaUAboGQd3xkE532eSJOWrzEyY9hl8/CrMmgDr/4yuKI3sch/qaZ3O7mLCUO6V9dYhVuFfuKI0Ynl4WXz0tnFJwYMUKwkH14Z6deCIltDmCKhcKfjc3kxFS61d34/6peGsLPeMW3bCslRYlQ7fb4NpW2FpKvyyE3bsmrgqI5S4RyYDWd8sOd0bhpbnMpkX7Jp9K0ImkEImP5DBDwS4hQDVCXAc8VxBEu1JMEjVv08OwwIqtosuuijX9XFxcSQnJ1OxYkVatmzJmWeeSVJSLlVFe2B4qn+V35fCF6/Bz9MguTi0PhXa94QSMYbNmDVhz8eb9W328DQpCW6+F26/KvY+rY6Czqfmftz4eOjcOfilf74GxeGTunDTSpi+fffy1iXhiZpwWA5/pJTIwz1lyRjbLA1Ap0xYG7FsC/BSAL4LwLh4KOP9qiRJ+1VaKkwZC6NfhVnfwI7tu6tLE9kdmiaSvct9qLAvk93haWh5rFA062PYXWAY2jcxGZJLQLVq0LgJNGkGTZtC504GpP8UZRKhaWloCpx0YPS6rRkwbjPMSoU5O4K9lf7Y9aH6Tgi+YSLfdCF5HfQvFMzHx9ohcoTeDAJk8BsBhhBgKNspBZxIIpdRnI4kk2yQKimL1atXs27dOrZvD/4BXaZMGYoVK8a6desIBALExcURGqV0xIgRDBs2jLfffpvixYvndtgcGZ7qX2PKRzDw8mA3qJC5X8PYgXDnaKhUK3r7vFR5xuUwg2S3C4Izgz79IPyya9b64iXgjPOg30OQ6E+esmheEsbXhwWpsDINaiTlHJqGnFEc3k/JeX2JODixWPbl9wWig9NIcwmGqLd4jypJ0l+2IwW+GgJfvg3zJkFa2u4K06yRUgK71yUSDLGyro/VwzoyQM3M8m9ofVwilCgJNQ6GNu2gdVto1AhatQ5+aK9/n9IJ0P1A6B6xbGcApqbC3B0wLRW+S4MVgeAwURmhe8PQGy6O3QFrrt38s64MEBmc7v7YIPiRQSbBD/U/JI0PSaM4mRxHEr0pQQ+Kk2SQKgl47LHH6N27N+eccw7XXHMNlSsHS+83b97Ma6+9xuDBg3nttdeoWbMmI0eOZMCAAbz88stcd911+3Q+J4zSv8K63+CGZtHBaaSG7eCeT6OXrVoOvZoFKwJycs9gOO7MnNcHAjD/R0hNgXoNcx7rVNoXGQHouA5mpsdef3tpuKNM9LLNAaiduXvCiFjqALNidb+SJEl7FAgEP6Afdj8smBIMUCO74u+MeJz1/+PIZZGTOmVdF7qlTY+1Ph7ii0G1GtD22OBXkxbQuKlBqfZeegDmpMH0dJiYBt+lw+oMSEuEzNCYEpC9y3HoeULWwSUiAtO49CzLIkUPYhFHJqWA4yjGnRzAUexb9ZhU1IQnjBpaABNGnffPmTCqV69elCpVihdffDHm+uuvv56NGzfy5ptvAnDXXXcxY8YMxo4du0/ns/5N/wrj38g5OAVYMAmW/wiHNNm9rPohcEJ3GP9B7H0ObgBH76H7fVwcNGq6t62V8iYhDkaUh2s3wajU3X9glYmD60rBbTGGo1hP7sEpQE7D9c4jnYFs4wtSyQTaU4xrKEVLB+iRJImFU+Czl2Hie8Eu+ezqkh+a0Dz0/28iu8cvzTpfT+RET1nr67JWlYb+TUiAkqWhQXNoezy0aQ8t20DpGPcB0t5KioNWxYJfV+5atiETpqXBNzvh653BnkvbQuOnhoSrUuNiVJ9C9Gi8WYPT0McMEApWA8BW4BO2M4btlCWOCyjFFZSlGTG6Wkn6R/vxxx+57bbbclzftm1bHn744fDzpk2bMnr06H0+n+Gp/tYyM2DBl7D+VyhbFRqdGBy/Katls/Z8rF/mRIenALcMhIyd8M2I6OUNWsB979j9XoWvXDy8dSCszIDpaVAsDo5LhpI5VJZUBkoAufT255AYyz4nlZ5sYEdEv6wPSOEjUniZAzkbB0eTJP37bNsIHz8ME9+FNb/uDjRjTWKeRLBiNHLSp5y64sPuADWqS/6uGdPLlIOmbaFdBzimMzRqtr+uSNqzA+PhxOJwYsSyaTthXCZ8mwHfE5wINRBPsDw1IesIvnsSClMziRWsBshgE5k8zw5eYC11SeIiynITFSmVbdo06W/CbGGvlC5dml9++SXH9cuXL48a33T79u2UKlVqn8/nt0d/Wwu/gnf/AxtW7l5WpjJ0fwxanBW9bfE8fPJeLMbPUfESwa75vy6CyWNhZzo0aQdN2/21tkv7W80EqJmH/LJkHJwTB2/mMhzFJVnuaVMJcAUbo4LTkAzgGjbSiWIc6M2qJOlfYtEkGHIbLJ0KGem7J3oKdZ2PJxgR7cyyXyg0jd/1b6wYKRDxxa5tkktD4+Zw7Klw+gVQteZ+vyTpLzkiEY6IeL40E97KhE+JY3YgjpS4yHc0e5hgItbovZB94IsMAgRYzE7uZhsPsppjKcXjVKcZJf/yNUkqutq3b8+QIUM44ogj6NSpU9S6qVOn8u6779J51wzcKSkpjBw5kvr16+/z+RzzVH9LK2bD050hPTX7uvgE6PsRNDh+97KpH8NTF+Z8vBJl4PlFUHzfP4iQ/jbWBODETFgSY90JwPvxkBxxP/s+KVzGhlyP+QhluQr7B0qS/rl2bIdvX4WPH4QtayAzsHvip6yz3EeOWxprXeS/kdtksCsuSoaqB0Pnc6DT2VDnMEhylBz9Te0IwBwCDCOTD+PgNzJJZye7R/YN/TSERI7qG7k86wRTkct2iwNqksi9VONCKpDsB/wqwsJjnn5cAGOenv7PGfN0zZo1nHvuuaxevZq6detSp04dkpKSWLZsGfPnz6dixYoMHTqUatWq0aZNG7Zt28aLL77Icccdt0/ns/JUf0vjn44dnEKwK//nj0eHp61OgXpHwOJpsfc542aDU/17VIqDL+NhYACGBGANUJtgxWmfuOjgFGBxtrqZ7BZn61IV9Afp/EIaFUmkjuNRSZL+hn5fAJ/8D2Z8uHsM/dDQjqHu+ZH/U0aOZRqqMA0JZPk3M2KfpJLQ5Hg49nTo2APKVdjfVyIVjmJxcARxHEECjwOriOd94vmYeL5jBztIIPtPUawar1gj/8bu1r+CNC5jCf9hCRdQkXuoRS3vRaV/jEqVKvHhhx/y7LPP8umnn7J48WIAypQpQ48ePbjuuuuoVKkS69ato0WLFnTv3n2fg1Ow8lR/U7dUgfTcBm0EHvkNikfMNL5tEwy6Eb4fERzHFKB0+WBweuq1+dZU6W/vZbZxC5ty3aYfZejH7h+430jjv/zGGDazc9fNbxtK8SDVORI/qZAkFX2LxsPI2+HXGRDYldVk7TQcWWEa2cE4M2K7yEhoZ8S6zHgoVRGOOBHOvQnqt8jPq5GKrkns5ElS+IodbCBAIPxTkvUnK2u9dtb1ketCwWrwPjQBaEdZnqIOrSibz1ck5V248vTTAqg8PemfU3ma1aZNm0hPT6dChQrE5TosyL6x8lRFzqZfYf57kLIByh8Kh50NyVmyloy02PtG2pllm1IHwLWD4IIHgxNIJReHhu0gyQ8gpVx1ozh3sjnmmKcQrA04L2LCqHXspAuL+ZXoH8KpbOM0ljCGurQ2QJUkFVFzPoKRN8O6ZdlHXIwjOPFTKDSNHMM01sRPWSd9ChCc5PTI0+Gky6BRm/3ffunvph2JtKMMAUrzDem8QSqjSGF9OAQNyVqRmvWnLhSWZq0HD46OOoGNHMF0GlKS56hPB8rnw9VIKkg7duxg2rRp/Pbbbxx//PFs2bKFHTt2UKlSpf16HsNTFSlf3QkzBu7+dB/g6zvhlFeh3sm7lx1yJCydlPNxKh8KpXL4v7B8teCXpLypSAK3U5r72BJz/dWUonbEfycvsiZbcBqSSib38zsjqZsvbZUkaV9kZsLcD+DjG2DL6t1d7uOJrjANCXUyDnXLzzqiYlS3/DgofSAc2xPOvAmq1s6vq5D+3uKI43iSOZ5koCzzSeMxtjCcLWwmQCD8MUUoRM360UToj8jsXflDFakBAsxnI52YysEU5wUacRKV8//ipNyEPpkriPP8g3z22Wfce++9bNgQnJ9j0KBBpKen85///IebbrqJSy65ZL+dy5GTVWRMHwjTn40OTgHStsCoi2DNT7uXHX917sdq33cPEzhK2is3U4bnKUe9iJC0Fgk8ygH054Cobd9jY67H+pot/Jntz1BJkgpeIADzPoKHa8K758LW1cG/LROAZIJ/LMX6mzb0R1TodjNA9N+kmQTHMG19Kjz4DbyzDvo+a3Aq7Y3DSGYQFdjAwXxCZU6kJMVIIPtPYKTQNG4hkV35Mwh+FJJBgHSWs4mTmUwdPucL/szPS5G0n82cOZMbb7yRcuXKceWVV4aXV65cmerVq/Poo4/yxRdf7LfzGZ6qSMjcGQxOc7JzB8x8YffzZqfDyXfE3vaYy+HYy/dv+yTBhZRkBpWYTWVmUpkfqUzfGN3vN+YweVRIANgUY5sUMkjZw76SJO0viz+DJ2vDsG6QujoYmGaNYhJ3LYuLsS5rWAoQFw+128A1g2HwerhrFBx+bP60X/q3iCOOkynFOKqxgTq8SFWaUIK48NRtuQmFqZFjoO6MeBxgGdvozHc04gu+Y22+XYeUq+QC+PoHeeGFF6hRowYffvghF110EaHpnBo2bMiHH35I7dq1eeONN/bb+ey2ryJh3SLYsir3bZaPj35+0u3QohtMfhPW/wplq8CRvaBWs/xrp/RvF0dcVBf9WBpSjMlZxpmKVJoEakb87z2WPxnAcibvqlg9inLcwCGcbBcqSVI+WDUNPrkc/pgdfJ7E7k6+8eyekoaIZRkR/4YEIp4XLwcdLodTb4MyFfOv7dK/XQniuZJyXEk5VpHO/fzBO6xlS3iwjaxj9EcNokHkJFK71weD1fms5xi+pB0VeZXWHJald5WkomPWrFlcdtlllChRgtTU1Kh1pUuX5uyzz+all17ab+czPFWB2rwEUtdC6YOhZNW92zdWN/wq9eHMB/dP2yTtH5dTkclsy3F9Tw6kxK6ODy/zK7ewIGr9ZDYymVk8TkOu4KB8bask6d9j+1oYfQH88lkwLklmdy1aHLunmEkkeozT+IhtQjIJVpnWaAqn3glHdHfIKKmgVSeJF6jJc9RgMOt5jNUsYFvE2KiRIitQQyK79O+uTJ3EahozirM4iDc5ilIFMhil/vX+YZWh+S0tLY0DDsj5A47ExMRsoepfYbd9FYg/p8AnHeCjFjC2M3xwGHzVE7atDK6v0ADK1sz9GId0zP92SvrrulOOXjnMXtqSktxDcMa29aRxJ4tyPM6dLGJdDhNPSZK0N76+DV6sDr9+lr2qNIlgMBoa3zRWF33YPU1NQjK0ugDumw//+wHa9DA4lQpTAnFcQgV+ojEzaMyZVCIx27iokZNLhUQGqjuj1mcS4EOWU5H36M+cfL4CSXurdu3aTJ06Ncf148eP5+CDD95v5zM8Vb5bMx0+Ox3WTt+9LJABv46GsV2ClajxCdD62pyPkVgcWvbN/7ZK+uviiGMgB/EutelEGWpTjCMoxZPUZCz1KLNrfKr3WM2ObFUBu+0gk/dYXVDNliT9A60YD69UhtmPQXx6sKo0FJZGSsjyb9Yq0wBQrCyc8Qg8ugEuexuq1s/ftkvaey0oxUc0YC1Hche1KJXryMWRXfmzLt8J7CSVVO5gKrV4l5msyd/G698tqQC+/kG6d+/Op59+yuuvv862bcFej3FxcWzcuJF7772XKVOmcMYZZ+y388UFQqOqSvnks9Nh9dc5r2/6X2hxZ/DxN/fA9GcgM+L/r+Ll4JRXoc6J+dlKSQXt/1jE0yzPdZvrOJgHaFAwDZIk/WPsTIVPToPfvoiuNQt1zIXsY5um7XqeTjA2CcUqyZXhlEeg9YWQ4KBn0t9KGpm8xCru5mc2kkL0T346wd8IkWP1R1ahZrD745MAcUAP6vIunUmwDk37ycyZM9mRAV1mtMz3c41rNZNiCdCyZf6fK78FAgFuvvlmxowZQ9yu7h+lSpVi27ZtBAIBjjnmGF566SUSEvIysdyeGZ4qX6X8Ce8dSvYeEhFKHwLdI3pCbP4NFnwAKeuhQn1o0A2SSuR3SyUVtFjjnWb1KA3oS7C7xXI28xkr2UkmR1OVZjgjhyQpuyUfwle9YGfK7tgjUihADUUkIZExSjpQpjqc/Di0OL8gWi0pP2US4CVW8H8sYB3bdy2NFZ6mEx2gRn7kElSWZEbQlROolf8N1z9eODz9sQDC0yb/nPA0ZOzYsXzyyScsX76cjIwMatasSZcuXejWrRvx8fvvQw7DU+WrTT/DiFa5b1PsQDjvl4Jpj6SiYwPpNOAbUnPoul+ceBbQnhLEcw0T+YClBCL+BD6GqrxBByrjpyuSJMhIh89OhtVfRk8LE6omjRSKRSJH1k7btV2JatDlUWhxYb43WVIheJal3McC1pHC7o9LYPdvi1B4mj04Df12iQO6cyjv0ZW4mKMkS3ljePr3YK259otABvw5Fhb3h2XPQsqK4PJSNSGpTO77HtAw/9snqeg5kCQepkHM2804oD8NKE8y/2EC77MkKjgFmMjvdGccmbmVtkuS/hXWTIH3K8C6L4PDuiUSnLg4geD/KVmHesv6R1AmUKw8dB0It68yOJX+ya6lDmvpyoM0pgzJxB4LNdRtPyTA7qB1JwHS+IB5VOI55rOuYBquf7bkAvjSPnPUHv1lm2fDDxdAyq+7ly38P6h1KTR6DOr2hAUv5bx/w8vzv42SiqZLqcVBlOBplvMN6wFoz4HcQG06UZFlbOZDluW4/2zWMY4VnMxBBdVkSVIRM+t2WPAoBAK7J3yC6I64GQQD08i+DgGCkUl8MTjyv9D+XoizgEz617iDBtzKodzKLJ5nPum5fiAfGuwjg8hQdR1bacwrPEEHbqBNPrdY+vdq2LBheGzTvIqLi+Onn37aL+c3PNVfkrYWpp0J6Vk/bMuEFa9C0gHQ8h5YOx3Wzsi+/6EXQe0eBdFSSUVVJyrSiYrs3FVDmhRRDzSOFdkqTrP6lF8NTyXpXyh9O4xvB5tm7w5GI//HSCRYI5ZA9vA0AATi4LALoNOzwQlKJf37JBHPAFrSj8O4gil8zC9kr0QNDQQSqxt/JpkEuJHPGMkiPqcniXbwlfa7I444ItuyefPmsX37dho0aEC9evUIBAIsX76cn376ifLly3P00Ufvt/MbnuovWfFmjOA0wi8vQZ2bocsYWPJu8Ct1DZStB/UvgYNOKbi2SiraYt1o7sxDl/z0HMZMlST9c21ZBN8cATs3B7vkZxIMSbNOApWw63nWKKRsQzjlA6h0eMG0V1LRVoUSjOQEvmcNZzOeFbt6RAWF7jVjdeMPEApXv2YRNXicH+hLdcoWUMv1jxBrfJn8Ok8eLFy4kIEDBzJ16lS2bt1K5cqV6dixI9dddx1lyuwel3HVqlUMGDCASZMmsXXrVurXr8+VV15Jx44dsx3z559/5qmnnuKHH34gNTWVpk2bct1119Gq1R4mydnlrbfeinr+ySefcPvtt/Pyyy/Tvn37qHWTJ0/m6quvpk2b/VcN/v/t3Xl4VOX58PHvmclCwr6LIogLIBVZ3HertrZYcKHu4l5327e2tVj7q9VWrbVSa6lLW7Glilap+1JcqVatWypQVMAFBREE2ck+c94/JpNMkskCJjMh+X6u61yZnPOcc+5oMszccz/344JR+lJe+yasebnxMXs8CH2PyEw8ktqXIlZyKI82OuY2DuJkduF5PmIOy+lKPuMZygCaaLgsSdoqLX8C3jw60XM/VXIybd0EajkpS8Lkwf6/hZEXOkVfUnqVxPkVb/Nz/kOs+pkldWEpqFlYKvmsU7NMXR45vMh57OPMKDVDUVERZXE48oMMLBi1UxH5kcYXjPrwww+ZOHEi0WiUU089lQEDBvD222/zyCOPsPPOO/P3v/+dwsJCVq5cyYknnsjatWuZNGkS/fv3Z+bMmcyfP5/f/OY3jB8/vvqaH3zwASeddBL5+fmccsopdO7cmRkzZvDpp58ybdq0LUpyjh8/nv3224+f/OQnaY/feOONPPvss8yaNWuzr52Olaf6cppR8FX3ha0kNddY+rI/2/AKy9MeH0Ahw+nCWO7gQ9ZU75/Ms5zHHlzH4URcAVWS2o33b4CFk2uKZ1KrQKJ1vk8VB/ruD0c9Dp16tmqIkrZyOUT4KWM5i2EcycPMr7cgVGqytH4f1HJi7MdU/sIJnM6eGYtbW7k2sqDTtddeS0VFBX//+98ZOnQoACeddBIjRozg2muvZcaMGZx77rlMnTqVZcuWMWPGjOpk7MSJEznhhBO49tprOfzwwyksLATg+uuvp7y8nAcffJDtt98egAkTJjBhwgSuvvpqHn/88c3uZ/rxxx9z8sknN3h82223Zfny9O8ht4TNONRsYRw2PAdf/AnWPQLxUuh1SOPnRAqg5z6ZiU9S+/RXvspIetXbP4BC7uRgTmBmrcQpJCoGbuUNbuDfmQpTktTK/ncBfDQ5MbMxdUt9u5VcMCr5JicOkAeHTIeJL5s4ldR829GZ/3EqN3MwUXKo/bFNSO1FpEg5FiekkjO4l6t5OrNBS19CeXk5b775JnvssUd14jTpmGOOAeCNN94gFovx6KOPMnr06FpVrPn5+Zx++umsWbOG2bNnA7Bq1SpeeukljjjiiOrEKUDPnj05/vjjef/995k7d+5mxzpgwABefPHFtMfCMOSf//wngwa1XPW3yVM1y6ZXYOEoWHwsLPsBfDIJ3hsO3XtAtEvD5w2cBLk9MhWlpPaoP4W8yNHcxxGcyTBOYxdu4yDmcDyv8TEr2dTgubfyJiW1plhJkrZG/zsevrgjUZiTmjiNULtNXEDtZGqX4XDiMhg6KXOxSmpfvsdoPuBsBtI9ZW+yzj01cZpsGpKc4l/Gz3mMS/hHhiLVVq3uJ4OtsTUhJyeHxx9/nF/84hf1jq1atQqASCTCokWLKC4uZvTo0fXGjRo1CoA5c+bU+tqcsZtj4sSJzJ49m8svv5x3332XTZs2sXHjRubMmcOFF17Im2++yamnnrrZ122I0/bVpNIFsPg4iBfX3h9bDSuugBE/g/dugYrahV/0PxqGXZu5OCW1X1EijGMw4xhca//TfNjoeeso5T8s5asMac3wJEmtaP7RsO7R2m9ckimKHBIpiij1a792vQJGX5e5OCW1X4PpxhIu5Cye4C+8XbU3WYGafFxZ52vCH3iWcsr4I6dkMGJp80UikVrVoammTZsGwD777MOKFSuARPVnXdtssw0AS5cuBaieOp9ubP/+/WuN3Rznnnsu7733Ho8++iiPPfZYrWNhGHLqqady0kknbfZ1G2LyVE1a9fv6idNqIZQ8Doe+B589AOvnQrQzDDgOuo3KaJiSOqBYMxovVzanObMkqU16dyKsfzRRTZrazzRZcZpMnCYn0AKQB4c/C30PymiokjqAuziKYxnKcdxHrNbsphiJZ6K6M54SCdY/8S/yifJ7TsxYrNrKtJGep+k8/PDDPPDAAwwYMIDjjz+eF154AaC6p2mqTp06AVBSUgLAxo0bAejcuXO9sQUFBbXGbo5IJMKUKVM46aSTeOaZZ1iyZAkAgwcP5pvf/GbaStcvw+SpmrThicaPl7wF8fUw8PTMxCNJSQcxiNf5tMHjBeQyiv7MZxm5RNmFfgQuICVJW4UPzoUNDyaSpMnEabLiFBIJ1Qi1k6q528HX50B+7wwGKqlDmcAuvM8ljOU21rCWmgrU1A/sU3uiJo5P5Wl6kM8vOCbTIUtb7KGHHuLKK6+ksLCQW265hc6dOxOGDS3PSPWxSCRS6/t059QduyX23ntv9t577y0+v7nseaomxcubHhM2Y4wktbRzGEthAw18QkKG0Yn9+TX7cSN78ivGch338HqGo5Qkba5l18D6OxOFODkkEqhR6vc4Tb6ZCYGeB8KRn5g4ldT6dqAnn/EjdiM5FTk1eZqcuh+jpv9pogfqL3mQP/OvjMerrUAb6Hla1x/+8AcmT55MYWEhf/rTn9h9992BmirSdBWjpaWlAHTt2nWzxzbmu9/9Lp9+2nDRTFMWL17MxRdfvMXnmzxVg8JKCGPQeZ/Gx+UOhNxtMxOTJKUaSDfuZSLdyK93rD9x3uYDPmdD9b4PWMWF3MtUZmcwSknS5lj7EKy8inrzBCJQvd516puYOLDtWXDgSxD47kZShuSTyzy+zzcZTv1npWSdfDKBmtzKOZ8/8jILMh6v1FwVFRVcccUV3HLLLfTv35+7776bPffcs/r4wIEDgZp+pqmS+5K9T5szNl0/1Lp69+7NuHHjuOaaa5g/f36zf5a3336bK664gvHjx9O3b99mn1eX0/ZVT9lTUHwLVLwMRKDLV6C0Eioa+G3pfT4E0YyGKEnVvsoQ3uMS7mUe8/icLuQxkp6cz98aPOeXPMUZ7EtXOmUwUklSU8o+gKXH1yRJIZF6SPYzTSZOI9RMht3hp7Bz/YWBJSkjnuQcvsP9VRWlyQrU1ARqbXFiHMEv+YQ/0JdumQ1WbVcb6Xkai8X4wQ9+wKxZsxg2bBh/+tOfqhd2Stpxxx3p2rUrc+fOrXf+nDlzABg7diwAI0eOJBKJMHfuXE499dS0Y8eMGdNkXFdddRXjxo3jqquu4t5772XgwIEcccQRjBgxgsGDB9OtWzfi8Thr1qzh008/paioiP/85z98/PHHDBkyhFtvvZWDDtryZugmT1VL8a2wcXLKjjjE50FPYF0llNX5jel5KvS5NJMRSlJ9XcjjO+xR/f0VPNzo+GLKeZS5nErr98eRJDVPWA4f7wt5sZpepslJsBFqlmGJUJNY3eFGGPTDDAcqSXX8iRPoQpSbebZqT7LfaVJqQhVKqWRffsIibiHihGC1Ib/73e+YNWsWu+++O9OmTUs7pT4nJ4dx48Zx//33U1RUVJ0oLSsrY/r06fTp04eDDz4YgD59+rD//vsza9YsLrnkErbffnsA1qxZwwMPPMDw4cMZMWJEs2Lba6+9eOKJJ5g1axbTpk3jrrvuIgjqr2cRhiGRSITRo0dz6aWXMm7cuLTjNkcQNtbpVR1KbDl8MYJ0H44lFED8bChfCjn9oMcpULhHA2MlKYvO5x7u5c1Gx1zLBC7lqxmKSJLUlE/HQ/njNZNfk2mG5PrVycmvIVAObPdrGPijrIQqSWn9P+7ndzwDlFDzxjpZgZr6rAYQ4QLGcRvnZz5QtRlFRUWUxeHIcGyr32tWUER+pKYqtK5ly5bxta99jVgsxmWXXVav4hQSydADDjiAlStXcuyxx1JSUsJZZ51F7969mTlzJvPnz2fKlCmMGzeu+pyFCxdy4okn0rlzZ84880zy8vK45557WLZsGXfddVetlgCbY+XKldXVpatXryYSidC3b1+222479t9/f3r16rVF103HylNVK51Bw4lTgBLoPgIKrs9URJK0ZXahX4uMkSRlxsZHIfZ4YlGopGjVVkniTUtqxcc2k02cSmp7buYEVrORv/FC1Z7UxaNidUbHuJ3HOJfD2YOdMxqnlM7rr79OZWUiKXTTTTelHbP33ntzwAEH0LdvX+69915uuukmpk+fTkVFBcOGDeOOO+7gkEMOqXXO0KFDmTFjBlOmTOHWW28lEomw2267ccMNNzB69Ogtjrdv376MHz9+i8/fHFaeqtqGy6Dkz42P6XxFYpOktmwF6xnBNVTUe5EKccopoIJj2I0d6M9pfJXBJlIlKWviFfBpVwjKak/XT32TUk6i+rQc6Hoi7HhfFgKVpGY6mOt4ibkkns0qqGk8ElK7KUlAD7qxmvsI6i2Tp46gqKiIshCOjGag8jRWRH7QcOWpGmZzDVWLbNsyYyQp2/rTjd9zAtE6L0LL+IJillDMF/ydl7iBmYziUn7bRI9USVLrWX0c5JVBPpCbsqVOkYuS6HOa/xUTp5LavheYzLb0IpEoTX6Yn6xCrUj5Ws5aVnMJt2UnUEnNYvJUAIQfhuTnxMiLxQji6YuRg86Qf1yGA5OkLXQKe/Ms3+N4xrIdPehOHNhAZ/KIpvzzFyfOVdzDk7yRvWAlqYOqWAg8nkiWJqfp51DzfTKBGgB0gaE+VUvaCkSJMI8byK3VdCQ5fb8iZSsHyriNf7COjdkJVm1DXgY2bTGTpx1cuCGk8rRKKsdUEP4sRmEsRrdNFRSWVkJqR4cAutwAkfoLrUlSm7UHg7mTSbzLVWxLlHxyGpwSNZXHMxydJGnjhMQbkqBqSwpIJE6Tb1ZCYPBLECnIcICStIV60ZXH+HHVd8mp+qkJ1OTjSkJKOIb/y06gkppk8rSDi51SSfhovPrDsCAPggLIi8cpLE1ML8g9CLr/AwpOz2KgkvQlrGEj77Kk0TGv8B4htgGXpEypfBOCBZBHSG7VlkfNolEBiTcrEaDbD6BgdLYilaQtcySjmcgBJBKnceovHBVWb7N5nSV8noUoJTUlp+khaq/ir8QJ/1U/URDkADmQH8Qp/HdIZKSNqyVt3XKa8VlhDlEb9UtSBpWeGpJHSKT6g6ug6kOsgICASqqSp72h92+yF6ckfRn38SO68x+KKaEmcZpMpCaTp4mPi07lKl60/2nHlIlp9SUZuEeWlJeXk5OTQyTSOjWiVp52YOET8SYGQPhcE2MkaSvQlUIOYESjY77JHhmKRpIU/ywkujBOlBiR6q2SCHFyiBMlrP44q8/TWQ1Vkr6UHKLcz0+o6X0ap/aiUcnep+W8xJuUUJqdQKWtzNq1a7nmmms48MADGT16NK+99hpvvvkmF1xwAR999FGL3svkaUdW1kJjJGkr8COOI5Lyz15InHLWsYllbGIpH7OQh5mdvQAlqQOpvCgkSoygTruUgDgBcSLEEwtI7QV5Y7MToyS1lKPYm8FsU/VdJTX9TlOfA+NAGZOZmunw1BbkZmBrR9auXcuJJ57IjBkzKCgoIKxas2fdunXMnj2bU089lSVLGm/btjlMnnZgwT5N/+8P9nEKq6T24TBGcQcX04MuxIlRzGeUsYaQCvKJ8jbvcTpXcSm/znaoktT+PVEJhARVVacBMRKJg5CgaoOQbq7lJ6mdeIRrqT9dP7mIVLISNc5dPJi1GKWtxdSpU/n000+56667+Pvf/16dPD388MP54x//SHFxMbfeemuL3c/kaQcWHBAnUrCJSOl6ImUbIFZR+/iIgOAQk6eS2o8TOZgF3M6+bEsuUEA+XehETvXyJPBXnuBR/pW9ICWpnYu/GCeoSE7TrySgsnrqflCdQI0THQXRftmOVpJaxih2ZiB9qUmepk7bT07dr2AD6/iQpdkLVNmRl4GtHXn++ec54YQT2G+//QiC2nmrgw8+mBNPPJHXXnutxe5n8rSjeqCYYNRyIps2EqksIVJeTE7xaiKl6yEMYXBA9N6cer+EkrS1K6WMObxHHrlVSdP6z3N38kjmA5OkDiL+84qqxGmsuso0MVU/UYEaVPU77XR3tiOVpJb1B35U9ShG/eRpMoFaxrX8MTsBSluJzz//nOHDhzd4fKeddmLlypUtdj+Tpx3R2+Vw3hdQFhJEgC5AJyAXIpQQHV9Czhs5BENMnEpqfz5hOWVUNDpmES3XH0eSVFvwWnn1tPzElNU4yb5/EeKJKfydQ6K7+VpUUvsygYPJIYeavqfJ6fqxqi2RQH2cZ7IXpLLDnqebpXfv3nz66acNHl+4cCE9e/ZssfuZPO2I/rAh8bxcJQCCXAg6JbbI68UE0QbPlqStWk+6NTmmVzPGSJI2X1gSEhTHqypMK6u+1mzVfVCPM3EqqX3aj5HUJErDNFucz1lGnHj2gpTauIMPPpj77ruPpUvrt7goKiri/vvv58ADD2yx+5k87YheLmv8+OcxWNh4VZYkba0GsQ37slu9/WHKaqcn8rVMhiRJHcdzlVVJ0tQpquVVjxM9TwMguNpP8iW1T1dxIYlqU6i9YFRqFWol77AoOwEqO6w83SyXXHIJubm5HHvssVxxxRUEQcB9993HBRdcwKRJkygoKOCiiy5qsfuZPO2Ios34JD/HT/sltV+/4EI6kUdIjDJWs5GP2chHbORjuhLnWA7NdoiS1D49WEZNsjTdFoNISHSIyVNJ7dPh7EsiaZr8ICld79M4T/Bc1mKU2rr+/ftz3333MWbMGF588UXCMGTWrFnMnj2b0aNH87e//Y2BAwe22P1yWuxK2noc2Qn+uLHh40NyYKi/GpLar33YjXu4hm9zHuWsBSAgIIeA9XzOt/kOs7iH7k7fl6SWNSeZKKg7HbWm92nQy8SppPatkE4Us5aaCtSkEKpamLzMa0DLVc6pDQuAvAzdpx0ZOHAgf/zjH9mwYQOLFy8mHo8zcOBAevfu3eL3svK0I7qwCxSGUFEMJWugZDWUb4R41YvYy7pC0M7+qiSpjhf4FyEVdKGQzhTQhQI6kU9AwDssYoqrnEpSy1tWTs0CUamLpaTs28XXoZLat/70JvHcl+hxWtP3OV6970M+ymaI0laja9eujBw5klGjRrVK4hSsPO2YInEoXAsry2o+9K8sh4pNcPFAOKNLNqOTpFYXJ849PAQkKk6DNB/D/o0HuZofZjo0SWrXgk0VhFX9/JIJgoQIEAVyYES2opOkzNiBbfmI91IWyks+FwZAQEiUNXyRxQiVcZmoPN2KXXHFFZt9ThAEXHfddS1yf5OnHdGkD2BVOXQOaj7oD0g0EH54OVzXF7o4XUpS+1VMCWtY1+iYVaymlFI60SlDUUlSB1Ce2uMvVbL/HwQ7WHkqqX3blr4ExIhUVZsGVQuXhlXJ0zhQSkk2Q5TalIceemizzzF5qi33ygaYW1zzfd3fgLWVcN8XcG6/jIYlSZlUSAHd6co6NjQ4phc9TJxKUksLYwRUVqcJqHqUbMQWUJ5oLyVJ7VgXColQWZU8rXnOC0gkUCOExKnIWnzKgmgs2xG0ac89l90F1EyedjRvbWrGmGI4t/VDkaRsiRDhFI7hNv5GWN2YPyCgpur+NI7LWnyS1G4FVdWlxKBO0iAxbT8KJXUXk5Kk9qWMTUSqK/BTnweDqirUuMkaKcV2222X1fv799jRdGrGGmGFriMmqf37ERdyHzNYwUeEVS9eAzqRQw+GsxuX8Z0sRyhJ7VBuHMqSPU+Ti6MkxYAcWFqaldAkKVOW8ynJXqdBSvI0JCAkQgB0Ijdr8SkbyrMdwFbl4YcfbnJMXl4effv2Zdddd6VLly+3to/J047mqB7w4yVQ2ch0qAk9MhWNJGXNNVzFRpaRS4QKgqoK1DIirOFSTqQXPbMdoiS1P4XAxkoSbxLj1KwsDYlFo+Lwrn3+JLVvS/iICLFkvT1A1VyokDgx4kTpSdcsRii1bZMnTyYIanqkh2HitUTqvqS8vDy+//3vc+aZZ27x/UyedjTb5sHZfeGPn0MYQmUJVJRAGIdoLuzZBw7plu0oJalVFfEWM7iHgIB88sgnj5Bkxz24nus4mZPJc9lLSWpZ/SPweQX1q06p+r4cFm7MfFySlEHLWVrvVWZAIkGTaG4SZzCDMh2WtNW4/fbbmTx5Mjk5OUyaNImddtqJ/Px8PvzwQ+677z6WL1/OJZdcQmVlJU8++SQ33HADAwcO5Igjjtii+zk/uyO6YXv4Th8o+wJK10GsHOKVQCn8bxlcvyjbEUpSq7qP++rtS/2MchUreZqnMxeQJHUUY/KonTiNk5jCn5zGH8LnzejRL0lbsXI2EJCoOs2p+pp8LZp4HHIA+2crPGVcSGJGRmtv7WdBxhdeeIEuXbrw5JNPcv7553PEEUdw0EEHccYZZzBz5kz69+/PZ599xvnnn88DDzzAyJEjmT59+hbfz+RpRxQNYP1a6BSHgkhi6xKp6Yd6/SJ46YushihJrelzVjQ5ZiWfZyASSepgvtmVmoWiKoGKlK9Vb+5iFbC8LHsxSlIrmsN/iQJ5VVsuiQRqLjVT+KPAkRyZnQClrcDTTz/NCSecQLdu9WdOd+7cmYkTJ/Lkk08CiWn7Rx11FAsWLNji+5k87Yi+KIeHlice5wSJrW5fiD9/nPm4JClDdmBIi4yRJG2mbybf5KRWnCarTmMkkqhlcK2vRSW1T9fxM/KpSZZGqx4nv48AUQJGsnv2glQWVGRgaz/KysqIRqMNHo9EIhQXF1d/X1hYSHn5li/KZfK0I1q4Ecrr9piq438bMhOLJGXBaUyqWt20kvo992AQgziEQzIfmCS1d91zoFNATdI0OYUwpGYBqRjcszRLAUpS6/oPzxAhMU0/tYQpoCaB2oOe5LhEjdSg4cOHc//997NpU/1WPyUlJcycOZNddtmlel9RUREDBw7c4vv519gRdWvG//au/mpIap/+x9tcx4/pQillJKaFxsmlkgIgSiGFTOUPRPx8UZJaxx6d4eVkNUhifelE0jSZSA1gTQiflMCggqyEKEmt4b+8TpwycqmdOE3W3if7oB7hlP0OaMurIjuiCy+8kPPPP5/x48dz2mmnMWTIEPLy8vjwww+5//77Wbx4MbfccgsAP/3pT3nkkUf47ne/u8X3M0PWEX2lG+zaBd5tZCXT47fNXDySlCELmM/pjGMTG8kjjwhRKignRiU5FHMkp/IjJjOc4dkOVZLar8sHwtHLqel7WlVtWmshixic8V94wQVTJLUfP+AM8qg9BTg5BypZkx8BfszPMh2atFU56KCD+M1vfsM111zDr3/9a4KqVpRhGNKtWzeuv/56vva1r7F27VpmzpzJoYceyplnnrnF9zN52lH9fBic/FbKQqcxqFgPlZsgEod7S6FbOZw6FCJBo5eSpK3FVK5nEzUfHOUQJYeaqqbB9DFxKkmtbUIfiEYglkycJr8mN4AIzF4GGyqga262IpWkFvMZS1jKe7WSp8mPjiIkOlIGQCGFDPX1aAdk5enmGjduHEcccQSvvPIKixcvpry8nCFDhnDAAQdQWFgIJBaP+ve//02fPn2+1L2CMAzDpoepXXpiBfzsPViwDkqWQ7wSciLQKVIzh+DbO8Gdh9VfUEqStjIlFLMnA6mkssEx3enJ67hIiSS1uiPegOdWkHizGIOGnpuP3A7+eXAGA5Ok1nEso1nInLTHKqhZMu/bnM0U7sxkaMqioqIiygg5cmzvVr/XrKIvyCdg7NixrX6v9sbK047sqP6J7RuPw0shRHJqN14BmPkBjB8Cx+6YlRAlqaVsYmOjiVOADazLUDSS1MFN3RV2/YyaytOk1OpTYNZSWLoJBnbObHyS1ILm8xbvM4coibfcySXykpVsuVWPA+D/uDE7QSrLKrIdwFZn48aNPPXUU6xatYpYLFbveBAEXHzxxS1yL5OnHd3yTfDacog2Uln6l3dNnkra6vWkN33oxyo+b3DMzuyawYgkqQMb3iWxGNQnpVU7UheOSk2gBnDI0/DBsdmIUpJaxEUcSR41tUrJxGlyoShITN3fjbH0pFcWIpS2LvPmzePss89m48aNNDSh3uSpWs6SjRCLNz7mow2ZiUWSWskXrOBFHmM3hvEiy4hXf+5f2ymcm/ngJKmjumsUHP5C1TfJzn+V1F44CvhwFUz5H1y2W2bjk6QWcCOXUsoXtZIvUWo+IkpWoQbAH/h7psNTm2Hl6ea4+eabKS0t5bvf/S4jR44kLy+vVe9n8rSj61fQMmMkqQ0KCbmV/2Mmt1FJBSHQmQoqKKOMToREq8d+k+M4kbOyF6wkdTSH9YPBXeDjddTUX6UmTsOa73/4CkwcDIO7ZjxMSdpSCyjiH0yttUBUUoREEjWZPN2VMezAzpkOUdoqFRUVcdZZZ3HhhRdm5H6RpoeoXRvcDfYf0PDxMIQD+8B/lsDGsszFJUkt4C5+xX3cQmXVJ7kB0IkCCsinBzCIHTiQw/kd05nCNCL+syhJmfXg/lUPkumD5ONKElU4FUA5hGWw10yIu9atpK1DGaVczMHkkehpmgPkQb0K1KBqu41HMx+k2pDyDGztRzQaZeDAgRm7n+8SBdfvC4VpipArNkK4CqY8D1//K+xyM/x4FpQ1vuCKJLUFpZRwP3+otz8Acskhlyhncy538hDf4BgTp5KUDWN7wkF9qak4TSZOk1P4K2q2lWvhiIeyEqYkba4L2Is4m8glkTzNI5EsjVA7gRoA3+I0tiFziSBpazdmzBjeeOONjN3Pd4qCMX3h6QnwzcEQqeoBmFMC8Q0QSXZfATZVwG1vwGkzsxaqJDXXHF5hE+sbHfMK/8xQNJKkBj12COQk35YkK1CTCdQYtSpQX1gEP3gh/XUkqY24lpP4jP+RD9WVp9GqLYdEIibZfb8ThVzDn7MTqNqIkFofFrba1n5mb/zwhz9k9uzZTJs2jZUrV7b6/ex5qoTd+8Dfj4R1ZbB8E3z1T1AZTT921vvw4mI4eIdMRihJmyVevXZpw5LT+SVJWdQ9D/6wL5z/IjVrUCcTqGne6E15HYZ0g0v2yGiYktQcf+FKXubvtZYmDaiZol9Z9TVC4pnu1/yDPPIzH6i0FfvhD39INBrlxhtv5MYbb0w7JggC3nnnnRa5n8lT1dY9H55ZBBubSCg88D+Tp5LatF3Zg1zyqaDhfs2jOTCDEUmSGnTeMLjzXXj9M2oSqKmJ0zi1FpT67j+hVz6cslumI5WkBj3MFB7kOnKrvq/zzFVdcZr8ehBHcyDfyHygaoMy0ZO0/STpe/ToQY8ePTJ2P5Onqm9tadNj1jVjjCRl0AZW8yx38h8epIQNDGZ39udg/sUzacfnU8AxnJPhKCVJDXrxW9D7L7CpjNprUien74c1X0MSraRygBNMoErKvie4hb/xA/Kqvk8+i0VIVJumfh8APejLDTyY4Sil9uFvf/tbRu9n8lT1jejX9Jhd+7Z+HJLUTCtZwi/4Jiv5JGVf4vFQBrEwZT9AAV34JXfT38b8ktR25OfAy8fAmBkQpi4gFaNmGn+KMAYn/R1KyuGMsRkNVZJSPcwNzGByrUVlkj1OK0gkXpJ1hYnK0yjTeMsFS5XCytOWVlZWRn5+y/zMJk9V3/6DYERfeKeBpru5EThwIEx+EN79DLoXwPF7wLiREPXJX1Lm/Znv1kqcpirmE67gtyzgPUrZxC6M4hucTBe6ZThKSVKTRvWBPx8O5zxJrUrTWonT5JT+eCLJeuYDsHwD/PiQLAQsqaObzvd5iptJrhiSOk0fEkmXCmp6nAJcy2P0Z/sMRim1P2+//TZPPPEExcXFxOM1M1ZisRjr16/nrbfe4o033miRe5k8VXrTjoXxd8PK4tr7owFMHALjb4F4yj8JD78NB+4MM8+Hwo71aYak7FrBYubxfKNjPuYNvs/tGYpIkvSlnP0VKFoOf3izakfqFP5kIjUlgUoIkx+DBStg2gkZDlZSRxUSchNHMY+nyKPm455kkjT5kU9qj9MAOIsb2JdvZiNkqd146qmnuOyyywirZqoEQVDrcSQSYffdd2+x+1kmqPRG9INXz4MrDoLd+8PwPnDGaLj9m/D3V2snTpP+/T785OFMRyqpg1vGQsJ0qzGnWMp7GYpGktQiph4O43aq+ib1OT5Z05VMTcSqvpbDXf+GPX4DZU0sfCpJX1Ix67iCnXmPp8ilZop+HokkS3JLCqq+Hsl3OInLMxusthIVGdjaj7/+9a90796dO+64g9tuu40gCPjHP/7BPffcw2GHHUY0GuXnP/95i93P5Kka1q8LXHEI/Ps78PoF8PtvwXPzaTRHce/rsK64kQGS1LI606PJMV3o2fqBSJJa1hPfhgO2pybtkEyapq5fXUFNT9Q4FC2GAVfCohVZCFhSR7CYN/kx27GaD6uTpDnUTOvNIfGsFU05JwT2YyLf44+ZDVZqpxYtWsSJJ57IIYccwsEHH0xOTg4rVqxgjz324JZbbmHIkCHcdtttLXY/k6faPEXpewpWK6mAd5dnJhZJHc4mVvMvbuchfsLz3MJ6VrALe9GfHRs970BOzFCEkqQW9dIk2GdQnZ1VU/XrLiCVnDS7Zj3sejX85dWMhCip43iGG/k1exNjU71kSkBNAjU5TT9pD8ZzJTMzEqO2VuUZ2NqPsrIyBg1KvD6IRqMMGjSIBQsWVH8/fvx45s2b12L3s+epNk9BbsuMkaTN9CrT+Qc/pILS6n2PczVH8TNO5Zf8ltMIa/XFS9iZvdiX4zIZqiSppQQBvHIWHHYX/OvDqp1hypaU2mEwDrEQzvoz3PMKPPU9yPFtj6QtV8Ymfs8hLOUtcqnpbZrahRlq+ppGqsYEwF5M5EcmTqUW1bt3b1avXl39/cCBA3n//ferv+/evTurVq1qsftZearNc/Toxo8P6QO7D0w8fnk+XH9fYntjQauHJqn9WsBs7uPSWolTgBgVPMr/kUMFP+Q+BjOy+lgeBRzOWfyEh8klL9MhS5JaSiSAF86Co3elpparoUWkKqnphRqDZ+dB70vhJV+LStoy83mcn9GXZbxFhJrepsnp+XVLh1IbjRzGOSZO1UxWnm6OPfbYg5kzZ/LFF18AsNNOO/H6669TXJxoI1lUVESPHj1a7H5BmFyOSmqO1ZvggBvg07Xpj/9pEhy6M5z8K3hzUe1jB34F7r4cenVt9TAltS+3cSzv8VyDx7dnDD/kXwB8ykJK2cgAdqaQbpkKUZKUCRc9DLf9m8SbwGQCtZKaytO6MxCq+qQGwEn7wt0XQsT6EUlNq6CMuxjPIp4BEh/J1BWrsyX3VRIwnqv4NldlJFZtvYqKiigjzpFjm2iR2AJmFQ0inwhjx45t9Xu1tvfee4/jjz+eaDTK888/z5o1axg/fjw77LAD2267LS+//DLHHXcc1157bYvcz1cO2jy9OsOT34V9h9Te37cLTD0ZTtgTTriufuIU4N/zYdKvMxOnpHZlYVVitCFL+C8lrANgO4ayE2NNnEpSe3TrMXDLsRCpuxRLcjGp1H2VJBaUqoSwAu59EXqdA0/9N3PxStoqzeFerqMXH/MMOdRUm0brjEt+n9rfNCCHi7jPxKk2U0UGtvZj+PDh3H333Rx00EH06tWLnXbaiV/96ld89tln/Pvf/2bPPffkBz/4QYvdz8pTbbn5y+Ddz6B7ARwyFPJy4IU5cPTVjZ/3/K9gz6GZiVFSu/B9ehGvtzBIbb9iCQV0z1BEkqSs+s9iOPR3UJZ8Q5icqp+UrEJN7YWakmA9cFd49CfQs0umIpa0FVjPUmZwFJ8zt/oZBBLPHsnUU2qVKVX7k880+XTjCl5jAMMzFrO2bjWVpx+0+r1mFe3UbipPG1JeXk5paSndurVsIY2d07XlvrJtYkv1bDM+yX+myOSppHqW8joLeIxKShnI3gzjaHKqepUO46u8WzVlKp1B7GHiVJI6kn13gCXXwF43wsef1zmYTJImq0+hXrrj3/+D/pPgsmPg2tMgWreeTFJHUkkZT/Nd3uZO4sSIUDNNN/lRTC6JRGmU+lP448AAdmMyr9IJP5TR5grJTE/S9l87mZeXR15ey6934bR9tSwLmSVtpnI2cS/HMp0jeI3f8RZ38AjncCsjWc4cAA7jewSN/JN1OP8vQ9FKktqMvl3ho6vhpL2pPZk2+Xo0WTNWt06sakxFOdzwd+hzMtw7u5WDldQWhYQUcTu30Ie5/JEgTWfTHBKJk4CaBEpQZ8yhfI+fM8/EqdROmTxVyzp0980bs34j3P4AnP5TOOfn8I9noaJ99eKQ1LgnuZSP0iwGtZHPuI9jKWMDQzmYU7iVPAprjckhn2P5FaM5OlPhSpLakiCAe8+Ghy6E/LqVJsnq07qLSMVJ1I9VJVXXroZTfgk7TYJX52cgaEltwUc8yZ8YyGwuJGQjERIfw+RSPzma/HimbgIlh858l9mcyM2tHK3av/IMbNpS9jxVy4rH4YDLYH4DK8XtNRSe+1Xi8Wvz4PjLYe2G2mN2HQIPT4EBfVs3VklZt46l3MpIwrTrlyZ8jRvZi/MBKGE9b/EAq/mEHmzLWL5NF3pnKlxJUlu2ZiMcegPM/YSaxTGSidKkxjoXkkjGjtoR/vJjGLVz68csKeM+5WWe5SzW8j5hnWnMySn6UD/VVE5Nb9MKYAcO4nxmkUdBa4esdizR8zTGkWPntvq9ZhXtTj7Rdt3ztLVYeaqWFYnAA1fCVwbVPzZmJ5jx48TjjcVwQprEKcC7H8HZP2/NKCW1EZ/wUqOJU4DFzK5+XEA3DuQcJnA1B3O+iVNJUo2eXWDOL+DmUyE3l/RvdeIpX9NM5Q8r4e33YMzZsP8F8PaiVg1ZUuYs5xXuYySPcBAbWUQkTf/HHGqqTht6BomQz8n8jUt50cSpWlBFBjZtKReMUssb2Bde+W1i8agX5kAkgCPGwKGjasbc+09YkyZxmvTyHJizAEYNa/14JbWqkJCQSiLk1jvWWB/TzRkjSVK1730dTt4HvnED/HcRiVRIuj6oqZKLS4WJLYzBq0Uw9jTY+yvwm+/CgaMzEr6klrWUp/kPl7GWdwgJqxd8ipJIkNZNKUVJPBtEqHnGSD6D7MAhnMHj5NvbVOpQTJ6qdQQBfG1sYkvnP80oSX/tfyZPpa3YGuYzj5v4hEeJU04PvsKuXMDOnE5Q9Zn+YA4hQi7xRj4J3YmvZSpkSVJ70a87FF0H97wE598KmzbVGVC34iyZOE1OyiXxOIzDa/+Fg86CYTvAT86GSd9KvNaV1GbFibGQ2/kfv6aYREu5HBJJ04BEgjT56jOHmr96qN/vNDGmOycwg2GMa8Wo1bFZGbq5ysvL+dvf/sYzzzzDp59+ym9/+1sKCgp48MEHueiii+jdu+VmKVrOo+yo19A/jbz6VWqEIRTNhWdfhE+WtnxcklrE5/yHpzicxcwkXtUxai3zeZVL+Q/fqx7XlW3YnVMbvE43BrIbJ7Z6vJKkdurUg+CL6XDKoYn2Umnf/oQpW0rilBi1kqwLFsMZ/wf9vwo//i1sKm7NyCVtgTLW8Abf5QF68waXVCdOIfHXn7oYVE7K/nQSU/Rz2IOLuZxVJk6lNqSsrIzTTz+dG2+8kXfffZdVq1ZRUVHBkiVLuOeeezjllFP44osvWux+Jk+VHUcd1PjxnCgcuV/tfU89B3seCYdOhOPOgZGHwXFnw9JlrRenpC3yH75HJenfVC7iL6zg5ervv86NjOB46n7O35uhnMwj5NpLSpL0ZeTnwj2XwcLbYOSOJP69SX0bFKb5mm5afwyogJUr4dd/gt77wbfOh3kLWjV8SU37gv/wL47gMbbhfX5PnHXkkD7hkUyaBtS8+kx9FRqv2rMt+3Mpn/BNphJ10q5aXXkGts03Z84cdt11V1577bV6x84880yGDRuWdqs7ftGiRVx00UXst99+jBkzhjPOOIO33npri2ICuP3225kzZw7XXnstzz33HGGY+Df8yCOP5KqrrmLp0qXccccdW3z9unwGUHZ8Y38YNRTmLEx//MzxMKBvzffPvginXAyxlBeyYQjPvgRHngwvPQy9erZqyJKaZyVvsJZ3Gx2ziOn05wAAcsjnGO7kICazgMeppJTt2IsdObx6er8kSV/aTgNg7u/hyTfgnCmwfEWdAXWTqKliJFIqYc3jsgp44nl4ajYMGQg/Pg9OPwby81vtR5BUo5JNfMQdvM/vKGEJISEBkEfirzTZ1xRqepdCzccncWo6Iqd2Ru7CDozjbraveq0qdVSLFy/m4osvJh6Ppz2+YMECdtttN04//fR6x3baaafqxx988AGnnHIK+fn5TJo0ic6dOzNjxgzOOOMMpk2bxt57773ZsT355JMcffTRTJw4kTVr1lTvD4KAk08+mXfeeYfZs2fzk5/8ZLOvnY7JU2VHNAoP3QTf+QU893rN/tycROL0hu/VHn/1TbUTp6mWLIM/z4DLL269eCU12yaWNGNM/bYbvdmF/fl+a4QkSVKNcXvBZ/fC7x+Gq6alWcS0bvI02QsVapKoKeIx+OAjOG8yfO/ncOzXYfKFMHJ4KwQvaS1vsJBf8jmziFFW/bFGqmTSNJlATZ/6qd20o5B+HMgURjTSUkrqKJ555hmuvPJK1q1bl/b4559/zurVqznuuOM4+uijG73W9ddfT3l5OQ8++CDbb789ABMmTGDChAlcffXVPP744wSb2Ut82bJlnHXWWQ0e33333XnkkUc265qNcdq+sqdPT3hoCrz+N/jdj+C2n8C7D8JNP4CclLz+B4thzjuNX+sfT7RqqJLgMx7iNb7B0/TjOXbgf3yPTXxYb1wh2zV5rUIGtEaIkiQ136XHwKqH4ednQvfu1Lw1qvsGLnW97bopmGSf1Ko6t5INMOMBGHUYbL8HXHk9fPhx68QvdSCbeI8FXMYL9Oc19mY1jxKhjAiJirB0VWHRlMeNdTvOow8H81vOY4WJU2VR25m2f95553HJJZfQt29fvvWtb6Uds2BBomXNLrvs0ui1Vq1axUsvvcQRRxxRnTgF6NmzJ8cffzzvv/8+c+c2Y0HxOrp27dpoT9NPPvmErl27bvZ1G2LyVNk3fAicdTScOg769ap/fMPGpq/R2Ji1a+Hvf4e77oI5c7Y4TKkje4+fMoczWMMrxCmlgtUs5S5e5WDW8Xatsf3Yh+4MbfR6OzOpFaOVJKmZIhG46gz44lH4v7OhZw/qJ0+TNW3pateSi0qlJlHDRHuppcvgultgl71h9/3g2usT/VIlNUspS/mAn/Aaw3iDr7CM3wKfEyXxVxolsQAUJBIb0TTXaGwxqDz6sx83ci6fM4b/19LhS1utDz/8kMsuu4yHHnqIHXbYIe2Y9957D4ChQxPv+4qLi9NO759TlYMZPXp0vWOjRo2qNWZz7LvvvjzwwANs2FB39ggsWbKE++67j7322muzr9sQp+2r7dtpByjoBCWlDY8ZuWv9fWEI110Ht9wCJSU1+/fZB6ZNg5RPPSQ1bC1vsphb0h6rZD3/42IOSFkACmAffstzTCRG/b/bHTmZARzSKrFKkrRFolG45hz4+Vlw493wuxnwWbInarI7Yl11p/PXnTgcB9ZBvBzmfQ7zXoNrfg7DhsGFF8Jpp0ELVsVI7UEFq/mcu1jOnyjhQ2JUVH88kRQl8VdZQU0SNZbytSmd2IE9uZwRXNjS4UtbKCTxG52J+zTtySefJC8vr9ExyeTpQw89xHe+8x1WrVpFQUEBX//615k8eTK9eiUK45YvXw7AgAH1Zx72798fgKVL67d0a8qll17Kt7/9bY4++mgOOeQQgiDgueee47nnnuPBBx8kFotxwQUXbPZ1G2Llqdq+rl3g5GMaH3N+miq2X/8abrihduIU4LXX4FvfguL0K4FLqm0Jf2n0+AbmsY6iWvu24SC+wdMMYjxBVR1AN3Zmb37DAdzWWqFKkvTlRCLw49Nh2T/hr7+AXXck/XrcUHuBqXTT+deQmCaZMq68HObPg/93MfTvBXuPgV9dC+83sIiq1AEUM49PuYp5jOBt+vEpP6KcBQRUNDglP7noE9ROaqRrupFIvgb0Zg8O4R+cwEcmTqVGNJU4BVi4MPHv1jvvvMPll1/O7373OyZMmMCjjz7KySefzPr16wHYuDExS7hz5871rlFQUABASd2cTTMMGTKEu+66i8LCQu69917CMOTuu+/m7rvvpmfPntx+++0MH95yvcetPNXW4ZeTYd578Mbb9Y9dfhEcdmDtfcXF8PvfN3y9jz6C++6Ds89u0TClrUUpC1jH00CMLhxEZ/ZocGwJi5u8XjEf0Z2xtfb1ZjSHck9VI/8KcunyJaOWJCmDTv9WYnt1DvzyjzDrpaoFTJtTuVNGYhp/HTlULetdNc3/nbfh3bfh+qtg0GA45DA4aRIceHDL/RxSGxMSZyPPspq/sZ5/UcanQLz6Lysg8acSJ/FXlPy+7l9UsiY8NWEa1nkcIZcBHMNuXEkvRrXGjyO1kOb3JG0LTjrpJDZt2sS5555LJJL4COMb3/gGQ4YM4Ve/+hV33nkn3//+9wnDxF9l8muq5L7k+Ztr99135/HHH2fBggV89NFHxONxBg4cyG677bbF12yIyVNtHbp0hifvhgefhPsegdVrYNjOcM7JsN+e9ce/+CJUfdLRoMcfbzp5unEDrF8HffpBMz59kdq6GJv4mPNYy2O19ndhf4YwnVz61TsnP82++mP6N3gsSj5R8jc/WEmS2oL9RsETf4DPVsLN0+G2u2HjJggDqjKhaU4qq/qacizZqBES78JS39fFY7D4Q/j4Q7j3LujcGfY/EI45AQ79Gmyzbcv/XFKGhIRU8gmbeJp13EcJrxGjlBgxQhJ9S5OJ0lQRahKk6dIg6WrBg6ojOXRnJ37AzlxIPr1b9OeRBKeemn5xtVNPPZUbb7yRl156ie9///vVFafpqktLSxMt3r7swk7Dhg1j2LBhX+oaTTF5qq1Hfj6cfGxia0pZWdNjyhv5ZGfBO/C7a+GFp6CyErr1gImnwfd+Ap2tntPWK13iFGAjr/ABExnGiwR1Xopux6l8xswGr1nADvTkgBaPVZKkNmVAX7jhB/DL78KDs+Dnv4dFH0KsnIb7oqZInWOcmglKZnySjRzDGBSvhxeehH89CZEoDNoB9twfjpwAR3wLOnVq6Z9OalFxNrGJRyjmYUp5jRhLCVOqS5N/BrGqLbngU92epVFq/rLqflSRfByr/j6H7nyFEVzHNnyDwC6F2qpsXZWnDcnLy6Nbt25s2rQJgIEDBwI1vU9TNdYPtSlTp05tckwQBFx88cWbfe10TJ6qfdpzz0Tj/1gjLcP32Sf9/nfnwSnfSFSdJq1fC3dNhbdehbufhKreHNLWpIT30iZOk4qZw3qepTtfq7W/N4fRn2NYwcP1zgnIYVdurJdwlSSp3crNhRO/ldg+/ASuuQUefwZWr4IwmebJhTSLJgINl9ClJldT36XFY7D4g8T26AzIzYHBO8JXvwkHfQ12HQX9Nv+Np9SSYiyhgv9SwjOU8E8q+YQ4MeJVqc3UKfm5JKpM49QkR0PSJ08be4UZqxqRQz+24zh24ScUMrDFfiZJ6S1YsIDLLruM/fbbj5/+9Ke1jn3xxResWbOG3XffHYCRI0cSiUSYO3duvWrVOXPmADBmzJjNjqGx5GkQBIRhaPJUatJ228HRR8ODD6Y/XlAAZ52V/tgNP62dOE019y14YDqcfn7LxCl9CeW8RBmPEVJGLnvRiYkENJzYX8/TTV5zPbPqJU8DAkYxjY/YnU/4M2UsA6AXh7Azk+ll1akkqaPacRD85TeJD+wffAr+fDf861UoiwMbaVZ/1GTVaVLqO7RkVWqy6WMYg4oYfPQufPwu/GVKogp124Ewdn/46ng48BvOlFKrCgkJ2UAZj1DGk1TwGnE+JaSC1N/5ZHVpmu6/5FBTZxdNGdNQI4z6CujN4WzPhfTlm36Qr3agItsBNNvgwYNZuXIlDz/8MGeffTbbblvTWuamm24C4NhjEzOG+/Tpw/7778+sWbO45JJL2H777QFYs2YNDzzwAMOHD2fEiBGbHcP06dPr7YvFYqxcuZJHHnmEFStW8Mc//nFLfry0gjBd11apPVi3DiZOhNdfr72/oACmT4cjj6x/zorP4KDhVU38G/CV0fDwi43fu6ICXn4Kln4IPfvAoUdD5y/Xx0NKirOBdZxMBbV/DyP0pTv3k9vA4k/L+S3LuKrRa/fhbAZxc4PHQ2KUsYIIBeTRc7NjlySp3du0Ce7+B0ybDkUvQWVVQik5LT9K7WRpshQP6led1j1OmsfJ5CpAUPVN956J16xjDoCxB8E+h1cdkzZfSCUxZhHjZSp5lQreJs46QgLixKsrR2ufU5MKqloerZ5k9Wnq2Nrp15pp/TECQnLoxv5swzn049tEGykakLYWRUVFlFHJkWPvbfV7zSo6mXxyGDt2bNODq/z+979n6tSpTJ8+nX1SZu8+9thj/OhHP6JPnz6ccsopdO3aleeee45XX32V8ePH85vf/KZ67MKFCznxxBPp3LkzZ555Jnl5edxzzz0sW7aMu+66iz33TLOOzZcQhiGnnXYau+66a73K2C1l5anar+7d4emnYdYseOghKCmBMWNg0iTo2zf9OV+sbDxxCrDq88aPv/EC/OJ8+CKlp8dvfwQXXA3fPm/zfgYpjQ1cUi9xChBnJWuZSG/mEKF7veNdObDJa3floEaPB0TphItWSJLUoM6d4fzTE9u6dYkP7W+7DT54H6is/Vqzbj4z3fep0/zrVqjWWzEnhEgIm76AN56Dt55LTPOPRqDPABg+FkbsBbuMgn2+Bjm5SKnCsIQ4TxOGbxOniJAiQlYQEhILKiFI/OolEp9h9TpodZOjyYLqGDX5/c2v2goIyaWAXdmG79Kfk4hS+KV+Pqnt2rp6no4fP54+ffpwxx138Kc//YlYLMaQIUP42c9+xsknn1xr7NChQ5kxYwZTpkzh1ltvJRKJsNtuu3HDDTcwevToFo8tCAKOOuoobr311hZLnlp5KqVa/QUcsEtikaiG7LEf3Dcr/bEP3oFzD4XyBnpcXfMXOPy4Lxul2okw3EAYfwpYD8FuRCL7NnlOjE/4gt1pbFGKLvyKQi5Ke2whX2cj/0l7LI9BjOC/RPCNlCRJLe6zz+Cvf4VH/gHvzq1aaKpKsiq1buVp6kJSUFN1WrcaNbkvWdmavGY05Viq/NxEQrVHb9h5JOz4FdhpdxhzGPTbfst/Rm1Vwvj7hPGXCMP/EobvEjKPkDWEkRCCCgjqJzxjAYRVv0/JylGoXzEKtStKY9R/9Zo8P5ZyPJFs7UQBo+nBSfRlErn0aoGfVmqbaipP/9rq95pVdMZmV55ure68805uueWW6r6qX5aVp1KqXr3ha+PhqYcaHnNSA71SAe6b2nDiFGD6Tc1LnsZi8N9Z8NEcyC+EfY6G/js0fZ62GrHYTcRjNwLF1fviwUiiOXcRBEMbPK+cl2lqNd9yXmwweTqEv/EB36aY2v+I5DGInXnQxKkkSa1lwACYPDmxrVwJ/3wcHn8YZj8HpcUQhLWzT8mEZ7pSl7qLTiXHplamRkkvF4hXJK67ZhkULYOiWZATTZyTmwd9BsL2w2DwbjBoBOx5JEQi0KVX4qu2DmEMwjWEVELlExBfQBj+jzB8F1hOIn0ZEiaXaapKwAdVLzXDZFlpimhYVWUa1Cz2BLX7liY11igiJDllPyAOBHShK9+gJ8fRnSPJsT2UOpytq/K0rSovL2fevHn89a9/Zaeddmqx65o8leq64jp4+w34bGn9Y1+fAOOPb/jcV/7Z+LXfnwcrlkL/RlaBXDwPbjoFPv+4Zt+Mn8Ghk+A7N0PUP9utXSx2O/HY1fX2h+E8KivGk5P7KkGQ/hP2oMF3Qs0bk0t/hvEv1vMs65lFSCVdOIgeHG3iVJKkTOnbFyadldgAXn0Z7p4GLz6TaP0UpiwcklySPFW6hGpqpqqhlwI5dR4n51LnAEEs8bi8BJYtSmyvPwG5kUQQefnQrRf02Ra2Gw47joX+Q6CwBwzbD3Lzm/3jq4WFJVD+KsTXQeX7UFkEsYXAMkLWQFAOQZQwmph2HwBhVaVzrXx9VUYzTP7+pPvdo+bXJqjzuF5YdR7XTN0PiJNDLgMpYBw9OZtC2n8lnKSWM3z4cIJGenmHYcjkyZNb7H5mYaS6BmwHD86GaVPh0b/DmtWw01A46Ww48UyINpK8ijUy3b96TKzhYxvXwHXHwLqVtfeHIbwwHTp3h0nXNuOHqPLFJ7BiARR0hx32cqGAlhJWQMlfofgvEPsQIv2g4FQoPB8i3Ro/NawgHrupkRGfEY//hWj0srRH8zgUyKOxTybzSLMYWoqACN35Ot35eqPjJElShux3QGIDWPgezJ4F/3oaXnsRijcl9gdhIpkVoenmkQ295AtSviYfJ9sFQO0sWADkhInqxQAIS2H9ssT20Vvw0t2J15a5eZATJJKnPfpDv8HQdzD02wkG7Z7oq9p3Z+g7ZLP+kyhF+QKo/ATiZVD+X6j4CGKfQPxjiH8OQVmi120kdbXuMPFrElD1rr+SoBLCqqR5EId46u9BlSCsassb1H5cb0ydfel+JZPveuJVv7QB3SngYAr4JoV8nTx23KL/HFL71Ixcgqrttddeaffn5OTQr18/Jk6cyN57791i97PnqdSSJp8CLz3e8PFtBsEDcxue7vTo7+Ce/2v4/PxCuH0hFDaeoGPNp3Df9+DdZ2oWJegzBI7+BYye0Pi5HUXJuxBbB/k7Q26f5p8XVsDak6DsmfrHckZAr6cg0vA0o3j8VWKVTSQ3g73IyX2uweMbuIwS/pz2WIRB9OYNAlcflSSpffjkI7h/OrzwBHz0HlQUJxKaOdSfvp+cRJLa7xRqkqU5aY7XvU6Qsj/5OJfaSdWkZD/W1H6rARAJIBoAcYjmASEEUYjmQu9tE9P/e2wLfXdMPM7vAoP2qYozCtvunrh+JNq+PvwPq9aWDyug+H+Jny1eBpvehPhGiH+RSIxWLoNwNcQ+AyohEqv671xB4kFKC6fUnrh1euZWv9FP7ZEbVCVQSSRAwzR1IWGkqjIViKeZuh8PEltqT9PKqvsl7xmSQ4wuRBlBARPowiRyXHRUqqem5+ltrX6vWUUXdpiepy3NylOpJZ14Mfz7idqrqNY93lifqDnPNn79smJ492XY45sNjyleB7eMg1Uf1d6/6iOYNgnOnQG7H9X4fZI2roC3psGiJyFWAdvvC3ueB/1GNO/8VLFy2PARRPOh6w6bf351TO/DpkWQ2xN67rP5L6jXPQufXgXF8xLfB7nQ82jY/kbI7d30+SV3pU+cAlS+Axt/Cd0aqywta0aQjfe76cKvCCmllBmkvnjOYTe6c4+JU0mS2pNBQ+CHVyW20hJ4+3X4z/Pwxkuw4C0o2ZhIbsXj6StTm3qplO54akVimurE6v2RlMepi1NFqkoWA1IWxqqAoBRWLYBVQE4EIlWvYyK5EK9MJEtzoonFrIIwUbXavT/kdoL8rtC1D+QVQqee0Llf4nVgp57QfSCUr4OC3tB9B9j0KRT0gy7bwsalUNgf8rpC8fLE40gelK6Cgr6J+5ethk59IV4OZWsS51ZsgPLVULgdlH2eeFwwCIo/gvK1ULg9bFoAleshtweUfgiVG4FKqFyR+JA+VgIVKyG2HsIyiK1I/HeIBCnJ0DgEOTWtGiJBorFoasI7JFHGmUtNQ9KkZHlnMqda9TtQ691ISO3fjdTK0jT/a5sSr/c1UnXJ7uSyN7kcRC6Hkc8eBORtwR2kjiYkMz1PrZ3cUiZPpZY05gD44c0w5Qf1p/BPPA+Ov6Dx88PGFwICEi+MG/PKX+onTquvH8JjVzcvebpiPtwzAYpTWgisfAfeng4T/gi7fbvpa0DihfCcG2DBnYkXqQC9RsKoyTB4fPOuAVC8GOZdCl/8q2Zf4RDY9Tro38xk8Lpn4f3jE9UaSWEFrJ4JJfNh+PMQ7dxEHHc1frzkXuj6SwjSJzCDYDRQAJQ0eIkg2KfRWwTk0Y1b6cyPKeMJQkrJZU/yOLjx2CRJ0tatUwHse0hiS/poAfzrCXjteVg4D1Yvq3qtE9ROtKUkzRp8/5wuSdrQ5/4NLU7V0PjkO8+QmiRgMp54VeIwqISwMpEQjACxUlizoSaJuCI1yLAq2VqVGYzkQk685nGyyjOaC5HKRKI1mlNVxRkkzo3EaqpdiVclfKNALBFfTg6Jyk8SSd14RdUH95HEa8ho1SJbVCV+o2HivKAqAZruv0VqMjRZAVyrx21Yf1zyv13q41TJxGjdx6nSNSBtQJiSOA+C2qfGg8SKUnFyCRlIDqOJchidmECUQQ1fVJK+hIcffniLzjvmmGNa5P5O25daw8rP4Ml7YOkH0LMffPNkGDK86fP+8Wu4/5cNH8/rBLe+B13TLyYEwK8PhiVvN36fya/Cdl9pfMzte8Oq99Ifi+bBpfOhS//GrwHwwiT4+JE0BwI48DbY+ZSmr1G2El4+GEo/TXMwAns+AP2+1vR13tk/MUWqIYNuhH7nN36NFf0TTfkb0/d/EG34xWOs8v8Rj09r4Ghu1YJRQxu/hyRJUl1hCJs2wAfvwPMPwryXYdkHsGENVJQnptJHq97+pZviX6vfaZrjqWNS15lMLS7MTTM29XrBZo6p+zjVlo6pG2e681JjCuo8bmhM8rrpHteNaUvHNDY+GU/V41pv9FP+P4YpP1M8zbT9eHJaf7SqPJV8oBcEQwmCA4kykQg7A10ImixtltSYxLT9Co4c+5tWv9esoh+ST+5WOW2/qQWi6grDkCAIePfdd1vk/laeSq2h7wA444ebf97hZ8Ljt0Dx+vTHDz6l8cQpQNnGpu/T1JjFLzWcOIXE9Ku3p8OBP2r8Op+91EDiFCCEN38KQ75d1QurER//qYHEKUAcFl7TdPK05J3GE6cAq+9vOnka6QOxJY0MyIGg4Z6nAJHo9YThR4ThC3WO5BPNucPEqSRJ2jJBAF26wah9ExtAZSV8thiWfwxvPAfz/wOfLoQNqxOLQMXDRFI1SEm1NVSlmvq+NV31YkPva1P3N1SZGmnG49R7NlT5SgP7mxqf7udJTneP1nnc0BgaeZxqc8ekxtZE1WiDUlssVJ0fBsmHQdXyUhHCaB5B0A8iw4kE+xHhMIJgO2DHzUpcSFJLuvjii+s9BxUXFzNt2jSOPvpott9++1a9v8lTqS3p0Q8m/wN+czKsX1X72N4T4IxfNX2N7XaHz99v+HhOPvTfpfFrrGzGpzOfv9P0mA/va/x46Sr49BkY1MS0+88ebPz4+jmw6UPo3MiKnZXrGr9Gc8cUnAobG/n/0OlbEOna6CWCoIBozsOE4WzC+ExCNhAEuxGJnE4QbNN0DJIkSc2VkwPb75zY9jq8Zv9ni2HtSni/COb9Gz6eC6s/hZINUFmeWEkoUjWVPd1U8HTJvZaa09icZGvQwOPU5GJzErJNSU6vh81PhjY0vu4U+s3RUIVsUrT27hBqFvUiSFSTEhJG8iDSHYKBEIwkEj0UIiMJIv0JgoGbGZQkta5LL7203r41a9Ywbdo0jjnmGPbbb79Wvb/JU6mtGbYP/OEdePVB+PC/kN8Z9jsWhoxq3vkHfwf+20iycY9vQ+cmqlfzuzV9n07dmx5TuqoZY75oekxsU9NjKpuopu20c2JxqNSeUnUVNKO1QuFFUPIPiC2qfyzSE7r8X9PXAIIgIAi+CpGvNmu8JElSixqwQ2LbdS8YXzXzprwMVn+WSKC+NQuWvAvLF8KnC6BsbaI6NQgSC4nGqalYTfbiDKhJtKYm9OomWltCQ8nTL6Opqs7WaHjX0P0iacY0tHhXdduFZAVxzUpTQTRCGO0NuUMhZxhB9CuQcwRB0BmCbQmCuv0LJGVHI+9TlXUmT6W2KK8THHJKYttcOx8A3/o/ePwX9Y8NGgPHNaN6deg4yC2EiuKGx3ylGQtGddu56THdmzGm2+6NTNsHol2g806NXyO3L/ScAKv/0fCYvuc0HUukB/SeBRuuhtL7q/qf5kD+OOj6M8hpoqpXkiSprcrLh212SDweMrL+8S+WwvzZsGY5rPgQPn0H1iyB4tWJxZ3CMiBSleCL1T43zWrv9RKs6RY9Sk28NjQ+NenZVGuBxjRV1dmctgRfpkVB6jVS2xPUa1WQUtqakw/RQsjpBbnbQ+5uEB0C0W0g73CI9rMrqSR9SSZPpfboyB/BsK/CS3+G5e9CQQ/Y8wTYYyLkdmr6/E7d4MDL4YWfpz++85Ew+MCmrzP0LJg/lQY/pu8xHPrv3/R1Bp8Pnz/V8PGBp0BO56avs/2voWQ+lKTp57rNZdCtmVWgkT7Q/ffQ7QaILYdI78S0J0mSpPas90A4+LT6+8tLYO1yyMmDhS/BF4th3Wew+iNYsxQ2rYENK4EKCKrmwQeVQByCCITx+v1Fk4nPxqbKJxOPja1CH00Zkzqdvm7SsjkVoM1JhkYb2J96n+T+aGqlaCQRZBCF3Gjiv1M0J/HfNKcv5PaEvEGQMwii20LeTtBpXwjLIWc7CJpYQ0BSG1ee7QDUCJOnUnu1w56JbUsdcBnkdYFXpsCGZYl9uZ1h9CQ4PE1Vazrdd4a9roU3flL/WF4POOiPzbtO38Ng58nwfpqq2Z77w7Brmned3L4w/HlY9bfE4lCxdVCwK/Q9t/mJ01RBIeQ00mdVkiSpI8grgH5DEo/3PSn9mNKNUF4M0VxYNBsqSqFyE3w2DzZ9AaXrYP2yRHuAeCmUrQMqEglGAgiqEguRHIhXVCVYoxCprHocSfRoBaq7fcaBSFWCMvVxjPqtBdKJUJPojAQQSRYEpDR4jabcNzcnkfQMgNxcoLIq9tzEfSPxxM+f1ytRMZrXE3IHQLQH5PSBwt0gWgDRztDl0MSCXpEuECls6v+AJKkVmTyV1LC9zoOxZ8PytyFWDv1HQn7jiyHV85VLoNfu8O7tsPINiOTB4PEw4iLoMqj51xn6E+j3dfj4Tti0EHJ7wXYnwjZHQ2QzejVFu0D/CxObJEmSMqNTl8QGMPrYpseXbUwkSiuKE4uZ5hYmWgOsmg+RfKjYBOs+BuIQL0n00Y+XJSoxKzYk+rKGFYnzIyFQCbESyKmq6oyVQm5eVWK1LJG0DOMQVkJOIUTDxLHcwkTiNBpN7A+iEOkEuV0gmpdIgub3TVSIBpFEK6lIVeK086iqPGs5dB4JQU4i3mgzZkwB0Ix1CCS1E1aetmUmTyU1LpoD232JClaAAQcnti+rx56JTZIkSe1bflWiNbcTDD6gZv/2rbuisiSp7Xn44Yfr7du0KbGw9Msvv8yKFSvSnnfMMce0yP1NnkqSJEmSJElZU5HtANq0yZMnEwTpe63ceeed9faFYUgQBCZPJUmSJEmSJLVvF198cYPJ00wweSpJkiRJkiRlRUhmep6GTQ9poy699NKs3j/S9BBJkiRJkiRJ6nisPJUkSZIkSZKyJhOVp9pSVp5KkiRJkiRJUhpWnkqSJEmSJElZU5HtANQIK08lSZIkSZIkKQ0rTyVJkiRJkqSssedpW2blqSRJkiRJkiSlYeWpJEmSJEmSlDX2PG3LrDyVJEmSJEmSpDRMnkqSJEmSJElSGk7blyRJkiRJkrLGafttmZWnkiRJkiRJkpSGlaeSJEmSJElSlkSozHYIaoSVp5IkSZIkSZKUhpWnkiRJkiRJUpZYedq2WXkqSZIkSZIkSWlYeSpJkiRJkiRlRZihytMwA/don6w8lSRJkiRJkqQ0rDyVJEmSJEmSsiAgMz1Pg1a/Q/tl5akkSZIkSZIkpWHlqSRJkiRJkpQledkOQI2y8lSSJEmSJEmS0rDyVJIkSZIkScqS3GwHoEZZeSpJkiRJkiRJaVh5KkmSJEmSJGWJPU/bNitPJUmSJEmSJCkNK08lSZIkSZKkLLHytG2z8lSSJEmSJEmS0rDyVJIkSZIkScoSk3Ntm5WnkiRJkiRJkpSGyVNJkiRJkiRJSsPKYEmSJEmSJClLXDCqbbPyVJIkSZIkSZLSsPJUkiRJkiRJyoKAzFSeBhm4R3tl5akkSZIkSZIkpWHlqSRJkiRJkpQludkOQI2y8lSSJEmSJEmS0rDyVJIkSZIkScqSTPQ81Zaz8lSSJEmSJEmS0rDyVJIkSZIkScoSK0/bNitPJUmSJEmSJCkNK08lSZIkSZKkLMnNdgBqlJWnkiRJkiRJkmqZM2cOu+66K6+99lq9Y8uWLePyyy/nwAMPZPTo0Zxwwgk899xzaa+zaNEiLrroIvbbbz/GjBnDGWecwVtvvdXa4bcYk6eSJEmSJElSluRlYNtcixcv5uKLLyYej9c7tnLlSk477TSeffZZJk6cyOWXX05lZSUXXXQRjz32WK2xH3zwAaeccgpz585l0qRJ/L//9/9Yvnw5Z5xxBq+//voWRJZ5TtuXJEmSJEmSBMAzzzzDlVdeybp169Ienzp1KsuWLWPGjBmMHTsWgIkTJ3LCCSdw7bXXcvjhh1NYWAjA9ddfT3l5OQ8++CDbb789ABMmTGDChAlcffXVPP744wRBkJkfbAtZeSpJkiRJkiRlQUBmKk+bm54877zzuOSSS+jbty/f+ta36h2PxWI8+uijjB49ujpxCpCfn8/pp5/OmjVrmD17NgCrVq3ipZde4ogjjqhOnAL07NmT448/nvfff5+5c+c2M7LsMXkqSZIkSZIkiQ8//JDLLruMhx56iB122KHe8UWLFlFcXMzo0aPrHRs1ahSQ6JWa+rU5Y9syp+1LkiRJkiRJWZKb7QBSPPnkk+TlNdwldcWKFQAMGDCg3rFtttkGgKVLlwKwfPnyBsf279+/1ti2zMpTSZIkSZIkSY0mTgE2bNgAUN3TNFWnTp0AKCkpAWDjxo0AdO7cud7YgoKCWmPbMpOnkiRJkiRJkpoUhmGTxyKRSK3v051Td2xb5rR9SZIkSZIkKUsar/VsW5JVpOkqRktLSwHo2rXrZo9ty9p+eleSJEmSJElS1g0cOBCo6WeaKrkv2fu0OWPT9UNta0yeSpIkSZIkSVmSm4Gtpey444507dqVuXPn1js2Z84cAMaOHQvAyJEjiUQijY4dM2ZMC0bXOkyeSpIkSZIkSWpSTk4O48aN480336SoqKh6f1lZGdOnT6dPnz4cfPDBAPTp04f999+fWbNmsWTJkuqxa9as4YEHHmD48OGMGDEi4z/D5rLnqSRJkiRJkpQlW1PPU4BLL72U559/nu985zucddZZ9O7dm5kzZ7Jw4UKmTJlCfn5+9dgf//jHnHjiiZx88smceeaZ5OXlcc8997Bu3Tpuvvnm7P0Qm8HkqSRJkiRJkqRm6du3L/feey833XQT06dPp6KigmHDhnHHHXdwyCGH1Bo7dOhQZsyYwZQpU7j11luJRCLstttu3HDDDYwePTo7P8BmCsIwDLMdhCRJkiRJktSRFBUVUUkZD489stXvdUzRLHLIr+5Hquaz56kkSZIkSZIkpeG0fUmSJEmSJClLtraepx2NlaeSJEmSJEmSlIaVp5IkSZIkSVIWBGSm8jTIwD3aKytPJUmSJEmSJCkNK08lSZIkSZKkLMnNdgBqlJWnkiRJkiRJkpSGlaeSJEmSJElSlmSi56m2nJWnkiRJkiRJkpSGlaeSJEmSJElSllh52rZZeSpJkiRJkiRJaVh5KkmSJEmSJGVJbrYDUKOsPJUkSZIkSZKkNEyeSpIkSZIkSVIaTtuXJEmSJEmSssQFo9o2K08lSZIkSZIkKQ0rTyVJkiRJkqQssfK0bbPyVJIkSZIkSZLSsPJUkiRJkiRJyoKAzCTnggzco72y8lSSJEmSJEmS0rDyVJIkSZIkScoSe562bVaeSpIkSZIkSVIaVp5KkiRJkiRJWWLladtm5akkSZIkSZIkpWHlqSRJkiRJkpQludkOQI2y8lSSJEmSJEmS0rDyVJIkSZIkScoSe562bVaeSpIkSZIkSVIaVp5KkiRJkiRJWWLladtm5akkSZIkSZIkpWHlqSRJkiRJkpQFAZCbgfuEGbhHe2XlqSRJkiRJkiSlYfJUkiRJkiRJktJw2r4kSZIkSZKUJZlYMKosA/dor6w8lSRJkiRJkqQ0rDyVJEmSJEmSssTK07bNylNJkiRJkiRJSsPKU0mSJEmSJClLcrMdgBpl5akkSZIkSZIkpWHlqSRJkiRJkpQlVp62bVaeSpIkSZIkSVIaVp5KkiRJkiRJWZKX7QDUKCtPJUmSJEmSJCkNK08lSZIkSZKkLLHnadtm5akkSZIkSZIkpWHlqSRJkiRJkpQFAZnpeRpk4B7tlZWnkiRJkiRJkpSGlaeSJEmSJElSluTEsx2BGmPlqSRJkiRJkiSlYeWpJEmSJEmSlCWRymxHoMZYeSpJkiRJkiRJaVh5KkmSJEmSJGWJladtm5WnkiRJkiRJkpSGyVNJkiRJkiRJSsNp+5IkSZIkSVKWOG2/bbPyVJIkSZIkSZLSsPJUkiRJkiRJyoYQgkxUnoZAkIH7tENWnkqSJEmSJElSGlaeSpIkSZIkSdlSnu0A1BgrTyVJkiRJkiQpDStPJUmSJEmSpGxpQ5WnV155JTNnzkx77Prrr+e4444DYNmyZdx888288sorbNy4kaFDh3L++edz+OGHZzLcjDB5KkmSJEmSJIkFCxaw3Xbb8b3vfa/esbFjxwKwcuVKTjvtNNauXcukSZPo378/M2fO5KKLLuI3v/kN48ePz3TYrcrkqSRJkiRJkpQtFdkOICEej7No0SK++tWvcvTRRzc4burUqSxbtowZM2ZUJ1QnTpzICSecwLXXXsvhhx9OYWFhpsJudfY8lSRJkiRJkjq4xYsXU1payi677NLgmFgsxqOPPsro0aOrE6cA+fn5nH766axZs4bZs2dnINrMMXkqSZIkSZIkZUtFBrZmeO+99wAYOnQoACUlJcRisVpjFi1aRHFxMaNHj653/qhRowCYM2dO8264lTB5KkmSJEmSJHVwCxYsAOCll17isMMOY/To0YwaNYqLLrqIJUuWALBixQoABgwYUO/8bbbZBoClS5dmKOLMsOepJEmSJEmSlC3l2Q4gIZk8ffvtt7nwwgvp2bMnRUVFTJ8+nf/+97888MADbNiwASBtT9NOnToBiYrV9sTkqSRJkiRJktTBjRs3jhEjRnDeeedVJ0KPOOIIRo8ezaWXXspvf/tbDj300AbPD8MQgEikfU10N3kqSZIkSZIkZUNI5ipPg8YPT5gwIe3+r3/96wwYMIB///vfHHXUUUD66tLS0lIAunbt+uXibGPaVypYkiRJkiRJUovq3bs3mzZtYuDAgQAsX7683pjkvmTv0/bC5KkkSZIkSZKULRUZ2JqwevVqxo8fzyWXXFI/vIoKPv74YwYPHsyOO+5I165dmTt3br1xc+bMAWDs2LHN/tG3BiZPJUmSJEmSpA6sV69exGIxXnjhBf73v//VOnbHHXewYcMGjj32WHJychg3bhxvvvkmRUVF1WPKysqYPn06ffr04eCDD850+K0qCJPdXCVJkiRJkiRlRFFREcTLGFtxZOvfK3cWRPIbrQp97bXXOPfcc8nPz+fUU0+lX79+vPbaa8yaNYu9996badOmkZuby8qVKzn22GMpKSnhrLPOonfv3sycOZP58+czZcoUxo0b1+o/TyaZPJUkSZIkSZIyrDp5uik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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def build_template_3d(angles_deg: np.ndarray, shape_name: str) -> np.ndarray:\n", - " hue = (np.asarray(angles_deg, dtype=np.float64) % 360.0) / 360.0\n", - " if shape_name == \"SemicircularShape\":\n", - " theta = np.pi * hue\n", - " else:\n", - " theta = 2.0 * np.pi * hue\n", - "\n", - " x = np.cos(theta)\n", - " y = np.sin(theta)\n", - " z = np.zeros_like(theta)\n", - " return np.column_stack([x, y, z])\n", - "\n", - "fig = plt.figure(figsize=(12, 5), dpi=140)\n", - "for panel_idx, row in enumerate(optimal_layers_df.itertuples(index=False), start=1):\n", - " dataset_name = row.dataset\n", - " optimal_layer = int(row.optimal_layer)\n", - " bundle = scans[dataset_name]\n", - " X_opt = np.asarray(bundle[\"layer_data\"][optimal_layer], dtype=np.float64)\n", - " angles_deg = bundle[\"angles_deg\"]\n", - " template = build_template_3d(angles_deg, global_winner_shape)\n", - "\n", - " stage_1_3d = UserProvidedSMDSParametrization(y=template, n_components=3, name=f\"{dataset_name}_template\")\n", - " smds_3d = SupervisedMDS(stage_1=stage_1_3d, alpha=1e-4)\n", - " coords = smds_3d.fit_transform(X_opt, template)\n", - "\n", - " ax = fig.add_subplot(1, 2, panel_idx, projection=\"3d\")\n", - " sc = ax.scatter(\n", - " coords[:, 0],\n", - " coords[:, 1],\n", - " coords[:, 2],\n", - " c=angles_deg,\n", - " cmap=\"hsv\",\n", - " s=22,\n", - " alpha=0.95,\n", - " edgecolors=\"none\",\n", - " )\n", - " ax.set_title(f\"{dataset_name} | layer {optimal_layer}\", fontsize=10)\n", - " ax.set_axis_off()\n", - " ax.view_init(elev=24, azim=38)\n", - " ranges = np.ptp(coords, axis=0)\n", - " ranges[ranges == 0.0] = 1.0\n", - " ax.set_box_aspect(ranges)\n", - "\n", - "cbar = fig.colorbar(sc, ax=fig.axes, fraction=0.03, pad=0.02)\n", - "cbar.set_label(\"Hue angle (deg)\")\n", - "plt.suptitle(f\"3D SMDS Projection with : {global_winner_shape}\", fontsize=12, weight=\"bold\", y=1.02)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "8be61efa", - "metadata": {}, - "source": [ - "## 9) Causal Intervention: Neural Joystick (Blue -> Red)\n", - "\n", - "For each domain, we train a linear decoder from activations to RGB, then apply latent steering:\n", - "\n", - "$$x_{steered} = x_{blue} + alpha (x_{red} - x_{blue})$$\n", - "\n", - "If hue is causally encoded, decoded output shifts from blue toward red." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "36b27233", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datasetdecoder_msedecoder_r2decoded_blue_huedecoded_steered_huedecoded_red_hue
0full_simple2.029280e-301.0240.0349.4117650.0
1wild_textured7.226774e-311.0240.0349.4117650.0
\n", - "
" - ], - "text/plain": [ - " dataset decoder_mse decoder_r2 decoded_blue_hue \\\n", - "0 full_simple 2.029280e-30 1.0 240.0 \n", - "1 wild_textured 7.226774e-31 1.0 240.0 \n", - "\n", - " decoded_steered_hue decoded_red_hue \n", - "0 349.411765 0.0 \n", - "1 349.411765 0.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def hue_to_rgb(angle_deg: float) -> tuple[float, float, float]:\n", - " return colorsys.hsv_to_rgb((angle_deg % 360.0) / 360.0, 1.0, 1.0)\n", - "\n", - "def rgb_to_hue_deg(rgb: np.ndarray) -> float:\n", - " r, g, b = np.clip(rgb, 0.0, 1.0)\n", - " hue, _, _ = colorsys.rgb_to_hsv(float(r), float(g), float(b))\n", - " return (hue * 360.0) % 360.0\n", - "\n", - "def mean_vector_at_angle(X: np.ndarray, angles_deg: np.ndarray, target_deg: float) -> np.ndarray:\n", - " circular_delta = np.abs(((angles_deg - target_deg + 180.0) % 360.0) - 180.0)\n", - " idx = np.where(circular_delta == circular_delta.min())[0]\n", - " return X[idx].mean(axis=0)\n", - "\n", - "alpha = 0.85\n", - "joystick_rows = []\n", - "panel_data = []\n", - "\n", - "for row in optimal_layers_df.itertuples(index=False):\n", - " dataset_name = row.dataset\n", - " optimal_layer = int(row.optimal_layer)\n", - " bundle = scans[dataset_name]\n", - " angles_deg = bundle[\"angles_deg\"]\n", - " X_opt = np.asarray(bundle[\"layer_data\"][optimal_layer], dtype=np.float64)\n", - "\n", - " y_rgb = np.array([hue_to_rgb(a) for a in angles_deg], dtype=np.float64)\n", - " decoder = LinearRegression()\n", - " decoder.fit(X_opt, y_rgb)\n", - " recon_rgb = np.clip(decoder.predict(X_opt), 0.0, 1.0)\n", - "\n", - " blue_vec = mean_vector_at_angle(X_opt, angles_deg, 240.0)\n", - " red_vec = mean_vector_at_angle(X_opt, angles_deg, 0.0)\n", - " steered_vec = blue_vec + alpha * (red_vec - blue_vec)\n", - "\n", - " rgb_blue = np.clip(decoder.predict(blue_vec.reshape(1, -1))[0], 0.0, 1.0)\n", - " rgb_steered = np.clip(decoder.predict(steered_vec.reshape(1, -1))[0], 0.0, 1.0)\n", - " rgb_red = np.clip(decoder.predict(red_vec.reshape(1, -1))[0], 0.0, 1.0)\n", - "\n", - " hue_blue = rgb_to_hue_deg(rgb_blue)\n", - " hue_steered = rgb_to_hue_deg(rgb_steered)\n", - " hue_red = rgb_to_hue_deg(rgb_red)\n", - "\n", - " joystick_rows.append(\n", - " {\n", - " \"dataset\": dataset_name,\n", - " \"decoder_mse\": float(mean_squared_error(y_rgb, recon_rgb)),\n", - " \"decoder_r2\": float(r2_score(y_rgb, recon_rgb)),\n", - " \"decoded_blue_hue\": float(hue_blue),\n", - " \"decoded_steered_hue\": float(hue_steered),\n", - " \"decoded_red_hue\": float(hue_red),\n", - " }\n", - " )\n", - "\n", - " panel_data.append((dataset_name, rgb_blue, rgb_steered, rgb_red, hue_blue, hue_steered, hue_red))\n", - "\n", - "joystick_df = pd.DataFrame(joystick_rows).sort_values(\"dataset\").reset_index(drop=True)\n", - "display(joystick_df)\n", - "\n", - "fig, axes = plt.subplots(len(panel_data), 3, figsize=(9.5, 2.8 * len(panel_data)), dpi=140)\n", - "if len(panel_data) == 1:\n", - " axes = np.asarray([axes])\n", - "\n", - "for r, item in enumerate(panel_data):\n", - " dataset_name, rgb_blue, rgb_steered, rgb_red, hue_blue, hue_steered, hue_red = item\n", - " columns = [\n", - " (\"Blue seed\", rgb_blue, hue_blue),\n", - " (f\"Steered alpha={alpha:.2f}\", rgb_steered, hue_steered),\n", - " (\"Red reference\", rgb_red, hue_red),\n", - " ]\n", - " for c, (title, rgb, hue) in enumerate(columns):\n", - " patch = np.ones((50, 50, 3), dtype=np.float64) * rgb.reshape(1, 1, 3)\n", - " axes[r, c].imshow(patch)\n", - " axes[r, c].set_title(f\"{dataset_name}\\n{title}\\nHue~{hue:.1f} deg\", fontsize=9)\n", - " axes[r, c].axis(\"off\")\n", - "\n", - "plt.suptitle(\"Neural Joystick: latent color steering\", fontsize=12, weight=\"bold\", y=1.01)\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "41fd182c", - "metadata": {}, - "source": [ - "## 10) Expanded Neural Joystick Examples\n", - "\n", - "We expand the joystick with:\n", - "- Multiple steering directions (`Blue→Red`, `Red→Green`, `Green→Blue`)\n", - "- Alpha sweeps to visualize controllability curves\n", - "- Additional color-swatch trajectories for qualitative examples\n" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "06746192", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datasetpairsource_degtarget_degalphadecoded_huetarget_error_deg
0full_simpleBlue→Red240.00.0-0.25240.0-1.200000e+02
1full_simpleBlue→Red240.00.00.00240.0-1.200000e+02
2full_simpleBlue→Red240.00.00.25260.0-1.000000e+02
3full_simpleBlue→Red240.00.00.50300.0-6.000000e+01
4full_simpleBlue→Red240.00.00.75340.0-2.000000e+01
5full_simpleBlue→Red240.00.01.000.00.000000e+00
6full_simpleBlue→Red240.00.01.250.00.000000e+00
7full_simpleRed→Green0.0120.0-0.250.0-1.200000e+02
8full_simpleRed→Green0.0120.00.000.0-1.200000e+02
9full_simpleRed→Green0.0120.00.2520.0-1.000000e+02
10full_simpleRed→Green0.0120.00.5060.0-6.000000e+01
11full_simpleRed→Green0.0120.00.75100.0-2.000000e+01
12full_simpleRed→Green0.0120.01.00120.05.684342e-14
13full_simpleRed→Green0.0120.01.25120.01.136868e-13
14full_simpleGreen→Blue120.0240.0-0.25120.0-1.200000e+02
15full_simpleGreen→Blue120.0240.00.00120.0-1.200000e+02
16full_simpleGreen→Blue120.0240.00.25140.0-1.000000e+02
17full_simpleGreen→Blue120.0240.00.50180.0-6.000000e+01
\n", - "
" - ], - "text/plain": [ - " dataset pair source_deg target_deg alpha decoded_hue \\\n", - "0 full_simple Blue→Red 240.0 0.0 -0.25 240.0 \n", - "1 full_simple Blue→Red 240.0 0.0 0.00 240.0 \n", - "2 full_simple Blue→Red 240.0 0.0 0.25 260.0 \n", - "3 full_simple Blue→Red 240.0 0.0 0.50 300.0 \n", - "4 full_simple Blue→Red 240.0 0.0 0.75 340.0 \n", - "5 full_simple Blue→Red 240.0 0.0 1.00 0.0 \n", - "6 full_simple Blue→Red 240.0 0.0 1.25 0.0 \n", - "7 full_simple Red→Green 0.0 120.0 -0.25 0.0 \n", - "8 full_simple Red→Green 0.0 120.0 0.00 0.0 \n", - "9 full_simple Red→Green 0.0 120.0 0.25 20.0 \n", - "10 full_simple Red→Green 0.0 120.0 0.50 60.0 \n", - "11 full_simple Red→Green 0.0 120.0 0.75 100.0 \n", - "12 full_simple Red→Green 0.0 120.0 1.00 120.0 \n", - "13 full_simple Red→Green 0.0 120.0 1.25 120.0 \n", - "14 full_simple Green→Blue 120.0 240.0 -0.25 120.0 \n", - "15 full_simple Green→Blue 120.0 240.0 0.00 120.0 \n", - "16 full_simple Green→Blue 120.0 240.0 0.25 140.0 \n", - "17 full_simple Green→Blue 120.0 240.0 0.50 180.0 \n", - "\n", - " target_error_deg \n", - "0 -1.200000e+02 \n", - "1 -1.200000e+02 \n", - "2 -1.000000e+02 \n", - "3 -6.000000e+01 \n", - "4 -2.000000e+01 \n", - "5 0.000000e+00 \n", - "6 0.000000e+00 \n", - "7 -1.200000e+02 \n", - "8 -1.200000e+02 \n", - "9 -1.000000e+02 \n", - "10 -6.000000e+01 \n", - "11 -2.000000e+01 \n", - "12 5.684342e-14 \n", - "13 1.136868e-13 \n", - "14 -1.200000e+02 \n", - "15 -1.200000e+02 \n", - "16 -1.000000e+02 \n", - "17 -6.000000e+01 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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datasetpairalphamean_abs_target_error
0full_simpleBlue→Red-0.25120.0
1full_simpleBlue→Red0.00120.0
2full_simpleBlue→Red0.25100.0
3full_simpleBlue→Red0.5060.0
4full_simpleBlue→Red0.7520.0
5full_simpleBlue→Red1.000.0
6full_simpleBlue→Red1.250.0
7full_simpleGreen→Blue-0.25120.0
8full_simpleGreen→Blue0.00120.0
9full_simpleGreen→Blue0.25100.0
10full_simpleGreen→Blue0.5060.0
11full_simpleGreen→Blue0.7520.0
12full_simpleGreen→Blue1.000.0
13full_simpleGreen→Blue1.250.0
14full_simpleRed→Green-0.25120.0
15full_simpleRed→Green0.00120.0
16full_simpleRed→Green0.25100.0
17full_simpleRed→Green0.5060.0
\n", - "
" - ], - "text/plain": [ - " dataset pair alpha mean_abs_target_error\n", - "0 full_simple Blue→Red -0.25 120.0\n", - "1 full_simple Blue→Red 0.00 120.0\n", - "2 full_simple Blue→Red 0.25 100.0\n", - "3 full_simple Blue→Red 0.50 60.0\n", - "4 full_simple Blue→Red 0.75 20.0\n", - "5 full_simple Blue→Red 1.00 0.0\n", - "6 full_simple Blue→Red 1.25 0.0\n", - "7 full_simple Green→Blue -0.25 120.0\n", - "8 full_simple Green→Blue 0.00 120.0\n", - "9 full_simple Green→Blue 0.25 100.0\n", - "10 full_simple Green→Blue 0.50 60.0\n", - "11 full_simple Green→Blue 0.75 20.0\n", - "12 full_simple Green→Blue 1.00 0.0\n", - "13 full_simple Green→Blue 1.25 0.0\n", - "14 full_simple Red→Green -0.25 120.0\n", - "15 full_simple Red→Green 0.00 120.0\n", - "16 full_simple Red→Green 0.25 100.0\n", - "17 full_simple Red→Green 0.50 60.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def circular_hue_error(pred_deg: float, target_deg: float) -> float:\n", - " return float(((pred_deg - target_deg + 180.0) % 360.0) - 180.0)\n", - "\n", - "steering_pairs = [\n", - " (240.0, 0.0, \"Blue→Red\"),\n", - " (0.0, 120.0, \"Red→Green\"),\n", - " (120.0, 240.0, \"Green→Blue\"),\n", - "]\n", - "alphas = np.array([-0.25, 0.00, 0.25, 0.50, 0.75, 1.00, 1.25], dtype=np.float64)\n", - "\n", - "expanded_rows = []\n", - "swatch_cache = []\n", - "\n", - "for row in optimal_layers_df.itertuples(index=False):\n", - " dataset_name = row.dataset\n", - " optimal_layer = int(row.optimal_layer)\n", - " bundle = scans[dataset_name]\n", - "\n", - " X_opt = np.asarray(bundle[\"layer_data\"][optimal_layer], dtype=np.float64)\n", - " angles = np.asarray(bundle[\"angles_deg\"], dtype=np.float64)\n", - " y_rgb = np.array([hue_to_rgb(a) for a in angles], dtype=np.float64)\n", - "\n", - " decoder = LinearRegression()\n", - " decoder.fit(X_opt, y_rgb)\n", - "\n", - " for source_deg, target_deg, label in steering_pairs:\n", - " source_vec = mean_vector_at_angle(X_opt, angles, source_deg)\n", - " target_vec = mean_vector_at_angle(X_opt, angles, target_deg)\n", - " direction = target_vec - source_vec\n", - "\n", - " for alpha_val in alphas:\n", - " steered_vec = source_vec + alpha_val * direction\n", - " rgb_out = np.clip(decoder.predict(steered_vec.reshape(1, -1))[0], 0.0, 1.0)\n", - " hue_out = rgb_to_hue_deg(rgb_out)\n", - " err = circular_hue_error(hue_out, target_deg)\n", - "\n", - " expanded_rows.append(\n", - " {\n", - " \"dataset\": dataset_name,\n", - " \"pair\": label,\n", - " \"source_deg\": source_deg,\n", - " \"target_deg\": target_deg,\n", - " \"alpha\": float(alpha_val),\n", - " \"decoded_hue\": float(hue_out),\n", - " \"target_error_deg\": float(err),\n", - " }\n", - " )\n", - "\n", - " if label == \"Blue→Red\":\n", - " swatch_cache.append((dataset_name, float(alpha_val), rgb_out, float(hue_out)))\n", - "\n", - "expanded_df = pd.DataFrame(expanded_rows)\n", - "display(expanded_df.head(18))\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 4), dpi=140, sharey=True)\n", - "for ax, dataset_name in zip(axes, sorted(expanded_df[\"dataset\"].unique())):\n", - " sub = expanded_df[expanded_df[\"dataset\"] == dataset_name]\n", - " for _, _, label in steering_pairs:\n", - " part = sub[sub[\"pair\"] == label].sort_values(\"alpha\")\n", - " ax.plot(part[\"alpha\"], part[\"decoded_hue\"], marker=\"o\", linewidth=1.8, label=label)\n", - " ax.set_title(f\"{dataset_name}: decoded hue vs alpha\")\n", - " ax.set_xlabel(\"Steering alpha\")\n", - " ax.grid(True, linestyle=\"--\", alpha=0.35)\n", - "axes[0].set_ylabel(\"Decoded hue (deg)\")\n", - "axes[0].set_ylim(-5, 365)\n", - "axes[0].legend(loc=\"best\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# Swatch trajectories for Blue→Red\n", - "for dataset_name in sorted(expanded_df[\"dataset\"].unique()):\n", - " entries = [item for item in swatch_cache if item[0] == dataset_name]\n", - " entries = sorted(entries, key=lambda t: t[1])\n", - "\n", - " fig, axes = plt.subplots(1, len(entries), figsize=(1.4 * len(entries), 2.6), dpi=140)\n", - " if len(entries) == 1:\n", - " axes = [axes]\n", - "\n", - " for ax, (_, alpha_val, rgb_out, hue_out) in zip(axes, entries):\n", - " patch = np.ones((40, 40, 3), dtype=np.float64) * rgb_out.reshape(1, 1, 3)\n", - " ax.imshow(patch)\n", - " ax.set_title(f\"a={alpha_val:.2f}\\n{hue_out:.1f}°\", fontsize=8)\n", - " ax.axis(\"off\")\n", - "\n", - " plt.suptitle(f\"{dataset_name}: Blue→Red steering trajectory\", fontsize=11, weight=\"bold\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "summary_expanded = (\n", - " expanded_df.groupby([\"dataset\", \"pair\", \"alpha\"])\n", - " .agg(mean_abs_target_error=(\"target_error_deg\", lambda s: float(np.mean(np.abs(s)))))\n", - " .reset_index()\n", - ")\n", - "display(summary_expanded.head(18))\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/family_tree.ipynb b/docs/notebooks/family_tree.ipynb deleted file mode 100644 index b772ea1..0000000 --- a/docs/notebooks/family_tree.ipynb +++ /dev/null @@ -1,608 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4a39b307", - "metadata": {}, - "source": [ - "# Family Tree Experiment\n", - "\n", - "## Experiment Overview\n", - "\n", - "The family tree experiment tests whether hierarchical concepts like family relationships intrinsically organize themselves into hierarchical structures in LLM latent spaces.\n", - "\n", - "SMDS allows studying embeddings with a hierarchical nature. Concepts such as family trees may intrinsically take such organization in the latent space. These are functional steps to validate this hypothesis:\n", - "\n", - "A dataset of paragraphs describing a family tree is prepared. Unique names are sampled from a known set (see emailed resources) and organized into a family tree. This family tree is then parsed into a text that describes it. E.g. \"Anna's parents are Sofia and Luke. Sofia's parents are Agnes and Robert. Luke's parents are George and Daniela.\" describes a family tree from a child to its grandparents;\n", - "The paragraphs are fed to an LLM and activations in correspondance to the names tokens are recorded (see emailed resources);\n", - "Manifold search is applied on these activations, using as features quantities like tree distance (1 for parent-child, 2 for grandparent-child, ...). Several hypothesis manifolds are compared and one is identified as the winner.\n", - "\n", - "### Hypothesis\n", - "Concepts such as family trees may intrinsically take hierarchical organization in the latent space of LLMs.\n", - "\n", - "### Methodology\n", - "1. **Data Generation**: Create paragraphs describing family relationships (parent-child: distance 1, grandparent-child: distance 2)\n", - "2. **Activation Recording**: Feed paragraphs to LLMs and record activations at token positions corresponding to family member names\n", - "3. **Manifold Search**: Apply SMDS (Supervised Multidimensional Scaling) on activations using tree distance as features\n", - "4. **Analysis**: Compare hypothesis shapes (especially Hierarchical vs. Circular) to identify winning manifold\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "bf6f1034", - "metadata": {}, - "source": [ - "## Data generation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "170faada", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import random\n", - "\n", - "import pandas as pd\n", - "\n", - "\n", - "def load_names():\n", - " path = \"smds/demos/resources/names.csv\"\n", - " if not os.path.exists(path):\n", - " path = os.path.abspath(os.path.join(os.path.dirname(__file__), \"../resources/names.csv\"))\n", - "\n", - " df = pd.read_csv(path)\n", - " return df[\"name\"].tolist()\n", - "\n", - "\n", - "def generate_family_tree_data(names, n_samples=50):\n", - " data = []\n", - "\n", - " for _ in range(n_samples):\n", - " if len(names) < 7:\n", - " raise ValueError(\"Not enough names in the dataset to generate a depth-2 tree (needs 7 unique names).\")\n", - "\n", - " family_names = random.sample(names, 7)\n", - "\n", - " child = family_names[0]\n", - " p1, p2 = family_names[1], family_names[2]\n", - " gp1, gp2 = family_names[3], family_names[4]\n", - " gp3, gp4 = family_names[5], family_names[6]\n", - "\n", - " base_text = (\n", - " f\"{child}'s parents are {p1} and {p2}. \"\n", - " f\"{p1}'s parents are {gp1} and {gp2}. \"\n", - " f\"{p2}'s parents are {gp3} and {gp4}. \"\n", - " f\"Therefore, the family's youngest member is {child}.\"\n", - " )\n", - "\n", - " for i in range(7):\n", - " target_name = family_names[i]\n", - "\n", - " text = base_text + f\" The family member is {target_name}.\"\n", - "\n", - " if i == 0:\n", - " dist = 0\n", - " elif 1 <= i <= 2:\n", - " dist = 1\n", - " elif 3 <= i <= 6:\n", - " dist = 2\n", - "\n", - " entry = {\"text\": text, \"names\": family_names, \"target_map\": {target_name: dist}}\n", - " data.append(entry)\n", - "\n", - " return pd.DataFrame(data)" - ] - }, - { - "cell_type": "markdown", - "id": "bc90ed5b", - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "source": [ - "## Models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab06847b", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import numpy as np\n", - "import torch\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer\n", - "\n", - "from smds.demos.family_tree.data_generation import generate_family_tree_data, load_names\n", - "from smds.shapes.discrete_shapes.hierarchical import HierarchicalShape\n", - "from smds.pipeline.discovery_pipeline import discover_manifolds, DEFAULT_SHAPES\n", - "\n", - "random.seed(42)\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "\n", - "def find_last_token_idx(tokenizer, text, target):\n", - " encoding = tokenizer(text, return_offsets_mapping=True, add_special_tokens=True)\n", - " offset_mapping = encoding[\"offset_mapping\"]\n", - "\n", - " target_start = text.rfind(target)\n", - " if target_start == -1:\n", - " return -1\n", - " target_end = target_start + len(target)\n", - "\n", - " matched_idx = -1\n", - " for idx, (tok_start, tok_end) in enumerate(offset_mapping):\n", - " if tok_start == tok_end == 0:\n", - " continue\n", - "\n", - " if not (tok_end <= target_start or tok_start >= target_end):\n", - " matched_idx = idx\n", - " pass\n", - "\n", - " return matched_idx\n", - "\n", - "\n", - "def get_activations_all_layers(df, model, tokenizer):\n", - " model.eval()\n", - "\n", - " layer_data = {}\n", - "\n", - " print(\"Recording activations for all layers...\")\n", - " for _, row in tqdm(df.iterrows(), total=len(df)):\n", - " text = row[\"text\"]\n", - " target_map = row[\"target_map\"]\n", - "\n", - " input_ids = tokenizer(text, return_tensors=\"pt\").to(model.device)\n", - "\n", - " with torch.no_grad():\n", - " outputs = model(**input_ids, output_hidden_states=True)\n", - "\n", - " for layer_idx, hidden_state_tensor in enumerate(outputs.hidden_states):\n", - " if layer_idx not in layer_data:\n", - " layer_data[layer_idx] = {\"activations\": [], \"distances\": []}\n", - "\n", - " hidden_state = hidden_state_tensor.squeeze(0).cpu().numpy()\n", - "\n", - " for name, dist in target_map.items():\n", - " idx = find_last_token_idx(tokenizer, text, name)\n", - " if idx != -1 and idx < len(hidden_state):\n", - " vect = hidden_state[idx]\n", - " layer_data[layer_idx][\"activations\"].append(vect)\n", - " layer_data[layer_idx][\"distances\"].append(dist)\n", - "\n", - " final_data = {}\n", - " for layer_idx, data in layer_data.items():\n", - " if len(data[\"activations\"]) > 0:\n", - " final_data[layer_idx] = (\n", - " np.array(data[\"activations\"]),\n", - " np.array(data[\"distances\"])\n", - " )\n", - "\n", - " return final_data\n", - "\n", - "\n", - "def main():\n", - " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - " model_name = \"gpt2\"\n", - " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", - " model = AutoModelForCausalLM.from_pretrained(model_name).to(device)\n", - "\n", - " all_names = load_names()\n", - " df = generate_family_tree_data(all_names, n_samples=50)\n", - "\n", - " all_layers_data = get_activations_all_layers(df, model, tokenizer)\n", - "\n", - " hierarchical_shape = HierarchicalShape(level_distances=[5.0, 1.0])\n", - "\n", - " summary_results = []\n", - "\n", - " for layer_idx in sorted(all_layers_data.keys()):\n", - " print(f\"Analyzing Layer {layer_idx}\")\n", - " X, y = all_layers_data[layer_idx]\n", - " y = y.astype(np.float64)\n", - "\n", - " y_hier = np.zeros((len(y), 2), dtype=np.float64)\n", - " y_hier[:, 1] = y\n", - "\n", - " res_hier, _ = discover_manifolds(\n", - " X,\n", - " y_hier,\n", - " shapes=[hierarchical_shape],\n", - " experiment_name=f\"family_tree_layer_{layer_idx}_hier\",\n", - " model_name=\"gpt2\",\n", - " n_folds=5,\n", - " n_jobs=-1,\n", - " save_results=False,\n", - " create_visualization=False,\n", - " )\n", - "\n", - " if not res_hier.empty:\n", - " row = res_hier.iloc[0]\n", - " summary_results.append({\n", - " \"layer\": layer_idx,\n", - " \"shape\": \"HierarchicalShape\",\n", - " \"stress\": row[\"mean_scale_normalized_stress\"]\n", - " })\n", - "\n", - " res_shapes, _ = discover_manifolds(\n", - " X,\n", - " y,\n", - " shapes=DEFAULT_SHAPES,\n", - " experiment_name=f\"family_tree_layer_{layer_idx}_shapes\",\n", - " model_name=\"gpt2\",\n", - " n_folds=5,\n", - " n_jobs=-1,\n", - " save_results=False,\n", - " create_visualization=False,\n", - " )\n", - "\n", - " if not res_shapes.empty:\n", - " for _, row in res_shapes.iterrows():\n", - " summary_results.append({\n", - " \"layer\": layer_idx,\n", - " \"shape\": row[\"shape\"],\n", - " \"stress\": row[\"mean_scale_normalized_stress\"]\n", - " })\n", - "\n", - " summary_df = pd.DataFrame(summary_results)\n", - " print(summary_df)\n", - "\n", - " plt.figure(figsize=(12, 8))\n", - " for shape_name in summary_df[\"shape\"].unique():\n", - " subset = summary_df[summary_df[\"shape\"] == shape_name]\n", - " subset = subset.sort_values(\"layer\")\n", - " plt.plot(subset[\"layer\"], subset[\"stress\"], marker='o', label=shape_name)\n", - "\n", - " plt.xlabel(\"Layer\")\n", - " plt.ylabel(\"Stress\")\n", - " plt.title(\"Stress vs Layer by Shape\")\n", - " plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n", - " plt.grid(True)\n", - " plt.tight_layout()\n", - " plt.savefig(\"stress_vs_layer.png\")\n", - " print(\"Plot saved to stress_vs_layer.png\")\n", - "\n", - "\n", - "main()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c365e1f3", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "\n", - "import numpy as np\n", - "import torch\n", - "from tqdm import tqdm\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n", - "\n", - "from smds.demos.family_tree.data_generation import generate_family_tree_data, load_names\n", - "\n", - "random.seed(42)\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "\n", - "def find_last_token_idx(tokenizer, text, target):\n", - " encoding = tokenizer(text, return_offsets_mapping=True, add_special_tokens=True)\n", - " offset_mapping = encoding[\"offset_mapping\"]\n", - "\n", - " target_start = text.rfind(target)\n", - " if target_start == -1:\n", - " return -1\n", - " target_end = target_start + len(target)\n", - "\n", - " matched_idx = -1\n", - " for idx, (tok_start, tok_end) in enumerate(offset_mapping):\n", - " if tok_start == tok_end == 0:\n", - " continue\n", - "\n", - " if not (tok_end <= target_start or tok_start >= target_end):\n", - " matched_idx = idx\n", - " pass\n", - "\n", - " return matched_idx\n", - "\n", - "\n", - "def get_activations(df, model, tokenizer, layer=-1):\n", - " model.eval()\n", - " activations = []\n", - " distances = []\n", - "\n", - " print(\"Recording activations...\")\n", - " for _, row in tqdm(df.iterrows(), total=len(df)):\n", - " text = row[\"text\"]\n", - " target_map = row[\"target_map\"]\n", - "\n", - " input_ids = tokenizer(text, return_tensors=\"pt\").to(model.device)\n", - "\n", - " with torch.no_grad():\n", - " outputs = model(**input_ids, output_hidden_states=True)\n", - "\n", - " hidden_state = outputs.hidden_states[layer].squeeze(0).cpu().numpy()\n", - "\n", - " for name, dist in target_map.items():\n", - " idx = find_last_token_idx(tokenizer, text, name)\n", - " if idx != -1 and idx < len(hidden_state):\n", - " vect = hidden_state[idx]\n", - " activations.append(vect)\n", - " distances.append(dist)\n", - " else:\n", - " pass\n", - "\n", - " return np.array(activations), np.array(distances)\n", - "\n", - "\n", - "def main():\n", - " print(\"Setting up experiment...\")\n", - " bnb_config = BitsAndBytesConfig(\n", - " load_in_4bit=True,\n", - " bnb_4bit_compute_dtype=torch.float16,\n", - " bnb_4bit_use_double_quant=True,\n", - " )\n", - "\n", - " model_name = \"meta-llama/Llama-3.1-8B-Instruct\"\n", - " try:\n", - " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", - " model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config, device_map=\"auto\")\n", - " except Exception as e:\n", - " print(f\"Error loading model {model_name}: {e}\")\n", - " raise e\n", - "\n", - " print(\"Generating data...\")\n", - " all_names = load_names()\n", - " df = generate_family_tree_data(all_names, n_samples=50)\n", - " print(f\"Generated {len(df)} family trees.\")\n", - " print(f\"Sample text: {df.iloc[0]['text']}\")\n", - "\n", - " X, y = get_activations(df, model, tokenizer, layer=-1)\n", - "\n", - " print(f\"Collected data shape: X={X.shape}, y={y.shape}\")\n", - "\n", - " print(\"\\nRunning Manifold Discovery Pipeline...\")\n", - "\n", - " from smds.pipeline.discovery_pipeline import discover_manifolds\n", - "\n", - " y = y.astype(np.float64)\n", - " X = X.astype(np.float64)\n", - "\n", - " results_df, save_path = discover_manifolds(\n", - " X,\n", - " y,\n", - " experiment_name=\"family_tree_experiment\",\n", - " model_name=\"llama\",\n", - " n_folds=5,\n", - " n_jobs=-1,\n", - " save_results=True,\n", - " create_visualization=True,\n", - " )\n", - "\n", - " print(\"\\nPipeline Results:\")\n", - " print(results_df[[\"shape\", \"mean_scale_normalized_stress\", \"std_scale_normalized_stress\", \"error\"]])\n", - "\n", - " if not results_df.empty:\n", - " winner = results_df.iloc[0]\n", - " print(f\"\\nWinner: {winner['shape']} (Mean Score: {winner['mean_scale_normalized_stress']:.4f})\")\n", - "\n", - " print(\"\\nRunning Hierarchical Analysis...\")\n", - " from smds.shapes.discrete_shapes.hierarchical import HierarchicalShape\n", - "\n", - " y_hier = np.zeros((len(y), 2), dtype=np.float64)\n", - " y_hier[:, 1] = y\n", - "\n", - " hierarchical_shape = HierarchicalShape(level_distances=[5.0, 1.0])\n", - "\n", - " results_hier, _ = discover_manifolds(\n", - " X,\n", - " y_hier,\n", - " shapes=[hierarchical_shape],\n", - " save_path=save_path, # Append to existing results\n", - " n_folds=5,\n", - " n_jobs=-1,\n", - " save_results=True,\n", - " create_visualization=True,\n", - " )\n", - "\n", - " print(\"\\nHierarchical Results:\")\n", - " print(results_hier[[\"shape\", \"mean_scale_normalized_stress\", \"std_scale_normalized_stress\", \"error\"]])\n", - "\n", - "\n", - "main()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb247937", - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "\n", - "import numpy as np\n", - "import torch\n", - "from tqdm import tqdm\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n", - "\n", - "from smds.demos.family_tree.data_generation import generate_family_tree_data, load_names\n", - "\n", - "random.seed(42)\n", - "np.random.seed(42)\n", - "torch.manual_seed(42)\n", - "\n", - "\n", - "def find_last_token_idx(tokenizer, text, target):\n", - " encoding = tokenizer(text, return_offsets_mapping=True, add_special_tokens=True)\n", - " offset_mapping = encoding[\"offset_mapping\"]\n", - "\n", - " target_start = text.rfind(target)\n", - " if target_start == -1:\n", - " return -1\n", - " target_end = target_start + len(target)\n", - "\n", - " matched_idx = -1\n", - " for idx, (tok_start, tok_end) in enumerate(offset_mapping):\n", - " if tok_start == tok_end == 0:\n", - " continue\n", - "\n", - " if not (tok_end <= target_start or tok_start >= target_end):\n", - " matched_idx = idx\n", - " pass\n", - "\n", - " return matched_idx\n", - "\n", - "\n", - "def get_activations(df, model, tokenizer, layer=-1):\n", - " model.eval()\n", - " activations = []\n", - " distances = []\n", - "\n", - " print(\"Recording activations...\")\n", - " for _, row in tqdm(df.iterrows(), total=len(df)):\n", - " text = row[\"text\"]\n", - " target_map = row[\"target_map\"]\n", - "\n", - " input_ids = tokenizer(text, return_tensors=\"pt\").to(model.device)\n", - "\n", - " with torch.no_grad():\n", - " outputs = model(**input_ids, output_hidden_states=True)\n", - "\n", - " hidden_state = outputs.hidden_states[layer].squeeze(0).cpu().numpy()\n", - "\n", - " for name, dist in target_map.items():\n", - " idx = find_last_token_idx(tokenizer, text, name)\n", - " if idx != -1 and idx < len(hidden_state):\n", - " vect = hidden_state[idx]\n", - " activations.append(vect)\n", - " distances.append(dist)\n", - " else:\n", - " pass\n", - "\n", - " return np.array(activations), np.array(distances)\n", - "\n", - "\n", - "def main():\n", - " print(\"Setting up experiment...\")\n", - " bnb_config = BitsAndBytesConfig(\n", - " load_in_4bit=True,\n", - " bnb_4bit_compute_dtype=torch.float16,\n", - " bnb_4bit_use_double_quant=True,\n", - " )\n", - "\n", - " model_name = \"Qwen/Qwen2.5-7B-Instruct\"\n", - " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", - "\n", - " model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config, device_map=\"auto\")\n", - "\n", - " print(\"Generating data...\")\n", - " all_names = load_names()\n", - " df = generate_family_tree_data(all_names, n_samples=50)\n", - " print(f\"Generated {len(df)} family trees.\")\n", - " print(f\"Sample text: {df.iloc[0]['text']}\")\n", - "\n", - " X, y = get_activations(df, model, tokenizer, layer=-1)\n", - "\n", - " print(f\"Collected data shape: X={X.shape}, y={y.shape}\")\n", - "\n", - " print(\"\\nRunning Manifold Discovery Pipeline...\")\n", - "\n", - " from smds.pipeline.discovery_pipeline import discover_manifolds\n", - "\n", - " y = y.astype(np.float64)\n", - " X = X.astype(np.float64)\n", - "\n", - " results_df, save_path = discover_manifolds(\n", - " X,\n", - " y,\n", - " experiment_name=\"family_tree_experiment\",\n", - " model_name=\"qwen\",\n", - " n_folds=5,\n", - " n_jobs=-1,\n", - " save_results=True,\n", - " create_visualization=True,\n", - " )\n", - "\n", - " print(\"\\nPipeline Results:\")\n", - " print(results_df[[\"shape\", \"mean_scale_normalized_stress\", \"std_scale_normalized_stress\", \"error\"]])\n", - "\n", - " if not results_df.empty:\n", - " winner = results_df.iloc[0]\n", - " print(f\"\\nWinner: {winner['shape']} (Mean Score: {winner['mean_scale_normalized_stress']:.4f})\")\n", - "\n", - " print(\"\\nRunning Hierarchical Analysis...\")\n", - " from smds.shapes.discrete_shapes.hierarchical import HierarchicalShape\n", - "\n", - " y_hier = np.zeros((len(y), 2), dtype=np.float64)\n", - " y_hier[:, 1] = y\n", - "\n", - " hierarchical_shape = HierarchicalShape(level_distances=[5.0, 1.0])\n", - "\n", - " results_hier, _ = discover_manifolds(\n", - " X,\n", - " y_hier,\n", - " shapes=[hierarchical_shape],\n", - " save_path=save_path, # Append to existing results\n", - " n_folds=5,\n", - " n_jobs=-1,\n", - " save_results=True,\n", - " create_visualization=True,\n", - " )\n", - "\n", - " print(\"\\nHierarchical Results:\")\n", - " print(results_hier[[\"shape\", \"mean_scale_normalized_stress\", \"std_scale_normalized_stress\", \"error\"]])\n", - "\n", - "\n", - "\n", - "main()\n" - ] - }, - { - "cell_type": "markdown", - "id": "175186d3", - "metadata": {}, - "source": [ - "### Conclusion\n", - "- **GPT-2:** 0.694 (Circular) vs. 0.749 (Hierarchical)\n", - "- **Llama:** 0.841 (Circular) vs. 0.878 (Hierarchical)\n", - "- **Qwen:** 0.787 (Circular) vs. 0.834 (Hierarchical)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "supervised-multidimensional-scaling (3.12.3)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/world_cities_continents.csv b/docs/notebooks/world_cities_continents.csv new file mode 100644 index 0000000..dc50d1d --- /dev/null +++ b/docs/notebooks/world_cities_continents.csv @@ -0,0 +1,14777 @@ +city,country,continent,lat,lon,population +A Coruña,Spain,Europe,43.365,-8.41,249261 +A Yun Pa,Vietnam,Asia,13.3939,108.4408,53720 +Aabenraa,Denmark,Europe,55.0362,9.418,15685 +Aachen,Germany,Europe,50.7756,6.0836,252769 +Aalborg,Denmark,Europe,57.0337,9.9166,143598 +Aalen,Germany,Europe,48.8333,10.1,69147 +Aaley,Lebanon,Asia,33.8,35.6,35000 +Aalsmeer,Netherlands,Europe,52.2667,4.75,31991 +Aalst,Belgium,Europe,50.9383,4.0392,88854 +Aalten,Netherlands,Europe,51.925,6.5808,27120 +Aarau,Switzerland,Europe,47.3923,8.0446,21503 +Aarhus,Denmark,Europe,56.1572,10.2107,290598 +Aars,Denmark,Europe,56.8038,9.5158,8657 +Aasiaat,Greenland,North America,68.7097,-52.8694,3069 +Aba,Nigeria,Africa,5.1167,7.3667,1160000 +Abadiânia,Brazil,South America,-16.2039,-48.7069,11452 +Abaetetuba,Brazil,South America,-1.7178,-48.8828,158188 +Abaeté,Brazil,South America,-19.1583,-45.4522,22360 +Abaiara,Brazil,South America,-7.3589,-39.0458,10038 +Abaji,Nigeria,Africa,8.4756,6.9433,127900 +Abakan,Russia,Asia,53.7167,91.4667,181709 +Abancay,Peru,South America,-13.6333,-72.8833,72277 +Abarkūh,Iran,Asia,31.1256,53.2625,27524 +Abaré,Brazil,South America,-8.7208,-39.115,17639 +Abasolo,Mexico,North America,20.45,-101.517,29002 +Abay,Kazakhstan,Asia,49.6311,72.8539,25550 +Abbeville,France,Europe,50.1058,1.8358,22406 +Abbotsford,Canada,North America,49.05,-122.3167,153524 +Abbottabad,Pakistan,Asia,34.1558,73.2194,148587 +Abdulino,Russia,Asia,53.6667,53.6333,17274 +Abdurahmoni Jomí,Tajikistan,Asia,37.9458,68.8117,18800 +Abejorral,Colombia,South America,5.7894,-75.4281,20287 +Abelardo Luz,Brazil,South America,-26.565,-52.3278,17392 +Abengourou,Côte d’Ivoire,Africa,6.7297,-3.4964,164424 +Abeokuta,Nigeria,Africa,7.1608,3.3483,888924 +Abhar,Iran,Asia,36.1464,49.2225,99285 +Abhā,Saudi Arabia,Asia,18.2167,42.5,236157 +Abidjan,Côte d’Ivoire,Africa,5.3364,-4.0267,4980000 +Abilene,United States,North America,32.4543,-99.7384,127130 +Abim,Uganda,Africa,2.7019,33.66,24400 +Abinsk,Russia,Asia,44.8667,38.15,14378 +Aboisso,Côte d’Ivoire,Africa,5.4667,-3.2,100903 +Abomey,Benin,Africa,7.1856,1.9881,90195 +Abra Pampa,Argentina,South America,-22.7167,-65.7,9425 +Abrantes,Portugal,Europe,39.4667,-8.2,39325 +Abre Campo,Brazil,South America,-20.3008,-42.4778,13927 +Abreu e Lima,Brazil,South America,-7.9117,-34.9028,98462 +Abu Dhabi,United Arab Emirates,Asia,24.4667,54.3667,1483000 +Abuja,Nigeria,Africa,9.0667,7.4833,3770000 +Abéché,Chad,Africa,13.8331,20.8347,76492 +Abī al Khaşīb,Iraq,Asia,30.4464,47.9781,240300 +Abū Ghurayb,Iraq,Asia,33.3056,44.1848,189000 +Acacías,Colombia,South America,3.9878,-73.7597,75252 +Acajutiba,Brazil,South America,-11.6619,-38.0169,13795 +Acala,Mexico,North America,16.5533,-92.8069,15001 +Acambay,Mexico,North America,19.9539,-99.8431,56847 +Acanceh,Mexico,North America,20.8133,-89.4524,10968 +Acandí,Colombia,South America,8.5333,-77.2333,12095 +Acapetahua,Mexico,North America,15.2333,-92.7667,26899 +Acapulco de Juárez,Mexico,North America,16.8636,-99.8825,673478 +Acarape,Brazil,South America,-4.2242,-38.7083,16288 +Acaraú,Brazil,South America,-2.8858,-40.12,48968 +Acari,Brazil,South America,-6.4358,-36.6389,10597 +Acarigua,Venezuela,South America,9.5597,-69.2019,203358 +Acará,Brazil,South America,-1.9608,-48.1969,52126 +Acatenango,Guatemala,North America,14.5544,-90.9439,28780 +Acatic,Mexico,North America,20.7803,-102.91,21530 +Acatlán,Mexico,North America,20.2333,-103.5333,25250 +Acatzingo,Mexico,North America,18.9817,-97.7822,52078 +Acaxochitlán,Mexico,North America,20.1667,-98.2,46065 +Acayucan,Mexico,North America,17.9422,-94.9103,83817 +Accra,Ghana,Africa,5.5461,-0.2067,491817 +Acevedo,Colombia,South America,1.8192,-75.8964,39877 +Achaguas,Venezuela,South America,7.7667,-68.2333,38523 +Acharnés,Greece,Europe,38.0833,23.7333,108169 +Achinsk,Russia,Asia,56.2817,90.5039,99665 +Achuapa,Nicaragua,North America,13.05,-86.5833,13797 +Achí,Colombia,South America,8.5667,-74.55,23051 +Acopiara,Brazil,South America,-6.095,-39.4528,44962 +Acoyapa,Nicaragua,North America,11.9667,-85.1667,16946 +Acquaviva,San Marino,Europe,43.9457,12.4185,2145 +Actopan,Mexico,North America,19.5036,-96.6192,43388 +Acámbaro,Mexico,North America,20.0361,-100.7314,56597 +Acıpayam,Turkey,Europe,37.425,29.3539,54888 +Ad Dammām,Saudi Arabia,Asia,26.4333,50.1,903312 +Ad Darbāsīyah,Syria,Asia,37.0728,40.652,8551 +Ad Duraykīsh,Syria,Asia,34.8951,36.143,13244 +Ad Dīwānīyah,Iraq,Asia,31.9892,44.9247,391600 +Ada,Serbia,Europe,45.8,20.1333,10547 +Adalar,Turkey,Europe,40.8578,29.12,8586 +Adamantina,Brazil,South America,-21.6847,-51.0733,35111 +Adamstown,Pitcairn Islands,Oceania,-25.0667,-130.0833,47 +Adana,Turkey,Europe,37.0,35.3213,1765981 +Addis Ababa,Ethiopia,Africa,9.03,38.74,5704000 +Adelaide,Australia,Oceania,-34.9275,138.6,1387290 +Aden,Yemen,Asia,12.8,45.0333,863000 +Adilcevaz,Turkey,Europe,38.8058,42.7469,15059 +Adjumani,Uganda,Africa,3.3772,31.7906,37100 +Ado-Ekiti,Nigeria,Africa,7.6211,5.2214,446749 +Adolfo Gonzáles Chaves,Argentina,South America,-38.0333,-60.0833,12914 +Adrar,Algeria,Africa,27.8667,-0.2833,68276 +Aduku,Uganda,Africa,2.0194,32.72,10700 +Adustina,Brazil,South America,-10.5328,-38.1,14201 +Adygeysk,Russia,Asia,44.8832,39.1902,13224 +Adzopé,Côte d’Ivoire,Africa,6.1035,-3.8648,156488 +Adıyaman,Turkey,Europe,37.7639,38.2778,267131 +Afak,Iraq,Asia,32.0889,45.2506,75000 +Afega,Samoa,Oceania,-13.8019,-171.8217,1998 +Afgooye,Somalia,Africa,2.1381,45.1212,79400 +Afogados da Ingazeira,Brazil,South America,-7.7508,-37.6342,40241 +Afonso Bezerra,Brazil,South America,-5.4978,-36.5058,10839 +Afonso Cláudio,Brazil,South America,-20.0739,-41.1239,30455 +Afrânio,Brazil,South America,-8.5,-41.0,18674 +Afuá,Brazil,South America,-0.1569,-50.3869,37765 +Afyonkarahisar,Turkey,Europe,38.7578,30.5386,251799 +Afşin,Turkey,Europe,38.2464,36.9186,80044 +Agadez,Niger,Africa,16.9959,7.9828,117770 +Agadir,Morocco,Africa,30.4214,-9.5831,538000 +Agartala,India,Asia,23.8314,91.2869,522613 +Agboville,Côte d’Ivoire,Africa,5.9333,-4.2167,135082 +Agdangan,Philippines,Asia,13.8758,121.9122,12764 +Agege,Nigeria,Africa,6.6219,3.3258,683600 +Agen,France,Europe,44.2049,0.6212,32193 +Agidel,Russia,Asia,55.9,53.9333,14219 +Aginskoye,Russia,Asia,51.1031,114.5228,11491 +Agrestina,Brazil,South America,-8.45,-35.95,23779 +Agrigento,Italy,Europe,37.3107,13.5766,55512 +Agrínio,Greece,Europe,38.6167,21.4,50690 +Agua Blanca,Guatemala,North America,14.4833,-89.6494,17765 +Agua Prieta,Mexico,North America,31.3258,-109.5489,91029 +Agua de Dios,Colombia,South America,4.3781,-74.6739,13182 +Aguacatán,Guatemala,North America,15.35,-91.3167,59638 +Aguachica,Colombia,South America,8.3167,-73.6333,109621 +Aguada de Pasajeros,Cuba,North America,22.3847,-80.8461,31279 +Aguadas,Colombia,South America,5.6167,-75.4667,21237 +Aguadulce,Panama,North America,8.2415,-80.5398,51668 +Aguasay,Venezuela,South America,9.4249,-63.7308,11856 +Aguascalientes,Mexico,North America,21.876,-102.296,934424 +Aguazul,Colombia,South America,5.1731,-72.5547,43183 +Aguaí,Brazil,South America,-22.0603,-46.9736,34863 +Agudo,Brazil,South America,-29.645,-53.24,16041 +Agudos,Brazil,South America,-22.4739,-48.9836,37401 +Agudos do Sul,Brazil,South America,-25.9928,-49.335,10233 +Aguilares,Argentina,South America,-27.4333,-65.6167,32097 +Agustín Codazzi,Colombia,South America,10.0367,-73.2369,49233 +Agía Paraskeví,Greece,Europe,38.0117,23.82,62147 +Agía Varvára,Greece,Europe,38.0,23.65,26759 +Ahar,Iran,Asia,38.4797,47.0669,100641 +Aheqi,China,Asia,40.9365,78.4023,11910 +Ahlat,Turkey,Europe,38.7528,42.4944,27563 +Ahmedabad,India,Asia,23.0225,72.5714,8009000 +Ahmetli,Turkey,Europe,38.5183,27.9386,16963 +Ahram,Iran,Asia,28.8828,51.2756,15198 +Ahuachapán,El Salvador,North America,13.9167,-89.85,135325 +Ahualulco de Mercado,Mexico,North America,20.6167,-103.8667,23630 +Ahumada,Mexico,North America,30.6186,-106.5122,8575 +Ahvāz,Iran,Asia,31.3047,48.6783,1325000 +Aichach,Germany,Europe,48.45,11.1333,22222 +Aigáleo,Greece,Europe,37.992,23.678,65831 +Aihua,China,Asia,24.4167,100.1,115047 +Ailan Mubage,China,Asia,43.908,81.2777,778047 +Aimorés,Brazil,South America,-19.4958,-41.0639,25141 +Ainaro,Timor-Leste,Asia,-8.9969,125.505,3085 +Aioun,Mauritania,Africa,16.6614,-9.615,24199 +Aipe,Colombia,South America,3.2167,-75.2333,29177 +Airmadidi,Indonesia,Asia,1.4333,124.9833,31446 +Aiuaba,Brazil,South America,-6.5739,-40.1239,14076 +Aix-en-Provence,France,Europe,43.5263,5.4454,147933 +Aizkraukle,Latvia,Europe,56.6008,25.255,6853 +Ajaccio,France,Europe,41.9267,8.7369,75343 +Ajax,Canada,North America,43.8583,-79.0364,126666 +Ajdovščina,Slovenia,Europe,45.8861,13.9097,6843 +Ajdābiyā,Libya,Africa,30.77,20.22,416000 +Ajka,Hungary,Europe,47.1006,17.5522,26408 +Akat Amnuai,Thailand,Asia,17.5964,103.9816,9013 +Akdağmadeni,Turkey,Europe,39.6658,35.8836,22180 +Aketao,China,Asia,39.1349,75.9337,36270 +Akhaltsikhe,Georgia,Europe,41.6389,42.9861,10153 +Akhisar,Turkey,Europe,38.9239,27.84,177419 +Akhtubinsk,Russia,Asia,48.2833,46.1667,35635 +Akhty,Russia,Asia,41.4647,47.74,15285 +Akita,Japan,Asia,39.72,140.1026,302984 +Akjoujt,Mauritania,Africa,19.747,-14.391,11235 +Akkuş,Turkey,Europe,40.7931,37.0164,21258 +Akron,United States,North America,41.0798,-81.5219,538659 +Aksaray,Turkey,Europe,38.3742,34.0289,247147 +Aksay,Russia,Asia,47.25,39.8667,48372 +Akseki,Turkey,Europe,37.0486,31.79,10477 +Aksu,China,Asia,41.185,80.2904,535657 +Aktepe,Turkey,Europe,36.7,36.4833,8736 +Akure,Nigeria,Africa,7.25,5.195,847903 +Akyazı,Turkey,Europe,40.6833,30.6253,101782 +Akyurt,Turkey,Europe,40.1333,33.0833,40625 +Akçaabat,Turkey,Europe,41.0167,39.55,129290 +Akçadağ,Turkey,Europe,38.345,37.9686,27872 +Akçakale,Turkey,Europe,36.7108,38.9478,123721 +Akçakoca,Turkey,Europe,41.0878,31.1244,27878 +Akşehir,Turkey,Europe,38.3575,31.4164,93965 +Al Atārib,Syria,Asia,36.1391,36.825,10657 +Al Bayḑā’,Libya,Africa,32.7628,21.755,250000 +Al Başrah,Iraq,Asia,30.515,47.81,1326564 +Al Buraymī,Oman,Asia,24.2558,55.8025,67963 +Al Bāḩah,Saudi Arabia,Asia,20.0129,41.4677,21983 +Al Fallūjah,Iraq,Asia,33.35,43.7833,250884 +Al Fayyūm,Egypt,Africa,29.3,30.8333,519047 +Al Fujayrah,United Arab Emirates,Asia,25.1222,56.3344,93673 +Al Fuḩayş,Jordan,Asia,32.0167,35.7667,21908 +Al Fāw,Iraq,Asia,29.98,48.47,105080 +Al Ghardaqah,Egypt,Africa,27.2578,33.8117,214247 +Al Ghayz̧ah,Yemen,Asia,16.2106,52.1711,10948 +Al Ghizlānīyah,Syria,Asia,33.3967,36.4549,38473 +Al Hindīyah,Iraq,Asia,32.5467,44.2277,84100 +Al Hāshimīyah,Iraq,Asia,32.3744,44.6569,37300 +Al Jabāyish,Iraq,Asia,30.9549,46.9751,36100 +Al Jawf,Libya,Africa,24.2167,23.3,18587 +Al Juwayyidah,Jordan,Asia,31.9167,35.95,176400 +Al Jīzah,Jordan,Asia,31.6833,35.95,110097 +Al Karak,Jordan,Asia,31.1833,35.7,21678 +Al Khawr,Qatar,Asia,25.69,51.51,61877 +Al Khārjah,Egypt,Africa,25.44,30.55,49991 +Al Kiswah,Syria,Asia,33.3581,36.2419,43456 +Al Kūfah,Iraq,Asia,32.03,44.4,171305 +Al Mafraq,Jordan,Asia,32.3399,36.2052,580000 +Al Manşūrah,Egypt,Africa,31.05,31.3833,621953 +Al Marj,Libya,Africa,32.5005,20.83,169540 +Al Mayādīn,Syria,Asia,35.0198,40.4515,48922 +Al Mazyūnah,Oman,Asia,17.8486,52.62,10000 +Al Mazār ash Shamālī,Jordan,Asia,32.4725,35.7928,16000 +Al Maḩwīt,Yemen,Asia,15.4694,43.5453,16291 +Al Ma‘allā’,Yemen,Asia,12.7897,45.0064,49891 +Al Minyā,Egypt,Africa,28.0833,30.75,236043 +Al Miqdādīyah,Iraq,Asia,33.9786,44.9369,155968 +Al Misrākh,Yemen,Asia,13.47,44.0375,112653 +Al Mukallā,Yemen,Asia,14.5333,49.1333,594951 +Al Musayfirah,Syria,Asia,32.6326,36.3398,10466 +Al Musayyib,Iraq,Asia,32.7786,44.29,76454 +Al Muzayrīb,Syria,Asia,32.7108,36.0275,12640 +Al Mālikīyah,Syria,Asia,37.177,42.1401,39000 +Al Qardāḩah,Syria,Asia,35.4564,36.0591,8671 +Al Qaryatayn,Syria,Asia,34.2296,37.2407,37820 +Al Qunayţirah,Syria,Asia,33.1257,35.8236,153 +Al Qurayyāt,Saudi Arabia,Asia,31.3167,37.3667,167080 +Al Qurnah,Iraq,Asia,31.0155,47.4336,286073 +Al Quşayr,Syria,Asia,34.5078,36.5803,46772 +Al Quţayfah,Syria,Asia,33.7385,36.6007,26671 +Al Qāmishlī,Syria,Asia,37.03,41.23,184231 +Al Qā’im,Iraq,Asia,34.3925,40.9895,74100 +Al Wajh,Saudi Arabia,Asia,26.2324,36.4636,50000 +Al Wakrah,Qatar,Asia,25.18,51.61,87970 +Al Ḩajar al Aswad,Syria,Asia,33.4576,36.3042,101827 +Al Ḩamdānīyah,Iraq,Asia,36.2698,43.3754,35000 +Al Ḩamzah,Iraq,Asia,31.7244,44.9769,260000 +Al Ḩasakah,Syria,Asia,36.4833,40.75,188160 +Al Ḩayy,Iraq,Asia,32.1742,46.0433,82900 +Al Ḩazm,Yemen,Asia,16.1642,44.7767,16044 +Al Ḩillah,Iraq,Asia,32.4833,44.4333,541034 +Al Ḩişn,Jordan,Asia,32.4583,35.8583,35085 +Al Ḩudaydah,Yemen,Asia,14.8022,42.9511,404062 +Al ‘Amādīyah,Iraq,Asia,37.0919,43.487,11000 +Al ‘Amārah,Iraq,Asia,31.8333,47.15,511500 +Al ‘Aqabah,Jordan,Asia,29.5322,35.0061,140000 +Al ‘Arīsh,Egypt,Africa,31.1249,33.8006,178651 +Al ‘Ashārah,Syria,Asia,34.9213,40.5601,17537 +Al ‘Azīzīyah,Iraq,Asia,32.9094,45.0636,44868 +Ala-Buka,Kyrgyzstan,Asia,41.4083,71.4806,15426 +Alabel,Philippines,Asia,6.1023,125.2868,88294 +Alaca,Turkey,Europe,40.1683,34.8425,19510 +Aladağ,Turkey,Europe,37.5464,35.3986,15897 +Alagir,Russia,Asia,43.0333,44.2333,21315 +Alagoa Grande,Brazil,South America,-7.0822,-35.6,26062 +Alagoa Nova,Brazil,South America,-7.0708,-35.7583,20921 +Alagoinha,Brazil,South America,-6.95,-35.545,13725 +Alagoinhas,Brazil,South America,-12.1356,-38.4192,152327 +Alajuela,Costa Rica,North America,10.164,-84.2645,44374 +Alajärvi,Finland,Europe,63.0,23.8167,9113 +Alanya,Turkey,Europe,36.55,32.0,364180 +Alapayevsk,Russia,Asia,57.85,61.7,35972 +Alaplı,Turkey,Europe,41.1806,31.3861,20777 +Alashankou,China,Asia,45.1733,82.5546,11097 +Alausí,Ecuador,South America,-2.19,-78.85,6071 +Alavus,Finland,Europe,62.5917,23.6167,10933 +Alaçam,Turkey,Europe,41.61,35.595,24647 +Alaçatı,Turkey,Europe,38.2814,26.3742,10386 +Alaşehir,Turkey,Europe,38.35,28.5167,104717 +Alba Iulia,Romania,Europe,46.0669,23.57,64227 +Albacete,Spain,Europe,38.9956,-1.8558,174137 +Albania,Colombia,South America,11.1597,-72.5856,19429 +Albany,United States,North America,42.6664,-73.7987,602242 +Albergaria-a-Velha,Portugal,Europe,40.6936,-8.4806,25252 +Albertville,France,Europe,45.6767,6.3933,19706 +Albi,France,Europe,43.9289,2.1464,50605 +Albina,Suriname,South America,5.5,-54.05,5247 +Alblasserdam,Netherlands,Europe,51.8667,4.6667,20136 +Albufeira,Portugal,Europe,37.0889,-8.2511,44158 +Albuquerque,United States,North America,35.1054,-106.6465,767499 +Alcalde Díaz,Panama,North America,9.1227,-79.5567,41292 +Alcalá,Colombia,South America,4.6667,-75.75,12716 +Alcanena,Portugal,Europe,39.4589,-8.6689,13868 +Alcantara,Philippines,Asia,9.9715,123.4047,16910 +Alcobaça,Portugal,Europe,39.5522,-8.9775,53649 +Alcochete,Portugal,Europe,38.7553,-8.9608,17569 +Alcácer do Sal,Portugal,Europe,38.3725,-8.5133,13046 +Alcântara,Brazil,South America,-2.4089,-44.415,18467 +Alcântaras,Brazil,South America,-3.5889,-40.5458,11369 +Aldama,Mexico,North America,28.8386,-105.9111,18642 +Aldan,Russia,Asia,58.6167,125.4167,21490 +Aldeias Altas,Brazil,South America,-4.6278,-43.4708,23286 +Alebtong,Uganda,Africa,2.25,33.315,7900 +Aleg,Mauritania,Africa,17.058,-13.909,15521 +Alegre,Brazil,South America,-20.7639,-41.5328,29177 +Alegrete,Brazil,South America,-29.7839,-55.7911,72409 +Aleksandrov,Russia,Asia,56.4,38.7333,57053 +Aleksandrovac,Serbia,Europe,43.4553,21.0514,26522 +Aleksandrovsk,Russia,Asia,59.1667,57.6,10502 +Aleksandrovsk-Sakhalinskiy,Russia,Asia,50.9,142.15,8769 +Aleksandrów Kujawski,Poland,Europe,52.8767,18.6936,11719 +Alekseyevka,Russia,Asia,50.6333,38.7,36578 +Aleksin,Russia,Asia,54.5167,37.1,60842 +Aleksinac,Serbia,Europe,43.5383,21.7047,43258 +Alenquer,Brazil,South America,-1.9419,-54.7378,69377 +Alençon,France,Europe,48.4306,0.0931,25667 +Aleppo,Syria,Asia,36.2319,37.1681,2003671 +Aleshtar,Iran,Asia,33.8653,48.2622,33558 +Alessandria,Italy,Europe,44.9133,8.62,91059 +Alexandria,Egypt,Africa,31.1975,29.8925,4870000 +Alexandroúpoli,Greece,Europe,40.85,25.8667,38939 +Alexándreia,Greece,Europe,40.6333,22.45,12718 +Alexânia,Brazil,South America,-16.0819,-48.5069,27008 +Alfenas,Brazil,South America,-21.4289,-45.9469,78970 +Alfredo Chaves,Brazil,South America,-20.635,-40.75,13616 +Alfredo Wagner,Brazil,South America,-27.7,-49.3339,10481 +Alga,Kazakhstan,Asia,49.9028,57.3333,19705 +Algarrobo,Colombia,South America,10.1,-74.2667,14294 +Algeciras,Colombia,South America,2.5333,-75.3167,24708 +Algiers,Algeria,Africa,36.7325,3.0872,2364230 +Alhandra,Brazil,South America,-7.3504,-34.9284,15914 +Ali Sabieh,Djibouti,Africa,11.15,42.7167,37939 +Aliança,Brazil,South America,-7.6028,-35.2308,35741 +Aliağa,Turkey,Europe,38.8008,26.9728,104828 +Alibunar,Serbia,Europe,45.0808,20.9658,3431 +Alicante,Spain,Europe,38.3453,-0.4831,358720 +Alijó,Portugal,Europe,41.2764,-7.4749,11942 +Alingsås,Sweden,Europe,57.9333,12.5167,38509 +Aljustrel,Portugal,Europe,37.8775,-8.1653,9257 +Alkmaar,Netherlands,Europe,52.6333,4.75,111766 +Allen,United States,North America,33.1088,-96.6735,107684 +Allende,Mexico,North America,28.3333,-100.8333,21376 +Allentown,United States,North America,40.5961,-75.4756,632175 +Almada,Portugal,Europe,38.6803,-9.1583,177238 +Almaguer,Colombia,South America,1.9167,-76.8667,21374 +Almaty,Kazakhstan,Asia,43.24,76.915,2228675 +Almeirim,Brazil,South America,-1.5228,-52.5819,34280 +Almelo,Netherlands,Europe,52.3567,6.6625,73132 +Almenara,Brazil,South America,-16.1833,-40.7,40364 +Almere,Netherlands,Europe,52.3667,5.2167,214715 +Almería,Spain,Europe,36.8417,-2.4639,202675 +Almirante,Panama,North America,9.3,-82.4,12731 +Almirante Tamandaré,Brazil,South America,-25.325,-49.31,119825 +Almolonga,Guatemala,North America,14.8122,-91.4944,17305 +Almoloya,Mexico,North America,19.7,-98.4,10638 +Almoloya de Alquisiras,Mexico,North America,18.85,-99.85,14196 +Almoloya del Río,Mexico,North America,19.1586,-99.4886,8939 +Alofi,Niue,Oceania,-19.056,-169.921,597 +Alor Setar,Malaysia,Asia,6.1133,100.3729,217000 +Alotau,Papua New Guinea,Oceania,-10.3167,150.4333,15939 +Alotenango,Guatemala,North America,14.4878,-90.8058,28129 +Alphen aan den Rijn,Netherlands,Europe,52.1333,4.6667,114182 +Alpinópolis,Brazil,South America,-20.8639,-46.3878,19958 +Alpu,Turkey,Europe,39.7667,30.95,9966 +Alquízar,Cuba,North America,22.8067,-82.5828,29616 +Alta,Norway,Europe,69.9686,23.2714,15342 +Alta Floresta,Brazil,South America,-9.8758,-56.0858,58613 +Alta Floresta D’Oeste,Brazil,South America,-11.9283,-61.995,21494 +Alta Gracia,Argentina,South America,-31.6667,-64.4333,48140 +Altagracia,Nicaragua,North America,11.5667,-85.5833,19955 +Altagracia de Orituco,Venezuela,South America,9.8504,-66.38,40052 +Altamira,Brazil,South America,-3.2028,-52.2058,126279 +Altamirano,Mexico,North America,16.7361,-92.0389,10618 +Altay,China,Asia,47.8666,88.1166,142000 +Altdorf,Switzerland,Europe,46.8806,8.6394,9401 +Altenburg,Germany,Europe,50.985,12.4333,31580 +Altenholz,Germany,Europe,54.4,10.1333,9927 +Altepexi,Mexico,North America,18.35,-97.3167,21100 +Alterosa,Brazil,South America,-21.2489,-46.1428,13915 +Altinho,Brazil,South America,-8.49,-36.0589,20674 +Altinópolis,Brazil,South America,-21.0231,-47.3728,16203 +Alto Alegre dos Parecis,Brazil,South America,-12.1278,-61.8508,11479 +Alto Araguaia,Brazil,South America,-17.3147,-53.2153,19385 +Alto Garças,Brazil,South America,-16.9439,-53.5278,13052 +Alto Longá,Brazil,South America,-5.2511,-42.2103,14339 +Alto Paraná,Brazil,South America,-23.1289,-52.3189,13909 +Alto Parnaíba,Brazil,South America,-9.1108,-45.93,11109 +Alto Piquiri,Brazil,South America,-24.0278,-53.4408,9727 +Alto Rio Doce,Brazil,South America,-21.0258,-43.4108,10891 +Alto Río Senguer,Argentina,South America,-45.0167,-70.8167,1570 +Alto Santo,Brazil,South America,-5.5208,-38.2719,14155 +Alto do Rodrigues,Brazil,South America,-5.2878,-36.7619,12484 +Altos,Brazil,South America,-5.0389,-42.4608,47453 +Altos del Rosario,Colombia,South America,8.8,-74.1667,13669 +Altstätten,Switzerland,Europe,47.378,9.5488,11730 +Altônia,Brazil,South America,-23.8739,-53.9019,18742 +Altötting,Germany,Europe,48.2267,12.6783,10119 +Altındağ,Turkey,Europe,39.9636,32.9033,413994 +Altınekin,Turkey,Europe,38.3078,32.8686,14289 +Altınova,Turkey,Europe,40.6975,29.5114,9042 +Altınözü,Turkey,Europe,36.1,36.23,60344 +Alvand,Iran,Asia,36.1867,50.0639,93836 +Alvarado,Mexico,North America,18.7811,-95.7572,57035 +Alvarães,Brazil,South America,-3.2,-64.8333,15866 +Alvinópolis,Brazil,South America,-20.1069,-43.0489,15059 +Alvorada,Brazil,South America,-12.48,-49.125,8802 +Alvorada D’Oeste,Brazil,South America,-11.3417,-62.2861,13117 +Alvorada do Sul,Brazil,South America,-22.78,-51.2308,10326 +Alytus,Lithuania,Europe,54.4,24.05,51856 +Alzey,Germany,Europe,49.7458,8.1153,19530 +Alès,France,Europe,44.1281,4.0817,45025 +Além Paraíba,Brazil,South America,-21.8878,-42.7039,30717 +Alīgūdarz,Iran,Asia,33.4025,49.6903,78690 +Alūksne,Latvia,Europe,57.4239,27.0503,6175 +Am-Timan,Chad,Africa,11.0333,20.2833,38261 +Amacuzac,Mexico,North America,18.6,-99.37,17598 +Amadora,Portugal,Europe,38.75,-9.2333,171454 +Amagá,Colombia,South America,6.05,-75.7,30376 +Amahai,Indonesia,Asia,-3.3439,128.9322,50478 +Amalfi,Colombia,South America,6.9167,-75.0833,22714 +Amaliáda,Greece,Europe,37.8,21.35,26315 +Amambaí,Brazil,South America,-23.1039,-55.2258,39325 +Amancio,Cuba,North America,20.8197,-77.5844,36132 +Amapá,Brazil,South America,2.0528,-50.7928,7943 +Amaraji,Brazil,South America,-8.3758,-35.4522,18205 +Amarante,Portugal,Europe,41.2728,-8.0825,56264 +Amarante do Maranhão,Brazil,South America,-5.5669,-46.7419,31292 +Amares,Portugal,Europe,41.6333,-8.35,18889 +Amargosa,Brazil,South America,-13.03,-39.605,31108 +Amarillo,United States,North America,35.1984,-101.8316,205100 +Amarāvati,India,Asia,16.5131,80.5165,103000 +Amasya,Turkey,Europe,40.65,35.8331,114921 +Amatenango de la Frontera,Mexico,North America,15.5333,-92.0833,31735 +Amatenango del Valle,Mexico,North America,16.5167,-92.4,11283 +Amatepec,Mexico,North America,18.65,-100.15,27026 +Amatitlán,Guatemala,North America,14.4833,-90.6167,152296 +Amatitán,Mexico,North America,20.835,-103.731,15344 +Amatlán de los Reyes,Mexico,North America,18.8457,-96.9149,9123 +Amaturá,Brazil,South America,-3.3639,-68.1978,10819 +Amatán,Mexico,North America,17.3667,-92.8167,24512 +Ambato,Ecuador,South America,-1.2422,-78.6289,177316 +Ambatondrazaka,Madagascar,Africa,-17.8329,48.4167,47649 +Amberg,Germany,Europe,49.4444,11.8483,42676 +Ambon,Indonesia,Asia,-3.6967,128.1783,372249 +Ambositra,Madagascar,Africa,-20.5294,47.2425,41078 +Ambovombe,Madagascar,Africa,-25.1754,46.0882,114230 +Ambrolauri,Georgia,Europe,42.5194,43.15,2015 +Amdjarass,Chad,Africa,16.0658,22.8431,20850 +Ameca,Mexico,North America,20.5486,-104.0431,37871 +Amecameca de Juárez,Mexico,North America,19.75,-99.1711,31687 +Americana,Brazil,South America,-22.7392,-47.3314,242018 +Amersfoort,Netherlands,Europe,52.155,5.3875,157462 +Amfilochía,Greece,Europe,38.85,21.1667,9154 +Amherst,United States,North America,43.0117,-78.757,130209 +Amiens,France,Europe,49.892,2.299,134780 +Amioûn,Lebanon,Asia,34.3,35.8,10000 +Amlash,Iran,Asia,37.0944,50.1928,15047 +Amman,Jordan,Asia,31.9497,35.9328,4007526 +Amnat Charoen,Thailand,Asia,15.85,104.6333,26118 +Amolatar,Uganda,Africa,1.6517,32.825,12900 +Amontada,Brazil,South America,-3.3608,-39.8308,42156 +Ampana,Indonesia,Asia,-0.8667,121.5833,40299 +Amparo,Brazil,South America,-22.7031,-46.765,72677 +Ampelókipoi,Greece,Europe,40.65,22.9167,35846 +Ampère,Brazil,South America,-25.915,-53.4728,19620 +Amstelveen,Netherlands,Europe,52.3,4.85,94435 +Amsterdam,Netherlands,Europe,52.3728,4.8936,1477213 +Amstetten,Austria,Europe,48.1167,14.8667,23569 +Amudat,Uganda,Africa,1.9522,34.9444,1900 +Amuntai,Indonesia,Asia,-2.4177,115.2494,41554 +Amuria,Uganda,Africa,2.03,33.6431,8900 +Amuru,Uganda,Africa,2.8186,31.8642,10800 +América Dourada,Brazil,South America,-11.455,-41.4358,15137 +Américo Brasiliense,Brazil,South America,-21.7361,-48.1114,38202 +An Cabhán,Ireland,Europe,53.9908,-7.3606,11741 +An Châu,Vietnam,Asia,21.3333,106.85,9416 +An Nabk,Syria,Asia,34.017,36.7333,52502 +An Najaf,Iraq,Asia,32.029,44.3396,724700 +An Nhơn,Vietnam,Asia,13.917,109.083,350000 +An Nu‘mānīyah,Iraq,Asia,32.5562,45.4129,71000 +An Nāşirīyah,Iraq,Asia,31.0439,46.2575,541600 +An Phú,Vietnam,Asia,10.85,105.0833,11108 +Anaco,Venezuela,South America,9.4819,-64.4917,106000 +Anadia,Portugal,Europe,40.4333,-8.4333,27532 +Anadyr,Russia,Asia,64.7333,177.5167,13043 +Anagé,Brazil,South America,-14.6119,-41.1358,25438 +Anaheim,United States,North America,33.839,-117.8574,344553 +Anajatuba,Brazil,South America,-3.2644,-44.6197,26988 +Anajás,Brazil,South America,-0.9869,-49.94,28011 +Anamur,Turkey,Europe,36.0243,32.8026,65920 +Anamã,Brazil,South America,-3.58,-61.4039,9962 +Ananindeua,Brazil,South America,-1.3658,-48.3719,478778 +Ananás,Brazil,South America,-6.3658,-48.0728,10325 +Anapa,Russia,Asia,44.8944,37.3167,85747 +Anapoima,Colombia,South America,4.5503,-74.5361,14519 +Anapurus,Brazil,South America,-3.6719,-43.1158,13793 +Anastácio,Brazil,South America,-20.4839,-55.8069,22477 +Anchieta,Brazil,South America,-20.8056,-40.6444,29984 +Anchorage,United States,North America,61.1508,-149.1091,289069 +Ancona,Italy,Europe,43.6167,13.5167,98356 +Ancón,Panama,North America,8.9627,-79.5474,29761 +Anda,China,Asia,46.424,125.352,357535 +Andalgalá,Argentina,South America,-27.6,-66.3167,14068 +Andalucía,Colombia,South America,4.1667,-76.1667,25467 +Andaraí,Brazil,South America,-12.8069,-41.3308,13080 +Andes,Colombia,South America,5.5833,-75.9167,43269 +Andijon,Uzbekistan,Asia,40.7833,72.3333,747800 +Andirá,Brazil,South America,-23.0508,-50.2289,19878 +Andkhōy,Afghanistan,Asia,36.95,65.1167,39730 +Andong,"Korea, South",Asia,36.5592,128.7289,153306 +Andorinha,Brazil,South America,-10.345,-39.8328,15012 +Andorra la Vella,Andorra,Europe,42.5,1.5,22615 +Andradas,Brazil,South America,-22.0678,-46.5689,32968 +Andradina,Brazil,South America,-20.8958,-51.3789,59783 +Andrelândia,Brazil,South America,-21.74,-44.3089,11927 +Andria,Italy,Europe,41.2317,16.3083,97146 +Andrijevica,Montenegro,Europe,42.73,19.79,1048 +André Fernandes,Brazil,South America,-15.9658,-41.4978,9110 +Andīmeshk,Iran,Asia,32.4714,48.3489,240113 +Andırın,Turkey,Europe,37.5764,36.3506,31224 +Anenii Noi,Moldova,Europe,46.8817,29.2308,10872 +Ang Thong,Thailand,Asia,14.5925,100.4572,13738 +Angarsk,Russia,Asia,52.55,103.9,217365 +Angatuba,Brazil,South America,-23.4903,-48.4128,24161 +Angeles City,Philippines,Asia,15.1472,120.5847,462928 +Angelim,Brazil,South America,-8.8833,-36.2833,10241 +Angelópolis,Colombia,South America,6.1333,-75.7,8946 +Angers,France,Europe,47.4736,-0.5542,157555 +Angical,Brazil,South America,-12.0069,-44.6939,13732 +Angicos,Brazil,South America,-5.6658,-36.6008,11632 +Angol,Chile,South America,-37.8,-72.7167,48608 +Angostura,Colombia,South America,6.8667,-75.3333,10500 +Angoulême,France,Europe,45.65,0.16,41423 +Anguera,Brazil,South America,-12.1508,-39.2458,11031 +Anguo,China,Asia,38.418,115.327,363091 +Anicuns,Brazil,South America,-16.4608,-49.9619,18503 +Aniva,Russia,Asia,46.7167,142.5167,9686 +Ankara,Turkey,Europe,39.93,32.85,5864049 +Ankaran,Slovenia,Europe,45.5793,13.7379,3224 +Anlu,China,Asia,31.2667,113.6667,498356 +Ann Arbor,United States,North America,42.2759,-83.731,314647 +Annaba,Algeria,Africa,36.9,7.7667,257359 +Annaberg-Buchholz,Germany,Europe,50.58,13.0022,19470 +Annapolis,United States,North America,38.9706,-76.5047,40744 +Annecy,France,Europe,45.916,6.133,131272 +Anolaima,Colombia,South America,4.7617,-74.464,12911 +Anori,Brazil,South America,-3.7728,-61.6442,17194 +Anorí,Colombia,South America,7.0736,-75.1458,16447 +Anqing,China,Asia,30.5318,117.1153,4165284 +Ansbach,Germany,Europe,49.3,10.5833,42311 +Anserma,Colombia,South America,5.2381,-75.784,33674 +Ansermanuevo,Colombia,South America,4.8,-76.0,19557 +Ansião,Portugal,Europe,39.9167,-8.4333,11642 +Ansongo,Mali,Africa,15.665,0.5028,32709 +Antalya,Turkey,Europe,36.8874,30.7075,1344000 +Antananarivo,Madagascar,Africa,-18.91,47.525,1275207 +Antas,Brazil,South America,-10.4,-38.3328,14206 +Antenor Navarro,Brazil,South America,-6.7289,-38.4489,17964 +Antigua Guatemala,Guatemala,North America,14.5667,-90.7333,59091 +Antilla,Cuba,North America,20.8411,-75.7206,21332 +Antioch,United States,North America,37.9787,-121.796,333347 +Antipolo,Philippines,Asia,14.5842,121.1763,887399 +Antofagasta,Chile,South America,-23.65,-70.4,348517 +Antonina,Brazil,South America,-25.4289,-48.7119,18091 +Antony,France,Europe,48.7539,2.2975,64026 +Antsirabe,Madagascar,Africa,-19.8667,47.0333,265018 +Antsiranana,Madagascar,Africa,-12.2765,49.3115,129320 +Antsohihy,Madagascar,Africa,-14.8661,47.9834,38253 +Antwerp,Belgium,Europe,51.2178,4.4003,565039 +Antônio Cardoso,Brazil,South America,-12.435,-39.12,11146 +Antônio Carlos,Brazil,South America,-27.5169,-48.7678,11224 +Antônio Dias,Brazil,South America,-19.6528,-42.8719,9219 +Antônio Prado,Brazil,South America,-28.8578,-51.2828,12833 +Anuradhapura,Sri Lanka,Asia,8.335,80.4108,63208 +Anykščiai,Lithuania,Europe,55.5344,25.1072,8638 +Anzoátegui,Colombia,South America,4.6339,-75.0972,18849 +Anápolis,Brazil,South America,-16.3269,-48.9528,288085 +Anísio de Abreu,Brazil,South America,-9.1889,-43.0458,9407 +Anār,Iran,Asia,30.8656,55.2703,15532 +Aomori,Japan,Asia,40.8228,140.7469,425962 +Aosta,Italy,Europe,45.7372,7.3206,33093 +Apac,Uganda,Africa,1.985,32.535,67700 +Apan,Mexico,North America,19.7,-98.4333,39247 +Apapa,Nigeria,Africa,6.45,3.3667,217362 +Aparecida,Brazil,South America,-22.8469,-45.2297,36185 +Aparecida do Taboado,Brazil,South America,-20.0869,-51.0939,27674 +Apartadó,Colombia,South America,7.8833,-76.6333,121003 +Apaseo el Alto,Mexico,North America,20.45,-100.6167,64443 +Apaseo el Grande,Mexico,North America,20.5531,-100.6347,85319 +Apatin,Serbia,Europe,45.6667,18.9833,23155 +Apatity,Russia,Asia,67.5667,33.4,69500 +Apaxco de Ocampo,Mexico,North America,19.98,-99.15,27521 +Apače,Slovenia,Europe,46.6972,15.9106,543 +Apeldoorn,Netherlands,Europe,52.21,5.97,138945 +Aperibé,Brazil,South America,-21.6208,-42.1028,11034 +Apia,Samoa,Oceania,-13.8333,-171.75,35974 +Apiaí,Brazil,South America,-24.5097,-48.8428,25211 +Apizaco,Mexico,North America,19.4167,-98.1333,81565 +Apiúna,Brazil,South America,-27.0358,-49.39,9811 +Apodi,Brazil,South America,-5.6639,-37.7989,34174 +Apolda,Germany,Europe,51.0167,11.5167,22896 +Aporá,Brazil,South America,-11.66,-38.0808,15922 +Apostolove,Ukraine,Europe,47.6594,33.7167,16356 +Appenzell,Switzerland,Europe,47.3306,9.4086,5750 +Appingedam,Netherlands,Europe,53.3167,6.8667,11721 +Appleton,United States,North America,44.278,-88.3892,234701 +Apsheronsk,Russia,Asia,44.4667,39.7333,39577 +Apt,France,Europe,43.8769,5.3969,10297 +Apucarana,Brazil,South America,-23.5508,-51.4608,130134 +Apuiarés,Brazil,South America,-3.9489,-39.4319,12540 +Apía,Colombia,South America,5.1,-75.95,12613 +Apóstoles,Argentina,South America,-27.9167,-55.7667,40858 +Aqköl,Kazakhstan,Asia,52.0,70.9333,14217 +Aqsū,Kazakhstan,Asia,52.0333,76.9167,69354 +Aqtaū,Kazakhstan,Asia,43.6525,51.1575,183350 +Aqtöbe,Kazakhstan,Asia,50.2836,57.2297,387807 +Aquidabã,Brazil,South America,-10.2833,-37.0333,20131 +Aquidauana,Brazil,South America,-20.4711,-55.7872,48029 +Aquiraz,Brazil,South America,-3.9008,-38.3908,60469 +Aquitania,Colombia,South America,5.5833,-72.8333,16087 +Ar Ramthā,Jordan,Asia,32.5589,36.0147,155693 +Ar Ramādī,Iraq,Asia,33.4258,43.2992,874543 +Ar Raqqah,Syria,Asia,35.9304,39.02,299824 +Ar Rastan,Syria,Asia,34.9267,36.7324,61176 +Ar Rayyān,Qatar,Asia,25.25,51.4,605712 +Ar Rumaythah,Iraq,Asia,31.5283,45.2039,115431 +Ar Rustāq,Oman,Asia,23.3908,57.4244,120000 +Ar Ruşayfah,Jordan,Asia,32.0178,36.0464,472604 +Ar Ruţbah,Iraq,Asia,33.0333,40.2833,27879 +Ar Ruḩaybah,Syria,Asia,33.7442,36.7039,30450 +Araban,Turkey,Europe,37.4247,37.6928,32933 +Aracaju,Brazil,South America,-10.9111,-37.0717,664908 +Aracataca,Colombia,South America,10.5937,-74.1928,40400 +Aracati,Brazil,South America,-4.5619,-37.77,61187 +Aracatu,Brazil,South America,-14.4278,-41.4619,13936 +Araci,Brazil,South America,-11.3328,-38.9669,47584 +Aracoiaba,Brazil,South America,-4.3708,-38.8139,25553 +Aracruz,Brazil,South America,-19.8203,-40.2733,103101 +Arad,Romania,Europe,46.175,21.3125,145078 +Arafat,Mauritania,Africa,18.0583,-15.9621,175969 +Aragarças,Brazil,South America,-15.8978,-52.2508,18390 +Aragoiânia,Brazil,South America,-16.9119,-49.4508,11890 +Aragua de Barcelona,Venezuela,South America,9.4575,-64.8261,27000 +Araguari,Brazil,South America,-18.6489,-48.1869,117808 +Araguatins,Brazil,South America,-5.6508,-48.1239,31918 +Araguaína,Brazil,South America,-7.1908,-48.2069,171301 +Araioses,Brazil,South America,-2.89,-41.9031,46771 +Araklı,Turkey,Europe,40.9333,40.05,48581 +Arakvāz-e Malekshāhī,Iran,Asia,33.3847,46.5997,11977 +Aral,China,Asia,40.548,81.281,158593 +Aramari,Brazil,South America,-12.0819,-38.4989,9833 +Aranyaprathet,Thailand,Asia,13.6928,102.5017,16937 +Aranzazu,Colombia,South America,5.3,-75.45,9854 +Aranđelovac,Serbia,Europe,44.3042,20.5561,46225 +Arapgir,Turkey,Europe,39.0333,38.4833,9964 +Arapiraca,Brazil,South America,-9.7525,-36.6611,234696 +Arapongas,Brazil,South America,-23.4189,-51.4239,119138 +Arapoti,Brazil,South America,-24.1578,-49.8269,25777 +Araputanga,Brazil,South America,-15.4708,-58.3528,14786 +Araquari,Brazil,South America,-26.37,-48.7219,23645 +Arar,Saudi Arabia,Asia,30.9753,41.0231,27449 +Arara,Brazil,South America,-6.8278,-35.7578,12212 +Araranguá,Brazil,South America,-28.935,-49.4858,71922 +Araraquara,Brazil,South America,-21.7939,-48.1758,242228 +Araras,Brazil,South America,-22.3569,-47.3839,130866 +Ararendá,Brazil,South America,-4.7528,-40.8328,11096 +Arari,Brazil,South America,-3.4539,-44.78,29472 +Ararica,Brazil,South America,-29.6139,-50.925,8525 +Araripe,Brazil,South America,-7.2128,-40.0458,19783 +Araripina,Brazil,South America,-7.55,-40.5667,85088 +Araruama,Brazil,South America,-22.8728,-42.3428,82803 +Araruna,Brazil,South America,-6.5321,-35.7398,17189 +Arataca,Brazil,South America,-15.2628,-39.4139,10191 +Aratuba,Brazil,South America,-4.4178,-39.045,11224 +Aratuípe,Brazil,South America,-13.0789,-39.0019,8677 +Arauca,Colombia,South America,7.0903,-70.7617,96814 +Araucária,Brazil,South America,-25.5928,-49.41,151666 +Arauquita,Colombia,South America,7.0303,-71.4356,41309 +Araure,Venezuela,South America,9.5667,-69.2167,11908 +Arauá,Brazil,South America,-11.2622,-37.6197,9947 +Aravan,Kyrgyzstan,Asia,40.5161,72.4996,32917 +Araxá,Brazil,South America,-19.5928,-46.9408,111691 +Araçagi,Brazil,South America,-6.8528,-35.3808,16646 +Araçariguama,Brazil,South America,-23.4386,-47.0614,22860 +Araçatuba,Brazil,South America,-21.2089,-50.4328,200124 +Araçoiaba,Brazil,South America,-7.79,-35.0908,19243 +Araçoiaba da Serra,Brazil,South America,-23.5053,-47.6142,31321 +Araçuaí,Brazil,South America,-16.85,-42.07,34297 +Araçás,Brazil,South America,-12.22,-38.2028,11557 +Araújos,Brazil,South America,-19.9478,-45.1658,9199 +Aračinovo,North Macedonia,Europe,42.0264,21.5617,7315 +Arbeláez,Colombia,South America,4.2725,-74.4151,12292 +Arboga,Sweden,Europe,59.4,15.8333,14057 +Arboledas,Colombia,South America,7.6667,-72.75,8984 +Arboletes,Colombia,South America,8.8517,-76.4283,44560 +Arcachon,France,Europe,44.65,-1.17,10895 +Arceburgo,Brazil,South America,-21.3639,-46.94,9177 +Arcos,Brazil,South America,-20.2914,-45.5397,32687 +Arcos de Valdevez,Portugal,Europe,41.85,-8.4167,22847 +Arcoverde,Brazil,South America,-8.4189,-37.0539,61600 +Ardabīl,Iran,Asia,38.2517,48.2975,529374 +Ardahan,Turkey,Europe,41.1111,42.7022,22927 +Ardakān,Iran,Asia,32.3094,54.0186,75271 +Ardal,Iran,Asia,31.9986,50.6622,10113 +Ardestān,Iran,Asia,33.3731,52.3689,15744 +Ardeşen,Turkey,Europe,41.19,40.9831,30645 +Ardon,Russia,Asia,43.1833,44.3167,18915 +Areado,Brazil,South America,-21.3589,-46.1458,13881 +Areal,Brazil,South America,-22.2308,-43.1058,11828 +Arealva,Brazil,South America,-22.0286,-48.9111,8613 +Areguá,Paraguay,South America,-25.3125,-57.3847,67487 +Areia,Brazil,South America,-6.9628,-35.6919,22633 +Areia Branca,Brazil,South America,-4.9558,-37.1369,24093 +Areiópolis,Brazil,South America,-22.6681,-48.665,11158 +Arendal,Norway,Europe,58.4617,8.7721,45891 +Arenápolis,Brazil,South America,-14.4503,-56.8461,9502 +Arequipa,Peru,South America,-16.3989,-71.5369,1008290 +Arezzo,Italy,Europe,43.4631,11.8781,96260 +Arganil,Portugal,Europe,40.2183,-8.0542,12145 +Argao,Philippines,Asia,9.88,123.6,78187 +Argelia,Colombia,South America,2.2431,-77.2786,27670 +Argentan,France,Europe,48.75,-0.02,13494 +Argenteuil,France,Europe,48.9478,2.2489,107135 +Argostóli,Greece,Europe,38.1733,20.4908,23574 +Argun,Russia,Asia,43.2944,45.8839,15148 +Argyroúpoli,Greece,Europe,37.9,23.75,33312 +Arhavi,Turkey,Europe,41.3333,41.3,17558 +Ariana,Tunisia,Africa,36.8625,10.1956,123079 +Aribinda,Burkina Faso,Africa,14.2292,-0.8633,45818 +Arica,Chile,South America,-18.4784,-70.3212,202131 +Aridaía,Greece,Europe,40.9667,22.05,18864 +Arifiye,Turkey,Europe,40.7167,30.3667,49340 +Arilje,Serbia,Europe,43.7531,20.0956,18792 +Arima,Trinidad and Tobago,North America,10.6333,-61.2833,65623 +Arinos,Brazil,South America,-15.9169,-46.1058,17272 +Aripuanã,Brazil,South America,-10.1767,-59.4439,26983 +Ariquemes,Brazil,South America,-9.9161,-63.0408,111148 +Arjona,Colombia,South America,10.2586,-75.3461,75615 +Arkhangelsk,Russia,Asia,64.5431,40.5375,351488 +Arles,France,Europe,43.6767,4.6278,51156 +Arlington,United States,North America,32.6998,-97.125,394769 +Arlit,Niger,Africa,18.7333,7.3833,78651 +Arlon,Belgium,Europe,49.6836,5.8167,30818 +Arlöv,Sweden,Europe,55.6333,13.0667,10284 +Armanāz,Syria,Asia,36.0836,36.5031,10296 +Armavir,Russia,Asia,45.0,41.1167,184546 +Armazém,Brazil,South America,-28.2619,-49.0178,8834 +Armação dos Búzios,Brazil,South America,-22.7469,-41.8819,40006 +Armenia,Colombia,South America,4.53,-75.68,301226 +Armutlu,Turkey,Europe,40.5167,28.8333,10843 +Arnhem,Netherlands,Europe,51.9833,5.9167,164096 +Arnstadt,Germany,Europe,50.8342,10.9464,28264 +Aroeiras,Brazil,South America,-7.545,-35.7078,18705 +Arouca,Portugal,Europe,40.9289,-8.2436,22359 +Arqalyq,Kazakhstan,Asia,50.2481,66.9278,28169 +Arraial do Cabo,Brazil,South America,-22.9658,-42.0278,30986 +Arraias,Brazil,South America,-12.9308,-46.9378,10287 +Arraiján,Panama,North America,8.9398,-79.6404,41041 +Arras,France,Europe,50.292,2.78,42621 +Arrecifes,Argentina,South America,-34.0667,-60.1167,32215 +Arriaga,Mexico,North America,16.2361,-93.8995,25366 +Arroio Grande,Brazil,South America,-32.2378,-53.0869,17558 +Arroio do Meio,Brazil,South America,-29.4008,-51.945,16951 +Arroio do Tigre,Brazil,South America,-29.3328,-53.0928,12058 +Arroio dos Ratos,Brazil,South America,-30.0769,-51.7289,14601 +Arroyohondo,Colombia,South America,10.1333,-75.5667,9907 +Arruda dos Vinhos,Portugal,Europe,38.9833,-9.0667,13391 +Arsanjān,Iran,Asia,29.9142,53.3058,17642 +Arsenyev,Russia,Asia,44.1667,133.25,47306 +Arsin,Turkey,Europe,40.95,39.9333,30911 +Arsuz,Turkey,Europe,36.4128,35.8867,101233 +Arta,Djibouti,Africa,11.5236,42.8472,11221 +Artashat,Armenia,Europe,39.9539,44.5506,21300 +Artemisa,Cuba,North America,22.8136,-82.7633,59130 +Artigas,Uruguay,South America,-30.4667,-56.4667,40658 +Artsyz,Ukraine,Europe,45.9944,29.4322,14355 +Artur Nogueira,Brazil,South America,-22.5731,-47.1725,50246 +Artvin,Turkey,Europe,41.1833,41.8181,25841 +Artëm,Russia,Asia,43.3667,132.2,108539 +Artëmovskiy,Russia,Asia,57.3564,61.8711,28775 +Arua,Uganda,Africa,3.0353,30.9108,61962 +Arujá,Brazil,South America,-23.3967,-46.3211,83939 +Arusha,Tanzania,Africa,-3.3667,36.6833,617631 +Arvada,United States,North America,39.832,-105.151,122835 +Arvayheer,Mongolia,Asia,46.2689,102.7575,30000 +Arvika,Sweden,Europe,59.6667,12.5667,14244 +Arys,Kazakhstan,Asia,42.4333,68.8,40707 +Arzamas,Russia,Asia,55.4,43.8167,103629 +Arzgir,Russia,Asia,45.3694,44.2281,13160 +Arês,Brazil,South America,-6.1939,-35.16,13251 +Arāk,Iran,Asia,34.0914,49.6933,520944 +As Safīrah,Syria,Asia,36.077,37.3725,106460 +As Salamīyah,Syria,Asia,35.0113,37.0532,110683 +As Salţ,Jordan,Asia,32.0333,35.7333,88900 +As Samāwah,Iraq,Asia,31.3167,45.2833,215000 +As Sukhnah,Syria,Asia,34.8853,38.8711,16173 +As Sulaymānīyah,Iraq,Asia,35.55,45.4333,878146 +As Suqaylibīyah,Syria,Asia,35.3667,36.3936,17313 +As Suwaydā’,Syria,Asia,32.7004,36.5666,64730 +Asaba,Nigeria,Africa,6.1833,6.7333,500000 +Asadābād,Iran,Asia,34.7825,48.1219,55703 +Asaka,Uzbekistan,Asia,40.6333,72.2333,62200 +Asarcık,Turkey,Europe,41.0314,36.2683,16001 +Asau,Samoa,Oceania,-13.5194,-172.6372,1133 +Asbest,Russia,Asia,57.0,61.4667,56533 +Ascensión,Mexico,North America,31.0928,-107.9964,13456 +Aschaffenburg,Germany,Europe,49.9667,9.15,72918 +Ascoli Piceno,Italy,Europe,42.8547,13.5753,45571 +Ash Shaddādah,Syria,Asia,36.0576,40.7348,15806 +Ash Shaykh Badr,Syria,Asia,34.9917,36.0835,47982 +Ash Shaykh Zuwayd,Egypt,Africa,31.2119,34.1106,64305 +Ash Shaykh ‘Uthmān,Yemen,Asia,12.8864,45.0156,105248 +Ash Shaykhān,Iraq,Asia,36.6959,43.352,17766 +Ash Shaţrah,Iraq,Asia,31.4175,46.1772,254000 +Ash Shiḩr,Yemen,Asia,14.7608,49.6069,68313 +Ash Shāmīyah,Iraq,Asia,31.9625,44.6008,300000 +Ash Shīḩānīyah,Qatar,Asia,25.3722,51.2047,161240 +Ash Shūnah ash Shamālīyah,Jordan,Asia,32.61,35.6081,25000 +Asha,Russia,Asia,55.0,57.2667,27442 +Asheville,United States,North America,35.5707,-82.5537,295040 +Ashgabat,Turkmenistan,Asia,37.9375,58.38,1030063 +Ashkezar,Iran,Asia,31.9997,54.2075,19123 +Ashmyany,Belarus,Europe,54.425,25.9375,16787 +Ashtarak,Armenia,Europe,40.2975,44.3617,21600 +Asino,Russia,Asia,57.0,86.1333,24913 +Asipovichy,Belarus,Europe,53.2933,28.4756,29103 +Asker,Norway,Europe,59.8331,10.4392,61523 +Askim,Norway,Europe,59.5832,11.1629,14488 +Asmara,Eritrea,Africa,15.3358,38.9411,963000 +Asprópyrgos,Greece,Europe,38.0667,23.5833,31381 +Assab,Eritrea,Africa,13.0078,42.7411,74405 +Assaré,Brazil,South America,-6.8739,-39.875,21697 +Assaí,Brazil,South America,-23.3728,-50.8408,13797 +Assen,Netherlands,Europe,52.9953,6.5606,68836 +Assis,Brazil,South America,-22.6619,-50.4119,101409 +Assomada,Cabo Verde,Africa,15.0949,-23.6654,12026 +Astana,Kazakhstan,Asia,51.1472,71.4222,1078362 +Astara,Azerbaijan,Europe,38.44,48.875,16800 +Asten,Netherlands,Europe,51.4031,5.7472,16817 +Asti,Italy,Europe,44.9,8.2069,73421 +Astolfo Dutra,Brazil,South America,-21.315,-42.8619,14138 +Astorga,Brazil,South America,-23.2328,-51.6658,25475 +Astrakhan,Russia,Asia,46.35,48.035,465524 +Astravyets,Belarus,Europe,54.6136,25.9553,15116 +Astrea,Colombia,South America,9.5,-73.9833,18434 +Asunción,Paraguay,South America,-25.2945,-57.6435,477346 +Asunción Mita,Guatemala,North America,14.3333,-89.7167,53141 +Asunción Nochixtlán,Mexico,North America,17.45,-97.2167,18525 +Aswān,Egypt,Africa,24.0889,32.8997,267913 +Asyūţ,Egypt,Africa,27.1869,31.1714,389307 +At Tall,Syria,Asia,33.6103,36.3107,63554 +At Tawāhī,Yemen,Asia,12.7833,44.9833,52984 +At-Bashy,Kyrgyzstan,Asia,41.1725,75.7968,14655 +Atacames,Ecuador,South America,0.8667,-79.8333,18948 +Ataco,Colombia,South America,3.6,-75.3833,13470 +Atakpamé,Togo,Africa,7.5269,1.1267,84979 +Atalaia,Brazil,South America,-9.5019,-36.0228,37512 +Atalaia do Norte,Brazil,South America,-4.3719,-70.1919,15314 +Ataléia,Brazil,South America,-18.0439,-41.11,13736 +Atambua,Indonesia,Asia,-9.1061,124.8925,76732 +Ataq,Yemen,Asia,14.5364,46.8331,37315 +Atar,Mauritania,Africa,20.5167,-13.05,24021 +Atasū,Kazakhstan,Asia,48.6903,71.6499,19616 +Atbasar,Kazakhstan,Asia,51.8,68.3333,29733 +Atenco,Mexico,North America,19.5167,-98.9167,42739 +Atescatempa,Guatemala,North America,14.175,-89.7417,20033 +Ath,Belgium,Europe,50.6167,3.7667,29164 +Ath Thawrah,Syria,Asia,35.8367,38.5481,84000 +Athens,Greece,Europe,37.9842,23.7281,3059764 +Ati,Chad,Africa,13.2133,18.3381,35311 +Atibaia,Brazil,South America,-23.1172,-46.5506,137187 +Atlanta,United States,North America,33.7628,-84.422,5211164 +Atlatlahucan,Mexico,North America,18.935,-98.9,25232 +Atlautla,Mexico,North America,19.0279,-98.7814,31900 +Atotonilco el Alto,Mexico,North America,20.5333,-102.5,64009 +Atoyac de Álvarez,Mexico,North America,17.2,-100.4333,20515 +Attapu,Laos,Asia,14.8,106.8333,19200 +Attard,Malta,Europe,35.8928,14.4383,10930 +Atuntaqui,Ecuador,South America,0.3317,-78.2189,25115 +Atushi,China,Asia,39.7162,76.1684,240368 +Atyraū,Kazakhstan,Asia,47.1167,51.8833,130916 +Auburn,United States,North America,32.6087,-85.4903,108327 +Auch,France,Europe,43.6465,0.5855,22825 +Auchi,Nigeria,Africa,7.0667,6.2667,150000 +Auckland,New Zealand,Oceania,-36.8492,174.7653,1470100 +Augsburg,Germany,Europe,48.3689,10.8978,303150 +Augusta,United States,North America,33.3645,-82.0708,439361 +Augustinópolis,Brazil,South America,-5.4658,-47.8878,17484 +Augusto Correa,Brazil,South America,-1.0219,-46.645,44573 +Augustów,Poland,Europe,53.8436,22.9794,29494 +Auki,Solomon Islands,Oceania,-8.7678,160.6978,7882 +Aurelino Leal,Brazil,South America,-14.3169,-39.3269,11179 +Aurich,Germany,Europe,53.4714,7.4836,43375 +Auriflama,Brazil,South America,-20.6858,-50.555,13692 +Aurillac,France,Europe,44.9261,2.4406,26189 +Aurora,United States,North America,39.7083,-104.7237,390201 +Austin,United States,North America,30.3005,-97.7522,1915031 +Autazes,Brazil,South America,-3.5797,-59.1306,40290 +Autlán de Navarro,Mexico,North America,19.7667,-104.3667,64931 +Autun,France,Europe,46.9517,4.2994,13144 +Auxerre,France,Europe,47.7986,3.5672,35236 +Avanhandava,Brazil,South America,-21.4608,-49.9497,13859 +Avanos,Turkey,Europe,38.715,34.8467,14968 +Avarua,Cook Islands,Oceania,-21.207,-159.771,4009 +Avaré,Brazil,South America,-23.0989,-48.9258,91232 +Aveiro,Portugal,Europe,40.6389,-8.6553,78450 +Avelino Lopes,Brazil,South America,-10.1369,-43.9489,10866 +Avellaneda,Argentina,South America,-34.6625,-58.3678,31892 +Avellino,Italy,Europe,40.9153,14.7897,52198 +Avesta,Sweden,Europe,60.15,16.2,11949 +Avignon,France,Europe,43.95,4.8075,91760 +Avranches,France,Europe,48.685,-1.3556,10225 +Awbārī,Libya,Africa,26.5833,12.7667,35000 +Aweil,South Sudan,Africa,8.7666,27.4,33537 +Awka,Nigeria,Africa,6.2069,7.0678,2171900 +Axapusco,Mexico,North America,19.7194,-98.7972,21915 +Axixá,Brazil,South America,-2.8369,-44.0528,11790 +Axixá do Tocantins,Brazil,South America,-5.6169,-47.7689,10262 +Axochiapan,Mexico,North America,18.5,-98.75,39174 +Ayacucho,Peru,South America,-13.1631,-74.2244,216444 +Ayagöz,Kazakhstan,Asia,47.9667,80.4333,41593 +Ayan,Russia,Asia,56.4583,138.1681,180 +Ayancık,Turkey,Europe,41.95,34.5833,13277 +Ayapango,Mexico,North America,19.1264,-98.8028,8864 +Ayapel,Colombia,South America,8.3167,-75.15,47247 +Ayaş,Turkey,Europe,40.0167,32.3333,12998 +Aybastı,Turkey,Europe,40.6867,37.3992,20969 +Aydın,Turkey,Europe,37.8481,27.8453,259027 +Aydıncık,Turkey,Europe,36.1417,33.3178,11468 +Ayní,Tajikistan,Asia,39.3975,68.5406,14862 +Ayotoxco de Guerrero,Mexico,North America,20.1,-97.4,8558 +Ayutla,Mexico,North America,20.8167,-104.2,12880 +Ayutla de los Libres,Mexico,North America,16.9,-99.2167,15370 +Ayvacık,Turkey,Europe,41.0036,36.6319,18928 +Ayvalık,Turkey,Europe,39.3167,26.6944,74030 +Az Zabadānī,Syria,Asia,33.7249,36.1005,26285 +Az Zaqāzīq,Egypt,Africa,30.5667,31.5,302840 +Az Zarqā’,Jordan,Asia,32.0833,36.1,481300 +Az Zaydīyah,Yemen,Asia,15.3292,43.0094,16246 +Az Zubayr,Iraq,Asia,30.3892,47.7081,370000 +Az Zāwīyah,Libya,Africa,32.7522,12.7278,200000 +Azambuja,Portugal,Europe,39.0667,-8.8667,21814 +Azare,Nigeria,Africa,11.6742,10.1911,105687 +Aznā,Iran,Asia,33.4597,49.4547,47489 +Azogues,Ecuador,South America,-2.7333,-78.8333,35763 +Azov,Russia,Asia,47.1083,39.4181,80381 +Azua,Dominican Republic,North America,18.46,-70.74,91345 +Azul,Argentina,South America,-36.7833,-59.85,55728 +Açailandia,Brazil,South America,-4.9469,-47.505,106550 +Açu,Brazil,South America,-5.5769,-36.9089,47904 +Açucena,Brazil,South America,-19.0728,-42.5458,8943 +Aígio,Greece,Europe,38.2506,22.0811,20422 +Aïn Defla,Algeria,Africa,36.2652,1.9703,65453 +Aïn Temouchent,Algeria,Africa,35.3,-1.1333,75558 +Añatuya,Argentina,South America,-28.4667,-62.8333,23286 +Ağcabədi,Azerbaijan,Europe,40.0489,47.4502,43000 +Ağdam,Azerbaijan,Europe,39.9833,46.9167,3770 +Ağdaş,Azerbaijan,Europe,40.65,47.4833,29600 +Ağrı,Turkey,Europe,39.7186,43.0508,148765 +Ağstafa,Azerbaijan,Europe,41.1189,45.4539,20200 +Ağsu,Azerbaijan,Europe,40.5703,48.4009,20200 +Aībak,Afghanistan,Asia,36.2653,68.0167,35560 +Aş Şanamayn,Syria,Asia,33.0719,36.1832,26268 +Aş Şuwayrah,Iraq,Asia,32.9256,44.7758,77200 +Aşkale,Turkey,Europe,39.9211,40.6947,21494 +Aţ Ţafīlah,Jordan,Asia,30.8375,35.6044,23512 +Aţ Ţūr,Egypt,Africa,28.2333,33.6167,12733 +Aḑ Ḑāli‘,Yemen,Asia,13.6957,44.7314,21783 +Ba Đồn,Vietnam,Asia,17.7547,106.4231,134000 +Baabda,Lebanon,Asia,33.8333,35.5333,84900 +Baalbek,Lebanon,Asia,34.0063,36.2073,81052 +Baarn,Netherlands,Europe,52.2117,5.2875,24792 +Babaeski,Turkey,Europe,41.4325,27.0931,29215 +Babahoyo,Ecuador,South America,-1.8167,-79.5167,98251 +Babati,Tanzania,Africa,-4.2167,35.75,67445 +Babruysk,Belarus,Europe,53.15,29.2333,207351 +Babylon,United States,North America,40.6925,-73.3586,217860 +Babīlā,Syria,Asia,33.471,36.3308,50880 +Babək,Azerbaijan,Europe,39.1519,45.4417,5600 +Bacabal,Brazil,South America,-4.225,-44.78,103711 +Baclayon,Philippines,Asia,9.6227,123.9135,22461 +Bacolod,Philippines,Asia,10.6765,122.9509,600783 +Bacuri,Brazil,South America,-1.7028,-45.1339,16290 +Bacău,Romania,Europe,46.5833,26.9167,136087 +Bad Dürkheim,Germany,Europe,49.4594,8.1681,18821 +Bad Ems,Germany,Europe,50.3381,7.7106,10002 +Bad Fallingbostel,Germany,Europe,52.8675,9.6967,12119 +Bad Hersfeld,Germany,Europe,50.8683,9.7075,30770 +Bad Homburg,Germany,Europe,50.2167,8.6,55995 +Bad Kissingen,Germany,Europe,50.2,10.0667,23245 +Bad Kreuznach,Germany,Europe,49.85,7.8667,52989 +Bad Neuenahr-Ahrweiler,Germany,Europe,50.5447,7.1133,27647 +Bad Neustadt,Germany,Europe,50.3219,10.2161,15720 +Bad Oldesloe,Germany,Europe,53.8117,10.3742,25104 +Bad Reichenhall,Germany,Europe,47.7247,12.8769,19087 +Bad Salzungen,Germany,Europe,50.8117,10.2333,23133 +Bad Schwalbach,Germany,Europe,50.1333,8.0667,11602 +Bad Segeberg,Germany,Europe,53.9167,10.3167,18891 +Bad Tölz,Germany,Europe,47.7603,11.5567,19360 +Badajoz,Spain,Europe,38.8803,-6.9753,150570 +Baden,Austria,Europe,48.0075,16.2344,26286 +Baden-Baden,Germany,Europe,48.7628,8.2408,57420 +Badian,Philippines,Asia,9.8694,123.3959,43735 +Badin,Pakistan,Asia,24.6572,68.8406,117455 +Badulla,Sri Lanka,Asia,6.9847,81.0564,42923 +Bady Bassitt,Brazil,South America,-20.9178,-49.445,27260 +Baependi,Brazil,South America,-21.9589,-44.89,18366 +Baetov,Kyrgyzstan,Asia,41.2667,74.957,10682 +Bafatá,Guinea-Bissau,Africa,12.1719,-14.6575,29556 +Bafoulabé,Mali,Africa,13.8064,-10.8322,19955 +Bafoussam,Cameroon,Africa,5.4667,10.4167,600000 +Bafra,Turkey,Europe,41.5722,35.9147,143109 +Bagadó,Colombia,South America,5.4167,-76.4167,13174 +Bagan Si Api-api,Indonesia,Asia,2.1667,100.8167,73360 +Bagh,Pakistan,Asia,33.9735,73.7918,33548 +Baghdad,Iraq,Asia,33.3153,44.3661,6183000 +Baghlān,Afghanistan,Asia,36.1328,68.7,83490 +Bago,Burma,Asia,17.3333,96.4833,284179 +Bagre,Brazil,South America,-1.9,-50.1639,13708 +Bagrāmī,Afghanistan,Asia,34.4911,69.2756,31680 +Baguio City,Philippines,Asia,16.4119,120.5933,366358 +Bagé,Brazil,South America,-31.3308,-54.1069,117938 +Bahawalnagar,Pakistan,Asia,29.9928,73.2536,141935 +Bahawalpur,Pakistan,Asia,29.3956,71.6836,762111 +Bahir Dar,Ethiopia,Africa,11.6,37.3833,243300 +Bahçe,Turkey,Europe,37.2,36.5833,16110 +Bahçesaray,Turkey,Europe,38.1286,42.8075,13495 +Bahía Blanca,Argentina,South America,-38.7167,-62.2667,301572 +Bahía Honda,Cuba,North America,22.9064,-83.1639,43483 +Bahía de Caráquez,Ecuador,South America,-0.5995,-80.4245,22209 +Bahār,Iran,Asia,34.9078,48.4394,673405 +Baia Mare,Romania,Europe,47.6567,23.5719,108759 +Baicheng,China,Asia,23.903,106.619,3571505 +Baidoa,Somalia,Africa,3.1167,43.65,1200000 +Baiquan,China,Asia,47.6018,126.0819,70472 +Baishan,China,Asia,41.944,126.4147,968373 +Baixa Grande,Brazil,South America,-11.96,-40.1678,18220 +Baixo Guandu,Brazil,South America,-19.5189,-41.0158,27819 +Baiyashi,China,Asia,26.4019,111.2864,130256 +Baiyin,China,Asia,36.5451,104.1389,1512110 +Baião,Brazil,South America,-2.7908,-49.6719,51641 +Baja,Hungary,Europe,46.1833,18.9536,32759 +Bajestān,Iran,Asia,34.5178,58.1844,11741 +Bajina Bašta,Serbia,Europe,43.9731,19.5597,26022 +Bajo Boquete,Panama,North America,8.78,-82.44,19000 +Bakersfield,United States,North America,35.3529,-119.0359,578542 +Bakhchysarai,Ukraine,Europe,44.7528,33.8608,27448 +Bakhmut,Ukraine,Europe,48.5947,38.0008,71094 +Baksan,Russia,Asia,43.6825,43.5339,40010 +Baku,Azerbaijan,Europe,40.3667,49.8352,2300500 +Bala,Turkey,Europe,39.5539,33.1211,20521 +Balad,Iraq,Asia,34.0147,44.1456,62817 +Balakən,Azerbaijan,Europe,41.7186,46.4165,1815 +Balancán,Mexico,North America,17.8,-91.5333,58524 +Balanga,Philippines,Asia,14.68,120.53,104173 +Balashikha,Russia,Asia,55.8167,37.9667,530311 +Balassagyarmat,Hungary,Europe,48.0712,19.2937,13917 +Balatonalmádi,Hungary,Europe,47.0292,18.0219,10000 +Balatonfüred,Hungary,Europe,46.95,17.8833,12925 +Balboa,Colombia,South America,2.0436,-77.2197,26384 +Balcarce,Argentina,South America,-37.8456,-58.2578,44796 +Balchik,Bulgaria,Europe,43.4233,28.142,12429 +Baler,Philippines,Asia,15.7583,121.5625,43785 +Baley,Russia,Asia,51.6,116.6333,11151 +Balingen,Germany,Europe,48.2731,8.8506,35054 +Baliqchi,Uzbekistan,Asia,40.9,71.8528,25100 +Balkanabat,Turkmenistan,Asia,39.5167,54.3667,188429 +Balkh,Afghanistan,Asia,36.7581,66.8981,77000 +Balmazújváros,Hungary,Europe,47.6167,21.35,16948 +Balneário de Camboriú,Brazil,South America,-26.9908,-48.635,139155 +Balneário do Rincão,Brazil,South America,-28.8344,-49.2361,15981 +Balpyq Bī,Kazakhstan,Asia,44.8947,78.2083,12654 +Balqash,Kazakhstan,Asia,46.8481,74.995,78002 +Balsa Nova,Brazil,South America,-25.5839,-49.6358,13395 +Balsas,Brazil,South America,-7.5325,-46.0356,107767 +Balta,Ukraine,Europe,47.94,29.6219,17854 +Baltimore,United States,North America,39.3051,-76.6144,2189589 +Baltiysk,Russia,Asia,54.65,19.9167,27032 +Balvi,Latvia,Europe,57.1292,27.2667,5584 +Balya,Turkey,Europe,39.75,27.5833,12451 +Balzan,Malta,Europe,35.8981,14.4533,3958 +Balzar,Ecuador,South America,-1.36,-79.9,32744 +Balzers,Liechtenstein,Europe,47.0667,9.5,4642 +Baláo,Ecuador,South America,-2.91,-79.81,12850 +Balıkesir,Turkey,Europe,39.6333,27.8833,314958 +Bam,Iran,Asia,29.0994,58.3436,127396 +Bama,Nigeria,Africa,11.5189,13.6842,118121 +Bamako,Mali,Africa,12.6458,-7.9922,4227569 +Bambari,Central African Republic,Africa,5.7653,20.6742,41356 +Bamberg,Germany,Europe,49.8914,10.8869,80580 +Bambuí,Brazil,South America,-20.0058,-45.9769,23546 +Bamenda,Cameroon,Africa,5.9614,10.1517,600000 +Bamiantong,China,Asia,44.9167,130.5167,197065 +Ban Bueng,Thailand,Asia,13.3142,101.1114,19346 +Ban Chang,Thailand,Asia,12.7208,101.0669,30983 +Ban Dung,Thailand,Asia,17.6986,103.2611,15836 +Ban Houayxay,Laos,Asia,20.2631,100.4336,17687 +Ban Laem,Thailand,Asia,13.2047,99.9796,12541 +Ban Na San,Thailand,Asia,8.8004,99.3637,19801 +Ban Phai,Thailand,Asia,16.06,102.731,28913 +Ban Pong,Thailand,Asia,13.8163,99.8774,17298 +Banabuiú,Brazil,South America,-5.31,-38.9208,17195 +Banamba,Mali,Africa,13.55,-7.45,30591 +Bananal,Brazil,South America,-22.6839,-44.3228,9969 +Bananeiras,Brazil,South America,-6.75,-35.6328,23134 +Banaybanay,Philippines,Asia,6.9664,126.0114,44451 +Banaz,Turkey,Europe,38.7333,29.75,35181 +Banda Aceh,Indonesia,Asia,5.55,95.3175,235305 +Banda del Río Salí,Argentina,South America,-26.85,-65.1667,63226 +Bandar Lampung,Indonesia,Asia,-5.45,105.2667,1166761 +Bandar Seri Begawan,Brunei,Asia,4.8903,114.9422,50000 +Bandar ‘Abbās,Iran,Asia,27.1961,56.2878,526648 +Bandar-e Anzalī,Iran,Asia,37.4708,49.47,118564 +Bandar-e Būshehr,Iran,Asia,28.9264,50.8514,223504 +Bandar-e Deylam,Iran,Asia,30.055,50.1653,25730 +Bandar-e Gaz,Iran,Asia,36.7739,53.9481,20742 +Bandar-e Genāveh,Iran,Asia,29.5808,50.5172,73472 +Bandar-e Khamīr,Iran,Asia,26.9533,55.5853,15320 +Bandar-e Lengeh,Iran,Asia,26.5594,54.885,30435 +Bandar-e Māhshahr,Iran,Asia,30.5567,49.1897,162797 +Bandar-e Torkaman,Iran,Asia,36.9014,54.0736,53970 +Bandar-e ‘Asalūyeh,Iran,Asia,27.4744,52.6114,13557 +Bandeirantes,Brazil,South America,-23.11,-50.3678,31273 +Bandiagara,Mali,Africa,14.35,-3.6111,25564 +Bandundu,Congo (Kinshasa),Africa,-3.3167,17.3667,143435 +Bandung,Indonesia,Asia,-6.912,107.6097,432557 +Bandırma,Turkey,Europe,40.35,27.9667,167363 +Banes,Cuba,North America,20.9697,-75.7117,34452 +Banfora,Burkina Faso,Africa,10.6308,-4.7589,117200 +Bang Ban,Thailand,Asia,14.3739,100.4853,11092 +Bang Bua Thong,Thailand,Asia,13.9102,100.4258,50141 +Bang Khla,Thailand,Asia,13.7214,101.2081,9311 +Bang Kruai,Thailand,Asia,13.805,100.4728,42390 +Bang Phae,Thailand,Asia,13.6916,99.9298,16130 +Bang Phlat,Thailand,Asia,13.8247,100.4908,12482 +Bang Racham,Thailand,Asia,14.9,100.3167,13937 +Bang Sao Thong,Thailand,Asia,13.595,100.8304,23630 +Bangalore,India,Asia,12.9789,77.5917,15386000 +Bangar,Brunei,Asia,4.7086,115.0739,451 +Bangassou,Central African Republic,Africa,4.7374,22.8195,31453 +Bangil,Indonesia,Asia,-7.5978,112.7846,84240 +Bangkalan,Indonesia,Asia,-7.0253,112.7519,89550 +Bangkinang,Indonesia,Asia,0.35,101.0333,37847 +Bangkok,Thailand,Asia,13.7525,100.4942,18007000 +Bangued,Philippines,Asia,17.6,120.62,50382 +Bangui,Central African Republic,Africa,4.3733,18.5628,889231 +Banhā,Egypt,Africa,30.4628,31.1797,157701 +Banja Luka,Bosnia and Herzegovina,Europe,44.7725,17.1925,185042 +Banjar,Indonesia,Asia,-7.3695,108.5414,203417 +Banjarmasin,Indonesia,Asia,-3.32,114.5925,662230 +Banjul,"Gambia, The",Africa,13.4581,-16.5786,413397 +Bankass,Mali,Africa,14.0775,-3.5158,30159 +Banlung,Cambodia,Asia,13.7394,106.9873,30399 +Bannu,Pakistan,Asia,32.9864,70.6044,62191 +Banovići,Bosnia and Herzegovina,Europe,44.4089,18.5287,17131 +Banská Bystrica,Slovakia,Europe,48.7353,19.1453,76018 +Banská Štiavnica,Slovakia,Europe,48.4586,18.8931,9628 +Banyuwangi,Indonesia,Asia,-8.2186,114.3669,117558 +Baní,Dominican Republic,North America,18.29,-70.33,145595 +Banī Suwayf,Egypt,Africa,29.0667,31.0833,193048 +Baocheng,China,Asia,18.6431,109.6994,33138 +Baoding,China,Asia,38.874,115.464,10546831 +Bar,Montenegro,Europe,42.1,19.1,15868 +Bar-le-Duc,France,Europe,48.7719,5.1603,14615 +Barabai,Indonesia,Asia,-2.5833,115.3833,55687 +Baracoa,Cuba,North America,20.3444,-74.4956,78056 +Baradero,Argentina,South America,-33.8,-59.5167,28537 +Barahona,Dominican Republic,North America,18.2079,-71.0996,83619 +Barakī Barak,Afghanistan,Asia,33.9692,68.9447,22622 +Baranavichy,Belarus,Europe,53.1333,26.0167,171361 +Baranoa,Colombia,South America,10.8,-74.9167,51730 +Baraúna,Brazil,South America,-5.08,-37.6169,18922 +Barbacena,Brazil,South America,-21.2258,-43.7739,125317 +Barbacoas,Colombia,South America,1.6717,-78.1397,38708 +Barbalha,Brazil,South America,-7.3111,-39.3042,61228 +Barberena,Guatemala,North America,14.3167,-90.3667,61079 +Barbosa,Colombia,South America,6.439,-75.333,53167 +Barbosa Ferraz,Brazil,South America,-24.03,-52.0119,10795 +Barcarena,Brazil,South America,-1.5058,-48.6258,126650 +Barcelona,Spain,Europe,41.3833,2.1833,4800000 +Barcelos,Portugal,Europe,41.5333,-8.6167,116752 +Barclayville,Liberia,Africa,4.6797,-8.2339,2733 +Barcs,Hungary,Europe,45.96,17.46,9672 +Barda,Russia,Asia,56.9167,55.5833,9972 +Bardaskan,Iran,Asia,35.2625,57.9703,28233 +Bardejov,Slovakia,Europe,49.295,21.2758,30267 +Bardsīr,Iran,Asia,29.9275,56.5764,25152 +Barendrecht,Netherlands,Europe,51.85,4.5333,48643 +Barentu,Eritrea,Africa,15.1167,37.6,18500 +Bari,Italy,Europe,41.1253,16.8667,316015 +Bariadi,Tanzania,Africa,-2.7919,33.9894,260927 +Barinas,Venezuela,South America,8.6333,-70.2,397279 +Bariri,Brazil,South America,-22.0744,-48.7403,35558 +Barishal,Bangladesh,Asia,22.7,90.3667,230000 +Barletta,Italy,Europe,41.3167,16.2833,92427 +Barnaul,Russia,Asia,53.3486,83.7764,623057 +Barneveld,Netherlands,Europe,52.1333,5.5833,59992 +Barobo,Philippines,Asia,8.5292,126.1217,53146 +Barouéli,Mali,Africa,13.0769,-6.8311,32739 +Barquisimeto,Venezuela,South America,10.0636,-69.3347,1240714 +Barra,Brazil,South America,-11.0889,-43.1419,51092 +Barra Bonita,Brazil,South America,-22.4947,-48.5581,36321 +Barra Mansa,Brazil,South America,-22.5439,-44.1708,169894 +Barra Velha,Brazil,South America,-26.6319,-48.685,15530 +Barra da Estiva,Brazil,South America,-13.6258,-41.3269,26026 +Barra de Santa Rosa,Brazil,South America,-6.72,-36.0608,12904 +Barra de Santo Antônio,Brazil,South America,-9.4,-35.5,16365 +Barra de São Francisco,Brazil,South America,-18.755,-40.8908,37597 +Barra do Bugres,Brazil,South America,-15.0725,-57.1811,35307 +Barra do Choça,Brazil,South America,-14.8808,-40.5789,36539 +Barra do Corda,Brazil,South America,-5.5031,-45.2369,88492 +Barra do Garças,Brazil,South America,-15.89,-52.2569,69210 +Barra do Mendes,Brazil,South America,-11.81,-42.0589,13836 +Barra do Piraí,Brazil,South America,-22.47,-43.8258,92883 +Barra do Ribeiro,Brazil,South America,-30.2908,-51.3008,12225 +Barra do Sul,Brazil,South America,-26.4682,-48.6104,14912 +Barra dos Coqueiros,Brazil,South America,-10.9089,-37.0389,41511 +Barracão,Brazil,South America,-26.2539,-53.6328,9759 +Barrancabermeja,Colombia,South America,7.0667,-73.85,300000 +Barrancas,Colombia,South America,11.0,-72.75,37554 +Barranco de Loba,Colombia,South America,8.95,-74.1167,14435 +Barranquilla,Colombia,South America,10.9833,-74.8019,1326588 +Barras,Brazil,South America,-4.2469,-42.2958,47938 +Barreira,Brazil,South America,-4.2869,-38.6428,17024 +Barreiras,Brazil,South America,-12.1528,-44.99,131849 +Barreirinha,Brazil,South America,-2.8025,-57.0686,22579 +Barreirinhas,Brazil,South America,-2.7469,-42.8258,39669 +Barreiro,Portugal,Europe,38.6631,-9.0725,78345 +Barreiros,Brazil,South America,-8.8183,-35.1864,42764 +Barretos,Brazil,South America,-20.5569,-48.5678,122485 +Barrie,Canada,North America,44.3711,-79.6769,154676 +Barrinha,Brazil,South America,-21.1936,-48.1639,31230 +Barro,Brazil,South America,-7.1769,-38.7819,19381 +Barro Alto,Brazil,South America,-11.7608,-41.9119,13453 +Barrocas,Brazil,South America,-11.5289,-39.0778,15203 +Barroquinha,Brazil,South America,-3.0189,-41.1369,14476 +Barros Cassal,Brazil,South America,-29.0928,-52.5828,9296 +Barroso,Brazil,South America,-21.1869,-43.9758,20080 +Barru,Indonesia,Asia,-4.3587,119.6399,44817 +Bartica,Guyana,South America,6.4,-58.6167,8532 +Bartolomé Masó,Cuba,North America,20.1686,-76.9428,56460 +Bartoszyce,Poland,Europe,54.25,20.8,22281 +Bartın,Turkey,Europe,41.6344,32.3375,81692 +Barueri,Brazil,South America,-23.5111,-46.8764,316473 +Baruta,Venezuela,South America,10.4322,-66.8739,240755 +Baruun-Urt,Mongolia,Asia,46.6829,113.2786,14297 +Barão de Cocais,Brazil,South America,-19.9458,-43.4869,30778 +Barão do Grajaú,Brazil,South America,-6.7558,-43.0239,18984 +Basarabeasca,Moldova,Europe,46.3336,28.9614,8471 +Basco,Philippines,Asia,20.45,121.97,9517 +Basel,Switzerland,Europe,47.5606,7.5906,173552 +Bashtanka,Ukraine,Europe,47.4056,32.4375,12180 +Basse Santa Su,"Gambia, The",Africa,13.3167,-14.2167,18414 +Basse-Terre,Guadeloupe,North America,16.0104,-61.7055,307 +Basseterre,Saint Kitts and Nevis,North America,17.3,-62.7333,13220 +Bastak,Iran,Asia,27.1953,54.3678,9959 +Bastogne,Belgium,Europe,50.0042,5.72,15894 +Bastos,Brazil,South America,-21.9167,-50.7333,21503 +Basuo,China,Asia,19.092,108.671,167636 +Bata,Equatorial Guinea,Africa,1.865,9.77,250770 +Batabanó,Cuba,North America,22.7167,-82.2833,25664 +Batagay,Russia,Asia,67.656,134.635,4266 +Bataguaçu,Brazil,South America,-21.7139,-52.4219,23031 +Bataiporã,Brazil,South America,-22.2953,-53.2711,11349 +Batalha,Brazil,South America,-4.0239,-42.0778,26300 +Batang,Indonesia,Asia,-6.9081,109.7304,136145 +Batangas,Philippines,Asia,13.83,121.0,351437 +Batatais,Brazil,South America,-20.8911,-47.585,62980 +Batgram,Pakistan,Asia,34.6833,73.0167,27083 +Batié,Burkina Faso,Africa,9.8833,-2.9167,17997 +Batken,Kyrgyzstan,Asia,40.0625,70.8194,27730 +Batman,Turkey,Europe,37.887,41.132,452157 +Batna,Algeria,Africa,35.55,6.1667,290645 +Baton Rouge,United States,North America,30.442,-91.1311,630063 +Batočina,Serbia,Europe,44.15,21.0833,11760 +Batroûn,Lebanon,Asia,34.25,35.65,45000 +Battalgazi,Turkey,Europe,38.4228,38.3656,307478 +Battambang,Cambodia,Asia,13.1028,103.1983,130000 +Batumi,Georgia,Europe,41.6458,41.6417,169095 +Baturité,Brazil,South America,-4.3289,-38.885,35218 +Baucau,Timor-Leste,Asia,-8.4757,126.4563,14961 +Bauchi,Nigeria,Africa,10.5,10.0,693700 +Baure,Nigeria,Africa,12.7833,8.7667,197425 +Bauru,Brazil,South America,-22.3147,-49.0606,366992 +Bauska,Latvia,Europe,56.4067,24.1875,9811 +Bauta,Cuba,North America,22.9919,-82.5492,23557 +Bautzen,Germany,Europe,51.1814,14.4242,38039 +Bayamo,Cuba,North America,20.3795,-76.6433,235107 +Bayan Hot,China,Asia,38.8556,105.7001,94445 +Bayanhongor,Mongolia,Asia,46.1944,100.7181,26252 +Bayburt,Turkey,Europe,40.2597,40.2278,48036 +Bayeux,Brazil,South America,-7.1333,-34.9333,82742 +Bayghanīn,Kazakhstan,Asia,48.6917,55.874,7657 +Bayjī,Iraq,Asia,34.9292,43.4931,60000 +Baymak,Russia,Asia,52.5833,58.3167,17833 +Bayombong,Philippines,Asia,16.48,121.15,67714 +Bayonne,France,Europe,43.49,-1.48,53312 +Bayramiç,Turkey,Europe,39.8128,26.61,15760 +Bayreuth,Germany,Europe,49.9481,11.5783,74907 +Bayt al Faqīh,Yemen,Asia,14.5161,43.3244,41652 +Bayındır,Turkey,Europe,38.2192,27.6481,40073 +Bazar-Korgon,Kyrgyzstan,Asia,41.0376,72.7459,41011 +Bazhou,China,Asia,31.868,106.748,2712894 +Baía da Traição,Brazil,South America,-6.6878,-34.9358,9224 +Bač,Serbia,Europe,45.3833,19.2333,6087 +Bačka Palanka,Serbia,Europe,45.25,19.4,25476 +Bačka Topola,Serbia,Europe,45.8167,19.6333,11930 +Bački Petrovac,Serbia,Europe,45.3606,19.5917,7452 +Bağlar,Turkey,Europe,37.9167,40.1833,406471 +Başkale,Turkey,Europe,38.0475,44.015,44168 +Baūyrzhan Momyshuly,Kazakhstan,Asia,42.6189,70.7597,12491 +Ba‘qūbah,Iraq,Asia,33.7447,44.6436,467900 +Bcharré,Lebanon,Asia,34.2511,36.0111,24000 +Beaumont,United States,North America,30.0849,-94.1451,146554 +Beaune,France,Europe,47.025,4.8397,20233 +Beauvais,France,Europe,49.4303,2.0952,55906 +Bebandem,Indonesia,Asia,-8.4046,115.5395,56800 +Bebedouro,Brazil,South America,-20.9494,-48.4792,77627 +Beberibe,Brazil,South America,-4.18,-38.1308,53114 +Becerril,Colombia,South America,9.7,-73.2833,20477 +Bedum,Netherlands,Europe,53.3003,6.6025,8650 +Beek,Netherlands,Europe,50.9394,5.7961,15875 +Beek en Donk,Netherlands,Europe,51.5161,5.6208,22805 +Beersheba,Israel,Asia,31.2522,34.7867,209000 +Behbahān,Iran,Asia,30.5942,50.2433,122604 +Behshahr,Iran,Asia,36.6936,53.5531,94702 +Beian,China,Asia,48.25,126.5167,308237 +Beichengqu,China,Asia,40.437,113.11,195229 +Beihai,China,Asia,21.481,109.12,1680000 +Beijing,China,Asia,39.9067,116.3975,18522000 +Beilen,Netherlands,Europe,52.8567,6.5111,14089 +Beira,Mozambique,Africa,-19.8333,34.85,530604 +Beirut,Lebanon,Asia,33.8981,35.5058,2421354 +Beja,Portugal,Europe,38.015,-7.8633,35854 +Bejaïa,Algeria,Africa,36.7511,5.0642,176139 +Bejucal,Cuba,North America,22.9328,-82.3869,28205 +Bela Crkva,Serbia,Europe,44.8975,21.4172,10675 +Bela Cruz,Brazil,South America,-3.0508,-40.1678,32775 +Bela Palanka,Serbia,Europe,43.2178,22.3067,12126 +Bela Vista,Brazil,South America,-22.1089,-56.5208,21613 +Bela Vista de Goiás,Brazil,South America,-16.9728,-48.9528,34445 +Bela Vista de Minas,Brazil,South America,-19.83,-43.0908,10167 +Bela Vista do Paraíso,Brazil,South America,-22.9969,-51.1908,14833 +Belalcázar,Colombia,South America,2.6547,-75.9928,36628 +Belaya Glina,Russia,Asia,46.0833,40.8583,9203 +Belaya Kalitva,Russia,Asia,48.1833,40.7667,40448 +Belebey,Russia,Asia,54.1,54.1333,59195 +Beledweyne,Somalia,Africa,4.736,45.204,62945 +Belen,Turkey,Europe,36.4889,36.1944,34449 +Belfort,France,Europe,47.64,6.85,45646 +Belgorod,Russia,Asia,50.6,36.6,328482 +Belgrade,Serbia,Europe,44.8178,20.4569,1197714 +Belize City,Belize,North America,17.4986,-88.1886,38500 +Belišće,Croatia,Europe,45.6825,18.4069,8884 +Bell Ville,Argentina,South America,-32.6333,-62.6833,33835 +Bella Vista,Argentina,South America,-28.4667,-59.05,29071 +Bellavista,Colombia,South America,6.5236,-76.9744,9941 +Bellevue,United States,North America,47.5951,-122.1535,151199 +Belley,France,Europe,45.7667,5.6833,9270 +Bellingham,United States,North America,48.7548,-122.469,128737 +Bellinzona,Switzerland,Europe,46.1956,9.0238,43785 +Bello,Colombia,South America,6.3333,-75.5667,561955 +Belluno,Italy,Europe,46.1408,12.2156,35529 +Belmonte,Brazil,South America,-15.8628,-38.8828,20121 +Belmopan,Belize,North America,17.2522,-88.7639,4000 +Belo Campo,Brazil,South America,-15.0378,-41.26,18412 +Belo Horizonte,Brazil,South America,-19.9281,-43.9419,5328000 +Belo Jardim,Brazil,South America,-8.3358,-36.4239,79507 +Belo Oriente,Brazil,South America,-19.22,-42.4839,23928 +Belo Vale,Brazil,South America,-20.4078,-44.0239,8627 +Belomorsk,Russia,Asia,64.5253,34.7658,7407 +Belorechensk,Russia,Asia,44.7686,39.8733,55010 +Beloretsk,Russia,Asia,53.9667,58.4,64525 +Belovodskoe,Kyrgyzstan,Asia,42.8333,74.1167,21275 +Beloyarskiy,Russia,Asia,63.7167,66.6667,19994 +Belterra,Brazil,South America,-2.6358,-54.9369,18099 +Beltinci,Slovenia,Europe,46.606,16.2328,2430 +Belém,Brazil,South America,-1.4558,-48.5039,1280614 +Belém de Maria,Brazil,South America,-8.6139,-35.8089,10378 +Belém de São Francisco,Brazil,South America,-8.7578,-38.9639,18301 +Belén,Nicaragua,North America,11.5,-85.8833,16428 +Belén de Escobar,Argentina,South America,-34.3333,-58.8167,54678 +Belén de Umbría,Colombia,South America,5.2,-75.8667,25276 +Belén de los Andaquíes,Colombia,South America,1.4161,-75.8725,11081 +Belëv,Russia,Asia,53.8125,36.1333,12746 +Belčišta,North Macedonia,Europe,41.3028,20.8303,297 +Ben,Iran,Asia,32.5447,50.7475,12971 +Ben Arous,Tunisia,Africa,36.7472,10.3333,96606 +Benavente,Portugal,Europe,38.9833,-8.8167,29019 +Bend,United States,North America,44.0563,-121.3095,113454 +Bender,Moldova,Europe,46.8333,29.4833,76158 +Benedikt,Slovenia,Europe,46.6086,15.8883,1078 +Beneditinos,Brazil,South America,-5.455,-42.36,9929 +Benedito Novo,Brazil,South America,-26.7828,-49.3639,10520 +Benemérito,Mexico,North America,16.5172,-90.6531,10026 +Benevento,Italy,Europe,41.1256,14.7811,58418 +Benevides,Brazil,South America,-1.3608,-48.245,63567 +Benghazi,Libya,Africa,32.1167,20.0667,631555 +Bengkalis,Indonesia,Asia,1.4897,102.0797,66211 +Bengkulu,Indonesia,Asia,-3.7956,102.2592,360495 +Benguela,Angola,Africa,-12.55,13.4167,512886 +Beni Abbès,Algeria,Africa,30.08,-2.1,10885 +Benin City,Nigeria,Africa,6.3333,5.6222,1780000 +Benito Juárez,Mexico,North America,20.8833,-98.2,17618 +Benjamin Constant,Brazil,South America,-4.3755,-70.0318,37648 +Benkovac,Croatia,Europe,44.0344,15.6128,9680 +Bensonville,Liberia,Africa,6.4456,-10.6097,4089 +Bent Jbaïl,Lebanon,Asia,33.1208,35.4336,30000 +Bentiu,South Sudan,Africa,9.2333,29.8333,7781 +Bento Gonçalves,Brazil,South America,-29.1708,-51.5189,123151 +Benua Baru,Indonesia,Asia,0.9878,117.98,24245 +Beočin,Serbia,Europe,45.1922,19.7203,8058 +Bequimão,Brazil,South America,-2.4489,-44.7828,19584 +Berane,Montenegro,Europe,42.84,19.86,11073 +Berat,Albania,Europe,40.7049,19.9497,36496 +Berazategui,Argentina,South America,-34.7167,-58.25,180523 +Berbérati,Central African Republic,Africa,4.2614,15.7894,76918 +Berdiansk,Ukraine,Europe,46.7556,36.7889,106311 +Berdychiv,Ukraine,Europe,49.9,28.5667,73046 +Berehove,Ukraine,Europe,48.2056,22.6472,23325 +Berettyóújfalu,Hungary,Europe,47.2167,21.55,13999 +Berezhany,Ukraine,Europe,49.4458,24.9361,17139 +Berezivka,Ukraine,Europe,47.2039,30.9128,9428 +Berezniki,Russia,Asia,59.4167,56.7833,134098 +Bergama,Turkey,Europe,39.1167,27.1833,105754 +Bergambacht,Netherlands,Europe,51.9328,4.7936,9801 +Bergamo,Italy,Europe,45.695,9.67,119534 +Bergeijk,Netherlands,Europe,51.3203,5.3592,18754 +Bergen,Norway,Europe,60.3925,5.3233,291189 +Bergen op Zoom,Netherlands,Europe,51.5,4.3,67514 +Bergerac,France,Europe,44.85,0.48,26852 +Bergheim,Germany,Europe,50.9667,6.65,62172 +Bergisch Gladbach,Germany,Europe,50.9918,7.1303,112660 +Berilo,Brazil,South America,-16.9519,-42.4658,9826 +Berisso,Argentina,South America,-34.8728,-57.8858,88470 +Berkeley,United States,North America,37.8722,-122.276,120223 +Berkovitsa,Bulgaria,Europe,43.2371,23.1251,12589 +Berlin,Germany,Europe,52.52,13.405,4679500 +Bern,Switzerland,Europe,46.948,7.4474,134506 +Bernardino de Campos,Brazil,South America,-23.0131,-49.4742,11157 +Bernardo de Irigoyen,Argentina,South America,-26.25,-53.9833,10889 +Bernay,France,Europe,49.09,0.6,9801 +Bernburg,Germany,Europe,51.8,11.7333,32106 +Berovo,North Macedonia,Europe,41.7078,22.8564,5850 +Berriozábal,Mexico,North America,16.8003,-93.2733,36084 +Bershad,Ukraine,Europe,48.3728,29.5325,11742 +Bertioga,Brazil,South America,-23.8539,-46.1389,64188 +Bertoua,Cameroon,Africa,4.5833,13.6833,88462 +Beruri,Brazil,South America,-3.9022,-61.3714,20718 +Beryslav,Ukraine,Europe,46.8333,33.4167,11895 +Berëzovskiy,Russia,Asia,56.9,60.8167,60566 +Besançon,France,Europe,47.24,6.02,120057 +Beshariq,Uzbekistan,Asia,40.4358,70.6103,22800 +Beshkent Shahri,Uzbekistan,Asia,38.8167,65.65,18900 +Beslan,Russia,Asia,43.1833,44.55,36011 +Besni,Turkey,Europe,37.6942,37.8614,37323 +Best,Netherlands,Europe,51.5108,5.3922,30216 +Betania,Colombia,South America,5.75,-75.9667,9286 +Betim,Brazil,South America,-19.9678,-44.1978,306675 +Betong,Thailand,Asia,5.7731,101.0725,24688 +Betulia,Colombia,South America,6.1122,-75.9839,17317 +Betun,Indonesia,Asia,-9.4953,124.9603,43016 +Betânia,Brazil,South America,-8.2767,-38.0339,11232 +Beuningen,Netherlands,Europe,51.8667,5.7667,26157 +Beverwijk,Netherlands,Europe,52.4833,4.65,41863 +Beydağ,Turkey,Europe,38.0833,28.2333,12030 +Beykoz,Turkey,Europe,41.1342,29.0922,247875 +Beyla,Guinea,Africa,8.6833,-8.6333,13204 +Beyləqan,Azerbaijan,Europe,39.7692,47.6156,12263 +Beyneū,Kazakhstan,Asia,45.3247,55.1958,40344 +Beypazarı,Turkey,Europe,40.1703,31.9211,48357 +Beyşehir,Turkey,Europe,37.6764,31.7261,77690 +Bezerros,Brazil,South America,-8.2333,-35.75,57371 +Bezhetsk,Russia,Asia,57.7667,36.7,21466 +Bečej,Serbia,Europe,45.6167,20.0333,30681 +Bełchatów,Poland,Europe,51.3667,19.3667,54338 +Beşikdüzü,Turkey,Europe,41.0494,39.2353,23774 +Beşiri,Turkey,Europe,37.921,41.292,11120 +Bhairāhawā,Nepal,Asia,27.5,83.45,63483 +Bhakkar,Pakistan,Asia,31.6278,71.0625,88472 +Bhisho,South Africa,Africa,-32.8494,27.4381,11192 +Bhopāl,India,Asia,23.2599,77.4126,1798218 +Bhubaneshwar,India,Asia,20.27,85.84,837737 +Biak,Indonesia,Asia,-1.18,136.08,103610 +Biankouma,Côte d’Ivoire,Africa,7.7333,-7.6167,71470 +Biała Podlaska,Poland,Europe,52.0333,23.1167,55429 +Białogard,Poland,Europe,54.0,15.9833,23142 +Białystok,Poland,Europe,53.1353,23.1456,294242 +Biberach,Germany,Europe,48.1,9.7833,34331 +Bicas,Brazil,South America,-21.725,-43.0589,14554 +Bicske,Hungary,Europe,47.4907,18.6363,11248 +Bida,Nigeria,Africa,9.0804,6.01,173849 +Bielefeld,Germany,Europe,52.0211,8.5347,338410 +Biella,Italy,Europe,45.5664,8.0533,42619 +Bielsk Podlaski,Poland,Europe,52.7667,23.2,24633 +Bielsko-Biała,Poland,Europe,49.8225,19.0444,169089 +Bieruń,Poland,Europe,50.1333,19.1,19334 +Biga,Turkey,Europe,40.2281,27.2422,57125 +Bigadiç,Turkey,Europe,39.3925,28.1311,48917 +Biguaçu,Brazil,South America,-27.4942,-48.6556,69486 +Bihać,Bosnia and Herzegovina,Europe,44.8147,15.8692,56261 +Bijeljina,Bosnia and Herzegovina,Europe,44.7567,19.2156,114663 +Bijelo Polje,Montenegro,Europe,43.04,19.75,12900 +Bikin,Russia,Asia,46.8167,134.2667,1126 +Bila Tserkva,Ukraine,Europe,49.7989,30.1153,207273 +Bilbao,Spain,Europe,43.2569,-2.9236,775000 +Bilecik,Turkey,Europe,40.1431,29.9792,74457 +Bileća,Bosnia and Herzegovina,Europe,42.8667,18.4333,10807 +Bilhorod-Dnistrovskyi,Ukraine,Europe,46.1833,30.35,47727 +Biliaivka,Ukraine,Europe,46.0,28.0,12355 +Bilibino,Russia,Asia,68.05,166.45,5449 +Billings,United States,North America,45.7891,-108.5526,133173 +Bilohirsk,Ukraine,Europe,45.0544,34.6022,16354 +Bilozerka,Ukraine,Europe,46.6278,32.4431,9258 +Bilthoven,Netherlands,Europe,52.1283,5.1986,21984 +Biltine,Chad,Africa,14.5275,20.9267,11840 +Bilwi,Nicaragua,North America,14.0281,-83.3808,86279 +Biləsuvar,Azerbaijan,Europe,39.4553,48.5458,20098 +Bimbo,Central African Republic,Africa,4.2558,18.4042,124176 +Bindura,Zimbabwe,Africa,-17.3,31.3333,50400 +Bingerville,Côte d’Ivoire,Africa,5.35,-3.9,204656 +Binghamton,United States,North America,42.1014,-75.9093,150564 +Bingöl,Turkey,Europe,38.8861,40.5017,133423 +Binnish,Syria,Asia,35.9566,36.7138,21848 +Bintuni,Indonesia,Asia,-2.1167,133.5333,34192 +Birao,Central African Republic,Africa,10.294,22.782,10178 +Birecik,Turkey,Europe,37.025,37.9769,93866 +Birendranagar,Nepal,Asia,28.6,81.6333,47914 +Birigui,Brazil,South America,-21.2889,-50.34,118979 +Biritiba-Mirim,Brazil,South America,-23.5728,-46.0389,32936 +Biritinga,Brazil,South America,-11.6169,-38.8,15146 +Birkirkara,Malta,Europe,35.8967,14.4625,24356 +Birmingham,United States,North America,33.5279,-86.7971,778756 +Birnin Kebbi,Nigeria,Africa,12.4504,4.1999,108164 +Birnin Konni,Niger,Africa,13.7904,5.2599,65252 +Birobidzhan,Russia,Asia,48.8,132.9333,73623 +Birsk,Russia,Asia,55.4167,55.5333,44295 +Birāṭnagar,Nepal,Asia,26.4833,87.2833,242548 +Birštonas,Lithuania,Europe,54.6,24.0333,3094 +Birżebbuġa,Malta,Europe,35.8256,14.5281,9736 +Biržai,Lithuania,Europe,56.2,24.75,10444 +Bishkek,Kyrgyzstan,Asia,42.8667,74.5667,1145044 +Biskra,Algeria,Africa,34.85,5.7333,204661 +Bismarck,United States,North America,46.8143,-100.7694,99060 +Bismil,Turkey,Europe,37.8467,40.6661,118698 +Bissau,Guinea-Bissau,Africa,11.85,-15.5667,492004 +Bistrica ob Sotli,Slovenia,Europe,46.0569,15.6625,235 +Bistriţa,Romania,Europe,47.1333,24.5,78877 +Bitburg,Germany,Europe,49.9667,6.5333,17465 +Bitlis,Turkey,Europe,38.4,42.1083,53023 +Bitola,North Macedonia,Europe,41.0319,21.3347,69287 +Bituruna,Brazil,South America,-26.1608,-51.5528,15533 +Biu,Nigeria,Africa,10.6111,12.195,176072 +Bizerte,Tunisia,Africa,37.2778,9.8639,162053 +Biên Hòa,Vietnam,Asia,10.95,106.8167,1575000 +Biłgoraj,Poland,Europe,50.55,22.7333,25459 +Bi’r al ‘Abd,Egypt,Africa,31.0,33.0167,15574 +Bjelovar,Croatia,Europe,45.8989,16.8422,36316 +Blace,Serbia,Europe,43.2906,21.2847,11754 +Black River,Jamaica,North America,18.0257,-77.8509,4261 +Bladel,Netherlands,Europe,51.3667,5.2167,20529 +Blagodarnyy,Russia,Asia,45.1,43.45,12715 +Blagoevgrad,Bulgaria,Europe,42.0227,23.0975,77441 +Blagoveshchensk,Russia,Asia,50.25,127.5333,239864 +Blantyre,Malawi,Africa,-15.7861,35.0058,800264 +Blaricum,Netherlands,Europe,52.2667,5.25,11952 +Bled,Slovenia,Europe,46.3688,14.114,4929 +Blenheim,New Zealand,Oceania,-41.5167,173.95,26400 +Blida,Algeria,Africa,36.4686,2.8319,99200 +Bloemfontein,South Africa,Africa,-29.1167,26.2167,256185 +Blois,France,Europe,47.5939,1.3281,47092 +Bloomington,United States,North America,40.4757,-88.9703,133107 +Bludenz,Austria,Europe,47.1333,9.8167,14956 +Bluefields,Nicaragua,North America,12.0,-83.75,56421 +Blumenau,Brazil,South America,-26.9189,-49.0658,361261 +Bo,Sierra Leone,Africa,7.9564,-11.74,233684 +Boa Esperança,Brazil,South America,-21.09,-45.5658,40219 +Boa Esperança do Sul,Brazil,South America,-21.9925,-48.3908,15018 +Boa Nova,Brazil,South America,-14.3628,-40.2078,13690 +Boa Viagem,Brazil,South America,-5.1278,-39.7319,50411 +Boa Vista,Brazil,South America,2.82,-60.6719,413486 +Boa Vista do Tupim,Brazil,South America,-12.66,-40.6089,16873 +Boac,Philippines,Asia,13.45,121.83,57283 +Boaco,Nicaragua,North America,12.4667,-85.6667,49839 +Bobigny,France,Europe,48.9106,2.4397,55270 +Bobo-Dioulasso,Burkina Faso,Africa,11.1833,-4.2833,903887 +Bobrynets,Ukraine,Europe,48.0578,32.1581,10396 +Boca da Mata,Brazil,South America,-9.6408,-36.22,21187 +Boca del Rio,Mexico,North America,19.1056,-96.1078,9947 +Bocaina,Brazil,South America,-22.1361,-48.5181,11810 +Bocaiúva,Brazil,South America,-17.1078,-43.815,48032 +Bocaiúva do Sul,Brazil,South America,-25.2058,-49.115,9050 +Bocana de Paiwas,Nicaragua,North America,12.7861,-85.1231,31762 +Bocas de Satinga,Colombia,South America,2.3469,-78.3256,32759 +Bocas del Toro,Panama,North America,9.3333,-82.25,12996 +Bochil,Mexico,North America,16.9833,-92.9167,14200 +Bochnia,Poland,Europe,49.9833,20.4333,28692 +Bochum,Germany,Europe,51.4819,7.2158,366385 +Boconó,Venezuela,South America,9.2461,-70.2617,100240 +Bodaybo,Russia,Asia,57.8667,114.2,8921 +Bodegraven,Netherlands,Europe,52.0822,4.7504,17954 +Boden,Sweden,Europe,65.8333,21.7,16847 +Bodocó,Brazil,South America,-7.7778,-39.9408,34478 +Bodrum,Turkey,Europe,37.0378,27.4242,175000 +Bodø,Norway,Europe,67.2827,14.3751,41720 +Boekel,Netherlands,Europe,51.6011,5.6742,10959 +Boende,Congo (Kinshasa),Africa,-0.281,20.876,36158 +Bofete,Brazil,South America,-23.1022,-48.2578,10879 +Boffa,Guinea,Africa,10.185,-14.043,2332 +Bogandé,Burkina Faso,Africa,12.9667,-0.15,21443 +Bogatić,Serbia,Europe,44.8333,19.4833,6488 +Bogdanci,North Macedonia,Europe,41.2031,22.5728,2951 +Bogdanovich,Russia,Asia,56.7803,62.0494,29895 +Bogoroditsk,Russia,Asia,53.7667,38.1333,29560 +Bogorodsk,Russia,Asia,56.1167,43.5,12300 +Bogotol,Russia,Asia,56.2,89.5167,18206 +Bogotá,Colombia,South America,4.7111,-74.0722,8034649 +Bogovinje,North Macedonia,Europe,41.9233,20.9133,25 +Bog’ot,Uzbekistan,Asia,41.35,60.8167,10700 +Bohinjska Bistrica,Slovenia,Europe,46.2742,13.9524,1767 +Bohodukhiv,Ukraine,Europe,50.1617,35.5256,14624 +Boise,United States,North America,43.6005,-116.2308,449428 +Boituva,Brazil,South America,-23.2833,-47.6722,62170 +Bojacá,Colombia,South America,4.7336,-74.3422,9256 +Bojnūrd,Iran,Asia,37.4722,57.3289,228931 +Bokhtar,Tajikistan,Asia,37.8364,68.7803,126700 +Boksitogorsk,Russia,Asia,59.4833,33.8333,15480 +Boké,Guinea,Africa,10.94,-14.3,116270 +Bol,Chad,Africa,13.46,14.74,11120 +Bolama,Guinea-Bissau,Africa,11.5776,-15.4742,5026 +Bole,China,Asia,44.8539,82.0514,235585 +Bolesławiec,Poland,Europe,51.2667,15.5667,37559 +Bolgatanga,Ghana,Africa,10.7904,-0.85,66685 +Bolhrad,Ukraine,Europe,45.6672,28.6128,14818 +Boli,China,Asia,45.7564,130.5759,95260 +Bolivia,Cuba,North America,22.075,-78.3503,15055 +Bollnäs,Sweden,Europe,61.352,16.3666,12381 +Bologna,Italy,Europe,44.4939,11.3428,387971 +Bologoye,Russia,Asia,57.8833,34.05,20234 +Bolu,Turkey,Europe,40.7347,31.6075,184682 +Bolvadin,Turkey,Europe,38.7167,31.05,33443 +Bolzano,Italy,Europe,46.4981,11.3548,106107 +Bolívar,Colombia,South America,1.9708,-76.9694,44868 +Bom Conselho,Brazil,South America,-9.17,-36.68,42085 +Bom Despacho,Brazil,South America,-19.7358,-45.2519,51737 +Bom Jardim,Brazil,South America,-7.7958,-35.5869,37629 +Bom Jesus,Brazil,South America,-4.42,-46.765,28599 +Bom Jesus da Lapa,Brazil,South America,-13.255,-43.4178,65550 +Bom Jesus da Serra,Brazil,South America,-14.3719,-40.5039,9730 +Bom Jesus do Galho,Brazil,South America,-19.8289,-42.3158,14536 +Bom Jesus do Itabapoana,Brazil,South America,-21.1339,-41.6797,35173 +Bom Jesus do Norte,Brazil,South America,-21.1139,-41.6719,9226 +Bom Jesus dos Perdões,Brazil,South America,-23.135,-46.4653,25985 +Bom Lugar,Brazil,South America,-4.22,-45.0389,12212 +Bom Princípio,Brazil,South America,-29.4889,-51.3528,9494 +Bom Repouso,Brazil,South America,-22.4708,-46.145,12649 +Bom Retiro do Sul,Brazil,South America,-29.6089,-51.9428,12294 +Bom Sucesso,Brazil,South America,-21.0328,-44.7578,17151 +Bombarral,Portugal,Europe,39.2672,-9.1581,13193 +Bombinhas,Brazil,South America,-27.1378,-48.5169,25058 +Bonanza,Nicaragua,North America,14.0167,-84.5833,18633 +Bonao,Dominican Republic,North America,18.95,-70.41,125338 +Bondoukou,Côte d’Ivoire,Africa,8.0304,-2.8,141568 +Bonfinópolis,Brazil,South America,-16.6178,-48.9628,10296 +Bonga,Ethiopia,Africa,7.2667,36.2333,20858 +Bongao,Philippines,Asia,5.0292,119.7731,116118 +Bongor,Chad,Africa,10.2806,15.3722,30518 +Bongouanou,Côte d’Ivoire,Africa,6.6517,-4.2041,74281 +Boninal,Brazil,South America,-12.7019,-41.8278,13622 +Bonita Springs,United States,North America,26.3558,-81.7859,445500 +Bonito,Brazil,South America,-8.47,-35.7289,37474 +Bonito de Santa Fé,Brazil,South America,-7.3128,-38.515,10252 +Bonn,Germany,Europe,50.7353,7.1022,335789 +Bonneville,France,Europe,46.0797,6.4014,13320 +Bontoc,Philippines,Asia,17.09,120.9772,24104 +Bonyhád,Hungary,Europe,46.3,18.53,12031 +Bonāb,Iran,Asia,37.3383,46.0539,75332 +Boosaaso,Somalia,Africa,11.28,49.18,700906 +Bopolu,Liberia,Africa,7.0667,-10.4875,2908 +Boqueirão,Brazil,South America,-7.4819,-36.135,17598 +Boquim,Brazil,South America,-11.1469,-37.6208,24188 +Boquira,Brazil,South America,-12.8231,-42.7306,21497 +Bor,South Sudan,Africa,6.2072,31.5591,315351 +Borba,Brazil,South America,-4.3878,-59.5939,28619 +Borborema,Brazil,South America,-21.62,-49.0739,14226 +Borda da Mata,Brazil,South America,-22.2739,-46.165,17404 +Bordeaux,France,Europe,44.84,-0.58,994920 +Bordj Bou Arreridj,Algeria,Africa,36.0667,4.7667,168346 +Bordj Mokhtar,Algeria,Africa,21.3289,0.9542,9323 +Borgo Maggiore,San Marino,Europe,43.945,12.4497,6871 +Borjomi,Georgia,Europe,41.8417,43.3828,11194 +Borken,Germany,Europe,51.8333,6.8667,43589 +Borlänge,Sweden,Europe,60.4833,15.4167,44898 +Borne,Netherlands,Europe,52.3,6.75,23668 +Bornova,Turkey,Europe,38.4697,27.2211,454470 +Borodino,Russia,Asia,55.9056,94.8997,15174 +Boromo,Burkina Faso,Africa,11.75,-2.9333,20193 +Borongan,Philippines,Asia,11.6094,125.4361,71961 +Borovichi,Russia,Asia,58.4,33.9167,47883 +Borovnica,Slovenia,Europe,45.9197,14.3572,2674 +Borovsk,Russia,Asia,55.2,36.5,12686 +Borovskoy,Kazakhstan,Asia,53.7964,64.1894,9434 +Boryeong,"Korea, South",Asia,36.3333,126.6167,101852 +Boryspil,Ukraine,Europe,50.35,30.95,64117 +Borås,Sweden,Europe,57.7304,12.92,114445 +Borçka,Turkey,Europe,41.3636,41.6792,11409 +Borāzjān,Iran,Asia,29.2694,51.22,155567 +Borūjen,Iran,Asia,31.9719,51.2917,57071 +Borūjerd,Iran,Asia,33.9111,48.7522,234997 +Bosanska Krupa,Bosnia and Herzegovina,Europe,44.8833,16.15,10196 +Bosconia,Colombia,South America,9.9761,-73.8903,40562 +Boshrūyeh,Iran,Asia,33.8681,57.4286,16426 +Bosilovo,North Macedonia,Europe,41.4406,22.7278,1273 +Boskoop,Netherlands,Europe,52.0667,4.65,16325 +Bossangoa,Central African Republic,Africa,6.4833,17.45,36478 +Boston,United States,North America,42.3188,-71.0852,4355184 +Botelhos,Brazil,South America,-21.6424,-46.3937,14828 +Botou,China,Asia,38.0833,116.5833,573842 +Botoşani,Romania,Europe,47.7486,26.6694,90010 +Bottrop,Germany,Europe,51.5247,6.9228,118705 +Botucatu,Brazil,South America,-22.8858,-48.445,139483 +Botuporã,Brazil,South America,-13.3819,-42.5228,11024 +Bouaflé,Côte d’Ivoire,Africa,6.9903,-5.7442,213967 +Bouaké,Côte d’Ivoire,Africa,7.6833,-5.0331,740000 +Bouar,Central African Republic,Africa,5.95,15.6,40353 +Bougouni,Mali,Africa,11.4177,-7.4832,59679 +Bouira,Algeria,Africa,36.38,3.9014,68545 +Boulder,United States,North America,40.0248,-105.2524,120121 +Boulogne-Billancourt,France,Europe,48.8352,2.2409,120205 +Boulogne-sur-Mer,France,Europe,50.7264,1.6147,41039 +Boulsa,Burkina Faso,Africa,12.6667,-0.5833,24200 +Boumerdes,Algeria,Africa,36.7603,3.4724,41685 +Bouna,Côte d’Ivoire,Africa,9.2667,-3.0,94883 +Boundiali,Côte d’Ivoire,Africa,9.5167,-6.4833,92792 +Bourem,Mali,Africa,16.9542,-0.3503,27486 +Bourg-en-Bresse,France,Europe,46.2056,5.2289,42065 +Bourges,France,Europe,47.0844,2.3964,64238 +Boussé,Burkina Faso,Africa,12.6667,-1.8975,25022 +Bovec,Slovenia,Europe,46.3378,13.5522,1586 +Bowling Green,United States,North America,36.9716,-86.4378,102247 +Boxmeer,Netherlands,Europe,51.6483,5.9444,29065 +Boxtel,Netherlands,Europe,51.5911,5.3275,32973 +Boyabat,Turkey,Europe,41.4653,34.7708,29093 +Bozdoğan,Turkey,Europe,37.6728,28.3103,32607 +Bozkurt,Turkey,Europe,37.8167,29.6167,12331 +Bozkır,Turkey,Europe,37.1886,32.2456,25307 +Bozoum,Central African Republic,Africa,6.3172,16.3783,20665 +Bozova,Turkey,Europe,37.3628,38.5256,52680 +Bozyazı,Turkey,Europe,36.1,32.9667,26812 +Bozüyük,Turkey,Europe,39.9078,30.0367,71806 +Boğazlıyan,Turkey,Europe,39.1942,35.2472,18791 +Brades,Montserrat,North America,16.7928,-62.2106,391 +Braga,Portugal,Europe,41.5503,-8.42,181494 +Bragado,Argentina,South America,-35.1167,-60.5,33222 +Bragança,Brazil,South America,-1.0536,-46.7656,128914 +Bragança Paulista,Brazil,South America,-22.9189,-46.5425,160665 +Brake,Germany,Europe,53.3333,8.4833,15102 +Brampton,Canada,North America,43.6833,-79.7667,656480 +Brandenburg,Germany,Europe,52.4167,12.5333,73921 +Brandon,United States,North America,27.9367,-82.3,116365 +Brandsen,Argentina,South America,-35.1667,-58.2167,30026 +Braniewo,Poland,Europe,54.3833,19.8333,16480 +Branquinha,Brazil,South America,-9.2458,-36.015,9603 +Brantford,Canada,North America,43.1667,-80.25,104688 +Brasil Novo,Brazil,South America,-3.2619,-52.6678,24718 +Brasilândia,Brazil,South America,-17.0087,-46.0086,15020 +Brasiléia,Brazil,South America,-11.01,-68.7478,26000 +Braslaw,Belarus,Europe,55.6391,27.0318,9419 +Braslovče,Slovenia,Europe,46.2884,15.0386,378 +Brasnorte,Brazil,South America,-12.155,-57.9789,17004 +Brasília,Brazil,South America,-15.7939,-47.8828,141742 +Brasília de Minas,Brazil,South America,-16.2078,-44.4289,32025 +Bratislava,Slovakia,Europe,48.1447,17.1128,475503 +Bratsk,Russia,Asia,56.1167,101.6,220097 +Bratunac,Bosnia and Herzegovina,Europe,44.1839,19.3308,20340 +Braunau am Inn,Austria,Europe,48.2583,13.035,17095 +Braunschweig,Germany,Europe,52.2667,10.5167,551000 +Brazzaville,Congo (Brazzaville),Africa,-4.2667,15.2667,2557100 +Brazópolis,Brazil,South America,-22.4739,-45.6078,14246 +Braço do Norte,Brazil,South America,-28.275,-49.1658,33773 +Braşov,Romania,Europe,45.6667,25.6167,237589 +Brdovec,Croatia,Europe,45.8703,15.7644,10737 +Breda,Netherlands,Europe,51.5875,4.775,184126 +Bregenz,Austria,Europe,47.505,9.7492,29620 +Brejetuba,Brazil,South America,-20.1458,-41.29,11687 +Brejinho,Brazil,South America,-6.1908,-35.3569,12202 +Brejo,Brazil,South America,-3.6839,-42.75,34120 +Brejo Santo,Brazil,South America,-7.4928,-38.985,51090 +Brejo da Madre de Deus,Brazil,South America,-8.1458,-36.3708,38109 +Brejo do Cruz,Brazil,South America,-6.3489,-37.4978,13613 +Brejão,Brazil,South America,-9.03,-36.5689,9079 +Brejões,Brazil,South America,-13.1039,-39.7958,12943 +Bremen,Germany,Europe,53.0758,8.8072,577026 +Bremerton,United States,North America,47.5436,-122.7121,228576 +Bremgarten,Switzerland,Europe,47.3528,8.34,8613 +Brescia,Italy,Europe,45.5389,10.2203,196446 +Bressuire,France,Europe,46.8408,-0.4872,19860 +Brest,Belarus,Europe,52.1347,23.6569,344470 +Breves,Brazil,South America,-1.6822,-50.4803,103497 +Breza,Bosnia and Herzegovina,Europe,44.021,18.261,14564 +Brezno,Slovakia,Europe,48.8036,19.6381,19790 +Brezovica,Slovenia,Europe,46.0207,14.4178,3221 +Brežice,Slovenia,Europe,45.9044,15.5925,6843 +Bria,Central African Republic,Africa,6.5369,21.9919,35204 +Briançon,France,Europe,44.8964,6.6356,10748 +Briceni,Moldova,Europe,48.3611,27.0839,5900 +Briceño,Colombia,South America,7.1111,-75.55,8673 +Bridgeport,United States,North America,41.1918,-73.1954,921660 +Bridgetown,Barbados,North America,13.0969,-59.6131,110000 +Brielle,Netherlands,Europe,51.9017,4.1625,17439 +Brig-Glis,Switzerland,Europe,46.3159,7.9876,13109 +Brignoles,France,Europe,43.4058,6.0615,17846 +Brikama,"Gambia, The",Africa,13.2667,-16.65,77700 +Brindisi,Italy,Europe,40.6383,17.9458,82694 +Brisbane,Australia,Oceania,-27.4678,153.0281,2706966 +Brive-la-Gaillarde,France,Europe,45.1583,1.5321,46769 +Brno,Czechia,Europe,49.1925,16.6083,400566 +Brockton,United States,North America,42.0821,-71.0242,105080 +Brod,Bosnia and Herzegovina,Europe,45.1333,17.9833,16619 +Brodnica,Poland,Europe,53.2597,19.3956,28865 +Brodósqui,Brazil,South America,-20.9908,-47.6589,25201 +Broken Arrow,United States,North America,36.038,-95.7806,115919 +Brokopondo,Suriname,South America,5.0667,-54.9667,2480 +Bronx,United States,North America,40.8501,-73.8662,1472654 +Brookhaven,United States,North America,40.832,-72.9517,487817 +Brooklyn,United States,North America,40.6501,-73.9496,2736074 +Brotas,Brazil,South America,-22.2839,-48.1269,23898 +Brotas de Macaúbas,Brazil,South America,-11.9989,-42.6258,11765 +Brovary,Ukraine,Europe,50.5111,30.79,109806 +Brownsville,United States,North America,25.9975,-97.458,223416 +Bruck an der Mur,Austria,Europe,47.4167,15.2667,15837 +Bruges,Belgium,Europe,51.2089,3.2242,118509 +Brumadinho,Brazil,South America,-20.1428,-44.2,38915 +Brumado,Brazil,South America,-14.2036,-41.6653,70268 +Brummen,Netherlands,Europe,52.0833,6.15,20884 +Brumunddal,Norway,Europe,60.8833,10.9333,11019 +Brunssum,Netherlands,Europe,50.95,5.9667,27670 +Brus,Serbia,Europe,43.3836,21.0336,16263 +Brusque,Brazil,South America,-27.0978,-48.9178,141385 +Brussels,Belgium,Europe,50.8467,4.3525,1249597 +Brvenica,North Macedonia,Europe,41.9672,20.9808,449 +Bryansk,Russia,Asia,53.2425,34.3667,373310 +Bryne,Norway,Europe,58.7333,5.65,12465 +Brzeg,Poland,Europe,50.8667,17.4833,34492 +Brzesko,Poland,Europe,49.9667,20.6167,16193 +Brzeziny,Poland,Europe,51.8,19.75,12117 +Brønderslev,Denmark,Europe,57.2651,9.9599,12884 +Brăila,Romania,Europe,45.2692,27.9575,154686 +Brčko,Bosnia and Herzegovina,Europe,44.8783,18.8092,39893 +Bua Yai,Thailand,Asia,15.584,102.4186,14168 +Buabidi,Panama,North America,8.4667,-81.7,420 +Buala,Solomon Islands,Oceania,-8.1448,159.5905,2813 +Buba,Guinea-Bissau,Africa,11.59,-14.99,7898 +Bubanza,Burundi,Africa,-3.0833,29.4,20031 +Buca,Turkey,Europe,38.35,27.1667,522404 +Bucak,Turkey,Europe,37.4592,30.5953,45865 +Bucaramanga,Colombia,South America,7.1333,-73.0,893040 +Bucha,Ukraine,Europe,50.5464,30.235,37321 +Buchanan,Liberia,Africa,5.8808,-10.0467,34270 +Bucharest,Romania,Europe,44.4325,26.1039,2412530 +Bucheon,"Korea, South",Asia,37.5,126.7833,772450 +Buda-Kashalyova,Belarus,Europe,52.7167,30.5667,8581 +Budakeszi,Hungary,Europe,47.5111,18.93,15862 +Budapest,Hungary,Europe,47.4983,19.0408,1686222 +Bududa,Uganda,Africa,1.01,34.3317,8700 +Budva,Montenegro,Europe,42.2881,18.8425,17479 +Budënnovsk,Russia,Asia,44.7833,44.1667,56488 +Buea,Cameroon,Africa,4.1667,9.2333,131325 +Buenaventura,Colombia,South America,3.8772,-77.0267,423927 +Buenavista,Colombia,South America,8.6833,-75.25,22711 +Bueng Kan,Thailand,Asia,18.3254,103.6704,20103 +Bueng Khong Long,Thailand,Asia,17.9667,104.045,10245 +Bueno Brandão,Brazil,South America,-22.4408,-46.3508,10911 +Buenos Aires,Argentina,South America,-34.6036,-58.3814,16710000 +Buenópolis,Brazil,South America,-17.8742,-44.1767,9150 +Buerarema,Brazil,South America,-14.9589,-39.3,14804 +Buesaco,Colombia,South America,1.3833,-77.1667,26229 +Buey Arriba,Cuba,North America,20.1736,-76.7494,30573 +Buffalo,United States,North America,42.9018,-78.8487,949547 +Bugalagrande,Colombia,South America,4.2167,-76.1667,20970 +Bugiri,Uganda,Africa,0.5694,33.7486,36000 +Bugojno,Bosnia and Herzegovina,Europe,44.05,17.45,36750 +Buguey,Philippines,Asia,18.2882,121.8331,32148 +Buguruslan,Russia,Asia,53.6583,52.4358,5400 +Buharkent,Turkey,Europe,37.9617,28.7422,13022 +Buikwe,Uganda,Africa,0.3442,33.03,18500 +Bujanovac,Serbia,Europe,42.4667,21.7667,41068 +Bujari,Brazil,South America,-9.8308,-67.9519,12917 +Bujaru,Brazil,South America,-1.515,-48.0447,29427 +Bujie,China,Asia,28.0269,105.0517,21827 +Bujumbura,Burundi,Africa,-3.3833,29.3667,1143202 +Bukavu,Congo (Kinshasa),Africa,-2.5061,28.8608,1133000 +Bukedea,Uganda,Africa,1.3475,34.0444,13900 +Bukhara,Uzbekistan,Asia,39.7667,64.4231,280187 +Bukoba,Tanzania,Africa,-1.3333,31.8167,144938 +Bukomansimbi,Uganda,Africa,-0.1667,31.6186,10000 +Bulancak,Turkey,Europe,40.9333,38.2333,49053 +Bulanık,Turkey,Europe,39.095,42.2667,27427 +Bulawayo,Zimbabwe,Africa,-20.17,28.58,665952 +Buldan,Turkey,Europe,38.045,28.8306,26630 +Bulgan,Mongolia,Asia,48.8125,103.5347,12323 +Bulisa,Uganda,Africa,2.1217,31.4156,9500 +Buloqboshi,Uzbekistan,Asia,40.6222,72.5028,32200 +Buluan,Philippines,Asia,6.7154,124.7854,57406 +Bulung’ur Shahri,Uzbekistan,Asia,39.76,67.27,29200 +Bundibugyo,Uganda,Africa,0.7125,30.06,22300 +Bungku,Indonesia,Asia,-2.5333,121.9667,25477 +Bungoma,Kenya,Africa,0.5666,34.5666,55857 +Bunia,Congo (Kinshasa),Africa,1.5667,30.25,900666 +Buntok,Indonesia,Asia,-1.719,114.8448,16774 +Buraydah,Saudi Arabia,Asia,26.3664,43.9628,467410 +Burayevo,Russia,Asia,55.8397,55.4044,9522 +Burbank,United States,North America,34.1879,-118.3235,105165 +Burco,Somalia,Africa,9.5221,45.5336,425000 +Burdur,Turkey,Europe,37.7194,30.2833,95436 +Burg,Germany,Europe,52.2725,11.855,22738 +Burgas,Bulgaria,Europe,42.503,27.4702,210284 +Burgos,Spain,Europe,42.35,-3.7067,175895 +Burgum,Netherlands,Europe,53.1922,5.9942,10058 +Burhaniye,Turkey,Europe,39.5,26.95,64283 +Buri,Brazil,South America,-23.7975,-48.5928,19965 +Buri Ram,Thailand,Asia,14.9942,103.1022,23364 +Buritama,Brazil,South America,-21.0667,-50.1475,16583 +Buriti,Brazil,South America,-3.9419,-42.925,29685 +Buriti Alegre,Brazil,South America,-18.1439,-49.0439,10495 +Buriti Bravo,Brazil,South America,-5.8369,-43.8339,22455 +Buriti do Tocantins,Brazil,South America,-5.3158,-48.2289,10307 +Buriti dos Lopes,Brazil,South America,-3.175,-41.8669,19654 +Buritirama,Brazil,South America,-10.7078,-43.6308,19589 +Buritis,Brazil,South America,-15.6178,-46.4228,20396 +Buritizeiro,Brazil,South America,-17.3508,-44.9619,23910 +Burlington,Canada,North America,43.3167,-79.8,186948 +Burnaby,Canada,North America,49.2667,-122.9667,249125 +Bursa,Turkey,Europe,40.1833,29.05,3101833 +Bururi,Burundi,Africa,-3.9333,29.6167,4478 +Burutu,Nigeria,Africa,5.35,5.5167,209666 +Busan,"Korea, South",Asia,35.18,129.075,3453198 +Bushenyi,Uganda,Africa,-0.5417,30.1878,43700 +Busia,Uganda,Africa,0.4669,34.09,64900 +Busko-Zdrój,Poland,Europe,50.4667,20.7167,15014 +Busovača,Bosnia and Herzegovina,Europe,44.0978,17.8783,18488 +Bussum,Netherlands,Europe,52.2833,5.1667,32410 +Buta,Congo (Kinshasa),Africa,2.8,24.7333,55313 +Butalangu,Uganda,Africa,0.8228,32.2428,3873 +Butaleja,Uganda,Africa,0.925,33.945,24000 +Butha-Buthe,Lesotho,Africa,-28.7833,28.2333,10000 +Butiá,Brazil,South America,-30.12,-51.9619,19084 +Butuan,Philippines,Asia,8.948,125.543,372910 +Butwāl,Nepal,Asia,27.7,83.45,118462 +Buurhakaba,Somalia,Africa,2.7837,44.0833,360795 +Buy,Russia,Asia,58.4833,41.5333,20564 +Buyende,Uganda,Africa,1.1475,33.1611,23039 +Buynaksk,Russia,Asia,42.8167,47.1167,69554 +Buzhum,Kyrgyzstan,Asia,40.0048,70.8352,11400 +Buzuluk,Russia,Asia,52.7833,52.25,88341 +Buzău,Romania,Europe,45.1531,26.8208,103481 +Buíque,Brazil,South America,-8.6233,-37.1564,52097 +Buôn Ma Thuột,Vietnam,Asia,12.6667,108.05,340000 +Buôn Trấp,Vietnam,Asia,12.4833,108.0167,23863 +Buşayrā,Jordan,Asia,30.7326,35.6094,10587 +Buşrá ash Shām,Syria,Asia,32.5174,36.4816,19683 +Bu’aale,Somalia,Africa,1.0833,42.5833,115000 +Byalynichy,Belarus,Europe,53.9956,29.7094,9670 +Byaroza,Belarus,Europe,52.55,24.9667,28376 +Bydgoszcz,Poland,Europe,53.1219,18.0003,337666 +Byerazino,Belarus,Europe,53.8333,28.9833,11366 +Bykhaw,Belarus,Europe,53.5167,30.25,16349 +Bytom,Poland,Europe,50.3483,18.9156,153274 +Bytów,Poland,Europe,54.1333,17.5,16739 +Bytča,Slovakia,Europe,49.2228,18.5581,11535 +Byumba,Rwanda,Africa,-1.5794,30.0694,36401 +Báguanos,Cuba,North America,20.7631,-76.0297,47851 +Bálsamo,Brazil,South America,-20.735,-49.5839,9596 +Bánovce nad Bebravou,Slovakia,Europe,48.7186,18.2581,16614 +Bátonyterenye,Hungary,Europe,47.9892,19.8286,11118 +Bålsta,Sweden,Europe,59.5833,17.5,13138 +Béchar,Algeria,Africa,31.6167,-2.2167,165627 +Béja,Tunisia,Africa,36.7333,9.1833,88251 +Békés,Hungary,Europe,46.7769,21.125,17526 +Békéscsaba,Hungary,Europe,46.6833,21.0833,54460 +Béni Mellal,Morocco,Africa,32.3394,-6.3608,209594 +Béthune,France,Europe,50.5303,2.6408,25342 +Béziers,France,Europe,43.3476,3.219,80815 +Bình Minh,Vietnam,Asia,10.0961,105.7894,18105 +Bôca do Acre,Brazil,South America,-8.7519,-67.3978,35447 +Böblingen,Germany,Europe,48.6856,9.0153,52093 +Bökönbaev,Kyrgyzstan,Asia,42.11,76.99,14267 +Büdelsdorf,Germany,Europe,54.3167,9.6833,10470 +Bünyan,Turkey,Europe,38.8486,35.8592,29704 +Büyükorhan,Turkey,Europe,39.75,28.9167,8940 +Büyükçekmece,Turkey,Europe,41.02,28.5775,277181 +Bābol,Iran,Asia,36.5475,52.6783,250217 +Bābolsar,Iran,Asia,36.7006,52.65,59966 +Bāfq,Iran,Asia,31.605,55.4056,45453 +Bāft,Iran,Asia,29.235,56.5989,34517 +Bāgh-e Malek,Iran,Asia,31.5247,49.8869,26343 +Bājil,Yemen,Asia,15.059,43.2873,46005 +Bāmyān,Afghanistan,Asia,34.825,67.8333,100000 +Bāneh,Iran,Asia,35.9969,45.8853,110218 +Bāniyās,Syria,Asia,35.1819,35.9487,43151 +Bāsht,Iran,Asia,30.3611,51.1558,10764 +Bāzārak,Afghanistan,Asia,35.3128,69.5158,24723 +Bāţūfah,Iraq,Asia,37.1744,43.0132,28000 +Bălţi,Moldova,Europe,47.7667,27.9167,97930 +Będzin,Poland,Europe,50.3333,19.1167,55695 +Bījār,Iran,Asia,35.8729,47.5993,53871 +Bīleh Savār,Iran,Asia,39.3794,48.3542,16188 +Bīrjand,Iran,Asia,32.8781,59.2161,203636 +Būkān,Iran,Asia,36.515,46.2075,193501 +Būlaevo,Kazakhstan,Asia,54.9056,70.4439,8433 +Bŭston,Tajikistan,Asia,40.5217,69.3308,15500 +Bərdə,Azerbaijan,Europe,40.3831,47.1186,38500 +Bạc Liêu,Vietnam,Asia,9.2833,105.7167,156110 +Bảo Lộc,Vietnam,Asia,11.5481,107.8075,170920 +Bắc Giang,Vietnam,Asia,21.2667,106.2,450000 +Bắc Kạn,Vietnam,Asia,22.1333,105.8333,45036 +Bắc Ninh,Vietnam,Asia,21.1833,106.05,287658 +Bến Tre,Vietnam,Asia,10.2333,106.3833,124449 +Caacupé,Paraguay,South America,-25.387,-57.14,56864 +Caapiranga,Brazil,South America,-3.3167,-61.2,8803 +Caaporã,Brazil,South America,-7.5158,-34.9078,21193 +Caazapá,Paraguay,South America,-26.2,-56.38,23996 +Cabaceiras do Paraguaçu,Brazil,South America,-12.5358,-39.1908,16559 +Cabadbaran,Philippines,Asia,9.12,125.53,80354 +Cabaiguán,Cuba,North America,22.0839,-79.4953,32000 +Cabarroguis,Philippines,Asia,16.5103,121.5222,33533 +Cabañas,Guatemala,North America,14.9333,-89.8,14817 +Cabeceiras de Basto,Portugal,Europe,41.5167,-8.0,16710 +Cabedelo,Brazil,South America,-6.9808,-34.8339,66519 +Cabimas,Venezuela,South America,10.4,-71.45,288595 +Cabinda,Angola,Africa,-5.56,12.19,739182 +Cabo Frio,Brazil,South America,-22.8789,-42.0189,126828 +Cabo Verde,Brazil,South America,-21.4719,-46.3958,11410 +Cabreúva,Brazil,South America,-23.3075,-47.1331,50429 +Cabricán,Guatemala,North America,15.0747,-91.6486,28124 +Cabrobó,Brazil,South America,-8.5119,-39.3069,30294 +Cabudare,Venezuela,South America,10.0331,-69.2633,70578 +Cacahoatán,Mexico,North America,17.1667,-92.1667,19108 +Cacequi,Brazil,South America,-29.8839,-54.825,11157 +Cacheu,Guinea-Bissau,Africa,12.2667,-16.1667,5674 +Cachipay,Colombia,South America,4.7308,-74.4375,9833 +Cachoeira,Brazil,South America,-12.6178,-38.9558,29250 +Cachoeira Alta,Brazil,South America,-18.7628,-50.9419,11513 +Cachoeira Paulista,Brazil,South America,-22.665,-45.0094,32294 +Cachoeira de Minas,Brazil,South America,-22.355,-45.7789,10555 +Cachoeira do Arari,Brazil,South America,-1.0108,-48.9628,23981 +Cachoeira do Sul,Brazil,South America,-30.0394,-52.8953,80070 +Cachoeira dos Índios,Brazil,South America,-6.9269,-38.6739,9151 +Cachoeiras de Macacu,Brazil,South America,-22.4628,-42.6528,56943 +Cachoeirinha,Brazil,South America,-29.9508,-51.0939,136258 +Cacimba de Dentro,Brazil,South America,-6.6417,-35.79,17178 +Cacimbinhas,Brazil,South America,-9.4,-36.99,9552 +Cacoal,Brazil,South America,-11.4386,-61.4472,73568 +Cacocum,Cuba,North America,20.7439,-76.3242,39153 +Caconde,Brazil,South America,-21.5289,-46.6439,18976 +Caculé,Brazil,South America,-14.5028,-42.2219,22462 +Cadaval,Portugal,Europe,39.25,-9.1,14525 +Cadereyta,Mexico,North America,20.691,-99.819,13347 +Cadereyta Jiménez,Mexico,North America,25.6,-100.0,75721 +Caen,France,Europe,49.1814,-0.3636,108398 +Caetano,Brazil,South America,-14.3378,-40.91,11266 +Caetanópolis,Brazil,South America,-19.295,-44.4189,11435 +Caetité,Brazil,South America,-14.0689,-42.475,52012 +Caeté,Brazil,South America,-19.88,-43.6697,45047 +Caetés,Brazil,South America,-8.7728,-36.6228,28827 +Cafarnaum,Brazil,South America,-11.6939,-41.4678,17466 +Cafayate,Argentina,South America,-26.0833,-65.9667,11785 +Cafelândia,Brazil,South America,-24.6178,-53.32,18997 +Cagayan de Oro,Philippines,Asia,8.48,124.65,728402 +Cagliari,Italy,Europe,39.2167,9.1167,148117 +Cagua,Venezuela,South America,10.1875,-67.4611,107932 +Cahors,France,Europe,44.4483,1.4414,19902 +Cahul,Moldova,Europe,45.9075,28.1944,30018 +Caiapônia,Brazil,South America,-16.9569,-51.81,14673 +Caibarién,Cuba,North America,22.5158,-79.4722,38479 +Caicedonia,Colombia,South America,4.3333,-75.8333,28825 +Caicó,Brazil,South America,-6.4578,-37.0978,61146 +Caieiras,Brazil,South America,-23.3642,-46.7406,102775 +Caimito,Cuba,North America,22.9575,-82.5964,36813 +Cairo,Egypt,Africa,30.0444,31.2358,20296000 +Cairu,Brazil,South America,-13.4869,-39.0439,17761 +Cajabamba,Peru,South America,-7.6237,-78.046,22830 +Cajamar,Brazil,South America,-23.3561,-46.8769,71805 +Cajamarca,Peru,South America,-7.1575,-78.5175,201329 +Cajari,Brazil,South America,-3.3208,-45.0108,16412 +Cajati,Brazil,South America,-24.7361,-48.1228,28494 +Cajazeiras,Brazil,South America,-6.89,-38.5619,63239 +Cajibío,Colombia,South America,2.6333,-76.6333,38703 +Cajicá,Colombia,South America,4.9167,-74.0333,82244 +Cajobi,Brazil,South America,-20.88,-48.8089,9133 +Cajolá,Guatemala,North America,14.9167,-91.6167,19333 +Cajueiro,Brazil,South America,-9.3967,-36.1536,16024 +Cajuru,Brazil,South America,-21.2753,-47.3042,26393 +Calabar,Nigeria,Africa,4.9767,8.3383,179392 +Calabozo,Venezuela,South America,8.9333,-67.4333,168605 +Calais,France,Europe,50.9481,1.8564,67585 +Calama,Chile,South America,-22.4667,-68.9333,157575 +Calamar,Colombia,South America,10.25,-74.9158,24573 +Calapan,Philippines,Asia,13.414,121.18,145786 +Calarcá,Colombia,South America,4.5333,-75.65,71605 +Caldas,Colombia,South America,6.09,-75.638,85385 +Caldas Novas,Brazil,South America,-17.7439,-48.6258,98622 +Caldas da Rainha,Portugal,Europe,39.4069,-9.1363,51729 +Caldeirão Grande,Brazil,South America,-11.02,-40.3028,13080 +Caldono,Colombia,South America,2.8,-76.5333,34132 +Calgary,Canada,North America,51.05,-114.0667,1306784 +Calheta de São Miguel,Cabo Verde,Africa,15.1875,-23.5917,4123 +Cali,Colombia,South America,3.4206,-76.5222,2838333 +Calicut,India,Asia,11.2588,75.7804,431560 +Califórnia,Brazil,South America,-23.65,-51.355,8710 +Calimaya,Mexico,North America,19.1608,-99.6172,47033 +Calimete,Cuba,North America,22.5339,-80.9097,27210 +Calkiní,Mexico,North America,20.3667,-90.05,14934 +Callao,Peru,South America,-12.0522,-77.1392,415888 +Caloocan City,Philippines,Asia,14.65,120.97,1661584 +Caloto,Colombia,South America,3.0333,-76.4167,25416 +Calpulalpan,Mexico,North America,19.5869,-98.5683,33263 +Caltanissetta,Italy,Europe,37.4915,14.0624,58532 +Calvillo,Mexico,North America,21.85,-102.7167,19742 +Calw,Germany,Europe,48.7167,8.7333,24448 +Calçado,Brazil,South America,-8.7419,-36.3339,11093 +Calçoene,Brazil,South America,2.4978,-50.9489,10612 +Cam Ranh,Vietnam,Asia,11.9136,109.1369,138510 +Camacan,Brazil,South America,-15.4189,-39.4958,22579 +Camagüey,Cuba,North America,21.3786,-77.9186,321992 +Camajuaní,Cuba,North America,22.4678,-79.7239,57600 +Camamu,Brazil,South America,-13.945,-39.1039,35382 +Camanducaia,Brazil,South America,-22.755,-46.145,26097 +Camapuã,Brazil,South America,-19.5308,-54.0439,13583 +Camaquã,Brazil,South America,-30.8528,-51.8153,62200 +Camarajibe,Brazil,South America,-8.0219,-34.9808,155054 +Camargo,Mexico,North America,27.667,-105.167,40221 +Camaçari,Brazil,South America,-12.6978,-38.3239,161727 +Cambará,Brazil,South America,-23.0458,-50.0739,22740 +Cambira,Brazil,South America,-23.5828,-51.5778,9460 +Camboriú,Brazil,South America,-27.025,-48.6539,103074 +Cambrai,France,Europe,50.1767,3.2356,31568 +Cambridge,Canada,North America,43.3972,-80.3114,138479 +Cambuci,Brazil,South America,-21.575,-41.9108,14616 +Cambuquira,Brazil,South America,-21.8583,-45.2911,12313 +Cambuí,Brazil,South America,-22.6119,-46.0578,29536 +Cambé,Brazil,South America,-23.2758,-51.2778,107208 +Cametá,Brazil,South America,-2.2439,-49.4958,134184 +Camoapa,Nicaragua,North America,12.3833,-85.5167,34962 +Camocim,Brazil,South America,-2.9008,-40.8428,62326 +Camocim de São Félix,Brazil,South America,-8.3589,-35.7619,17419 +Camotán,Guatemala,North America,14.8167,-89.3817,61958 +Campamento,Colombia,South America,6.9789,-75.2964,9091 +Campana,Argentina,South America,-34.1667,-58.9167,86860 +Campanha,Brazil,South America,-21.8389,-45.3997,15935 +Campeche,Mexico,North America,19.85,-90.5306,249623 +Campechuela,Cuba,North America,20.2333,-77.2792,41719 +Campestre,Brazil,South America,-21.7108,-46.2458,20696 +Campina Grande,Brazil,South America,-7.2306,-35.8811,419379 +Campina Grande do Sul,Brazil,South America,-25.3056,-49.0553,38769 +Campina Verde,Brazil,South America,-19.5358,-49.4858,18011 +Campina da Lagoa,Brazil,South America,-24.5919,-52.7989,15723 +Campinas,Brazil,South America,-22.9009,-47.0573,1213792 +Campinorte,Brazil,South America,-14.3139,-49.1519,12510 +Campo Alegre,Brazil,South America,-9.7819,-36.3508,32106 +Campo Alegre de Lourdes,Brazil,South America,-9.5158,-43.0108,30671 +Campo Belo,Brazil,South America,-20.8969,-45.2769,52277 +Campo Bom,Brazil,South America,-29.6789,-51.0528,62886 +Campo Erê,Brazil,South America,-26.3939,-53.0778,9623 +Campo Formoso,Brazil,South America,-10.5089,-40.3208,71377 +Campo Grande,Brazil,South America,-20.4686,-54.6222,663621 +Campo Largo,Brazil,South America,-25.4589,-49.5278,136327 +Campo Limpo,Brazil,South America,-23.2064,-46.7844,80847 +Campo Magro,Brazil,South America,-25.3689,-49.4508,20409 +Campo Maior,Brazil,South America,-4.8278,-42.1686,46893 +Campo Mourão,Brazil,South America,-24.0458,-52.3828,99432 +Campo Novo do Parecis,Brazil,South America,-13.675,-57.8919,45899 +Campo Redondo,Brazil,South America,-6.2428,-36.1828,10215 +Campo de la Cruz,Colombia,South America,10.3778,-74.8814,18354 +Campo do Brito,Brazil,South America,-10.7333,-37.4933,16749 +Campo do Meio,Brazil,South America,-21.1069,-45.83,11377 +Campoalegre,Colombia,South America,2.6867,-75.3256,34923 +Campobasso,Italy,Europe,41.561,14.6684,47075 +Campos,Brazil,South America,-21.7523,-41.3304,511168 +Campos Altos,Brazil,South America,-19.6958,-46.1708,12819 +Campos Belos,Brazil,South America,-13.0369,-46.7719,18108 +Campos Gerais,Brazil,South America,-21.235,-45.7589,26105 +Campos Lindos,Brazil,South America,-7.9939,-46.8678,8653 +Campos Novos,Brazil,South America,-27.4019,-51.225,36932 +Campos Sales,Brazil,South America,-7.0739,-40.3758,25135 +Campos do Jordão,Brazil,South America,-22.7394,-45.5914,50852 +Canalete,Colombia,South America,8.79,-76.2411,23520 +Cananea,Mexico,North America,30.9819,-110.3006,38113 +Cananéia,Brazil,South America,-25.015,-47.9269,12289 +Canapi,Brazil,South America,-9.1269,-37.605,15559 +Canarana,Brazil,South America,-11.685,-41.7689,24206 +Canatlán,Mexico,North America,24.52,-104.78,10342 +Canavieiras,Brazil,South America,-15.675,-38.9472,30906 +Canberra,Australia,Oceania,-35.2931,149.1269,381488 +Cancún,Mexico,North America,21.1606,-86.8475,888797 +Candeias,Brazil,South America,-12.6678,-38.5508,72382 +Candelaria,Philippines,Asia,13.9311,121.4233,137881 +Candelaria Loxicha,Mexico,North America,15.9167,-96.4833,10497 +Candelária,Brazil,South America,-29.6689,-52.7889,28906 +Candiba,Brazil,South America,-14.4108,-42.8669,13016 +Candiota,Brazil,South America,-31.5578,-53.6728,10710 +Canela,Brazil,South America,-29.3562,-50.8136,45488 +Canelinha,Brazil,South America,-27.265,-48.7678,12821 +Canelones,Uruguay,South America,-34.5167,-56.2833,19865 +Canguaretama,Brazil,South America,-6.38,-35.1289,29668 +Canguçu,Brazil,South America,-31.395,-52.6758,49680 +Cangzhou,China,Asia,38.3047,116.8387,7300783 +Canhotinho,Brazil,South America,-8.8819,-36.1908,24329 +Canillo,Andorra,Europe,42.5667,1.6,5781 +Canillá,Guatemala,North America,15.1667,-90.85,12724 +Canindé,Brazil,South America,-4.3589,-39.3119,74174 +Canindé de São Francisco,Brazil,South America,-9.6419,-37.7878,26834 +Cankova,Slovenia,Europe,46.7208,16.0225,416 +Cankuzo,Burundi,Africa,-3.2194,30.5528,3624 +Canoas,Brazil,South America,-29.92,-51.18,347657 +Canoinhas,Brazil,South America,-26.1769,-50.39,55016 +Cansanção,Brazil,South America,-10.6708,-39.4978,37439 +Cantagalo,Brazil,South America,-21.9808,-42.3678,19390 +Cantanhede,Portugal,Europe,40.3464,-8.5942,36595 +Cantaura,Venezuela,South America,9.3111,-64.3594,42000 +Cantel,Guatemala,North America,14.8112,-91.4555,47087 +Cantemir,Moldova,Europe,46.2781,28.2008,3429 +Canto do Buriti,Brazil,South America,-8.11,-42.9439,19365 +Canton,United States,North America,40.8078,-81.3676,293657 +Canudos,Brazil,South America,-9.9044,-39.1415,16105 +Canutama,Brazil,South America,-6.5339,-64.3828,16869 +Canápolis,Brazil,South America,-18.725,-49.2039,10608 +Cao Bằng,Vietnam,Asia,22.6667,106.2583,73549 +Cao Lãnh,Vietnam,Asia,10.4672,105.6303,211912 +Cap-Haïtien,Haiti,North America,19.75,-72.2,155505 +Capanema,Brazil,South America,-1.1958,-47.1808,70394 +Caparrapí,Colombia,South America,5.3442,-74.4917,10301 +Cape Coast,Ghana,Africa,5.1,-1.25,169894 +Cape Coral,United States,North America,26.6443,-81.9957,656464 +Cape Town,South Africa,Africa,-33.9253,18.4239,4770313 +Capela,Brazil,South America,-10.5028,-37.0528,31645 +Capela de Santana,Brazil,South America,-29.7,-51.325,11159 +Capela do Alto,Brazil,South America,-23.4706,-47.7347,20985 +Capela do Alto Alegre,Brazil,South America,-11.6678,-39.8378,10744 +Capelinha,Brazil,South America,-17.6908,-42.5158,31231 +Capelle aan den IJssel,Netherlands,Europe,51.9357,4.5782,67319 +Capellen,Luxembourg,Europe,49.6444,5.9897,1986 +Capilla del Señor,Argentina,South America,-34.2833,-59.1,9244 +Capim,Brazil,South America,-1.675,-47.775,30599 +Capim Branco,Brazil,South America,-19.5489,-44.1169,10663 +Capim Grosso,Brazil,South America,-11.3808,-40.0128,33235 +Capinzal,Brazil,South America,-27.3439,-51.6119,23314 +Capinópolis,Brazil,South America,-18.6819,-49.57,14655 +Capistrano,Brazil,South America,-4.47,-38.9008,17254 +Capitol Hill,Northern Mariana Islands,Oceania,15.2137,145.7546,2500 +Capitán Sarmiento,Argentina,South America,-34.1667,-59.7833,15965 +Capitão Enéas,Brazil,South America,-16.3239,-43.7108,14108 +Capitão Poço,Brazil,South America,-1.7464,-47.0594,54425 +Capitão de Campos,Brazil,South America,-4.4569,-41.9439,11100 +Capitólio,Brazil,South America,-20.615,-46.05,10380 +Capivari,Brazil,South America,-22.995,-47.5078,53152 +Capoeiras,Brazil,South America,-8.735,-36.6269,18338 +Capulhuac,Mexico,North America,19.2,-99.4667,30838 +Caputira,Brazil,South America,-20.1719,-42.2708,8936 +Capão Bonito,Brazil,South America,-24.0058,-48.3494,47118 +Capão da Canoa,Brazil,South America,-29.7608,-50.03,63594 +Capão do Leão,Brazil,South America,-31.7628,-52.4839,26487 +Caraballeda,Venezuela,South America,10.6131,-66.8506,48622 +Caracaraí,Brazil,South America,1.8158,-61.1278,20957 +Caracas,Venezuela,South America,10.4806,-66.9036,3242000 +Carache,Venezuela,South America,9.7072,-70.1933,32820 +Caracol,Brazil,South America,-9.2789,-43.33,10318 +Caraguatatuba,Brazil,South America,-23.62,-45.4128,78921 +Carambeí,Brazil,South America,-24.9178,-50.0969,23283 +Carandaí,Brazil,South America,-20.9539,-43.8058,23812 +Carangola,Brazil,South America,-20.7328,-42.0289,31240 +Carapebus,Brazil,South America,-22.1869,-41.6608,13847 +Carapicuíba,Brazil,South America,-23.5228,-46.8358,344596 +Carapó,Brazil,South America,-22.6339,-54.8219,30612 +Caratinga,Brazil,South America,-19.79,-42.1389,87360 +Carauari,Brazil,South America,-4.8828,-66.8958,28742 +Caravelas,Brazil,South America,-17.7319,-39.2658,20103 +Caraí,Brazil,South America,-17.1889,-41.695,19548 +Caraíbas,Brazil,South America,-14.6,-41.335,9940 +Caraúbas,Brazil,South America,-5.7928,-37.5569,18810 +Carbonia,Italy,Europe,39.1668,8.522,26250 +Carbonita,Brazil,South America,-17.5269,-43.0158,8512 +Carcassonne,France,Europe,43.21,2.35,46429 +Cardoso,Brazil,South America,-20.0819,-49.9139,11345 +Cardoso Moreira,Brazil,South America,-21.4878,-41.6158,12958 +Careiro,Brazil,South America,-3.7678,-60.3689,30792 +Careiro da Várzea,Brazil,South America,-3.197,-59.8267,30846 +Carepa,Colombia,South America,7.7578,-76.6553,47932 +Carhué,Argentina,South America,-37.1796,-62.7601,16245 +Cariacica,Brazil,South America,-20.2639,-40.42,324285 +Cariamanga,Ecuador,South America,-4.32,-79.56,13175 +Caridade,Brazil,South America,-4.2319,-39.1928,16377 +Carinhanha,Brazil,South America,-14.305,-43.765,28869 +Caripito,Venezuela,South America,10.1167,-63.1,64955 +Carira,Brazil,South America,-10.3581,-37.7008,19939 +Caririaçu,Brazil,South America,-7.0419,-39.2839,26320 +Cariré,Brazil,South America,-3.9508,-40.4728,17632 +Cariús,Brazil,South America,-6.5369,-39.4969,17015 +Carlos A. Carrillo,Mexico,North America,18.3748,-95.7544,24565 +Carlos Barbosa,Brazil,South America,-29.2978,-51.5039,20519 +Carlos Casares,Argentina,South America,-35.6167,-61.3667,21125 +Carlos Chagas,Brazil,South America,-17.7028,-40.7658,18615 +Carlos Manuel de Céspedes,Cuba,North America,21.5767,-78.2775,224488 +Carlosama,Colombia,South America,0.8658,-77.7294,8727 +Carlow,Ireland,Europe,52.8306,-6.9317,24272 +Carlsbad,United States,North America,33.1246,-117.2834,114500 +Carlópolis,Brazil,South America,-23.425,-49.7208,16905 +Carmel,United States,North America,39.965,-86.146,100501 +Carmen de Apicalá,Colombia,South America,4.15,-74.7333,8880 +Carmen de Areco,Argentina,South America,-34.3858,-59.8294,12008 +Carmen de Carupa,Colombia,South America,5.3503,-73.9053,9109 +Carmen de Patagones,Argentina,South America,-40.7833,-62.9667,20533 +Carmen de Viboral,Colombia,South America,6.0833,-75.3333,62581 +Carmo,Brazil,South America,-21.9339,-42.6089,17198 +Carmo da Cachoeira,Brazil,South America,-21.4608,-45.2239,11547 +Carmo da Mata,Brazil,South America,-20.5578,-44.8708,11019 +Carmo de Minas,Brazil,South America,-22.1219,-45.1289,13797 +Carmo do Cajuru,Brazil,South America,-20.1839,-44.7708,23479 +Carmo do Paranaíba,Brazil,South America,-19.0008,-46.3158,29011 +Carmo do Rio Claro,Brazil,South America,-20.9731,-46.1189,20954 +Carmo do Rio Verde,Brazil,South America,-15.3536,-49.7075,10186 +Carmópolis,Brazil,South America,-10.6478,-36.9889,13853 +Carmópolis de Minas,Brazil,South America,-20.5408,-44.635,14348 +Carnaubais,Brazil,South America,-5.3408,-36.8328,9714 +Carnaubal,Brazil,South America,-4.1669,-40.9428,17210 +Carnaubeira da Penha,Brazil,South America,-8.3219,-38.7439,12239 +Carnaíba,Brazil,South America,-7.805,-37.7939,18644 +Carneirinho,Brazil,South America,-19.6978,-50.6878,9422 +Carneiros,Brazil,South America,-9.4828,-37.3769,8999 +Carora,Venezuela,South America,10.1711,-70.0797,112600 +Carpentras,France,Europe,44.0558,5.0489,30854 +Carpina,Brazil,South America,-7.8508,-35.2547,84395 +Carrick on Shannon,Ireland,Europe,53.9469,-8.09,4743 +Carrollton,United States,North America,32.989,-96.8999,132741 +Carson City,United States,North America,39.1511,-119.7476,58364 +Cartagena,Colombia,South America,10.4,-75.5,914552 +Cartagena del Chairá,Colombia,South America,1.35,-74.8333,35473 +Cartago,Colombia,South America,4.7,-75.9167,142902 +Cartaxo,Portugal,Europe,39.1619,-8.7864,24462 +Caruaru,Brazil,South America,-8.2828,-35.9758,253634 +Carutapera,Brazil,South America,-1.195,-46.02,24238 +Carvajal,Venezuela,South America,9.33,-70.5767,58863 +Cary,United States,North America,35.7819,-78.8195,176686 +Caràzinho,Brazil,South America,-28.2839,-52.7864,62265 +Carúpano,Venezuela,South America,10.6722,-63.2403,173877 +Casa Branca,Brazil,South America,-21.7739,-47.0858,28083 +Casa Nova,Brazil,South America,-9.1619,-40.9708,55730 +Casablanca,Morocco,Africa,33.5333,-7.5833,3215935 +Casca,Brazil,South America,-28.5608,-51.9778,9465 +Cascais,Portugal,Europe,38.6972,-9.4217,214158 +Cascavel,Brazil,South America,-24.9558,-53.4553,348051 +Caseros,Argentina,South America,-34.6106,-58.5625,95785 +Caserta,Italy,Europe,41.0667,14.3333,72805 +Casilda,Argentina,South America,-33.05,-61.1667,35058 +Casillas,Guatemala,North America,14.4167,-90.25,26059 +Casimiro de Abreu,Brazil,South America,-22.4808,-42.2039,46110 +Casinhas,Brazil,South America,-7.7411,-35.7211,12967 +Cassilândia,Brazil,South America,-19.1128,-51.7339,20988 +Castanhal,Brazil,South America,-1.2969,-47.9219,134496 +Castaños,Mexico,North America,26.7833,-101.4167,27314 +Castelli,Argentina,South America,-25.95,-60.6167,27201 +Castellón de la Plana,Spain,Europe,39.9831,-0.0331,180379 +Castelo,Brazil,South America,-20.6039,-41.185,32756 +Castelo Branco,Portugal,Europe,39.8228,-7.4931,56109 +Castelo do Piauí,Brazil,South America,-5.3222,-41.5525,19715 +Castelsarrasin,France,Europe,44.0408,1.1078,14335 +Castilho,Brazil,South America,-20.8722,-51.4875,19873 +Castilla La Nueva,Colombia,South America,3.8333,-73.6833,13611 +Castlebar,Ireland,Europe,53.85,-9.3,13054 +Castres,France,Europe,43.6,2.25,42700 +Castricum,Netherlands,Europe,52.55,4.6667,36086 +Castries,Saint Lucia,North America,14.0108,-60.9894,70000 +Castro,Brazil,South America,-24.7893,-50.0123,71809 +Castro Alves,Brazil,South America,-12.7658,-39.4278,24712 +Castro Daire,Portugal,Europe,40.9,-7.9333,15339 +Cataguases,Brazil,South America,-21.3892,-42.6967,75540 +Catalão,Brazil,South America,-18.17,-47.9419,114427 +Catamarca,Argentina,South America,-28.4667,-65.7833,159139 +Catamayo,Ecuador,South America,-3.9833,-79.35,27026 +Catanduva,Brazil,South America,-21.1378,-48.9728,105847 +Catanduvas,Brazil,South America,-27.0708,-51.6619,10566 +Catania,Italy,Europe,37.5027,15.0873,311584 +Catanzaro,Italy,Europe,38.91,16.5875,84670 +Catarina,Guatemala,North America,14.85,-92.0833,35542 +Catarman,Philippines,Asia,12.4994,124.6328,97879 +Catbalogan,Philippines,Asia,11.78,124.88,106440 +Catemaco,Mexico,North America,18.4167,-95.1167,49451 +Catende,Brazil,South America,-8.6669,-35.7169,32156 +Catia La Mar,Venezuela,South America,10.6,-67.0333,661897 +Cativá,Panama,North America,9.3624,-79.8357,34558 +Catió,Guinea-Bissau,Africa,11.2833,-15.25,5081 +Catolé do Rocha,Brazil,South America,-6.3439,-37.7469,30661 +Catu,Brazil,South America,-12.3531,-38.3789,54970 +Catunda,Brazil,South America,-4.6478,-40.2008,10444 +Caturama,Brazil,South America,-13.3289,-42.2908,8841 +Catuípe,Brazil,South America,-28.25,-54.0119,8674 +Caucagua,Venezuela,South America,10.2822,-66.3772,35000 +Caucaia,Brazil,South America,-3.7328,-38.6558,355679 +Caucasia,Colombia,South America,7.9833,-75.2,90213 +Caucete,Argentina,South America,-31.65,-68.2833,28222 +Cauquenes,Chile,South America,-35.9667,-72.35,40441 +Cavalcante,Brazil,South America,-13.7978,-47.4578,9150 +Caxambu,Brazil,South America,-21.9769,-44.9328,21056 +Caxias,Brazil,South America,-4.8589,-43.3558,139756 +Caxias do Sul,Brazil,South America,-29.1678,-51.1789,360419 +Caxito,Angola,Africa,-8.58,13.6642,77276 +Cayambe,Ecuador,South America,0.0439,-78.1561,44559 +Cayenne,French Guiana,South America,4.933,-52.33,61550 +Cayo Mambí,Cuba,North America,20.6647,-75.2814,22286 +Cazin,Bosnia and Herzegovina,Europe,44.9667,15.9333,66149 +Cazones de Herrera,Mexico,North America,20.7044,-97.3106,24421 +Caçador,Brazil,South America,-26.7753,-51.015,79313 +Caçapava,Brazil,South America,-23.1008,-45.7069,91162 +Caçapava do Sul,Brazil,South America,-30.5119,-53.4908,32515 +Caçu,Brazil,South America,-18.5569,-51.1308,13774 +Caém,Brazil,South America,-11.1,-40.4333,10384 +Cañada de Gómez,Argentina,South America,-32.8167,-61.4,32108 +Cañasgordas,Colombia,South America,6.7497,-76.0258,13595 +Cañuelas,Argentina,South America,-35.0333,-58.7333,31901 +Ceará-Mirim,Brazil,South America,-5.6339,-35.4258,79115 +Cebu City,Philippines,Asia,10.32,123.75,964169 +Cedar Rapids,United States,North America,41.9662,-91.6783,192755 +Cedral,Brazil,South America,-20.9028,-49.2678,12618 +Cedro,Brazil,South America,-6.6069,-39.0619,22344 +Cegléd,Hungary,Europe,47.1772,19.7981,36391 +Celaya,Mexico,North America,20.5222,-100.8122,378143 +Celendín,Peru,South America,-6.8667,-78.15,26925 +Celje,Slovenia,Europe,46.2358,15.2675,37872 +Celldömölk,Hungary,Europe,47.2557,17.1491,9692 +Celle,Germany,Europe,52.6256,10.0825,70293 +Celorico de Basto,Portugal,Europe,41.3869,-8.0022,20098 +Cencheng,China,Asia,22.9167,111.0,724364 +Centar Župa,North Macedonia,Europe,41.4775,20.5578,354 +Centennial,United States,North America,39.5926,-104.8673,107386 +Centenário do Sul,Brazil,South America,-22.8208,-51.595,10832 +Central,Brazil,South America,-11.1542,-42.0814,16348 +Centralina,Brazil,South America,-18.5839,-49.1989,10207 +Cerejeiras,Brazil,South America,-13.1889,-60.8122,15890 +Ceres,Brazil,South America,-15.3078,-49.5978,22046 +Cereté,Colombia,South America,8.8833,-75.8,94935 +Cergy,France,Europe,49.0361,2.0631,69578 +Cerklje na Gorenjskem,Slovenia,Europe,46.2488,14.4855,1710 +Cerknica,Slovenia,Europe,45.7964,14.3581,4018 +Cerkno,Slovenia,Europe,46.1278,13.9875,1425 +Cerkvenjak,Slovenia,Europe,46.5706,15.9436,163 +Cerqueira César,Brazil,South America,-23.0356,-49.1661,19109 +Cerquilho Velho,Brazil,South America,-23.165,-47.7436,49802 +Cerrillos,Argentina,South America,-24.9,-65.4833,17634 +Cerritos,Mexico,North America,22.4275,-100.2783,14804 +Cerro Azul,Mexico,North America,21.192,-97.7409,23573 +Cerro Corá,Brazil,South America,-6.0458,-36.3458,11000 +Cerro de Pasco,Peru,South America,-10.6864,-76.2625,58899 +Cestos City,Liberia,Africa,5.4667,-9.5833,2578 +Cesário Lange,Brazil,South America,-23.2267,-47.9531,18375 +Cetinje,Montenegro,Europe,42.3894,18.9247,12460 +Ceyhan,Turkey,Europe,37.0289,35.8125,158922 +Ceylanpınar,Turkey,Europe,36.8461,40.0489,90440 +Cha-am,Thailand,Asia,12.8,99.9667,35581 +Chacabuco,Argentina,South America,-34.6333,-60.4667,38418 +Chachagüí,Colombia,South America,1.3605,-77.2835,12419 +Chachapoyas,Peru,South America,-6.2167,-77.85,32026 +Chachersk,Belarus,Europe,52.9161,30.9161,8927 +Chachoengsao,Thailand,Asia,13.6903,101.0703,60893 +Chaguanas,Trinidad and Tobago,North America,10.5167,-61.4,101297 +Chaguaramas,Venezuela,South America,9.3333,-66.2667,15000 +Chai Nat,Thailand,Asia,15.1872,100.1283,14469 +Chai Prakan,Thailand,Asia,19.7322,99.1403,16090 +Chaigoubu,China,Asia,37.7868,114.6107,73028 +Chaiyaphum,Thailand,Asia,15.8056,102.0311,58350 +Chaiyo,Thailand,Asia,14.6488,100.4788,12710 +Chajul,Guatemala,North America,15.4872,-91.0347,52019 +Chake Chake,Tanzania,Africa,-5.2395,39.77,49959 +Chakwal,Pakistan,Asia,32.9303,72.8556,299751 +Chalatenango,El Salvador,North America,14.0333,-88.9333,32272 +Chalco,Mexico,North America,19.2647,-98.8975,310130 +Chalkída,Greece,Europe,38.4625,23.595,59125 +Chalma,Mexico,North America,21.2167,-98.4,13479 +Chalon-sur-Saône,France,Europe,46.7806,4.8528,44592 +Chaltyr,Russia,Asia,47.2848,39.4823,15334 +Chalándri,Greece,Europe,38.0167,23.8,77102 +Cham,Germany,Europe,49.2167,12.65,17593 +Chambas,Cuba,North America,22.1967,-78.9133,41000 +Chambéry,France,Europe,45.57,5.9118,60251 +Chamical,Argentina,South America,-30.3667,-66.3167,12919 +Champaign,United States,North America,40.1142,-88.2737,148477 +Champerico,Guatemala,North America,14.293,-91.914,38570 +Champotón,Mexico,North America,19.35,-90.7167,30881 +Chamusca,Portugal,Europe,39.3594,-8.4808,8530 +Chanal,Mexico,North America,16.6489,-92.2214,9533 +Chandler,United States,North America,33.2825,-111.8514,278123 +Chandīgarh,India,Asia,30.75,76.78,1055450 +Changchun,China,Asia,43.897,125.326,4408154 +Changde,China,Asia,29.031,111.699,5279102 +Changhua,Taiwan,Asia,24.0667,120.5333,226564 +Changling,China,Asia,44.27,123.99,55841 +Changsha,China,Asia,28.228,112.939,4766296 +Changting,China,Asia,25.867,116.3167,87458 +Changuinola,Panama,North America,9.4333,-82.5167,31223 +Changwon,"Korea, South",Asia,35.2708,128.6631,1009998 +Changzhi,China,Asia,36.195,113.117,3180884 +Changzhou,China,Asia,31.811,119.974,3601079 +Chang’an,China,Asia,29.4768,113.4504,433200 +Chaniá,Greece,Europe,35.5167,24.0167,53910 +Chanthaburi,Thailand,Asia,12.6086,102.1039,23130 +Chaozhou,China,Asia,23.658,116.622,2656600 +Chapa de Mota,Mexico,North America,19.8144,-99.5269,31737 +Chapada dos Guimarães,Brazil,South America,-15.4608,-55.75,18990 +Chapadinha,Brazil,South America,-3.7419,-43.36,81386 +Chapaev,Kazakhstan,Asia,50.2,51.1667,8476 +Chapala,Mexico,North America,20.2961,-103.1911,21596 +Chaparral,Colombia,South America,3.75,-75.5833,50367 +Chapecó,Brazil,South America,-27.0958,-52.6178,224013 +Chaplynka,Ukraine,Europe,46.3644,33.5331,9415 +Charallave,Venezuela,South America,10.2431,-66.8622,129214 +Charalá,Colombia,South America,6.25,-73.0833,10211 +Charata,Argentina,South America,-27.2167,-61.2,26497 +Charcas,Mexico,North America,23.1261,-101.1128,14117 +Charleroi,Belgium,Europe,50.4167,4.4442,204322 +Charleston,United States,North America,32.8168,-79.9687,726389 +Charleville-Mézières,France,Europe,49.7719,4.7161,24668 +Charlotte,United States,North America,35.2083,-80.8303,1436613 +Charlottesville,United States,North America,38.0375,-78.4855,102802 +Charlottetown,Canada,North America,46.2403,-63.1347,52390 +Charqueada,Brazil,South America,-22.5097,-47.7781,16440 +Charqueadas,Brazil,South America,-29.955,-51.625,35012 +Charsadda,Pakistan,Asia,34.15,71.7333,105414 +Chartres,France,Europe,48.456,1.484,37990 +Charām,Iran,Asia,30.7461,50.7461,15218 +Chascomús,Argentina,South America,-35.575,-58.0089,33607 +Chatham,Canada,North America,42.4229,-82.1324,103988 +Chattanooga,United States,North America,35.066,-85.2481,410795 +Chattogram,Bangladesh,Asia,22.335,91.8325,2581643 +Chaumont,France,Europe,48.1117,5.1389,21418 +Chaval,Brazil,South America,-3.0339,-41.2439,12462 +Chaves,Portugal,Europe,41.7399,-7.4707,41243 +Chavinda,Mexico,North America,20.0167,-102.45,10258 +Chavusy,Belarus,Europe,53.8075,30.9714,9817 +Chaykovskiy,Russia,Asia,56.7667,54.15,74913 +Chaïdári,Greece,Europe,38.0167,23.65,47051 +Chañaral,Chile,South America,-26.3444,-70.6219,13143 +Chebarkul,Russia,Asia,54.9833,60.3667,44671 +Cheboksary,Russia,Asia,56.15,47.2333,496350 +Chekhov,Russia,Asia,55.15,37.45,86164 +Chekmagush,Russia,Asia,55.1333,54.6667,11382 +Chelyabinsk,Russia,Asia,55.1547,61.3758,1177058 +Chemnitz,Germany,Europe,50.8333,12.9167,250681 +Chenalhó,Mexico,North America,16.9333,-92.5667,47371 +Chengdu,China,Asia,30.66,104.0633,14645000 +Chengguan,China,Asia,35.4623,113.8054,740435 +Chengjiao,China,Asia,35.3984,114.0649,476867 +Chengjiao Chengguanzhen,China,Asia,34.4362,104.0306,67826 +Chengxiang,China,Asia,31.564,121.174,709500 +Chennai,India,Asia,13.0825,80.275,12395000 +Chenzhou,China,Asia,25.77,113.016,4667134 +Chenārān,Iran,Asia,36.6406,59.1186,53879 +Cheongju,"Korea, South",Asia,36.6333,127.4833,852018 +Chepes,Argentina,South America,-31.35,-66.6,6020 +Chepo,Panama,North America,9.1678,-79.0935,20420 +Cherbourg,France,Europe,49.63,-1.62,25370 +Cheremkhovo,Russia,Asia,53.15,103.0833,53958 +Cherkasy,Ukraine,Europe,49.4444,32.0597,269836 +Cherkessk,Russia,Asia,44.2167,42.05,123100 +Chernigovka,Russia,Asia,44.3406,132.5689,10124 +Chernihiv,Ukraine,Europe,51.4939,31.2947,282747 +Chernivtsi,Ukraine,Europe,48.3,25.9333,264298 +Chernogolovka,Russia,Asia,56.0,38.3667,18472 +Chernushka,Russia,Asia,56.5167,56.1,33092 +Chernyakhovsk,Russia,Asia,54.6347,21.8119,35705 +Cherskiy,Russia,Asia,68.7501,161.33,3707 +Chervonohrad,Ukraine,Europe,50.3867,24.2289,64297 +Chervyen,Belarus,Europe,53.7078,28.4322,10545 +Cherán,Mexico,North America,19.6833,-101.95,19081 +Chesapeake,United States,North America,36.6778,-76.3023,251153 +Chetumal,Mexico,North America,18.5036,-88.3053,169028 +Cheyenne,United States,North America,41.135,-104.7902,81378 +Chełm,Poland,Europe,51.1322,23.4778,59546 +Chełmno,Poland,Europe,53.3492,18.4231,18837 +Chiang Klang,Thailand,Asia,19.2938,100.8617,9721 +Chiang Mai,Thailand,Asia,18.7953,98.9986,1198000 +Chiang Rai,Thailand,Asia,19.9094,99.8275,69888 +Chiantla,Guatemala,North America,15.35,-91.45,93780 +Chiapa de Corzo,Mexico,North America,16.7069,-93.0139,55931 +Chiatura,Georgia,Europe,42.2894,43.2836,12803 +Chiayi,Taiwan,Asia,23.48,120.4497,263188 +Chiba,Japan,Asia,35.6073,140.1064,975014 +Chibok,Nigeria,Africa,10.8697,12.8467,66105 +Chicacao,Guatemala,North America,14.5333,-91.3167,67399 +Chicago,United States,North America,41.8375,-87.6866,8489066 +Chicamán,Guatemala,North America,15.3483,-90.7994,43139 +Chichicastenango,Guatemala,North America,14.9333,-91.1167,150932 +Chichigalpa,Nicaragua,North America,12.5667,-87.0333,44769 +Chichihualco,Mexico,North America,17.655,-99.674,10079 +Chichiriviche,Venezuela,South America,10.9343,-68.2771,18960 +Chiché,Guatemala,North America,15.0106,-91.065,32271 +Chiclayo,Peru,South America,-6.763,-79.8366,552508 +Chico,United States,North America,39.7578,-121.8161,109411 +Chicoloapan,Mexico,North America,19.4167,-98.9,175053 +Chiconcuac,Mexico,North America,19.55,-98.9,19656 +Chiconquiaco,Mexico,North America,19.75,-96.8167,14394 +Chicoutimi,Canada,North America,48.4333,-71.0667,103934 +Chiesanuova,San Marino,Europe,43.9045,12.4208,1143 +Chieti,Italy,Europe,42.3511,14.1674,48455 +Chifeng,China,Asia,42.255,118.8825,4035967 +Chignahuapan,Mexico,North America,19.8333,-98.0333,66464 +Chigorodó,Colombia,South America,7.6675,-76.6808,84183 +Chihuahua,Mexico,North America,28.6369,-106.0769,925762 +Chilapa de Álvarez,Mexico,North America,17.5944,-99.1778,120790 +Chilas,Pakistan,Asia,35.4194,74.0944,214000 +Chilcuautla,Mexico,North America,20.3333,-99.2333,15284 +Chilecito,Argentina,South America,-29.1667,-67.5,33724 +Chilibre,Panama,North America,9.154,-79.6202,53955 +Chillán,Chile,South America,-36.6,-72.1167,164270 +Chilpancingo,Mexico,North America,17.55,-99.5,225728 +Chilón,Mexico,North America,17.1167,-92.2833,8982 +Chimalhuacán,Mexico,North America,19.4131,-98.9839,614453 +Chimaltenango,Guatemala,North America,14.6622,-90.8208,116642 +Chimbas,Argentina,South America,-31.5,-68.5319,87258 +Chimboy Shahri,Uzbekistan,Asia,42.9295,59.782,36917 +Chimichagua,Colombia,South America,9.25,-73.8167,30303 +Chimoio,Mozambique,Africa,-19.1167,33.45,238976 +Chimá,Colombia,South America,9.15,-75.6333,13492 +China,Mexico,North America,25.7033,-99.2365,10000 +Chinampa de Gorostiza,Mexico,North America,21.3667,-97.7333,16741 +Chinandega,Nicaragua,North America,12.6167,-87.15,121793 +Chinautla,Guatemala,North America,14.7,-90.5,124365 +Chinchiná,Colombia,South America,4.9825,-75.6036,50673 +Chinggil,China,Asia,46.6673,90.3786,16098 +Chinhoyi,Zimbabwe,Africa,-17.3497,30.1944,90800 +Chiniot,Pakistan,Asia,31.7194,72.9842,477781 +Chinique,Guatemala,North America,15.0411,-91.0269,13547 +Chinsali,Zambia,Africa,-10.5522,32.0692,15198 +Chinácota,Colombia,South America,7.6167,-72.6,16348 +Chinú,Colombia,South America,9.0833,-75.3333,50313 +Chipata,Zambia,Africa,-13.6453,32.6464,327059 +Chiquimula,Guatemala,North America,14.7833,-89.5333,111505 +Chiquimulilla,Guatemala,North America,14.0833,-90.3833,64871 +Chiquinquirá,Colombia,South America,5.6333,-73.75,85274 +Chiradzulu,Malawi,Africa,-15.6746,35.1407,1580 +Chiriguaná,Colombia,South America,9.3667,-73.6,27006 +Chisec,Guatemala,North America,15.8125,-90.3217,69325 +Chisinau,Moldova,Europe,47.0228,28.8353,639000 +Chita,Russia,Asia,52.05,113.4667,333159 +Chitagá,Colombia,South America,7.1333,-72.6667,10554 +Chitipa,Malawi,Africa,-9.7024,33.2697,17743 +Chitral,Pakistan,Asia,35.8461,71.7858,49780 +Chitré,Panama,North America,7.9667,-80.4333,60 +Chivacoa,Venezuela,South America,10.16,-68.9097,59059 +Chivilcoy,Argentina,South America,-34.9,-60.0167,58152 +Chivolo,Colombia,South America,10.0261,-74.6211,24932 +Chizhou,China,Asia,30.6654,117.4916,1342764 +Chlef,Algeria,Africa,36.1647,1.3317,155134 +Choachí,Colombia,South America,4.5297,-73.9236,10729 +Chocamán,Mexico,North America,19.0167,-97.0333,21102 +Chocontá,Colombia,South America,5.1467,-73.6825,25257 +Chodzież,Poland,Europe,52.9833,16.9,18044 +Choele Choel,Argentina,South America,-39.2667,-65.6833,10146 +Choix,Mexico,North America,26.7086,-108.3253,9306 +Chojnice,Poland,Europe,53.7,17.55,39275 +Chok Chai,Thailand,Asia,14.7333,102.1667,8793 +Chokurdakh,Russia,Asia,70.6183,147.8946,2506 +Cholargós,Greece,Europe,38.0,23.8,31304 +Cholet,France,Europe,47.06,-0.8783,54074 +Cholpon-Ata,Kyrgyzstan,Asia,42.6494,77.0822,11520 +Chom Thong,Thailand,Asia,18.4901,98.6747,15618 +Choma,Zambia,Africa,-16.7711,26.9922,51842 +Chon Buri,Thailand,Asia,13.3611,100.985,342959 +Chone,Ecuador,South America,-0.6833,-80.1,54629 +Chongqing,China,Asia,29.5637,106.5504,12135000 +Chongshan,China,Asia,18.7751,109.5169,107100 +Chongzuo,China,Asia,22.377,107.365,2088692 +Chopinzinho,Brazil,South America,-25.8558,-52.5228,19083 +Chornomorske,Ukraine,Europe,45.5019,32.7025,11267 +Chorozinho,Brazil,South America,-4.3003,-38.4978,20274 +Chorrochó,Brazil,South America,-8.98,-39.0939,10579 +Chortkiv,Ukraine,Europe,49.0075,25.7906,28279 +Chortoq,Uzbekistan,Asia,41.0689,71.8153,53400 +Chorzów,Poland,Europe,50.3,18.95,104193 +Choró,Brazil,South America,-4.8428,-39.1408,12113 +Chos Malal,Argentina,South America,-37.3833,-70.2667,15567 +Choszczno,Poland,Europe,53.1667,15.4,14399 +Chota,Peru,South America,-6.5614,-78.6486,54611 +Choybalsan,Mongolia,Asia,48.0706,114.5228,38150 +Choyr,Mongolia,Asia,46.3611,108.3611,10434 +Christchurch,New Zealand,Oceania,-43.5311,172.6361,383200 +Chrysoúpoli,Greece,Europe,40.9833,24.7,14970 +Chrzanów,Poland,Europe,50.1333,19.4,35091 +Chuarrancho,Guatemala,North America,14.8181,-90.5153,17057 +Chudovo,Russia,Asia,59.1167,31.6667,14302 +Chuguyevka,Russia,Asia,44.1667,133.8583,11101 +Chuhuiv,Ukraine,Europe,49.8356,36.6864,31018 +Chuimatan,China,Asia,35.7166,102.8771,26044 +Chula Vista,United States,North America,32.6281,-117.0144,275030 +Chum Phae,Thailand,Asia,16.5443,102.0992,31597 +Chumbicha,Argentina,South America,-28.8667,-66.2333,2572 +Chumphon,Thailand,Asia,10.4939,99.18,33633 +Chuncheon,"Korea, South",Asia,37.8667,127.7333,281596 +Chupinguaia,Brazil,South America,-12.5522,-60.8997,9324 +Chuqung,China,Asia,33.3667,97.1333,10659 +Chur,Switzerland,Europe,46.8521,9.5297,35373 +Churapcha,Russia,Asia,61.9928,132.4317,10342 +Churumuco de Morelos,Mexico,North America,18.6167,-101.6333,14366 +Chusovoy,Russia,Asia,58.3167,57.8167,44780 +Chyhyryn,Ukraine,Europe,49.0772,32.6492,8539 +Châlons-en-Champagne,France,Europe,48.9575,4.365,43218 +Château-Gontier,France,Europe,47.8286,-0.7028,11805 +Château-Thierry,France,Europe,49.04,3.4,15068 +Châteaubriant,France,Europe,47.7178,-1.375,12231 +Châteaudun,France,Europe,48.0717,1.3387,12898 +Châteauroux,France,Europe,46.8103,1.6911,43079 +Châtellerault,France,Europe,46.8178,0.5461,31105 +Chã Grande,Brazil,South America,-8.2378,-35.4619,20546 +Chã da Alegria,Brazil,South America,-8.0008,-35.2133,11102 +Chía,Colombia,South America,4.85,-74.05,124309 +Chíos,Greece,Europe,38.3725,26.1375,27015 +Chüy,Kyrgyzstan,Asia,42.8197,75.2823,12688 +Chābahār,Iran,Asia,25.2928,60.6497,106739 +Chārīkār,Afghanistan,Asia,35.0131,69.1689,96093 +Chơn Thành,Vietnam,Asia,11.4292,106.6572,121083 +Chợ Lách,Vietnam,Asia,10.2647,106.13,11836 +Chợ Mới,Vietnam,Asia,10.55,105.4,12898 +Ch’ŏngjin,"Korea, North",Asia,41.7833,129.7667,667929 +Ciamis,Indonesia,Asia,-7.3281,108.3336,99750 +Cianjur,Indonesia,Asia,-6.82,107.1408,176368 +Cianorte,Brazil,South America,-23.6628,-52.605,79527 +Cibinong,Indonesia,Asia,-6.485,106.842,365387 +Cibitoke,Burundi,Africa,-2.8886,29.12,23885 +Cicuco,Colombia,South America,9.2667,-74.65,13120 +Cidade Gaúcha,Brazil,South America,-23.38,-52.945,11467 +Cidade Velha,Cabo Verde,Africa,14.9167,-23.6042,2148 +Cide,Turkey,Europe,41.8922,33.0044,11087 +Cidelândia,Brazil,South America,-5.1739,-47.7819,12878 +Cidreira,Brazil,South America,-30.1811,-50.2056,16583 +Ciechanów,Poland,Europe,52.8667,20.6333,42705 +Ciego de Ávila,Cuba,North America,21.8481,-78.7631,143449 +Cienfuegos,Cuba,North America,22.1456,-80.4364,164924 +Cieszyn,Poland,Europe,49.7484,18.6332,33958 +Cifuentes,Cuba,North America,22.6208,-80.0661,26135 +Cihanbeyli,Turkey,Europe,38.6581,32.9281,50677 +Cihuatlán,Mexico,North America,19.25,-104.5667,40139 +Cilacap,Indonesia,Asia,-7.7167,109.017,1174964 +Cili,China,Asia,29.4225,111.1219,130000 +Cimerak,Indonesia,Asia,-7.7422,108.4244,43500 +Cimitarra,Colombia,South America,6.3167,-73.95,49593 +Cimişlia,Moldova,Europe,46.52,28.7842,11997 +Cincinnati,United States,North America,39.1413,-84.506,1704916 +Cinfães,Portugal,Europe,41.0667,-8.0833,20427 +Cintalapa de Figueroa,Mexico,North America,16.6978,-93.7203,49201 +Cipó,Brazil,South America,-11.1,-38.5169,17230 +Circasia,Colombia,South America,4.6167,-75.6333,26705 +Cirkulane,Slovenia,Europe,46.3453,15.9952,451 +Ciro Redondo,Cuba,North America,22.0189,-78.7031,28370 +Cisneros,Colombia,South America,6.5383,-75.0886,11555 +Citlaltépec,Mexico,North America,21.3297,-97.8789,12109 +City of Isabela,Philippines,Asia,6.7,121.97,130379 +City of Parañaque,Philippines,Asia,14.5008,120.9915,689992 +Ciudad Acuña,Mexico,North America,29.3242,-100.9317,160225 +Ciudad Altamirano,Mexico,North America,18.3472,-100.6531,25850 +Ciudad Apodaca,Mexico,North America,25.7817,-100.1886,523370 +Ciudad Benito Juárez,Mexico,North America,25.65,-100.0833,308285 +Ciudad Bolívar,Venezuela,South America,8.1219,-63.55,422578 +Ciudad Choluteca,Honduras,North America,13.3014,-87.1842,152519 +Ciudad Constitución,Mexico,North America,25.0322,-111.6703,43805 +Ciudad Cuauhtémoc,Mexico,North America,21.2833,-98.3667,9740 +Ciudad Darío,Nicaragua,North America,12.7167,-86.1167,41014 +Ciudad Fernández,Mexico,North America,21.95,-100.0,48106 +Ciudad General Escobedo,Mexico,North America,25.7933,-100.1583,454967 +Ciudad Guadalupe Victoria,Mexico,North America,24.4497,-104.1225,16506 +Ciudad Guayana,Venezuela,South America,8.3667,-62.65,978202 +Ciudad Guzmán,Mexico,North America,19.7,-103.4667,115000 +Ciudad Hidalgo,Mexico,North America,19.6917,-100.5536,122619 +Ciudad Lerdo,Mexico,North America,25.55,-103.5167,79669 +Ciudad López Mateos,Mexico,North America,19.55,-99.2833,489937 +Ciudad Madero,Mexico,North America,22.2475,-97.8375,197216 +Ciudad Mante,Mexico,North America,22.7333,-98.95,84787 +Ciudad Manuel Doblado,Mexico,North America,20.7303,-101.9533,37145 +Ciudad Melchor Múzquiz,Mexico,North America,27.8667,-101.5,38992 +Ciudad Melchor de Mencos,Guatemala,North America,17.0667,-89.15,30682 +Ciudad Miguel Alemán,Mexico,North America,26.4003,-99.0253,19998 +Ciudad Obregón,Mexico,North America,27.4939,-109.9389,329404 +Ciudad Ojeda,Venezuela,South America,10.2,-71.3,128941 +Ciudad Piar,Venezuela,South America,7.4522,-63.32,40918 +Ciudad Real,Spain,Europe,38.9833,-3.9167,75909 +Ciudad Río Bravo,Mexico,North America,25.9861,-98.0889,101481 +Ciudad Sabinas Hidalgo,Mexico,North America,26.5,-100.1833,32047 +Ciudad Sandino,Nicaragua,North America,12.1655,-86.3579,127517 +Ciudad Santa Catarina,Mexico,North America,25.6833,-100.45,306322 +Ciudad Serdán,Mexico,North America,18.9833,-97.45,23824 +Ciudad Tecún Umán,Guatemala,North America,14.6667,-92.15,27365 +Ciudad Tula,Mexico,North America,23.0,-99.72,10043 +Ciudad Valles,Mexico,North America,21.9833,-99.0167,177022 +Ciudad Victoria,Mexico,North America,23.7389,-99.1431,305155 +Ciudad Vieja,Guatemala,North America,14.5233,-90.7667,41010 +Ciudad de Allende,Mexico,North America,25.2833,-100.0167,35289 +Ciudad de Atlixco,Mexico,North America,18.9,-98.45,89314 +Ciudad de Huajuapam de León,Mexico,North America,17.8,-97.7667,45321 +Ciudad de Huitzuco,Mexico,North America,18.3,-99.35,16025 +Ciudad del Carmen,Mexico,North America,18.6333,-91.8333,191238 +Ciudad del Este,Paraguay,South America,-25.5167,-54.6161,308983 +Cizre,Turkey,Europe,37.332,42.187,130916 +Ciénaga,Colombia,South America,11.0069,-74.25,129414 +Ciénaga de Oro,Colombia,South America,8.8833,-75.6167,64226 +Ciénega de Flores,Mexico,North America,25.95,-100.1833,14268 +Clarines,Venezuela,South America,9.9433,-65.1661,15000 +Clarington,Canada,North America,43.935,-78.6083,101427 +Clarksville,United States,North America,36.5692,-87.3413,218159 +Claveria,Philippines,Asia,12.9035,123.2457,42142 +Clearwater,United States,North America,27.979,-82.7623,117075 +Clemencia,Colombia,South America,10.5833,-75.3333,13821 +Clermont,France,Europe,49.3797,2.4133,10704 +Clermont-Ferrand,France,Europe,45.7831,3.0824,147751 +Clervaux,Luxembourg,Europe,50.05,6.0333,1468 +Cleveland,United States,North America,41.4764,-81.6805,1679247 +Clevelândia,Brazil,South America,-26.3958,-52.4708,15070 +Clonmel,Ireland,Europe,52.355,-7.7039,18369 +Cloppenburg,Germany,Europe,52.85,8.05,37280 +Clorinda,Argentina,South America,-25.2833,-57.7167,47240 +Clovis,United States,North America,36.8278,-119.683,122719 +Cluj-Napoca,Romania,Europe,46.7667,23.5833,286598 +Cláudio,Brazil,South America,-20.4428,-44.7658,30159 +Coacalco,Mexico,North America,19.6333,-99.0931,293444 +Coacoatzintla,Mexico,North America,19.65,-96.9333,10482 +Coalcomán de Vázquez Pallares,Mexico,North America,18.7833,-103.1667,18444 +Coaraci,Brazil,South America,-14.6408,-39.5511,16549 +Coari,Brazil,South America,-4.085,-63.1408,67096 +Coatepec,Mexico,North America,19.4522,-96.9614,92127 +Coatepec Harinas,Mexico,North America,18.9,-99.7167,36269 +Coatepeque,Guatemala,North America,14.7,-91.8667,120737 +Coatzacoalcos,Mexico,North America,18.1414,-94.4344,319187 +Cobija,Bolivia,South America,-11.0333,-68.7333,46267 +Coburg,Germany,Europe,50.2667,10.9667,42139 +Cobán,Guatemala,North America,15.4833,-90.3667,228664 +Cocal,Brazil,South America,-3.4708,-41.555,28212 +Cochabamba,Bolivia,South America,-17.3883,-66.1597,856198 +Cochoapa el Grande,Mexico,North America,17.2,-98.45,15572 +Cochrane,Chile,South America,-47.2547,-72.575,2789 +Coconuco,Colombia,South America,2.25,-76.4167,14952 +Cocorná,Colombia,South America,6.0569,-75.1853,14743 +Cocorote,Venezuela,South America,10.3217,-68.7825,39310 +Cocos,Brazil,South America,-14.1839,-44.5339,19151 +Cocotitlán,Mexico,North America,19.2167,-98.85,12142 +Cocula,Mexico,North America,20.39,-103.82,29267 +Codajás,Brazil,South America,-3.8369,-62.0569,23549 +Codó,Brazil,South America,-4.455,-43.8858,111146 +Coelho Neto,Brazil,South America,-4.2567,-43.0128,49621 +Coello,Colombia,South America,4.3333,-74.9167,9887 +Coesfeld,Germany,Europe,51.95,7.1667,37259 +Coeur d'Alene,United States,North America,47.704,-116.7936,128680 +Coevorden,Netherlands,Europe,52.661,6.7405,35483 +Cognac,France,Europe,45.7,-0.33,18466 +Cogua,Colombia,South America,5.0667,-73.9833,24092 +Coimbra,Portugal,Europe,40.2111,-8.4289,140816 +Coité do Nóia,Brazil,South America,-9.6322,-36.5786,10643 +Cojedes,Venezuela,South America,9.6167,-68.9167,14044 +Cojutepeque,El Salvador,North America,13.7167,-88.9333,50315 +Colares,Brazil,South America,-0.9369,-48.2819,12868 +Colatina,Brazil,South America,-19.5389,-40.6308,111788 +Colima,Mexico,North America,19.2433,-103.7247,146965 +Colina,Chile,South America,-33.2017,-70.6703,146209 +Colinas,Brazil,South America,-6.0258,-44.2489,35803 +Colinas do Tocantins,Brazil,South America,-8.0589,-48.475,34233 +College Station,United States,North America,30.5852,-96.2959,215768 +Colmar,France,Europe,48.0817,7.3556,67360 +Colméia,Brazil,South America,-8.7289,-48.765,8941 +Colomba,Guatemala,North America,14.7167,-91.7333,56487 +Colombia,Cuba,North America,20.9906,-77.4158,31482 +Colombo,Sri Lanka,Asia,6.9167,79.8333,752993 +Colonia,"Micronesia, Federated States of",Asia,9.5167,138.1333,3126 +Colonia del Sacramento,Uruguay,South America,-34.4714,-57.8442,26231 +Colorado,Brazil,South America,-22.8378,-51.9728,22896 +Colorado Springs,United States,North America,38.8674,-104.7605,638421 +Colorado do Oeste,Brazil,South America,-13.1167,-60.5417,15663 +Colotenango,Guatemala,North America,15.4167,-91.7167,44823 +Colotlán,Mexico,North America,22.2,-103.3,19689 +Columbia,United States,North America,34.0378,-80.9036,605083 +Columbus,United States,North America,39.9862,-82.9855,1578153 +Colíder,Brazil,South America,-10.8128,-55.455,31370 +Colón,Panama,North America,9.365,-79.875,78000 +Colônia Leopoldina,Brazil,South America,-8.9089,-35.725,15816 +Comala,Mexico,North America,19.3208,-103.7603,9649 +Comalapa,Mexico,North America,15.65,-92.15,21727 +Comalcalco,Mexico,North America,18.2801,-93.2018,214877 +Comandante Fontana,Argentina,South America,-25.3333,-59.6833,4277 +Comapa,Guatemala,North America,14.1167,-89.9167,37218 +Comayagua,Honduras,North America,14.4528,-87.6379,144785 +Comendador,Dominican Republic,North America,18.8833,-71.7,27908 +Comendador Levy Gasparian,Brazil,South America,-22.0289,-43.205,8741 +Comitancillo,Guatemala,North America,15.0833,-91.7167,80612 +Comitán,Mexico,North America,16.25,-92.1333,113479 +Como,Italy,Europe,45.8103,9.0861,83184 +Comodoro,Brazil,South America,-13.6593,-59.7941,18238 +Comodoro Rivadavia,Argentina,South America,-45.8647,-67.4808,201228 +Comonfort,Mexico,North America,20.7167,-100.7667,82216 +Compiègne,France,Europe,49.4149,2.8231,40808 +Compostela,Mexico,North America,21.2389,-104.9,17573 +Comrat,Moldova,Europe,46.3003,28.6572,20113 +Conakry,Guinea,Africa,9.5092,-13.7122,1667864 +Conceição,Brazil,South America,-7.5619,-38.5089,18860 +Conceição da Aparecida,Brazil,South America,-21.0939,-46.2039,10371 +Conceição da Barra,Brazil,South America,-18.5928,-39.7319,26494 +Conceição da Feira,Brazil,South America,-12.5058,-38.9989,20800 +Conceição das Alagoas,Brazil,South America,-19.915,-48.3878,28381 +Conceição de Jacuípe,Brazil,South America,-12.3269,-38.765,35308 +Conceição de Macabu,Brazil,South America,-22.085,-41.8678,21104 +Conceição do Almeida,Brazil,South America,-12.7794,-39.17,17165 +Conceição do Araguaia,Brazil,South America,-8.2578,-49.265,44617 +Conceição do Castelo,Brazil,South America,-20.3678,-41.2439,10910 +Conceição do Coité,Brazil,South America,-11.5639,-39.2828,67825 +Conceição do Mato Dentro,Brazil,South America,-19.0369,-43.425,17438 +Conceição do Rio Verde,Brazil,South America,-21.8808,-45.085,12541 +Conceição dos Ouros,Brazil,South America,-22.4128,-45.7978,10880 +Concepción,Chile,South America,-36.8282,-73.0514,719944 +Concepción Chiquirichapa,Guatemala,North America,14.85,-91.6167,20920 +Concepción Huista,Guatemala,North America,15.6167,-91.6833,21208 +Concepción Las Minas,Guatemala,North America,14.5167,-89.45,12615 +Concepción Tutuapa,Guatemala,North America,15.2833,-91.7833,84138 +Concepción del Uruguay,Argentina,South America,-32.4833,-58.2333,100728 +Conchal,Brazil,South America,-22.33,-47.1728,22676 +Conchas,Brazil,South America,-23.0134,-48.0078,15232 +Concord,United States,North America,37.9722,-122.0016,525233 +Concordia,Argentina,South America,-31.4,-58.0333,199634 +Concórdia,Brazil,South America,-27.2339,-52.0278,81646 +Condado,Brazil,South America,-7.5858,-35.1058,24587 +Conde,Brazil,South America,-11.8139,-37.6108,20426 +Condega,Nicaragua,North America,13.35,-86.4,28481 +Condeixa-a-Nova,Portugal,Europe,40.1167,-8.5,17078 +Condeúba,Brazil,South America,-14.895,-41.9689,17053 +Condoto,Colombia,South America,5.1,-76.65,14660 +Confresa,Brazil,South America,-10.6439,-51.5689,35075 +Congonhal,Brazil,South America,-22.1528,-46.0389,11083 +Congonhas,Brazil,South America,-20.5,-43.8578,52890 +Conguaco,Guatemala,North America,14.05,-90.0333,29887 +Consacá,Colombia,South America,1.2,-77.4667,9099 +Conselheiro Lafaiete,Brazil,South America,-20.66,-43.7858,131621 +Conselheiro Pena,Brazil,South America,-19.1719,-41.4719,20824 +Consolación del Sur,Cuba,North America,22.5083,-83.5172,87500 +Constantina,Brazil,South America,-27.735,-52.9919,10385 +Constantine,Algeria,Africa,36.35,6.6,448374 +Constanţa,Romania,Europe,44.1667,28.6333,263707 +Contagem,Brazil,South America,-19.9319,-44.0539,538017 +Contamana,Peru,South America,-7.3333,-75.0167,15036 +Contenda,Brazil,South America,-25.6758,-49.535,19128 +Contla,Mexico,North America,19.3333,-98.1667,27610 +Contramaestre,Cuba,North America,20.3,-76.2506,106002 +Convención,Colombia,South America,8.8333,-73.2,18112 +Copacabana,Colombia,South America,6.3333,-75.5,73574 +Copenhagen,Denmark,Europe,55.6805,12.5615,1366301 +Copiapó,Chile,South America,-27.3664,-70.3331,158438 +Coqueiral,Brazil,South America,-21.1889,-45.4408,9023 +Coquimatlán,Mexico,North America,19.2038,-103.8086,14892 +Coquimbo,Chile,South America,-29.9531,-71.3433,204068 +Coquitlam,Canada,North America,49.2839,-122.7919,148625 +Coral Springs,United States,North America,26.2702,-80.2591,133962 +Coração de Jesus,Brazil,South America,-16.685,-44.365,25377 +Coração de Maria,Brazil,South America,-12.2333,-38.75,22495 +Corbélia,Brazil,South America,-24.7989,-53.3069,17470 +Cordeiro,Brazil,South America,-22.0289,-42.3608,20783 +Cordeirópolis,Brazil,South America,-22.4819,-47.4569,24514 +Coreaú,Brazil,South America,-3.5328,-40.6569,19981 +Corella,Philippines,Asia,9.68,123.92,9479 +Coremas,Brazil,South America,-7.0139,-37.9458,14683 +Coribe,Brazil,South America,-13.8289,-44.4539,13990 +Corinto,Colombia,South America,3.1739,-76.2594,33489 +Cork,Ireland,Europe,51.9,-8.4731,222333 +Cornélio Procópio,Brazil,South America,-23.1808,-50.6469,45206 +Coro,Venezuela,South America,11.417,-69.67,195227 +Coroaci,Brazil,South America,-18.6219,-42.2858,10884 +Coroatá,Brazil,South America,-4.13,-44.1239,59566 +Coromandel,Brazil,South America,-18.4728,-47.2,28894 +Corona,United States,North America,33.8616,-117.5649,158839 +Coronda,Argentina,South America,-31.9667,-60.9167,18115 +Coronel Dorrego,Argentina,South America,-38.7,-61.2667,11510 +Coronel Fabriciano,Brazil,South America,-19.5189,-42.6289,104736 +Coronel Freitas,Brazil,South America,-26.9089,-52.7028,10388 +Coronel Oviedo,Paraguay,South America,-25.45,-56.44,52400 +Coronel Suárez,Argentina,South America,-37.4667,-61.9333,42276 +Coronel Vivida,Brazil,South America,-25.98,-52.5678,23331 +Coroneo,Mexico,North America,20.1333,-100.3333,10347 +Corozal,Colombia,South America,9.3333,-75.25,70853 +Corpus Christi,United States,North America,27.7254,-97.3767,339432 +Corralillo,Cuba,North America,22.9856,-80.5831,24791 +Correia Pinto,Brazil,South America,-27.585,-50.3608,15727 +Corrente,Brazil,South America,-10.4428,-45.1619,27278 +Correntes,Brazil,South America,-9.1289,-36.33,17131 +Correntina,Brazil,South America,-13.3428,-44.6369,30583 +Corrientes,Argentina,South America,-27.4833,-58.8167,352646 +Cortazar,Mexico,North America,20.4828,-100.9611,61658 +Cortês,Brazil,South America,-8.47,-35.5436,10198 +Coruche,Portugal,Europe,38.9581,-8.5283,19944 +Corumbaíba,Brazil,South America,-18.1419,-48.5619,9164 +Corumbá,Brazil,South America,-19.0089,-57.6528,96268 +Corumbá de Goiás,Brazil,South America,-15.9239,-48.8089,10562 +Corupá,Brazil,South America,-26.425,-49.2428,11847 +Coruripe,Brazil,South America,-10.1256,-36.1756,57294 +Corzuela,Argentina,South America,-26.9333,-60.9667,10470 +Cosamaloapan,Mexico,North America,18.3676,-95.7986,57147 +Cosautlán,Mexico,North America,19.3333,-96.9833,16353 +Cosenza,Italy,Europe,39.3,16.25,63760 +Cosmorama,Brazil,South America,-20.4778,-49.7778,8719 +Cosmópolis,Brazil,South America,-22.6458,-47.1961,66807 +Cosne sur Loire,France,Europe,47.4122,2.9275,9786 +Cospicua,Malta,Europe,35.8822,14.5222,5395 +Cosquín,Argentina,South America,-31.2436,-64.4653,47785 +Costa Marques,Brazil,South America,-12.445,-64.2272,12627 +Costa Mesa,United States,North America,33.6667,-117.9135,110538 +Cosío,Mexico,North America,22.3667,-102.3,15577 +Cota,Colombia,South America,4.8167,-74.1,32691 +Cotabato,Philippines,Asia,7.22,124.25,325079 +Cotacachi,Ecuador,South America,0.3,-78.2667,10526 +Cotegipe,Brazil,South America,-12.0278,-44.2578,13063 +Cotia,Brazil,South America,-23.6039,-46.9189,148987 +Cotija de la Paz,Mexico,North America,19.8167,-102.6833,19018 +Cotonou,Benin,Africa,6.3667,2.4333,679012 +Cotorra,Colombia,South America,9.05,-75.8,16215 +Cottbus,Germany,Europe,51.7606,14.3342,100010 +Cotuí,Dominican Republic,North America,19.06,-70.15,76554 +Couva,Trinidad and Tobago,North America,10.4167,-61.45,48858 +Coveñas,Colombia,South America,9.4167,-75.7,11270 +Covilhã,Portugal,Europe,40.2833,-7.5,51797 +Coxim,Brazil,South America,-18.5067,-54.76,33459 +Coyaima,Colombia,South America,3.8333,-75.0833,28443 +Coyhaique,Chile,South America,-45.5667,-72.0667,61210 +Coyotepec,Mexico,North America,19.75,-99.1711,41810 +Coyuca de Catalán,Mexico,North America,18.3256,-100.6992,42069 +Cozumel,Mexico,North America,20.5104,-86.9493,77236 +Craiova,Romania,Europe,44.3333,23.8167,234140 +Crateús,Brazil,South America,-5.1778,-40.6778,76390 +Crato,Brazil,South America,-7.2342,-39.4094,133031 +Cravinhos,Brazil,South America,-21.3403,-47.7294,34110 +Craíbas,Brazil,South America,-9.6178,-36.7678,25397 +Cremona,Italy,Europe,45.1333,10.0247,70637 +Criciúma,Brazil,South America,-28.6775,-49.3697,211369 +Crikvenica,Croatia,Europe,45.1737,14.6924,9980 +Crissiumal,Brazil,South America,-27.5,-54.1008,12886 +Cristais,Brazil,South America,-20.8758,-45.5189,12197 +Cristalina,Brazil,South America,-16.7689,-47.6139,34116 +Cristina,Brazil,South America,-22.2119,-45.2639,10374 +Cristino Castro,Brazil,South America,-8.8178,-44.2239,10503 +Cristinápolis,Brazil,South America,-11.4756,-37.7553,18029 +Crisópolis,Brazil,South America,-11.5108,-38.15,19729 +Criuleni,Moldova,Europe,47.2167,29.1614,6708 +Crixás,Brazil,South America,-14.5489,-49.9689,17065 +Crna Trava,Serbia,Europe,42.8101,22.299,434 +Croatá,Brazil,South America,-4.4,-40.9108,17481 +Crotone,Italy,Europe,39.0833,17.1167,58445 +Cruces,Cuba,North America,22.3419,-80.2761,28977 +Cruz,Brazil,South America,-2.9178,-40.1719,29761 +Cruz Alta,Brazil,South America,-28.6386,-53.6064,58913 +Cruz Grande,Mexico,North America,16.7333,-99.1333,20175 +Cruz Machado,Brazil,South America,-26.0169,-51.3469,15978 +Cruz das Almas,Brazil,South America,-12.67,-39.1019,53049 +Cruz del Eje,Argentina,South America,-30.7333,-64.8,30680 +Cruz do Espírito Santo,Brazil,South America,-7.14,-35.0864,17461 +Cruzeiro,Brazil,South America,-22.5772,-44.9583,82571 +Cruzeiro do Oeste,Brazil,South America,-23.785,-53.0728,23831 +Cruzeiro do Sul,Brazil,South America,-7.6308,-72.67,91888 +Cruzília,Brazil,South America,-21.8389,-44.8078,15362 +Créteil,France,Europe,48.7911,2.4628,92859 +Csongrád,Hungary,Europe,46.7111,20.1403,15409 +Csorna,Hungary,Europe,47.6167,17.25,9621 +Cuajinicuilapa,Mexico,North America,16.4717,-98.4153,26627 +Cuapiaxtla de Madero,Mexico,North America,18.9167,-97.8167,10101 +Cuatro Ciénegas de Carranza,Mexico,North America,26.9861,-102.0664,10395 +Cuauhtémoc,Mexico,North America,28.405,-106.8667,180638 +Cuautepec de Hinojosa,Mexico,North America,20.15,-98.4333,45527 +Cuautitlán,Mexico,North America,19.6833,-99.1833,178847 +Cuautitlán Izcalli,Mexico,North America,19.65,-99.25,140059 +Cuautla,Mexico,North America,18.8167,-98.95,194786 +Cubatão,Brazil,South America,-23.8953,-46.4256,131626 +Cubulco,Guatemala,North America,15.1083,-90.6306,60706 +Cuenca,Ecuador,South America,-2.8974,-79.0045,361524 +Cuencamé de Ceniceros,Mexico,North America,24.8667,-103.7,9848 +Cuernavaca,Mexico,North America,18.9186,-99.2342,338650 +Cuerámaro,Mexico,North America,20.6258,-101.6739,27308 +Cuetzala del Progreso,Mexico,North America,18.2833,-99.8333,9166 +Cuetzalan,Mexico,North America,20.0333,-97.5167,47983 +Cuiabá,Brazil,South America,-15.5958,-56.0969,483346 +Cuichapa,Mexico,North America,18.76,-96.8167,12238 +Cuihua,China,Asia,27.7377,103.8574,46324 +Cuijk,Netherlands,Europe,51.7269,5.8794,24931 +Cuilapa,Guatemala,North America,14.2833,-90.3,46974 +Cuilapan de Guerrero,Mexico,North America,16.9972,-96.7817,21597 +Cuilco,Guatemala,North America,15.4,-91.9667,67297 +Cuito,Angola,Africa,-12.3833,16.9333,512706 +Cuitzeo del Porvenir,Mexico,North America,19.9686,-101.1394,28227 +Cuité,Brazil,South America,-6.485,-36.1569,19719 +Cujubim,Brazil,South America,-9.3628,-62.5853,14863 +Culemborg,Netherlands,Europe,51.95,5.2333,29121 +Culfa,Azerbaijan,Europe,38.9558,45.6308,12500 +Culiacán,Mexico,North America,24.8069,-107.3939,808416 +Cumanayagua,Cuba,North America,22.1522,-80.2011,47118 +Cumandá,Ecuador,South America,-2.2058,-79.1339,11442 +Cumaná,Venezuela,South America,10.4564,-64.1675,405626 +Cumaral,Colombia,South America,4.2694,-73.4864,21397 +Cumaribo,Colombia,South America,4.4333,-69.8,43138 +Cumaru,Brazil,South America,-8.0058,-35.6969,15920 +Cumayeri,Turkey,Europe,40.8736,30.9508,10908 +Cumbal,Colombia,South America,0.9078,-77.7908,40492 +Cunday,Colombia,South America,4.0833,-74.6667,9544 +Cunduacán,Mexico,North America,18.0667,-93.1667,137257 +Cuneo,Italy,Europe,44.3833,7.55,55744 +Cunha,Brazil,South America,-23.0744,-44.9597,22110 +Cunha Porã,Brazil,South America,-26.8939,-53.1678,10953 +Cunén,Guatemala,North America,15.3333,-91.0333,47250 +Cupira,Brazil,South America,-8.6169,-35.95,23518 +Cuquío,Mexico,North America,20.929,-103.0229,17980 +Curaçá,Brazil,South America,-8.9919,-39.9078,34180 +Curicó,Chile,South America,-34.9833,-71.2333,136954 +Curimatá,Brazil,South America,-10.0358,-44.3058,11270 +Curitiba,Brazil,South America,-25.4297,-49.2719,1773718 +Curitibanos,Brazil,South America,-27.2828,-50.5839,40045 +Curití,Colombia,South America,6.6667,-73.0,12065 +Curiúva,Brazil,South America,-24.0328,-50.4578,13647 +Currais Novos,Brazil,South America,-6.2608,-36.515,40791 +Curralinho,Brazil,South America,-1.8139,-49.795,33903 +Curumaní,Colombia,South America,9.2,-73.55,23018 +Cururupu,Brazil,South America,-1.8278,-44.8678,31558 +Curuzú Cuatiá,Argentina,South America,-29.7833,-58.0833,34470 +Curuá,Brazil,South America,-1.8878,-55.1169,14117 +Curuçá,Brazil,South America,-0.7339,-47.855,26160 +Curvelo,Brazil,South America,-18.7564,-44.4308,80616 +Cusco,Peru,South America,-13.525,-71.9722,428450 +Custódia,Brazil,South America,-8.0875,-37.6431,37699 +Cutervo,Peru,South America,-6.3778,-78.8167,51272 +Cuxhaven,Germany,Europe,53.8611,8.6944,48713 +Cuyotenango,Guatemala,North America,14.5333,-91.5667,36201 +Czarnków,Poland,Europe,52.9,16.5667,10097 +Częstochowa,Poland,Europe,50.8,19.1167,213107 +Człuchów,Poland,Europe,53.65,17.3667,13343 +Cà Mau,Vietnam,Asia,9.1833,105.15,226372 +Cáceres,Spain,Europe,39.4833,-6.3667,96441 +Cáchira,Colombia,South America,7.75,-73.1667,10970 +Cádiz,Spain,Europe,36.535,-6.2975,110914 +Cáqueza,Colombia,South America,4.4053,-73.9478,17048 +Cárdenas,Cuba,North America,23.0428,-81.2036,86700 +Cássia,Brazil,South America,-20.5828,-46.9219,17155 +Cândido Mendes,Brazil,South America,-1.4467,-45.7167,20278 +Cândido Mota,Brazil,South America,-22.7464,-50.3869,31346 +Cândido Sales,Brazil,South America,-15.505,-41.2389,25247 +Cândido de Abreu,Brazil,South America,-24.5669,-51.3333,14809 +Céu Azul,Brazil,South America,-25.1469,-53.8489,11087 +Cêrro Azul,Brazil,South America,-24.8239,-49.2608,16134 +Cêrro Grande,Brazil,South America,-30.6,-51.75,9178 +Cêrro Largo,Brazil,South America,-28.1489,-54.7378,13705 +Cícero Dantas,Brazil,South America,-10.6,-38.3828,30907 +Cómbita,Colombia,South America,5.75,-73.25,14632 +Córdoba,Argentina,South America,-31.4167,-64.1833,2106734 +Côn Đảo,Vietnam,Asia,8.6931,106.5767,8827 +Cúa,Venezuela,South America,10.1667,-66.8833,127900 +Cúcuta,Colombia,South America,7.8942,-72.5039,806378 +Călăraşi,Romania,Europe,44.2,27.3333,58211 +Căuşeni,Moldova,Europe,46.6442,29.4139,12874 +Cēsis,Latvia,Europe,57.3131,25.2747,14699 +Cəbrayıl,Azerbaijan,Europe,39.4,47.0261,6070 +Cəlilabad,Azerbaijan,Europe,39.2042,48.4958,25846 +Cần Thơ,Vietnam,Asia,10.0333,105.7833,1237300 +Cẩm Phả,Vietnam,Asia,21.0167,107.3167,190232 +Dabakala,Côte d’Ivoire,Africa,8.3667,-4.4333,78634 +Dabas,Hungary,Europe,47.1833,19.3167,17542 +Dabeiba,Colombia,South America,7.0,-76.25,22954 +Dabola,Guinea,Africa,10.75,-11.1167,38617 +Dabou,Côte d’Ivoire,Africa,5.3256,-4.3767,138083 +Dachau,Germany,Europe,48.2603,11.4342,48337 +Dadu,Pakistan,Asia,26.7325,67.7792,146179 +Dadukou,China,Asia,26.5824,101.7184,1212203 +Daegu,"Korea, South",Asia,35.8717,128.6017,2376044 +Daejeon,"Korea, South",Asia,36.35,127.385,1475221 +Daet,Philippines,Asia,14.1142,122.9564,111700 +Daga,Bhutan,Asia,27.0667,89.8833,1146 +Dagua,Colombia,South America,3.6667,-76.7,36891 +Dagupan City,Philippines,Asia,16.043,120.334,174302 +Dahuaishu,China,Asia,36.2601,111.6743,151037 +Dahūk,Iraq,Asia,36.85,42.9833,330600 +Daireaux,Argentina,South America,-36.6,-61.75,16804 +Daiyue,China,Asia,39.5284,112.8056,35894 +Dajabón,Dominican Republic,North America,19.5667,-71.71,28071 +Dakar,Senegal,Africa,14.6726,-17.432,1438725 +Dalaba,Guinea,Africa,10.656,-12.272,6349 +Dalaguete,Philippines,Asia,9.7612,123.5349,74596 +Dalain Hob,China,Asia,41.9645,101.0667,16927 +Dalaman,Turkey,Europe,36.7667,28.8,47482 +Dalanzadgad,Mongolia,Asia,43.57,104.4258,19396 +Dalfsen,Netherlands,Europe,52.5064,6.2606,28901 +Dalgān,Iran,Asia,27.4825,59.4464,10292 +Dali,China,Asia,34.7953,109.9378,109696 +Dallas,United States,North America,32.7935,-96.7667,5843632 +Dalnegorsk,Russia,Asia,44.55,135.5833,33030 +Dalnerechensk,Russia,Asia,45.9333,133.7333,23028 +Daloa,Côte d’Ivoire,Africa,6.89,-6.45,421871 +Daly City,United States,North America,37.6862,-122.4685,102560 +Damanhūr,Egypt,Africa,31.05,30.4667,244043 +Damascus,Syria,Asia,33.502,36.2981,2584771 +Damaturu,Nigeria,Africa,11.7444,11.9611,88014 +Damboa,Nigeria,Africa,11.15,12.75,233500 +Damietta,Egypt,Africa,31.4167,31.8214,206664 +Damongo,Ghana,Africa,9.083,-1.8188,2014 +Damān,India,Asia,20.42,72.85,44282 +Damāvand,Iran,Asia,35.6961,52.0467,48380 +Danbury,United States,North America,41.4015,-73.4709,175074 +Dangchengwan,China,Asia,39.5161,94.8728,9792 +Danghara,Tajikistan,Asia,38.0983,69.3467,31100 +Dangriga,Belize,North America,16.9667,-88.2167,10217 +Dang‘ara,Uzbekistan,Asia,40.5831,70.91,11500 +Danilov,Russia,Asia,58.1833,40.1667,14746 +Danilovgrad,Montenegro,Europe,42.61,19.05,5156 +Dano,Burkina Faso,Africa,11.1436,-3.0628,25922 +Daoukro,Côte d’Ivoire,Africa,7.0586,-3.9646,101136 +Dapaong,Togo,Africa,10.8667,0.2,58071 +Daqing,China,Asia,46.589,125.104,2781562 +Dar Naim,Mauritania,Africa,18.0973,-15.9327,144043 +Dar es Salaam,Tanzania,Africa,-6.8161,39.2803,7962000 +Darende,Turkey,Europe,38.5472,37.5111,24588 +Dargaz,Iran,Asia,37.4425,59.1072,36762 +Dargeçit,Turkey,Europe,37.544,41.72,27147 +Darhan,Mongolia,Asia,49.4867,105.9228,74738 +Darién,Colombia,South America,3.9167,-76.6667,16054 +Darmstadt,Germany,Europe,49.8722,8.6528,164792 +Darnah,Libya,Africa,32.7648,22.6391,90000 +Darreh Shahr,Iran,Asia,33.1431,47.3794,21900 +Daru,Papua New Guinea,Oceania,-9.0833,143.2,12879 +Daruvar,Croatia,Europe,45.6,17.2167,10105 +Darwin,Australia,Oceania,-12.4381,130.8411,139902 +Dashiqiao,China,Asia,40.6167,122.5167,607098 +Datu Odin Sinsuat,Philippines,Asia,7.1889,124.1839,116768 +Datça,Turkey,Europe,36.7283,27.6869,25029 +Daugavpils,Latvia,Europe,55.8714,26.5161,77799 +Dauis,Philippines,Asia,9.625,123.865,52492 +Daule,Ecuador,South America,-1.8667,-79.9833,161498 +Davao,Philippines,Asia,7.07,125.6,1910167 +Davenport,United States,North America,41.5565,-90.6053,282356 +David,Panama,North America,8.4333,-82.4333,82907 +Davie,United States,North America,26.0789,-80.287,106412 +Davlekanovo,Russia,Asia,54.2167,55.0333,21834 +Davos,Switzerland,Europe,46.8091,9.8398,10862 +Davutlar,Turkey,Europe,37.7333,27.3,15877 +Dawei,Burma,Asia,14.0833,98.2,146964 +Dax,France,Europe,43.71,-1.05,21716 +Daxing,China,Asia,22.9968,102.395,54872 +Dayr Abū Sa‘īd,Jordan,Asia,32.5025,35.6922,29590 +Dayr az Zawr,Syria,Asia,35.3304,40.13,271800 +Dayr Ḩāfir,Syria,Asia,36.1569,37.7078,35409 +Dayr ‘Aţīyah,Syria,Asia,34.0954,36.7679,10984 +Dayton,United States,North America,39.7805,-84.2003,667936 +Dazhou,China,Asia,31.2093,107.4678,5385422 +Daşkəsən,Azerbaijan,Europe,40.5209,46.0814,10566 +Daşoguz,Turkmenistan,Asia,41.8333,59.9667,201142 +Da’an,China,Asia,45.5,124.2833,270911 +Dearborn,United States,North America,42.3127,-83.213,107846 +Debar,North Macedonia,Europe,41.525,20.5272,14561 +Debe,Trinidad and Tobago,North America,10.2,-61.45,7200 +Debrecen,Hungary,Europe,47.53,21.6392,201704 +Dedza,Malawi,Africa,-14.3667,34.3333,30928 +Deggendorf,Germany,Europe,48.8333,12.9667,35757 +Degollado,Mexico,North America,20.4667,-102.15,21226 +Dehdasht,Iran,Asia,30.7964,50.5658,57036 +Dehgolān,Iran,Asia,35.2778,47.4175,25992 +Dehlorān,Iran,Asia,32.6892,47.2689,32941 +Dehra Dūn,India,Asia,30.345,78.029,578420 +Dehāqān,Iran,Asia,31.9386,51.6475,16899 +Delft,Netherlands,Europe,52.0117,4.3592,109435 +Delfzijl,Netherlands,Europe,53.3333,6.9167,24716 +Delhi,India,Asia,28.61,77.23,32226000 +Delicias,Mexico,North America,28.1931,-105.4717,150506 +Delmenhorst,Germany,Europe,53.0506,8.6317,78979 +Delmiro Gouveia,Brazil,South America,-9.3886,-37.9992,52262 +Delta,Canada,North America,49.0847,-123.0586,108455 +Deltona,United States,North America,28.905,-81.2137,224303 +Delémont,Switzerland,Europe,47.3653,7.3472,12682 +Delčevo,North Macedonia,Europe,41.9661,22.7747,9644 +Delījān,Iran,Asia,33.9903,50.6836,40902 +Demerval Lobão,Brazil,South America,-5.3578,-42.6758,12489 +Demir Hisar,North Macedonia,Europe,41.2208,21.2031,2431 +Demir Kapija,North Macedonia,Europe,41.4114,22.2422,3275 +Demirci,Turkey,Europe,39.045,28.6581,36620 +Demre,Turkey,Europe,36.2472,29.9828,27691 +Den Chai,Thailand,Asia,17.9837,100.0522,11542 +Den Helder,Netherlands,Europe,52.9333,4.75,56582 +Dendermonde,Belgium,Europe,51.0333,4.1,45673 +Denekamp,Netherlands,Europe,52.3794,7.0089,9125 +Denizli,Turkey,Europe,37.7833,29.0964,651200 +Denpasar,Indonesia,Asia,-8.6717,115.2339,670210 +Denton,United States,North America,33.2175,-97.1418,455152 +Denver,United States,North America,39.762,-104.8758,2691349 +Dera Allahyar,Pakistan,Asia,28.4167,68.1667,80908 +Dera Ghazi Khan,Pakistan,Asia,30.0331,70.64,399064 +Dera Ismail Khan,Pakistan,Asia,31.8314,70.9019,109686 +Derbent,Russia,Asia,42.0578,48.2889,127084 +Derik,Turkey,Europe,37.3658,40.2697,62028 +Derince,Turkey,Europe,40.7539,29.8231,146374 +Derinkuyu,Turkey,Europe,38.3736,34.7347,10912 +Derventa,Bosnia and Herzegovina,Europe,44.98,17.91,12680 +Derzhavīnsk,Kazakhstan,Asia,51.1,66.3167,6307 +Des Moines,United States,North America,41.5725,-93.6105,560170 +Descalvado,Brazil,South America,-21.9039,-47.6189,31756 +Descanso,Brazil,South America,-26.8247,-53.5018,8530 +Despotovac,Serbia,Europe,44.0833,21.4333,22995 +Dessau-Rosslau,Germany,Europe,51.8333,12.2333,79686 +Destrnik,Slovenia,Europe,46.5006,15.875,186 +Det Udom,Thailand,Asia,14.906,105.0784,14264 +Detmold,Germany,Europe,51.9378,8.8833,74835 +Detroit,United States,North America,42.3834,-83.1024,3716929 +Detva,Slovakia,Europe,48.5572,19.4208,13955 +Deurne,Netherlands,Europe,51.4639,5.7947,32437 +Deutschlandsberg,Austria,Europe,46.8161,15.215,11721 +Develi,Turkey,Europe,38.3886,35.4925,66840 +Deventer,Netherlands,Europe,52.25,6.2,101236 +Devrek,Turkey,Europe,41.2333,31.9667,57069 +Dexing,China,Asia,28.9306,117.5947,293201 +Deyang,China,Asia,31.127,104.398,3456161 +Deyr,Iran,Asia,27.8403,51.94,24083 +Dezfūl,Iran,Asia,32.3786,48.4144,264709 +Dezhou,China,Asia,37.436,116.359,5568235 +Deán Funes,Argentina,South America,-30.4333,-64.35,21518 +Deçan,Kosovo,Europe,42.5378,20.2911,3803 +Dhaka,Bangladesh,Asia,23.7289,90.3944,19134000 +Dhamār,Yemen,Asia,14.55,44.4017,146346 +Dhuusamarreeb,Somalia,Africa,5.5375,46.3875,447 +Dhībān,Syria,Asia,35.015,40.5103,9000 +Di Linh,Vietnam,Asia,11.5778,108.0751,27645 +Diadema,Brazil,South America,-23.6858,-46.6228,393237 +Diamante,Argentina,South America,-32.0667,-60.65,19142 +Diamantina,Brazil,South America,-18.2489,-43.6,47702 +Diamantino,Brazil,South America,-14.4089,-56.4458,21941 +Dianbu,China,Asia,31.8883,117.4608,225808 +Dianga,China,Asia,34.0636,103.2136,15993 +Dianópolis,Brazil,South America,-11.6258,-46.8203,17739 +Diapaga,Burkina Faso,Africa,12.0667,1.7833,15515 +Dias d’Ávila,Brazil,South America,-12.6128,-38.2969,71485 +Dibulla,Colombia,South America,11.2667,-73.3,37854 +Dicle,Turkey,Europe,38.375,40.0733,36114 +Didim,Turkey,Europe,37.3756,27.2678,97000 +Didymóteicho,Greece,Europe,41.35,26.5,8611 +Diego Martin,Trinidad and Tobago,North America,10.7167,-61.5667,25370 +Diekirch,Luxembourg,Europe,49.8681,6.1567,7047 +Diemen,Netherlands,Europe,52.3333,4.9667,31334 +Diepholz,Germany,Europe,52.6072,8.3711,17608 +Dieppe,France,Europe,49.925,1.075,28599 +Diffa,Niger,Africa,13.3171,12.6089,30988 +Differdange,Luxembourg,Europe,49.5222,5.8917,15455 +Digne-les-Bains,France,Europe,44.0933,6.2364,17694 +Digora,Russia,Asia,43.1581,44.1569,9994 +Digos,Philippines,Asia,6.75,125.35,188376 +Dijon,France,Europe,47.3167,5.0167,159941 +Dikhil,Djibouti,Africa,11.1167,42.3667,35000 +Dikili,Turkey,Europe,39.0667,26.8833,47360 +Diksmuide,Belgium,Europe,51.0333,2.865,16739 +Dili,Timor-Leste,Asia,-8.5594,125.5795,222323 +Dillingen,Germany,Europe,48.5667,10.4667,20070 +Dimbokro,Côte d’Ivoire,Africa,6.6505,-4.71,70198 +Dimitrovgrad,Serbia,Europe,43.0167,22.7833,8043 +Dinan,France,Europe,48.4564,-2.0489,14966 +Dinant,Belgium,Europe,50.26,4.9101,13544 +Dingcheng,China,Asia,19.6803,110.3681,86367 +Dingli,Malta,Europe,35.8603,14.3814,3608 +Dingolfing,Germany,Europe,48.6333,12.5,20927 +Dinguiraye,Guinea,Africa,11.299,-10.726,6062 +Dingxi,China,Asia,35.608,104.592,2524097 +Dingzhou,China,Asia,38.516,114.99,1095986 +Dionísio Cerqueira,Brazil,South America,-26.255,-53.64,15008 +Diourbel,Senegal,Africa,14.655,-16.2314,100445 +Dipolog,Philippines,Asia,8.5872,123.3408,138141 +Dire Dawa,Ethiopia,Africa,9.6,41.8667,277000 +Diriamba,Nicaragua,North America,11.85,-86.2333,57542 +Diriomo,Nicaragua,North America,11.8667,-86.05,22352 +Diré,Mali,Africa,16.2667,-3.4,12092 +Dispur,India,Asia,26.1397,91.7925,534872 +Distracción,Colombia,South America,10.9,-72.8833,17112 +Divača,Slovenia,Europe,45.682,13.9702,1734 +Divino,Brazil,South America,-20.6139,-42.1489,20706 +Divinolândia,Brazil,South America,-21.6614,-46.7392,11086 +Divinópolis,Brazil,South America,-20.1389,-44.8839,231091 +Divisópolis,Brazil,South America,-15.7258,-41.0,10213 +Divnogorsk,Russia,Asia,55.9594,92.3619,27477 +Divo,Côte d’Ivoire,Africa,5.8372,-5.3572,294559 +Divriği,Turkey,Europe,39.3667,38.1167,10800 +Diyadin,Turkey,Europe,39.54,43.6772,20302 +Diyarbakır,Turkey,Europe,37.91,40.24,1791373 +Diébougou,Burkina Faso,Africa,10.9667,-3.25,25688 +Diéma,Mali,Africa,14.5333,-9.2,30592 +Djambala,Congo (Brazzaville),Africa,-2.54,14.7519,24734 +Djanet,Algeria,Africa,24.555,9.4853,14655 +Djelfa,Algeria,Africa,34.6667,3.25,158679 +Djenné,Mali,Africa,13.9,-4.55,32944 +Djibloho,Equatorial Guinea,Africa,1.5889,10.8225,2000 +Djibo,Burkina Faso,Africa,14.1011,-1.6306,61456 +Djibouti,Djibouti,Africa,11.5944,43.1481,603900 +Djougou,Benin,Africa,9.7,1.6667,237040 +Dmitrov,Russia,Asia,56.35,37.5333,62869 +Dnipro,Ukraine,Europe,48.4675,35.04,968502 +Do Gonbadān,Iran,Asia,30.36,50.7839,96728 +Doba,Chad,Africa,8.66,16.85,25650 +Dobele,Latvia,Europe,56.6258,23.2811,8589 +Dobje,Slovenia,Europe,46.1369,15.3945,114 +Doboj,Bosnia and Herzegovina,Europe,44.7333,18.1333,68514 +Dobrada,Brazil,South America,-21.5167,-48.3939,8592 +Dobrich,Bulgaria,Europe,43.5667,27.8333,87361 +Dobrna,Slovenia,Europe,46.3381,15.2293,536 +Dobrova,Slovenia,Europe,46.0533,14.4144,921 +Dobrovnik,Slovenia,Europe,46.6514,16.3525,948 +Dobrovo,Slovenia,Europe,45.9989,13.5296,334 +Dobrush,Belarus,Europe,52.4167,31.3167,18083 +Dobryanka,Russia,Asia,58.4667,56.4167,28545 +Doctor Arroyo,Mexico,North America,23.6667,-100.1667,9339 +Dodoma,Tanzania,Africa,-6.1731,35.7419,765179 +Doesburg,Netherlands,Europe,52.0167,6.1333,11064 +Doetinchem,Netherlands,Europe,51.9656,6.2889,58270 +Doha,Qatar,Asia,25.2867,51.5333,1186023 +Doi Lo,Thailand,Asia,18.4667,98.7833,12261 +Doi Saket,Thailand,Asia,18.8667,99.1333,10750 +Dois Córregos,Brazil,South America,-22.3661,-48.3803,26517 +Dois Irmãos,Brazil,South America,-29.58,-51.085,30709 +Dois Riachos,Brazil,South America,-9.3925,-37.1006,11067 +Dois Vizinhos,Brazil,South America,-25.75,-53.0569,44869 +Dok Kham Tai,Thailand,Asia,19.1624,99.9934,13450 +Dokkum,Netherlands,Europe,53.3253,5.9989,12365 +Dokolo,Uganda,Africa,1.9186,33.17,19810 +Dol,Slovenia,Europe,46.0884,14.6425,277 +Dole,France,Europe,47.0931,5.4906,23784 +Dolenjske Toplice,Slovenia,Europe,45.7545,15.0583,882 +Dolgoprudnyy,Russia,Asia,55.9333,37.5,119089 +Dolinsk,Russia,Asia,47.3167,142.8,11840 +Dolisie,Congo (Brazzaville),Africa,-4.2006,12.6792,83798 +Dolneni,North Macedonia,Europe,41.4264,21.4536,300 +Dolný Kubín,Slovakia,Europe,49.2117,19.2992,18095 +Dolores,Guatemala,North America,16.5142,-89.4158,30302 +Dolores Hidalgo Cuna de la Independencia Nacional,Mexico,North America,21.1516,-100.9369,67101 +Dolyna,Ukraine,Europe,48.9706,24.0111,20417 +Dolynska,Ukraine,Europe,48.1111,32.7647,18225 +Dom Basílio,Brazil,South America,-13.76,-41.7708,11884 +Dom Feliciano,Brazil,South America,-30.7039,-52.1078,13051 +Dom Pedrito,Brazil,South America,-30.9828,-54.6728,36981 +Dom Pedro,Brazil,South America,-5.0328,-44.4358,23053 +Domagnano,San Marino,Europe,43.95,12.4667,3565 +Dombarovskiy,Russia,Asia,50.755,59.5331,9556 +Dombóvár,Hungary,Europe,46.3769,18.131,17041 +Domingos Martins,Brazil,South America,-20.3628,-40.6589,30559 +Domodedovo,Russia,Asia,55.44,37.7619,156681 +Domokós,Greece,Europe,39.1333,22.3,9159 +Dompu,Indonesia,Asia,-8.5364,118.4633,57680 +Domžale,Slovenia,Europe,46.1394,14.595,13222 +Don Sak,Thailand,Asia,9.3168,99.6918,11934 +Dona Inês,Brazil,South America,-6.6136,-35.6267,10413 +Donauwörth,Germany,Europe,48.7,10.8,20108 +Donduşeni,Moldova,Europe,48.2167,27.5833,7101 +Donetsk,Ukraine,Europe,48.0028,37.8053,901645 +Dongchuan,China,Asia,25.4765,101.2847,78611 +Dongcun,China,Asia,38.28,111.6751,59772 +Dongen,Netherlands,Europe,51.6258,4.9433,26368 +Dongguan,China,Asia,23.021,113.752,10646000 +Donghua,China,Asia,35.2167,106.6,182449 +Dongning,China,Asia,44.1167,130.8167,195489 +Dongola,Sudan,Africa,19.1769,30.4839,56167 +Dongping,China,Asia,28.3817,111.2194,95000 +Dongta,China,Asia,38.0824,106.339,22040 +Dongtai,China,Asia,32.795,120.519,888410 +Dongxing,China,Asia,21.5478,107.9717,216053 +Dongyang,China,Asia,29.2667,120.2167,804398 +Donji Vakuf,Bosnia and Herzegovina,Europe,44.15,17.4,13985 +Donmatías,Colombia,South America,6.4833,-75.4333,24201 +Donostia,Spain,Europe,43.3214,-1.9856,189093 +Donskoy,Russia,Asia,53.9658,38.3247,63837 +Doorn,Netherlands,Europe,52.0333,5.35,10124 +Dordrecht,Netherlands,Europe,51.7958,4.6783,121434 +Dores de Campos,Brazil,South America,-21.1089,-44.0228,10007 +Dores do Indaiá,Brazil,South America,-19.4628,-45.6019,12630 +Dori,Burkina Faso,Africa,14.03,-0.03,46521 +Dormentes,Brazil,South America,-8.4469,-40.7708,17188 +Dornava,Slovenia,Europe,46.4367,15.9536,1263 +Dornbirn,Austria,Europe,47.4139,9.7444,48067 +Dorogobuzh,Russia,Asia,54.9167,33.3,9086 +Dortmund,Germany,Europe,51.5139,7.4653,595471 +Dorūd,Iran,Asia,33.4994,49.0531,121608 +Dospat,Bulgaria,Europe,41.6436,24.1597,10536 +Dosquebradas,Colombia,South America,4.8333,-75.6833,179301 +Dosso,Niger,Africa,13.0505,3.2081,58671 +Douai,France,Europe,50.3714,3.08,39833 +Douala,Cameroon,Africa,4.05,9.7,5768400 +Douentza,Mali,Africa,14.995,-2.9667,28005 +Douglas,Isle of Man,Europe,54.15,-4.4775,27938 +Douliu,Taiwan,Asia,23.7075,120.5439,107924 +Dourados,Brazil,South America,-22.2208,-54.8058,164949 +Dover,United States,North America,39.161,-75.5202,128045 +Dovzhansk,Ukraine,Europe,48.0778,39.6472,62691 +Dowa,Malawi,Africa,-13.6532,33.9385,7135 +Dowlatābād,Iran,Asia,32.8003,51.6958,40945 +Downey,United States,North America,33.9379,-118.1311,111793 +Doğanhisar,Turkey,Europe,38.1447,31.6781,14812 +Doğanşehir,Turkey,Europe,38.0958,37.8792,37697 +Doğubayazıt,Turkey,Europe,39.5472,44.0833,80061 +Do’stlik Shahri,Uzbekistan,Asia,40.5247,68.0358,18300 +Dracena,Brazil,South America,-21.4825,-51.5328,47043 +Drachten,Netherlands,Europe,53.1,6.1,45186 +Dragash,Kosovo,Europe,42.0265,20.6533,34827 +Dragomer,Slovenia,Europe,46.0189,14.382,1464 +Draguignan,France,Europe,43.5403,6.4667,40789 +Dragør,Denmark,Europe,55.5946,12.669,12327 +Drahichyn,Belarus,Europe,52.1833,25.15,14804 +Drammen,Norway,Europe,59.7439,10.2045,109416 +Dravograd,Slovenia,Europe,46.5903,15.0236,3088 +Drawsko Pomorskie,Poland,Europe,53.5333,15.8,11515 +Dresden,Germany,Europe,51.05,13.74,566222 +Dreux,France,Europe,48.7372,1.3664,31205 +Drobeta-Turnu Severin,Romania,Europe,44.6333,22.65,79865 +Drochia,Moldova,Europe,48.0333,27.75,13150 +Drohobych,Ukraine,Europe,49.35,23.5,73682 +Dronten,Netherlands,Europe,52.5242,5.7125,42011 +Druskininkai,Lithuania,Europe,54.0206,23.9725,13170 +Druten,Netherlands,Europe,51.8889,5.6044,18991 +Dráma,Greece,Europe,41.15,24.1467,44257 +Duartina,Brazil,South America,-22.4144,-49.4039,12567 +Duas Barras,Brazil,South America,-22.0508,-42.5219,10980 +Dubai,United Arab Emirates,Asia,25.2631,55.2972,3331420 +Dublin,Ireland,Europe,53.3497,-6.2603,592713 +Dubna,Russia,Asia,56.7364,37.1625,74032 +Dubno,Ukraine,Europe,50.3931,25.735,36901 +Dubovka,Russia,Asia,49.05,44.8333,14779 +Dubovskoye,Russia,Asia,47.4092,42.7575,8544 +Dubrovnik,Croatia,Europe,42.6403,18.1083,41562 +Ducheng,China,Asia,23.2445,111.5342,85659 +Dudelange,Luxembourg,Europe,49.4833,6.0833,21953 +Dudinka,Russia,Asia,69.4064,86.1764,19309 +Duekoué,Côte d’Ivoire,Africa,6.7419,-7.3492,220953 +Duga Resa,Croatia,Europe,45.4472,15.4958,10212 +Dugo Selo,Croatia,Europe,45.8,16.2333,17676 +Duisburg,Germany,Europe,51.4347,6.7625,503707 +Duitama,Colombia,South America,5.8333,-73.0167,132894 +Duiven,Netherlands,Europe,51.9472,6.0211,25066 +Duluth,United States,North America,46.7756,-92.1393,120443 +Dumaguete City,Philippines,Asia,9.3103,123.3081,134103 +Dumont,Brazil,South America,-21.2364,-47.9733,10023 +Dunajská Streda,Slovakia,Europe,47.9961,17.6147,23044 +Dunakeszi,Hungary,Europe,47.6297,19.1381,42747 +Dunaújváros,Hungary,Europe,46.9833,18.9167,41394 +Dundo,Angola,Africa,-7.3801,20.8351,177604 +Dunedin,New Zealand,Oceania,-45.8742,170.5036,36070 +Dunhua,China,Asia,43.3667,128.2333,392486 +Dunkerque,France,Europe,51.0383,2.3775,87013 +Dunleary,Ireland,Europe,53.3,-6.14,23857 +Duque Bacelar,Brazil,South America,-4.1558,-42.9439,10223 +Durango,Mexico,North America,24.025,-104.6675,654876 +Durazno,Uruguay,South America,-33.3667,-56.5167,34368 +Durham,United States,North America,35.9792,-78.9022,411362 +Durrës,Albania,Europe,41.3133,19.4458,153614 +Dursunbey,Turkey,Europe,39.5878,28.6275,33333 +Dushanbe,Tajikistan,Asia,38.5367,68.78,1564700 +Dutse,Nigeria,Africa,11.7011,9.3419,153000 +Duyên Hải,Vietnam,Asia,9.6331,106.4975,48210 +Dyurtyuli,Russia,Asia,55.4833,54.8667,31185 +Dzerzhinsk,Russia,Asia,56.2333,43.45,215259 +Dzerzhinskiy,Russia,Asia,55.6333,37.85,57434 +Dzhankoi,Ukraine,Europe,45.7086,34.3933,38622 +Działdowo,Poland,Europe,53.2333,20.1833,20670 +Dzierżoniów,Poland,Europe,50.7281,16.6511,31866 +Dzuunmod,Mongolia,Asia,47.7069,106.9528,14660 +Dzyarzhynsk,Belarus,Europe,53.6833,27.1333,29796 +Dáfni,Greece,Europe,37.95,23.7333,23431 +Dário Meira,Brazil,South America,-14.4358,-39.9078,10347 +Dédougou,Burkina Faso,Africa,12.4667,-3.4667,63617 +Dörtyol,Turkey,Europe,36.8428,36.2244,128941 +Döşemealtı,Turkey,Europe,37.0236,30.6011,79495 +Dún Dealgan,Ireland,Europe,54.0044,-6.4003,39004 +Düren,Germany,Europe,50.8,6.4833,93323 +Düsseldorf,Germany,Europe,51.2256,6.7767,631217 +Düzce,Turkey,Europe,40.8417,31.1583,194097 +Düziçi,Turkey,Europe,37.2431,36.4567,56724 +Düzköy,Turkey,Europe,40.8742,39.4256,13312 +Dāmghān,Iran,Asia,36.1642,54.3408,59106 +Dārat ‘Izzah,Syria,Asia,36.2825,36.8522,13525 +Dārayyā,Syria,Asia,33.4583,36.2326,84044 +Dąbrowa Górnicza,Poland,Europe,50.3214,19.1872,116930 +Dąbrowa Tarnowska,Poland,Europe,50.1667,20.9833,11549 +Dębica,Poland,Europe,50.05,21.4167,44047 +Dĩ An,Vietnam,Asia,10.9039,106.7678,73859 +Dīvāndarreh,Iran,Asia,35.9158,47.0228,34007 +Dūmā,Syria,Asia,33.5833,36.4,123494 +Dŭstí,Tajikistan,Asia,37.3486,68.6733,18800 +East Los Angeles,United States,North America,34.0326,-118.1691,114617 +Eau Claire,United States,North America,44.8197,-91.4948,108477 +Ebebiyín,Equatorial Guinea,Africa,2.15,11.3167,36565 +Ebersberg,Germany,Europe,48.0833,11.9667,12527 +Eberswalde,Germany,Europe,52.8333,13.8333,41704 +Ebéjico,Colombia,South America,6.3333,-75.7667,12158 +Ecatepec,Mexico,North America,19.6097,-99.06,1655015 +Ecatzingo,Mexico,North America,18.95,-98.75,9369 +Echternach,Luxembourg,Europe,49.8117,6.4217,5870 +Ecoporanga,Brazil,South America,-18.3728,-40.8308,21992 +Ed Daein,Sudan,Africa,11.4672,26.1317,264734 +Ed Damazin,Sudan,Africa,11.7704,34.35,186051 +Ed Damer,Sudan,Africa,17.59,33.96,122944 +Ede,Nigeria,Africa,7.7389,4.4361,159866 +Edelény,Hungary,Europe,48.2967,20.7442,9198 +Edinburg,United States,North America,26.3196,-98.1597,102561 +Edineţ,Moldova,Europe,48.1667,27.3167,15520 +Edirne,Turkey,Europe,41.6769,26.5556,180002 +Edison,United States,North America,40.536,-74.3697,107027 +Edmonton,Canada,North America,53.5344,-113.4903,1151635 +Edremit,Turkey,Europe,39.5922,27.02,167901 +Edéia,Brazil,South America,-17.3389,-49.9308,11747 +Eeklo,Belgium,Europe,51.1833,3.55,20890 +Eemnes,Netherlands,Europe,52.25,5.25,9362 +Eenhana,Namibia,Africa,-17.4658,16.3369,16588 +Eersel,Netherlands,Europe,51.3572,5.3147,19528 +Eger,Hungary,Europe,47.8989,20.3747,49499 +Egersund,Norway,Europe,58.45,6.0067,11629 +Egvekinot,Russia,Asia,66.3221,-179.1837,2248 +Eichstätt,Germany,Europe,48.8919,11.1839,13867 +Eidsvoll,Norway,Europe,60.3167,11.2333,24647 +Eijsden,Netherlands,Europe,50.7778,5.7108,11565 +Eindhoven,Netherlands,Europe,51.4344,5.4842,243730 +Einsiedeln,Switzerland,Europe,47.1278,8.7431,15550 +Eirunepé,Brazil,South America,-6.6597,-69.8744,33170 +Eisenach,Germany,Europe,50.9761,10.3206,42817 +Eisenberg,Germany,Europe,50.9681,11.9014,11196 +Eisenstadt,Austria,Europe,47.85,16.5167,14476 +Ejido,Venezuela,South America,8.3333,-71.4,99837 +Ejutla de Crespo,Mexico,North America,16.5672,-96.7308,17232 +Ekerö,Sweden,Europe,59.2667,17.7333,11524 +Ekibastuz,Kazakhstan,Asia,51.6667,75.3667,159760 +Ekinözü,Turkey,Europe,38.0617,37.1928,10290 +Eksjö,Sweden,Europe,57.6667,14.95,9701 +El Almendro,Nicaragua,North America,11.6833,-84.7,13363 +El Arenal,Mexico,North America,20.7,-103.6167,19900 +El Asintal,Guatemala,North America,14.6,-91.7333,43281 +El Ayote,Nicaragua,North America,12.5,-84.8167,12417 +El Bagre,Colombia,South America,7.5942,-74.8119,51862 +El Banco,Colombia,South America,8.9983,-73.9747,72131 +El Bayadh,Algeria,Africa,33.6803,1.0203,85577 +El Bordo,Colombia,South America,2.1667,-77.0833,37504 +El Cairo,Colombia,South America,4.75,-76.25,9976 +El Cajon,United States,North America,32.8017,-116.9604,104909 +El Calafate,Argentina,South America,-50.3378,-72.26,22844 +El Callao,Venezuela,South America,7.3463,-61.8271,21769 +El Carmen,Ecuador,South America,-0.2736,-79.4597,52366 +El Carmen de Atrato,Colombia,South America,5.8878,-75.1642,14049 +El Carmen de Bolívar,Colombia,South America,9.7167,-75.1333,66001 +El Carmen de Chucurí,Colombia,South America,6.6981,-73.5111,20858 +El Cerrito,Colombia,South America,3.6667,-76.1667,56470 +El Chal,Guatemala,North America,16.64,-89.65,15555 +El Charco,Colombia,South America,2.4775,-78.1111,42525 +El Chol,Guatemala,North America,14.9667,-90.4833,10445 +El Coco,Panama,North America,8.8701,-79.8041,19603 +El Colegio,Colombia,South America,4.5808,-74.4425,22247 +El Consejo,Venezuela,South America,10.2442,-67.245,41748 +El Copey,Colombia,South America,10.15,-73.9667,28550 +El Crucero,Nicaragua,North America,11.9833,-86.3167,13656 +El Cuá,Nicaragua,North America,13.3675,-85.6733,43305 +El Difícil,Colombia,South America,9.85,-74.2386,32637 +El Doncello,Colombia,South America,1.6833,-75.2833,22306 +El Dovio,Colombia,South America,4.5167,-76.2333,8508 +El Espinal,Mexico,North America,16.4906,-95.0444,8575 +El Estor,Guatemala,North America,15.5333,-89.35,82491 +El Fasher,Sudan,Africa,13.63,25.35,500000 +El Fuerte,Mexico,North America,26.4214,-108.62,12566 +El Geneina,Sudan,Africa,13.45,22.44,538390 +El Golea,Algeria,Africa,30.5833,2.8833,40195 +El Grullo,Mexico,North America,19.8,-104.2,25920 +El Hermel,Lebanon,Asia,34.3914,36.3958,22250 +El Jicaral,Nicaragua,North America,12.7333,-86.3833,10326 +El Jícaro,Nicaragua,North America,13.7167,-86.1333,25901 +El Kef,Tunisia,Africa,36.1822,8.7147,54701 +El Limón,Venezuela,South America,10.3003,-67.6336,106206 +El Meghaïer,Algeria,Africa,33.9506,5.9242,49793 +El Molino,Colombia,South America,10.6533,-72.9242,9121 +El Monte,United States,North America,34.0739,-118.0291,107066 +El Obeid,Sudan,Africa,13.1833,30.2167,418280 +El Oro de Hidalgo,Mexico,North America,19.8008,-100.1314,31847 +El Oued,Algeria,Africa,33.3611,6.8606,134699 +El Palmar,Guatemala,North America,14.65,-91.5833,31706 +El Paso,United States,North America,31.8476,-106.43,862494 +El Paujíl,Colombia,South America,1.5644,-75.3319,21462 +El Piñón,Colombia,South America,10.3333,-74.6667,16752 +El Playón,Colombia,South America,7.4767,-73.2081,11252 +El Progreso,Guatemala,North America,14.35,-89.85,25839 +El Pueblito,Mexico,North America,20.5397,-100.4414,115264 +El Quetzal,Guatemala,North America,14.7667,-91.8167,25888 +El Rama,Nicaragua,North America,12.3333,-84.6667,52482 +El Realejo,Nicaragua,North America,12.5333,-87.1667,8834 +El Retorno,Colombia,South America,2.3306,-72.6278,24562 +El Retén,Colombia,South America,10.6167,-74.2667,20981 +El Roble,Colombia,South America,9.1,-75.2,9786 +El Rodeo,Guatemala,North America,14.9167,-91.9667,19401 +El Rosal,Colombia,South America,4.8519,-74.2628,18958 +El Rosario,Mexico,North America,22.9922,-105.8572,16001 +El Salto,Mexico,North America,23.7823,-105.3585,24241 +El Sauce,Nicaragua,North America,12.8833,-86.5333,27900 +El Seibo,Dominican Republic,North America,18.77,-69.04,66867 +El Socorro,Venezuela,South America,8.9936,-65.7456,14049 +El Tablón,Colombia,South America,1.4269,-77.0969,13255 +El Tambo,Colombia,South America,2.4514,-76.8108,48094 +El Tarf,Algeria,Africa,36.767,8.317,25594 +El Tarra,Colombia,South America,8.5756,-73.0944,10957 +El Tejar,Guatemala,North America,14.65,-90.8,23780 +El Tigre,Venezuela,South America,8.8861,-64.2611,213524 +El Tocuyo,Venezuela,South America,9.7822,-69.7931,41327 +El Tortuguero,Nicaragua,North America,12.8167,-84.2,22324 +El Tumbador,Guatemala,North America,14.8667,-91.9333,52144 +El Valle del Espíritu Santo,Venezuela,South America,10.9833,-63.8833,45606 +El Viejo,Nicaragua,North America,12.6667,-87.1667,76775 +El Zulia,Colombia,South America,7.9333,-72.6,23921 +El Águila,Colombia,South America,4.9167,-76.0833,11069 +Elassóna,Greece,Europe,39.8833,22.1833,25459 +Elazığ,Turkey,Europe,38.6744,39.2228,387072 +Elbasan,Albania,Europe,41.1111,20.0806,78703 +Elbistan,Turkey,Europe,38.2014,37.1883,141307 +Elbląg,Poland,Europe,54.1667,19.4,115220 +Elburg,Netherlands,Europe,52.45,5.8333,23429 +Eldorado,Argentina,South America,-26.4,-54.6333,57323 +Eldoret,Kenya,Africa,0.5167,35.2833,289380 +Elefsína,Greece,Europe,38.0333,23.5333,24910 +Elektrostal,Russia,Asia,55.7833,38.4667,141778 +Elektrėnai,Lithuania,Europe,54.7886,24.6612,11753 +Elesbão Veloso,Brazil,South America,-6.2019,-42.14,13607 +Eleşkirt,Turkey,Europe,39.7981,42.6744,10191 +Elgin,United States,North America,42.0383,-88.324,114106 +Elias Fausto,Brazil,South America,-23.0428,-47.3739,17085 +Elista,Russia,Asia,46.3167,44.2667,103219 +Elizabeth,United States,North America,40.6658,-74.1913,135887 +Elk Grove,United States,North America,38.4161,-121.3842,177221 +Elkhart,United States,North America,41.6916,-85.9627,146511 +Elmadağ,Turkey,Europe,39.9208,33.2308,44379 +Elmalı,Turkey,Europe,36.7333,29.9,40774 +Eloy Alfaro,Ecuador,South America,-2.1733,-79.8311,295211 +Elvas,Portugal,Europe,38.88,-7.1628,20706 +Elverum,Norway,Europe,60.8833,11.5667,11633 +Elói Mendes,Brazil,South America,-21.61,-45.565,26336 +Embu,Kenya,Africa,-0.5333,37.45,41092 +Embu-Guaçu,Brazil,South America,-23.8322,-46.8114,66970 +Emden,Germany,Europe,53.3669,7.2061,50659 +Emet,Turkey,Europe,39.3333,29.2667,11431 +Emiliano Zapata,Mexico,North America,17.7446,-91.7651,22469 +Emirdağ,Turkey,Europe,39.0167,31.15,22978 +Emmeloord,Netherlands,Europe,52.7097,5.7508,26055 +Emmen,Netherlands,Europe,52.7833,6.9,107024 +Emmendingen,Germany,Europe,48.1214,7.8492,29035 +Empalme,Mexico,North America,27.9617,-110.8125,38886 +Empedrado,Argentina,South America,-27.9333,-58.7833,9258 +Encamp,Andorra,Europe,42.5361,1.5828,12826 +Encantado,Brazil,South America,-29.2358,-51.87,22962 +Encarnación,Paraguay,South America,-27.3472,-55.8739,69868 +Encarnación de Díaz,Mexico,North America,21.5167,-102.2333,51396 +Encheng,China,Asia,22.1833,112.3,483907 +Encrucijada,Cuba,North America,22.618,-79.866,31500 +Encruzilhada,Brazil,South America,-15.5314,-40.9094,16446 +Encruzilhada do Sul,Brazil,South America,-30.5439,-52.5219,23819 +Ende,Indonesia,Asia,-8.8333,121.65,87723 +Enerhodar,Ukraine,Europe,47.4989,34.6558,52237 +Engenheiro Beltrão,Brazil,South America,-23.7969,-52.2689,12454 +Engenheiro Paulo de Frontin,Brazil,South America,-22.55,-43.6778,12242 +Enghien,Belgium,Europe,50.6956,4.0411,13734 +Enkhuizen,Netherlands,Europe,52.7,5.3,18637 +Enköping,Sweden,Europe,59.6333,17.0667,21121 +Enna,Italy,Europe,37.5667,14.2667,25512 +Ennis,Ireland,Europe,52.8463,-8.9807,24253 +Enschede,Netherlands,Europe,52.2225,6.8925,159732 +Ensenada,Mexico,North America,31.8578,-116.6058,557430 +Enterprise,United States,North America,36.0091,-115.2278,232043 +Entre Ijuís,Brazil,South America,-28.36,-54.2678,9158 +Entre Rios,Brazil,South America,-11.9419,-38.0839,38098 +Entre Rios de Minas,Brazil,South America,-20.6708,-44.0658,14746 +Entrerríos,Colombia,South America,6.5667,-75.5167,8820 +Entroncamento,Portugal,Europe,39.4667,-8.4667,20206 +Enugu,Nigeria,Africa,6.5,7.5,1029400 +Envigado,Colombia,South America,6.1667,-75.5667,228848 +Epazoyucan,Mexico,North America,20.0177,-98.6361,11522 +Epe,Nigeria,Africa,6.5833,3.9833,181409 +Eqlīd,Iran,Asia,30.8928,52.6889,44341 +Erbaa,Turkey,Europe,40.6744,36.5681,75210 +Erbach,Germany,Europe,49.6583,8.9958,14099 +Erbil,Iraq,Asia,36.1911,44.0094,846000 +Erciş,Turkey,Europe,39.0311,43.3597,171000 +Erdek,Turkey,Europe,40.3986,27.7931,31902 +Erdemli,Turkey,Europe,36.6056,34.3111,151928 +Erdenet,Mongolia,Asia,49.0278,104.0444,106810 +Erding,Germany,Europe,48.2833,11.9,37169 +Erechim,Brazil,South America,-27.6339,-52.2739,105705 +Ereymentaū,Kazakhstan,Asia,51.6167,73.1,12518 +Ereğli,Turkey,Europe,37.5128,34.0483,150978 +Ergani,Turkey,Europe,38.2692,39.7617,136099 +Erie,United States,North America,42.1167,-80.0733,184332 +Erlangen,Germany,Europe,49.5833,11.0167,117806 +Ermelo,Netherlands,Europe,52.3,5.6331,27016 +Ermenek,Turkey,Europe,36.6389,32.8925,11629 +Ermoúpoli,Greece,Europe,37.4333,24.9167,13399 +Erongarícuaro,Mexico,North America,19.5833,-101.7167,15291 +Errachidia,Morocco,Africa,31.9319,-4.4244,97687 +Ertil,Russia,Asia,51.85,40.8,10024 +Ertis,Kazakhstan,Asia,53.3337,75.459,7772 +Eruh,Turkey,Europe,37.7497,42.1797,8895 +Erval d’Oeste,Brazil,South America,-27.1939,-51.495,21724 +Ervália,Brazil,South America,-20.84,-42.6569,20255 +Erzin,Turkey,Europe,36.9533,36.2033,41558 +Erzincan,Turkey,Europe,39.7464,39.4914,150714 +Erzurum,Turkey,Europe,39.9086,41.2769,767848 +Esbjerg,Denmark,Europe,55.467,8.45,71921 +Escada,Brazil,South America,-8.3592,-35.2236,69701 +Escaldes-Engordany,Andorra,Europe,42.5089,1.5408,15506 +Esch-sur-Alzette,Luxembourg,Europe,49.4969,5.9806,36625 +Eschen,Liechtenstein,Europe,47.2167,9.5167,4459 +Eschwege,Germany,Europe,51.1881,10.0528,19435 +Escondido,United States,North America,33.1348,-117.0723,149913 +Escuinapa,Mexico,North America,22.835,-105.778,30790 +Escuintla,Guatemala,North America,14.2978,-90.7869,170280 +Escuque,Venezuela,South America,9.25,-70.75,32901 +Escárcega,Mexico,North America,18.6067,-90.7344,29477 +Esfarāyen,Iran,Asia,37.0706,57.5056,59490 +Eshtehārd,Iran,Asia,35.725,50.365,29993 +Esik,Kazakhstan,Asia,43.35,77.4667,36715 +Esil,Kazakhstan,Asia,51.9556,66.4042,11551 +Eski-Nookat,Kyrgyzstan,Asia,40.2713,72.6131,18228 +Eskilstuna,Sweden,Europe,59.3674,16.516,69948 +Eskişehir,Turkey,Europe,39.7767,30.5206,820568 +Eslöv,Sweden,Europe,55.8333,13.3333,17748 +Eslāmshahr,Iran,Asia,35.5386,51.2228,448129 +Eslāmābād-e Gharb,Iran,Asia,34.1103,46.5275,90559 +Esmeralda,Cuba,North America,21.8561,-78.1114,29203 +Esmeraldas,Ecuador,South America,0.9667,-79.6528,155487 +Espargos,Cabo Verde,Africa,16.7546,-22.9453,6173 +Espera Feliz,Brazil,South America,-20.65,-41.9069,24102 +Esperantina,Brazil,South America,-3.9019,-42.2339,40970 +Esperanza,Argentina,South America,-31.4427,-60.9257,42082 +Esperança,Brazil,South America,-7.0333,-35.85,31231 +Espigão D’Oeste,Brazil,South America,-11.5247,-61.0128,25688 +Espinal,Colombia,South America,4.2,-74.8333,75828 +Espinho,Portugal,Europe,41.01,-8.64,31027 +Espinosa,Brazil,South America,-14.9081,-42.8103,30443 +Espita,Mexico,North America,21.0128,-88.3047,11551 +Espiye,Turkey,Europe,40.95,38.7333,26671 +Esplanada,Brazil,South America,-11.7958,-37.945,27230 +Espoo,Finland,Europe,60.2056,24.6556,314821 +Esposende,Portugal,Europe,41.5333,-8.7833,34254 +Espumoso,Brazil,South America,-28.725,-52.85,15173 +Esquel,Argentina,South America,-42.9,-71.3167,36687 +Esquipulas,Guatemala,North America,14.5667,-89.35,56253 +Esquipulas Palo Gordo,Guatemala,North America,14.9333,-91.8167,14018 +Essen,Germany,Europe,51.4508,7.0131,586608 +Esslingen,Germany,Europe,48.7333,9.3167,95881 +Estahbān,Iran,Asia,29.1269,54.0394,36410 +Estanzuela,Guatemala,North America,15.0,-89.5667,10872 +Estarreja,Portugal,Europe,40.75,-8.5667,26997 +Esteban Echeverría,Argentina,South America,-34.8167,-58.4667,109644 +Esteio,Brazil,South America,-29.8608,-51.1789,76137 +Estelí,Nicaragua,North America,13.0833,-86.35,112084 +Estiva,Brazil,South America,-22.4628,-46.0169,11502 +Estiva Gerbi,Brazil,South America,-22.2708,-46.945,11295 +Estreito,Brazil,South America,-6.5608,-47.4508,33294 +Estrela de Alagoas,Brazil,South America,-9.39,-36.76,15429 +Estremoz,Portugal,Europe,38.8422,-7.5881,14318 +Estrêla,Brazil,South America,-29.5019,-51.9658,32183 +Estrêla d’Oeste,Brazil,South America,-20.2878,-50.4008,9417 +Estância,Brazil,South America,-11.2678,-37.4378,65078 +Estância Velha,Brazil,South America,-29.6483,-51.1739,50672 +Esztergom,Hungary,Europe,47.7856,18.7403,28642 +Etchojoa,Mexico,North America,26.8667,-109.65,9831 +Etimesgut,Turkey,Europe,39.9458,32.6694,614891 +Etten-Leur,Netherlands,Europe,51.5706,4.6356,43869 +Etzatlán,Mexico,North America,20.7667,-104.0833,20011 +Euclides da Cunha,Brazil,South America,-10.5078,-39.0139,61456 +Eugene,United States,North America,44.0564,-123.1174,272737 +Eugenópolis,Brazil,South America,-21.0989,-42.1869,10801 +Eura,Finland,Europe,61.1333,22.1333,11184 +Eurajoki,Finland,Europe,61.2,21.7333,9143 +Euriápolis,Brazil,South America,-16.3778,-39.58,84120 +Euskirchen,Germany,Europe,50.6597,6.7917,60256 +Eusébio,Brazil,South America,-3.89,-38.4508,74170 +Eutin,Germany,Europe,54.1378,10.6181,17296 +Evansville,United States,North America,37.9881,-87.5341,206418 +Everett,United States,North America,47.9525,-122.1669,111083 +Evinayong,Equatorial Guinea,Africa,1.45,10.5667,3170 +Evren,Turkey,Europe,39.0167,33.8,40625 +Ewo,Congo (Brazzaville),Africa,-0.8742,14.8167,9062 +Extrema,Brazil,South America,-22.855,-46.3178,53482 +Extremoz,Brazil,South America,-5.7058,-35.3069,19572 +Exu,Brazil,South America,-7.5133,-39.7239,31843 +Eydhafushi,Maldives,Asia,5.1038,73.0704,2658 +Eyvān,Iran,Asia,33.8278,46.3061,31299 +Ezequiel Montes,Mexico,North America,20.6667,-99.8833,14053 +Ezhou,China,Asia,30.3914,114.8949,1079353 +Ezine,Turkey,Europe,39.79,26.3325,32000 +Eğil,Turkey,Europe,38.2572,40.0847,21434 +Eğirdir,Turkey,Europe,37.875,30.8506,16759 +Ełk,Poland,Europe,53.8214,22.3622,60390 +Eşfahān,Iran,Asia,32.6653,51.6703,2219343 +Eşme,Turkey,Europe,38.4,28.9667,15762 +E’erguna,China,Asia,50.243,120.181,68482 +Facatativá,Colombia,South America,4.8167,-74.3667,145080 +Fada,Chad,Africa,17.1833,21.5833,23786 +Fada Ngourma,Burkina Faso,Africa,12.05,0.3667,73200 +Faetano,San Marino,Europe,43.9256,12.4983,1177 +Fafe,Portugal,Europe,41.45,-8.1667,50633 +Fagersta,Sweden,Europe,60.0,15.7833,11130 +Fagundes,Brazil,South America,-7.355,-35.775,11049 +Fairfield,United States,North America,38.2583,-122.0335,150794 +Faisalabad,Pakistan,Asia,31.4167,73.0911,3203846 +Fakfak,Indonesia,Asia,-2.9167,132.3,18900 +Falan,Colombia,South America,5.1333,-74.95,9204 +Falköping,Sweden,Europe,58.1667,13.55,17939 +Falmouth,Jamaica,North America,18.49,-77.661,7779 +Falun,Sweden,Europe,60.613,15.647,37291 +Falāvarjān,Iran,Asia,32.5558,51.5106,37704 +Famagusta,Cyprus,Europe,35.125,33.9417,40920 +Famaillá,Argentina,South America,-27.05,-65.4,22924 +Famalicão,Portugal,Europe,41.4144,-8.5222,133832 +Fancheng,China,Asia,39.1891,113.2729,76088 +Fang,Thailand,Asia,19.9189,99.2136,30000 +Fangchenggang,China,Asia,21.6867,108.3547,953300 +Fangting,China,Asia,31.127,104.167,406775 +Fannūj,Iran,Asia,26.5753,59.6403,13070 +Fao Rai,Thailand,Asia,18.0179,103.3039,10978 +Farafangana,Madagascar,Africa,-22.8166,47.8332,34676 +Faranah,Guinea,Africa,10.0333,-10.7333,78108 +Fardīs,Iran,Asia,35.73,50.9822,181174 +Fargo,United States,North America,46.8651,-96.8292,225066 +Farg‘ona,Uzbekistan,Asia,40.3864,71.7864,299200 +Farias Brito,Brazil,South America,-6.9308,-39.5658,18217 +Farim,Guinea-Bissau,Africa,12.4833,-15.2167,46268 +Farkhor,Tajikistan,Asia,37.5,69.4,25300 +Faro,Portugal,Europe,37.0161,-7.935,64560 +Farroupilha,Brazil,South America,-29.225,-51.3478,55308 +Farsund,Norway,Europe,58.0833,6.8,9924 +Fartura,Brazil,South America,-23.3883,-49.51,16070 +Farum,Denmark,Europe,55.8099,12.3623,20317 +Farāh,Afghanistan,Asia,32.3436,62.1194,500000 +Farāshband,Iran,Asia,28.8617,52.0936,20320 +Farīmān,Iran,Asia,35.7011,59.8464,39515 +Fastiv,Ukraine,Europe,50.0833,29.9167,44014 +Fasā,Iran,Asia,28.9503,53.6272,110825 +Fatick,Senegal,Africa,14.3167,-16.4167,18416 +Fatsa,Turkey,Europe,41.0306,37.5,126775 +Fauske,Norway,Europe,67.2627,15.3913,9572 +Faxinal,Brazil,South America,-24.0008,-51.32,16389 +Faxinal dos Guedes,Brazil,South America,-26.8528,-52.26,11192 +Faya,Chad,Africa,17.9169,19.1167,48090 +Fayetteville,United States,North America,36.0714,-94.1661,405556 +Fayrōz Kōh,Afghanistan,Asia,34.5225,65.2517,15000 +Fayzobod,Tajikistan,Asia,38.55,69.3167,10400 +Faīẕābād,Afghanistan,Asia,37.1166,70.58,64704 +Federación,Argentina,South America,-30.9833,-57.9167,16658 +Federal,Argentina,South America,-30.95,-58.8,16075 +Feijó,Brazil,South America,-8.1639,-70.3539,35426 +Feira,Portugal,Europe,40.9254,-8.5428,11040 +Feira Grande,Brazil,South America,-9.9,-36.6778,22712 +Feira Nova,Brazil,South America,-7.9508,-35.3889,21427 +Feira de Santana,Brazil,South America,-12.2669,-38.9669,480949 +Feke,Turkey,Europe,37.815,35.9125,15833 +Feldbach,Austria,Europe,46.95,15.8833,13369 +Feldkirchen,Austria,Europe,46.7167,14.1,14198 +Felgueiras,Portugal,Europe,41.3667,-8.2,58065 +Felidhoo,Maldives,Asia,3.4717,73.5472,506 +Felipe Carrillo Puerto,Mexico,North America,19.5786,-88.0453,25744 +Felixlândia,Brazil,South America,-18.7578,-44.8989,13978 +Feliz,Brazil,South America,-29.4508,-51.3058,13764 +Fengcheng,China,Asia,37.4328,112.0233,128571 +Fenglu,China,Asia,24.6506,102.935,173161 +Fengning,China,Asia,41.2108,116.6404,99673 +Fenoarivo Atsinanana,Madagascar,Africa,-17.3819,49.409,41734 +Ferdows,Iran,Asia,34.0192,58.1731,28695 +Fereydūn Kenār,Iran,Asia,36.685,52.5208,38154 +Fereydūnshahr,Iran,Asia,32.9419,50.1203,13475 +Ferizaj,Kosovo,Europe,42.3706,21.1553,108610 +Ferizli,Turkey,Europe,40.9333,30.4833,30741 +Ferkessédougou,Côte d’Ivoire,Africa,9.5928,-5.1944,160267 +Fermo,Italy,Europe,43.1604,13.7181,35789 +Fernandópolis,Brazil,South America,-20.2839,-50.2458,71186 +Ferrara,Italy,Europe,44.8353,11.6199,129340 +Ferraz de Vasconcelos,Brazil,South America,-23.5411,-46.3689,184700 +Ferreira do Zêzere,Portugal,Europe,39.7,-8.2833,8619 +Ferreiros,Brazil,South America,-7.4478,-35.2439,15026 +Ferros,Brazil,South America,-19.2319,-43.0228,9590 +Fervedouro,Brazil,South America,-20.7258,-42.2789,10445 +Fethiye,Turkey,Europe,36.6514,29.1231,177702 +Feyẕābād,Iran,Asia,35.0183,58.7839,18120 +Fgura,Malta,Europe,35.8725,14.5228,11670 +Fianarantsoa,Madagascar,Africa,-21.4333,47.0833,191776 +Fier,Albania,Europe,40.7167,19.55,55845 +Figeac,France,Europe,44.6086,2.0317,9757 +Figueira da Foz,Portugal,Europe,40.1508,-8.8618,62125 +Filadelfia,Paraguay,South America,-22.34,-60.03,18210 +Filadélfia,Brazil,South America,-10.7408,-40.1328,17897 +Filandia,Colombia,South America,4.6667,-75.6333,12066 +Filipstad,Sweden,Europe,59.7167,14.1667,10644 +Filomeno Mata,Mexico,North America,20.2,-97.7,18367 +Finike,Turkey,Europe,36.3,30.15,49720 +Finnsnes,Norway,Europe,69.2406,18.0086,4658 +Finspång,Sweden,Europe,58.7167,15.7833,12440 +Fiorentino,San Marino,Europe,43.9106,12.4556,2548 +Firminópolis,Brazil,South America,-16.5819,-50.305,10419 +Fish Town,Liberia,Africa,5.1964,-7.8756,3328 +Fishers,United States,North America,39.9588,-85.9659,100918 +Flandes,Colombia,South America,4.2833,-74.8,29478 +Flensburg,Germany,Europe,54.7819,9.4367,92667 +Flexeiras,Brazil,South America,-9.2728,-35.715,9618 +Flint,United States,North America,43.0236,-83.6921,291688 +Florence,Italy,Europe,43.7714,11.2542,360930 +Florencia,Colombia,South America,1.6139,-75.6128,191867 +Florencio Varela,Argentina,South America,-34.8167,-58.2833,146704 +Flores,Guatemala,North America,16.9297,-89.8917,44706 +Flores Costa Cuca,Guatemala,North America,14.65,-91.8167,24888 +Flores da Cunha,Brazil,South America,-29.0289,-51.1819,30892 +Flores de Goiás,Brazil,South America,-14.4489,-47.05,13744 +Floresta,Brazil,South America,-8.6008,-38.5678,24729 +Floresta Azul,Brazil,South America,-14.86,-39.66,10525 +Florestópolis,Brazil,South America,-22.8628,-51.3869,11446 +Floreşti,Moldova,Europe,47.8933,28.3014,11998 +Floriana,Malta,Europe,35.8933,14.5058,2205 +Floriano,Brazil,South America,-6.7669,-43.0225,60025 +Florianópolis,Brazil,South America,-27.5933,-48.553,342315 +Florida,Cuba,North America,21.5294,-78.2228,71676 +Floridablanca,Colombia,South America,7.2167,-73.0667,295724 +Florânia,Brazil,South America,-6.1269,-36.8178,10196 +Florø,Norway,Europe,61.5986,5.0172,8925 +Flying Fish Cove,Christmas Island,Asia,-10.4261,105.6728,1599 +Flórina,Greece,Europe,40.7833,21.4,17686 +Foammulah,Maldives,Asia,-0.2932,73.4236,7888 +Focşani,Romania,Europe,45.7,27.1797,66648 +Foggia,Italy,Europe,41.4584,15.5519,145348 +Foix,France,Europe,42.9664,1.6078,9731 +Fojnica,Bosnia and Herzegovina,Europe,43.9595,17.9029,12356 +Fokino,Russia,Asia,53.2128,34.4186,12538 +Fomboni,Comoros,Africa,-12.28,43.7425,18277 +Fomento,Cuba,North America,22.1053,-79.7203,31131 +Fonseca,Colombia,South America,10.8333,-72.8333,39662 +Fontainebleau,France,Europe,48.4097,2.7025,15787 +Fontana,United States,North America,34.0968,-117.4599,211921 +Fonte Boa,Brazil,South America,-2.5139,-66.0917,25871 +Fontenay-le-Comte,France,Europe,46.466,-0.8061,13806 +Fontoura Xavier,Brazil,South America,-28.9828,-52.3458,9550 +Forbach,France,Europe,49.19,6.9,21111 +Forchheim,Germany,Europe,49.7197,11.0581,33610 +Forlì,Italy,Europe,44.2225,12.0408,116440 +Formiga,Brazil,South America,-20.4644,-45.4264,67822 +Formosa,Argentina,South America,-26.1833,-58.1833,234000 +Formosa do Rio Preto,Brazil,South America,-11.0478,-45.1928,25899 +Formoso do Araguaia,Brazil,South America,-11.7969,-49.5289,18881 +Forquilha,Brazil,South America,-3.7978,-40.2608,24173 +Forquilhinha,Brazil,South America,-28.7469,-49.4719,31431 +Forssa,Finland,Europe,60.8167,23.625,16469 +Forst (Lausitz),Germany,Europe,51.7333,14.6333,17721 +Fort Collins,United States,North America,40.5477,-105.0656,339256 +Fort Lauderdale,United States,North America,26.1412,-80.1464,183032 +Fort Liberté,Haiti,North America,19.6656,-71.8448,34434 +Fort Portal,Uganda,Africa,0.6544,30.2744,60800 +Fort Smith,United States,North America,35.3495,-94.3695,128384 +Fort Wayne,United States,North America,41.0888,-85.1436,345279 +Fort Wellington,Guyana,South America,6.4,-57.6167,118 +Fort Worth,United States,North America,32.7817,-97.3474,941311 +Fort-Shevchenko,Kazakhstan,Asia,44.5167,50.2667,5559 +Fort-de-France,Martinique,North America,14.6,-61.0667,253995 +Fortaleza,Brazil,South America,-3.7275,-38.5275,4167996 +Fortaleza dos Nogueiras,Brazil,South America,-6.9639,-46.1769,12640 +Fortim,Brazil,South America,-4.45,-37.7833,17294 +Fortul,Colombia,South America,6.7922,-71.7756,26798 +Fortuna,Brazil,South America,-5.7328,-44.1578,16976 +Fortín de las Flores,Mexico,North America,18.9,-97.0,21370 +Forécariah,Guinea,Africa,9.43,-13.098,12358 +Foshan,China,Asia,23.0214,113.1216,9042500 +Fougères,France,Europe,48.3525,-1.1986,20602 +Foz do Iguaçu,Brazil,South America,-25.54,-54.5875,285415 +Foça,Turkey,Europe,38.6667,26.7667,34946 +Foča,Bosnia and Herzegovina,Europe,43.5,18.7833,12334 +Fraiburgo,Brazil,South America,-27.0258,-50.9208,33481 +Fraijanes,Guatemala,North America,14.4667,-90.4333,63721 +Franca,Brazil,South America,-20.5389,-47.4008,287737 +Franceville,Gabon,Africa,-1.6333,13.5833,110568 +Francisco Beltrão,Brazil,South America,-26.0808,-53.055,96666 +Francisco I. Madero,Mexico,North America,25.7753,-103.2731,50084 +Francisco Morato,Brazil,South America,-23.2817,-46.7425,168243 +Francisco Sá,Brazil,South America,-16.4777,-43.4943,23476 +Francistown,Botswana,Africa,-21.1736,27.5125,103417 +Franco da Rocha,Brazil,South America,-23.3286,-46.7244,145755 +Franeker,Netherlands,Europe,53.1875,5.54,12781 +Frankenthal,Germany,Europe,49.5333,8.35,49122 +Frankfort,United States,North America,38.1924,-84.8643,28452 +Frankfurt,Germany,Europe,50.1106,8.6822,775790 +Frankfurt (Oder),Germany,Europe,52.3419,14.5517,58818 +Frauenfeld,Switzerland,Europe,47.558,8.8964,22313 +Fray Bartolomé de Las Casas,Guatemala,North America,15.8456,-89.8658,77896 +Fray Bentos,Uruguay,South America,-33.1333,-58.3,24406 +Frecheirinha,Brazil,South America,-3.76,-40.8158,11832 +Fredericia,Denmark,Europe,55.5758,9.7617,41243 +Frederick,United States,North America,39.4337,-77.4141,196743 +Fredericksburg,United States,North America,38.2992,-77.4872,166746 +Frederico Westphalen,Brazil,South America,-27.3589,-53.3939,32627 +Fredericton,Canada,North America,45.9636,-66.6431,64614 +Frederikshavn,Denmark,Europe,57.4337,10.5333,22961 +Frederikssund,Denmark,Europe,55.8389,12.0676,17135 +Frederiksværk,Denmark,Europe,55.9677,12.0215,12815 +Fredonia,Colombia,South America,5.9167,-75.6667,20997 +Fredrikstad,Norway,Europe,59.2181,10.9298,63610 +Freetown,Sierra Leone,Africa,8.4833,-13.2331,951000 +Frei Miguelinho,Brazil,South America,-7.9408,-35.9106,13636 +Frei Paulo,Brazil,South America,-10.5494,-37.5344,15556 +Freiberg,Germany,Europe,50.9119,13.3428,41045 +Freiburg im Breisgau,Germany,Europe,47.995,7.85,237244 +Freising,Germany,Europe,48.4028,11.7489,49939 +Fremont,United States,North America,37.5265,-121.9843,228518 +Fresnillo,Mexico,North America,23.175,-102.8675,230865 +Fresno,United States,North America,36.783,-119.7939,724547 +Freudenstadt,Germany,Europe,48.4633,8.4111,24337 +Fria,Guinea,Africa,10.3804,-13.55,23729 +Fribourg,Switzerland,Europe,46.8,7.15,38365 +Friedberg,Germany,Europe,50.3333,8.75,31131 +Frisco,United States,North America,33.156,-96.8216,210238 +Frolovo,Russia,Asia,49.7667,43.6667,35661 +Fronteiras,Brazil,South America,-7.0878,-40.6158,10259 +Frontera,Mexico,North America,26.926,-101.449,75242 +Frontino,Colombia,South America,6.7833,-76.1333,20156 +Frosinone,Italy,Europe,41.6333,13.35,43417 +Frutal,Brazil,South America,-20.025,-48.9408,58588 +Fryazino,Russia,Asia,55.95,38.05,60580 +Frías,Argentina,South America,-28.6333,-65.1333,26649 +Fucheng,China,Asia,35.3725,103.7056,32463 +Fuding,China,Asia,27.2,120.2,542000 +Fuente de Oro,Colombia,South America,3.4667,-73.6167,11599 +Fuerte Olimpo,Paraguay,South America,-21.0696,-57.9,4498 +Fujin,China,Asia,47.25,132.0333,414090 +Fukang,China,Asia,44.1646,87.9536,165006 +Fukui,Japan,Asia,36.0641,136.2196,261474 +Fukuoka,Japan,Asia,33.59,130.4017,2286000 +Fukushima,Japan,Asia,37.7608,140.4747,284282 +Fulda,Germany,Europe,50.5508,9.6753,70366 +Fullerton,United States,North America,33.8841,-117.9279,141278 +Funadhoo,Maldives,Asia,6.1482,73.29,2104 +Funafuti,Tuvalu,Oceania,-8.5167,179.2,6320 +Funchal,Portugal,Europe,32.65,-16.9167,105795 +Fundación,Colombia,South America,10.5214,-74.1867,90514 +Fundão,Portugal,Europe,40.1333,-7.5,29213 +Funing,China,Asia,39.8879,119.2314,76460 +Funtua,Nigeria,Africa,11.5204,7.32,180475 +Funza,Colombia,South America,4.7167,-74.2167,90671 +Fuqing,China,Asia,25.7167,119.3833,1316000 +Furmanov,Russia,Asia,57.25,41.1333,29715 +Fusagasugá,Colombia,South America,4.3452,-74.3618,170241 +Fushë Kosovë,Kosovo,Europe,42.6639,21.0961,39948 +Fusui,China,Asia,22.6325,107.9047,85512 +Fuyang,China,Asia,30.0553,119.95,771000 +Fuyuan,China,Asia,48.3647,134.3079,126694 +Fuzhou,China,Asia,26.0743,119.2964,3671192 +Fu’an,China,Asia,27.1,119.6333,577000 +Fámjin,Faroe Islands,Europe,61.5264,-6.8769,83 +Fársala,Greece,Europe,39.3,22.3833,9520 +Fátima,Brazil,South America,-10.6,-38.2169,18481 +Fès,Morocco,Africa,34.0433,-5.0033,1112072 +Fómeque,Colombia,South America,4.4847,-73.8936,12214 +Førde,Norway,Europe,61.4519,5.8569,10339 +Fürstenfeldbruck,Germany,Europe,48.1778,11.2556,38187 +Fürth,Germany,Europe,49.4667,11.0,132032 +Füzuli,Azerbaijan,Europe,39.6003,47.1431,3132 +Fāmenīn,Iran,Asia,35.1139,48.9725,14019 +Fārsān,Iran,Asia,32.255,50.5631,30504 +Făleşti,Moldova,Europe,47.5722,27.7139,12074 +Fındıklı,Turkey,Europe,41.1333,41.0167,10886 +Fūman,Iran,Asia,37.2242,49.3125,27763 +Gaalkacyo,Somalia,Africa,6.7697,47.4308,745000 +Gabasumdo,China,Asia,35.2481,100.5903,27742 +Gaborone,Botswana,Africa,-24.6569,25.9086,235884 +Gabrovo,Bulgaria,Europe,42.8742,25.3178,51398 +Gabès,Tunisia,Africa,33.8833,10.1167,167863 +Gabú,Guinea-Bissau,Africa,12.2833,-14.2167,43556 +Gachancipá,Colombia,South America,4.9908,-73.8731,17026 +Gachetá,Colombia,South America,4.8176,-73.636,11086 +Gacko,Bosnia and Herzegovina,Europe,43.1667,18.5333,8990 +Gadžin Han,Serbia,Europe,43.2203,22.0258,8389 +Gafsa,Tunisia,Africa,34.4225,8.7842,120739 +Gagarin,Russia,Asia,55.55,35.0167,25374 +Gagarin Shahri,Uzbekistan,Asia,40.6619,68.1722,15200 +Gagnef,Sweden,Europe,60.5833,15.0667,10328 +Gagnoa,Côte d’Ivoire,Africa,6.1333,-5.9333,277044 +Gaibandha,Bangladesh,Asia,25.329,89.5415,59289 +Gaigirgordub,Panama,North America,9.5583,-78.9483,10 +Gainesville,United States,North America,29.6804,-82.3459,219946 +Gaizhou,China,Asia,40.4,122.35,559271 +Gakuch,Pakistan,Asia,36.1736,73.7667,10142 +Galanta,Slovakia,Europe,48.1914,17.7308,15277 +Galaosiyo Shahri,Uzbekistan,Asia,39.85,64.45,12600 +Galapa,Colombia,South America,10.8969,-74.886,60708 +Galaţi,Romania,Europe,45.4233,28.0425,217851 +Galeana,Mexico,North America,24.8333,-100.0667,38930 +Galeras,Colombia,South America,9.1667,-75.05,21548 +Galich,Russia,Asia,58.3833,42.35,12856 +Galle,Sri Lanka,Asia,6.0328,80.2156,93118 +Gallipoli,Turkey,Europe,40.4139,26.6703,31782 +Galveston,United States,North America,29.2484,-94.8913,202810 +Galway,Ireland,Europe,53.2729,-9.0418,83456 +Galátsi,Greece,Europe,38.0167,23.75,57909 +Galūgāh,Iran,Asia,36.7269,53.8089,21352 +Gamarra,Colombia,South America,8.3333,-73.6667,12444 +Gambēla,Ethiopia,Africa,8.25,34.5833,97643 +Gameleira,Brazil,South America,-8.5844,-35.3867,18214 +Gamprin,Liechtenstein,Europe,47.2167,9.5,1663 +Gandu,Brazil,South America,-13.7439,-39.4869,32178 +Gangneung,"Korea, South",Asia,37.75,128.9,210456 +Gangtok,India,Asia,27.33,88.62,100286 +Gannan,China,Asia,47.9117,123.4978,59239 +Ganzhou,China,Asia,25.831,114.933,7396873 +Gao,Mali,Africa,16.2667,-0.05,49483 +Gaobeidian,China,Asia,39.327,115.874,528903 +Gaocheng,China,Asia,30.0167,100.2667,47500 +Gaoping,China,Asia,30.7824,106.1281,2174000 +Gaoua,Burkina Faso,Africa,10.3167,-3.1667,45284 +Gaoual,Guinea,Africa,11.754,-13.213,7461 +Gaoyi,China,Asia,37.6069,114.6088,52129 +Gaoyou,China,Asia,32.845,119.508,709572 +Gaozhou,China,Asia,21.9244,110.8422,1328657 +Gap,France,Europe,44.5594,6.0786,40656 +Gar,China,Asia,32.1166,80.0583,10000 +Garagoa,Colombia,South America,5.0825,-73.3642,16944 +Garanhuns,Brazil,South America,-8.8903,-36.4928,142506 +Gararu,Brazil,South America,-9.9675,-37.0833,11601 +Garbahaarrey,Somalia,Africa,3.35,42.2667,43000 +García,Mexico,North America,25.8167,-100.5833,234698 +Garden Grove,United States,North America,33.7787,-117.9601,170603 +Gardēz,Afghanistan,Asia,33.6,69.2169,70641 +Garešnica,Croatia,Europe,45.5742,16.9408,8624 +Garibaldi,Brazil,South America,-29.2558,-51.5339,34335 +Garissa,Kenya,Africa,-0.4569,39.6583,65881 +Garland,United States,North America,32.91,-96.6304,245298 +Garmeh,Iran,Asia,36.9864,56.2881,10933 +Garmisch-Partenkirchen,Germany,Europe,47.5,11.0833,23112 +Garmsār,Iran,Asia,35.2206,52.3389,48672 +Garoowe,Somalia,Africa,8.4,48.4833,485000 +Garopaba,Brazil,South America,-28.0228,-48.6128,29959 +Garoua,Cameroon,Africa,9.3,13.4,600000 +Garut,Indonesia,Asia,-7.2167,107.9,127980 +Garuva,Brazil,South America,-26.0269,-48.855,11378 +Garwolin,Poland,Europe,51.8972,21.615,17279 +Garzón,Colombia,South America,2.1819,-75.6578,69823 +Garça,Brazil,South America,-22.2153,-49.6511,44532 +Gashua,Nigeria,Africa,12.8681,11.0464,125100 +Gaspar,Brazil,South America,-26.9308,-48.9589,72570 +Gastonia,United States,North America,35.2494,-81.1854,189767 +Gastre,Argentina,South America,-42.2667,-69.2167,602 +Gatchina,Russia,Asia,59.5833,30.1333,91719 +Gatineau,Canada,North America,45.4833,-75.65,291041 +Gavarr,Armenia,Europe,40.3667,45.1333,20765 +Gavrilov-Yam,Russia,Asia,57.3167,39.85,16084 +Gay,Russia,Asia,51.4678,58.4431,41619 +Gaya,Niger,Africa,11.8853,3.4549,45465 +Gayéri,Burkina Faso,Africa,12.65,0.5,15170 +Gaza,Gaza Strip,Africa,31.5069,34.456,590481 +Gaziantep,Turkey,Europe,37.0628,37.3792,2130432 +Gaziemir,Turkey,Europe,38.3214,27.1275,137754 +Gazipaşa,Turkey,Europe,36.2728,32.3131,53702 +Gbadolite,Congo (Kinshasa),Africa,4.2833,21.0167,198839 +Gbarnga,Liberia,Africa,6.998,-9.473,34046 +Gdańsk,Poland,Europe,54.3475,18.6453,749786 +Gdynia,Poland,Europe,54.5175,18.54,245222 +Gebze,Turkey,Europe,40.8,29.4333,407019 +Gedaref,Sudan,Africa,14.0333,35.3833,354927 +Gediz,Turkey,Europe,38.9939,29.3914,26662 +Geita,Tanzania,Africa,-2.8714,32.2294,361671 +Geldermalsen,Netherlands,Europe,51.8789,5.2897,10860 +Geldrop,Netherlands,Europe,51.4222,5.5578,28001 +Gelendzhik,Russia,Asia,44.575,38.0725,80706 +Gelsenkirchen,Germany,Europe,51.5167,7.1,265885 +Gemena,Congo (Kinshasa),Africa,3.25,19.7667,138527 +Gemerek,Turkey,Europe,39.1819,36.0681,10195 +Gemert,Netherlands,Europe,51.5558,5.6867,16820 +Gemlik,Turkey,Europe,40.4317,29.1561,120245 +General Acha,Argentina,South America,-37.3833,-64.6,12583 +General Alvear,Argentina,South America,-34.9667,-67.7,29909 +General Arenales,Argentina,South America,-34.2833,-61.2833,16350 +General Belgrano,Argentina,South America,-35.7667,-58.5,20791 +General Conesa,Argentina,South America,-40.1,-64.4167,5484 +General José de San Martín,Argentina,South America,-26.5375,-59.3417,31758 +General Juan Madariaga,Argentina,South America,-37.0167,-57.1333,18089 +General Las Heras,Argentina,South America,-34.9333,-58.95,18022 +General Martín Miguel de Güemes,Argentina,South America,-24.6667,-65.05,47348 +General Pico,Argentina,South America,-35.6667,-63.7333,57669 +General Pinedo,Argentina,South America,-27.3167,-61.2833,15741 +General Pánfilo Natera,Mexico,North America,22.665,-102.1097,23526 +General Roca,Argentina,South America,-39.0333,-67.5833,136 +General Rodríguez,Argentina,South America,-34.6167,-58.95,85315 +General Salgado,Brazil,South America,-20.6478,-50.3608,10312 +General San Martín,Argentina,South America,-34.5667,-58.5333,28339 +General Santos,Philippines,Asia,6.12,125.17,697315 +General Viamonte,Argentina,South America,-35.0,-61.0333,22649 +General Villegas,Argentina,South America,-35.0333,-63.0167,35251 +Geneva,Switzerland,Europe,46.2017,6.1469,203840 +Gengqing,China,Asia,31.8081,98.5786,10221 +Genhe,China,Asia,50.7833,121.5167,120942 +Gennep,Netherlands,Europe,51.6942,5.9733,17035 +Genoa,Italy,Europe,44.4072,8.934,558745 +Gent,Belgium,Europe,51.0536,3.7253,265086 +Gentio do Ouro,Brazil,South America,-11.4289,-42.5058,10884 +Genç,Turkey,Europe,38.75,40.556,20763 +George Town,Malaysia,Asia,5.4136,100.3294,708127 +Georgetown,Guyana,South America,6.8011,-58.155,235017 +Georgiyevsk,Russia,Asia,44.15,43.4667,61543 +Gera,Germany,Europe,50.8806,12.0833,94847 +Gerede,Turkey,Europe,40.8006,32.1986,23547 +Germencik,Turkey,Europe,37.8697,27.6058,44172 +Germersheim,Germany,Europe,49.2167,8.3667,21295 +Germī,Iran,Asia,39.0297,48.0803,28967 +Gerze,Turkey,Europe,41.8056,35.1981,19144 +Gerāsh,Iran,Asia,27.6667,54.1411,34469 +Getulina,Brazil,South America,-21.7986,-49.9286,11287 +Getúlio Vargas,Brazil,South America,-27.89,-52.2278,16602 +Gevaş,Turkey,Europe,38.2978,43.1056,26918 +Gevgelija,North Macedonia,Europe,41.1392,22.5025,4967 +Gex,France,Europe,46.3333,6.0578,13455 +Gexianzhuang,China,Asia,37.0694,115.6591,122917 +Geyve,Turkey,Europe,40.5089,30.29,50799 +Ghabāghib,Syria,Asia,33.1824,36.2253,11802 +Ghafurov,Tajikistan,Asia,40.22,69.73,20600 +Ghanzi,Botswana,Africa,-21.7,21.65,21420 +Ghardaïa,Algeria,Africa,32.4833,3.6667,120000 +Gharyān,Libya,Africa,32.1697,13.0167,187854 +Ghaznī,Afghanistan,Asia,33.5492,68.4233,143379 +Ghonchí,Tajikistan,Asia,39.9589,69.1369,17000 +Ghorāhī,Nepal,Asia,28.0408,82.4861,156164 +Ghotki,Pakistan,Asia,28.1,69.19,119879 +Ghulja,China,Asia,43.9,81.35,542507 +Ghāndīnagar,India,Asia,23.2231,72.65,195891 +Ghāt,Libya,Africa,24.9644,10.1781,22000 +Ghōriyān,Afghanistan,Asia,34.34,61.47,54000 +Gia Nghĩa,Vietnam,Asia,11.9833,107.7,61413 +Giannitsá,Greece,Europe,40.7833,22.4,30498 +Gibara,Cuba,North America,21.1072,-76.1367,71126 +Gibraltar,Gibraltar,Europe,36.14,-5.35,34003 +Gießen,Germany,Europe,50.5833,8.6667,94996 +Gifhorn,Germany,Europe,52.4886,10.5464,43941 +Gifu,Japan,Asia,35.4232,136.7608,400118 +Gigante,Colombia,South America,2.3867,-75.5461,35498 +Gilbert,United States,North America,33.31,-111.7463,271118 +Gilbués,Brazil,South America,-9.8319,-45.3439,10892 +Gilgit,Pakistan,Asia,35.9208,74.3083,8851 +Gilroy,United States,North America,37.0046,-121.5854,115448 +Gimpo,"Korea, South",Asia,37.7,126.6,352683 +Ginebra,Colombia,South America,3.75,-76.1667,21815 +Girardot,Colombia,South America,4.305,-74.8017,129834 +Girardota,Colombia,South America,6.3764,-75.4473,51662 +Girau do Ponciano,Brazil,South America,-9.8839,-36.8289,36102 +Giresun,Turkey,Europe,40.9153,38.3894,125682 +Girona,Spain,Europe,41.9844,2.8211,156423 +Giruá,Brazil,South America,-28.0278,-54.35,16013 +Girón,Colombia,South America,7.0708,-73.1731,152582 +Gisborne,New Zealand,Oceania,-38.6625,178.0178,34527 +Gislaved,Sweden,Europe,57.3,13.5333,10231 +Gitega,Burundi,Africa,-3.4283,29.925,135467 +Giurgiu,Romania,Europe,43.9008,25.9739,54551 +Giza,Egypt,Africa,29.987,31.2118,5598402 +Gizo,Solomon Islands,Oceania,-8.1056,156.8389,6154 +Giżycko,Poland,Europe,54.04,21.7589,28233 +Gjakovë,Kosovo,Europe,42.3803,20.4308,94556 +Gjilan,Kosovo,Europe,42.4689,21.4633,90178 +Gjirokastër,Albania,Europe,40.0758,20.1389,19836 +Gjøvik,Norway,Europe,60.7957,10.6916,20589 +Glarus,Switzerland,Europe,47.0333,9.0667,5996 +Glazov,Russia,Asia,58.1333,52.6667,87762 +Glendale,United States,North America,33.5791,-112.2311,250193 +Gleno,Timor-Leste,Asia,-8.7239,125.4361,8133 +Gliwice,Poland,Europe,50.2833,18.6667,174016 +Gllogovc,Kosovo,Europe,42.6283,20.8939,58531 +Glodeni,Moldova,Europe,47.7667,27.5167,8676 +Glyfáda,Greece,Europe,37.8667,23.75,89597 +Glória do Goitá,Brazil,South America,-7.9992,-35.2911,29347 +Gmunden,Austria,Europe,47.9181,13.7994,13191 +Gniezno,Poland,Europe,52.5358,17.5958,65452 +Gobabis,Namibia,Africa,-22.45,18.9667,19101 +Gobernador Gregores,Argentina,South America,-48.7667,-70.25,4497 +Gobernador Virasora,Argentina,South America,-28.05,-56.0333,30666 +Godoy Cruz,Argentina,South America,-32.9167,-68.8333,191299 +Godāwari̇̄,Nepal,Asia,28.91,80.59,78018 +Goes,Netherlands,Europe,51.5,3.8833,38950 +Goiana,Brazil,South America,-7.5606,-35.0025,80055 +Goianinha,Brazil,South America,-6.2669,-35.21,26741 +Goianira,Brazil,South America,-16.4958,-49.4258,71916 +Goianápolis,Brazil,South America,-16.5108,-49.0239,13967 +Goianésia,Brazil,South America,-15.3175,-49.1175,71075 +Goiatins,Brazil,South America,-7.71,-47.3139,12433 +Goiatuba,Brazil,South America,-18.0128,-49.3569,31130 +Goio-Erê,Brazil,South America,-24.185,-53.0278,28437 +Goirle,Netherlands,Europe,51.5203,5.0671,23952 +Goiás,Brazil,South America,-15.9339,-50.14,24071 +Goiânia,Brazil,South America,-16.6806,-49.2564,1093007 +Goleniów,Poland,Europe,53.5636,14.8281,22166 +Golmud,China,Asia,36.4072,94.9283,215214 +Golpāyegān,Iran,Asia,33.4508,50.2817,47849 +Golub-Dobrzyń,Poland,Europe,53.1,19.05,11722 +Golubac,Serbia,Europe,44.653,21.632,8331 +Golubovci,Montenegro,Europe,42.3344,19.2253,3110 +Golyshmanovo,Russia,Asia,56.3819,68.3715,13424 +Goma,Congo (Kinshasa),Africa,-1.6794,29.2336,249862 +Gombe,Uganda,Africa,0.1811,32.1142,15196 +Gomīshān,Iran,Asia,37.07,54.0769,19191 +Gonaïves,Haiti,North America,19.4456,-72.6883,356324 +Gonbad-e Kāvūs,Iran,Asia,37.2436,55.1658,151910 +Gondomar,Portugal,Europe,41.15,-8.5333,164257 +Gongzhuling,China,Asia,43.5053,124.8224,862313 +González,Mexico,North America,22.8281,-98.4306,11212 +Gonābād,Iran,Asia,34.3575,58.6783,40773 +Goodyear,United States,North America,33.2614,-112.3622,102891 +Goranboy,Azerbaijan,Europe,40.61,46.7872,7400 +Goražde,Bosnia and Herzegovina,Europe,43.6667,18.9778,12512 +Gorenja Vas,Slovenia,Europe,46.1069,14.1451,1132 +Gorgān,Iran,Asia,36.8369,54.4372,312223 +Gori,Georgia,Europe,41.9817,44.1124,44524 +Gorinchem,Netherlands,Europe,51.8306,4.9742,37410 +Gorizia,Italy,Europe,45.9352,13.6193,33506 +Gorišnica,Slovenia,Europe,46.4147,16.0139,856 +Gorlice,Poland,Europe,49.6547,21.1597,26405 +Gornja Radgona,Slovenia,Europe,46.681,15.9883,3086 +Gornji Grad,Slovenia,Europe,46.2961,14.8052,1016 +Gornji Milanovac,Serbia,Europe,44.0212,20.456,38985 +Gornji Petrovci,Slovenia,Europe,46.8053,16.2225,411 +Gornji Vakuf,Bosnia and Herzegovina,Europe,43.9333,17.5833,22304 +Gorno-Altaysk,Russia,Asia,51.95,85.9667,63214 +Gornozavodsk,Russia,Asia,58.3667,58.3333,10890 +Goroka,Papua New Guinea,Oceania,-6.0833,145.3833,21507 +Gorokhovets,Russia,Asia,56.2,42.7,12666 +Gorom-Gorom,Burkina Faso,Africa,14.45,-0.2333,9752 +Gorontalo,Indonesia,Asia,0.5422,123.0614,191897 +Goryachiy Klyuch,Russia,Asia,44.6333,39.1333,42272 +Gorzów Wielkopolski,Poland,Europe,52.7333,15.25,119964 +Goslar,Germany,Europe,51.9072,10.43,50253 +Gospić,Croatia,Europe,44.5469,15.3744,11502 +Gostivar,North Macedonia,Europe,41.8,20.9167,32814 +Gostynin,Poland,Europe,52.4167,19.4667,17624 +Gostyń,Poland,Europe,51.8792,17.0125,20210 +Gotha,Germany,Europe,50.9492,10.705,46300 +Gothenburg,Sweden,Europe,57.7075,11.9675,674529 +Gotvand,Iran,Asia,32.2433,48.8136,24216 +Gouda,Netherlands,Europe,52.0181,4.7056,73681 +Goundam,Mali,Africa,16.4144,-3.6708,16253 +Gourcy,Burkina Faso,Africa,13.2167,-2.35,40144 +Gourma Rharous,Mali,Africa,16.88,-1.9239,26115 +Gouré,Niger,Africa,13.9874,10.27,14639 +Gouveia,Portugal,Europe,40.5,-7.6,14046 +Gouvêa,Brazil,South America,-18.456,-43.7424,11331 +Governador Archer,Brazil,South America,-5.0219,-44.2708,9100 +Governador Dix-Sept Rosado,Brazil,South America,-5.4589,-37.5208,11938 +Governador Lindenberg,Brazil,South America,-19.2519,-40.4608,10869 +Governador Valadares,Brazil,South America,-18.8508,-41.9489,247131 +Goya,Argentina,South America,-29.1333,-59.25,72304 +Goyang,"Korea, South",Asia,37.65,126.8,1061929 +Goz-Beida,Chad,Africa,12.2236,21.4144,66107 +Gołdap,Poland,Europe,54.3161,22.3094,13378 +Gracias,Honduras,North America,14.589,-88.5814,47622 +Grad,Slovenia,Europe,46.8,16.1,620 +Gradačac,Bosnia and Herzegovina,Europe,44.8785,18.4276,39340 +Gradiška,Bosnia and Herzegovina,Europe,45.1458,17.2539,11122 +Gradsko,North Macedonia,Europe,41.5775,21.9428,2219 +Grajales,Mexico,North America,19.2275,-97.8039,14159 +Grajaú,Brazil,South America,-5.8194,-46.1386,70065 +Grajewo,Poland,Europe,53.65,22.45,21395 +Gramado,Brazil,South America,-29.3789,-50.8739,40134 +Granada,Spain,Europe,37.1781,-3.6008,232717 +Granados,Guatemala,North America,14.9139,-90.5233,14585 +Grand Junction,United States,North America,39.0877,-108.5673,141008 +Grand Prairie,United States,North America,32.6871,-97.0208,198564 +Grand Rapids,United States,North America,42.9619,-85.6562,611480 +Grand Turk,Turks and Caicos Islands,North America,21.459,-71.139,3700 +Grand-Bassam,Côte d’Ivoire,Africa,5.2,-3.7333,124567 +Grand-Lahou,Côte d’Ivoire,Africa,5.1333,-5.0167,28470 +Granja,Brazil,South America,-3.12,-40.8258,53344 +Grasse,France,Europe,43.6667,6.9167,48669 +Gravatal,Brazil,South America,-28.3308,-49.035,12435 +Gravataí,Brazil,South America,-29.9433,-50.9939,232629 +Gravatá,Brazil,South America,-8.2008,-35.565,86516 +Grave,Netherlands,Europe,51.7592,5.7383,12486 +Graz,Austria,Europe,47.0708,15.4386,292630 +Graça,Brazil,South America,-4.0458,-40.7528,13801 +Graçanicë,Kosovo,Europe,42.6011,21.1958,10675 +Gračanica,Bosnia and Herzegovina,Europe,44.7031,18.3101,45220 +Greeley,United States,North America,40.4152,-104.7706,143554 +Green Bay,United States,North America,44.5148,-87.9895,227679 +Greensboro,United States,North America,36.0956,-79.8271,345858 +Greenville,United States,North America,34.8354,-82.3646,408626 +Greifswald,Germany,Europe,54.0833,13.3833,60071 +Greiz,Germany,Europe,50.6547,12.1997,20220 +Grenaa,Denmark,Europe,56.4128,10.8956,14179 +Grenoble,France,Europe,45.1715,5.7224,156389 +Gresham,United States,North America,45.5021,-122.4413,112818 +Grevenmacher,Luxembourg,Europe,49.6806,6.4417,4981 +Grevená,Greece,Europe,40.085,21.4275,21421 +Greymouth,New Zealand,Oceania,-42.45,171.2075,7965 +Greytown,Nicaragua,North America,10.9167,-83.7,1307 +Grimstad,Norway,Europe,58.3405,8.5934,13304 +Groaíras,Brazil,South America,-3.9128,-40.3828,10910 +Grodzisk Mazowiecki,Poland,Europe,52.1089,20.625,33708 +Grodzisk Wielkopolski,Poland,Europe,52.2333,16.3667,15131 +Groesbeek,Netherlands,Europe,51.7833,5.9333,18870 +Groningen,Netherlands,Europe,53.2167,6.5667,233273 +Grosseto,Italy,Europe,42.7722,11.1089,81321 +Grossos,Brazil,South America,-4.98,-37.155,9924 +Grosuplje,Slovenia,Europe,45.9551,14.6562,7703 +Groznyy,Russia,Asia,43.3125,45.6986,291687 +Groß-Gerau,Germany,Europe,49.9192,8.485,26614 +Grudziądz,Poland,Europe,53.4875,18.755,90890 +Gryazi,Russia,Asia,52.5,39.9667,43771 +Gryfice,Poland,Europe,53.9147,15.1986,15863 +Gryfino,Poland,Europe,53.2531,14.4875,20297 +Grândola,Portugal,Europe,38.1768,-8.5689,14826 +Grão Mogol,Brazil,South America,-16.5589,-42.89,13901 +Grójec,Poland,Europe,51.8656,20.8675,17037 +Guabiruba,Brazil,South America,-27.0858,-48.9808,24543 +Guacara,Venezuela,South America,10.2536,-67.9,178000 +Guacarí,Colombia,South America,3.7667,-76.3333,35513 +Guachavés,Colombia,South America,1.2219,-77.6772,31856 +Guachetá,Colombia,South America,5.3856,-73.6856,11385 +Guachochi,Mexico,North America,26.8194,-107.07,14513 +Guachucal,Colombia,South America,0.9667,-77.7333,18845 +Guadalajara,Mexico,North America,20.6767,-103.3475,5525000 +Guadalajara de Buga,Colombia,South America,3.9,-76.3019,128945 +Guadalupe,Mexico,North America,25.6775,-100.2597,691931 +Guaduas,Colombia,South America,5.0694,-74.5981,41142 +Guaitarilla,Colombia,South America,1.1333,-77.5564,12011 +Guaiçara,Brazil,South America,-21.6219,-49.7986,11633 +Guajará-Mirim,Brazil,South America,-10.7828,-65.3394,38045 +Gualaquiza,Ecuador,South America,-3.38,-78.58,9157 +Gualeguay,Argentina,South America,-33.15,-59.3333,43009 +Gualeguaychú,Argentina,South America,-33.0167,-58.5167,109461 +Gualán,Guatemala,North America,15.1333,-89.3667,49709 +Guamal,Colombia,South America,9.1472,-74.23,28277 +Guamaré,Brazil,South America,-5.095,-36.325,15295 +Guamo,Colombia,South America,4.0833,-74.9167,31350 +Guamúchil,Mexico,North America,25.4639,-108.0794,63743 +Guanagazapa,Guatemala,North America,14.2333,-90.65,17458 +Guanajay,Cuba,North America,22.9306,-82.6881,22839 +Guanajuato,Mexico,North America,21.0178,-101.2567,194500 +Guanambi,Brazil,South America,-14.2228,-42.7808,87817 +Guanare,Venezuela,South America,9.0436,-69.7489,112286 +Guangming,China,Asia,45.3333,122.7833,307215 +Guangping,China,Asia,36.4778,114.9333,66384 +Guangzhou,China,Asia,23.13,113.26,26940000 +Guang’an,China,Asia,30.4564,106.6326,3205476 +Guanhães,Brazil,South America,-18.775,-42.9328,32244 +Guankou,China,Asia,28.1637,113.6433,1168056 +Guano,Ecuador,South America,-1.5833,-78.6333,9136 +Guanta,Venezuela,South America,10.2383,-64.5917,35000 +Guantánamo,Cuba,North America,20.1383,-75.2061,228436 +Guapiara,Brazil,South America,-24.185,-48.5328,17879 +Guapiaçu,Brazil,South America,-20.795,-49.22,21711 +Guapimirim,Brazil,South America,-22.5369,-42.9819,51696 +Guapé,Brazil,South America,-20.7619,-45.9178,13772 +Guapí,Colombia,South America,2.5703,-77.8856,29797 +Guapó,Brazil,South America,-16.8308,-49.5319,19545 +Guarabira,Brazil,South America,-6.855,-35.49,57484 +Guaraci,Brazil,South America,-20.4986,-48.9447,10778 +Guaraciaba,Brazil,South America,-26.5989,-53.5178,10796 +Guaraciaba do Norte,Brazil,South America,-4.1669,-40.7478,42053 +Guaramirim,Brazil,South America,-26.4728,-49.0028,46711 +Guaranda,Ecuador,South America,-1.6,-79.0,30755 +Guarani,Brazil,South America,-21.3519,-43.0469,8918 +Guaraniaçu,Brazil,South America,-25.1008,-52.8778,13735 +Guaranésia,Brazil,South America,-21.2989,-46.8028,19150 +Guarapari,Brazil,South America,-20.6578,-40.5108,124656 +Guarapuava,Brazil,South America,-25.395,-51.4578,182093 +Guararapes,Brazil,South America,-21.2608,-50.6428,32346 +Guararema,Brazil,South America,-23.415,-46.035,28344 +Guaratinga,Brazil,South America,-16.5856,-39.7819,20700 +Guaratinguetá,Brazil,South America,-22.8167,-45.2278,119073 +Guaratuba,Brazil,South America,-25.8828,-48.575,42062 +Guaraí,Brazil,South America,-8.8339,-48.51,24775 +Guarda,Portugal,Europe,40.5364,-7.2683,42541 +Guarenas,Venezuela,South America,10.4667,-66.6167,200417 +Guareí,Brazil,South America,-23.3728,-48.1842,18887 +Guariba,Brazil,South America,-21.36,-48.2283,40487 +Guarne,Colombia,South America,6.28,-75.4427,51265 +Guarujá,Brazil,South America,-23.9936,-46.2564,311230 +Guarulhos,Brazil,South America,-23.4667,-46.5333,1324781 +Guará,Brazil,South America,-20.4283,-47.8242,20911 +Guasave,Mexico,North America,25.5655,-108.4718,86361 +Guasca,Colombia,South America,4.8658,-73.8772,15727 +Guasdualito,Venezuela,South America,7.2467,-70.7292,105000 +Guasipati,Venezuela,South America,7.4667,-61.4667,21165 +Guastatoya,Guatemala,North America,14.85,-90.0667,27407 +Guatapé,Colombia,South America,6.2333,-75.1667,9121 +Guatemala City,Guatemala,North America,14.6417,-90.5133,3014000 +Guateque,Colombia,South America,5.0056,-73.4722,9603 +Guatire,Venezuela,South America,10.4717,-66.5406,200417 +Guaxupé,Brazil,South America,-21.305,-46.7128,50911 +Guayabal,Colombia,South America,4.9633,-74.9056,11490 +Guayaquil,Ecuador,South America,-2.19,-79.8875,3094420 +Guazacapán,Guatemala,North America,14.0667,-90.4167,22197 +Guaçuí,Brazil,South America,-20.7758,-41.6789,25492 +Guaíba,Brazil,South America,-30.1139,-51.325,92924 +Guaíra,Brazil,South America,-20.3178,-48.3108,34610 +Gubakha,Russia,Asia,58.8706,57.5933,22915 +Gubkin,Russia,Asia,51.2828,37.5353,83766 +Gudermes,Russia,Asia,43.35,46.1,66352 +Gudja,Malta,Europe,35.8483,14.5025,2997 +Gudofredo Viana,Brazil,South America,-1.4028,-45.78,10186 +Guelma,Algeria,Africa,36.45,7.4333,120004 +Guelmim,Morocco,Africa,28.9833,-10.0667,126729 +Guelph,Canada,North America,43.55,-80.25,144356 +Guernica,Argentina,South America,-34.9167,-58.3833,52529 +Guia Lopes da Laguna,Brazil,South America,-21.4578,-56.1142,9824 +Guidan Roumdji,Niger,Africa,13.6575,6.6958,17525 +Guigang,China,Asia,23.112,109.599,4316262 +Guiglo,Côte d’Ivoire,Africa,6.5436,-7.4933,171454 +Guilin,China,Asia,25.275,110.296,4931137 +Guimarães,Portugal,Europe,41.445,-8.2908,156830 +Guiping,China,Asia,23.4,110.0833,1511011 +Guiratinga,Brazil,South America,-16.3489,-53.7578,10966 +Guisa,Cuba,North America,20.2608,-76.5381,44566 +Guixi,China,Asia,28.2925,117.2454,540572 +Guiyang,China,Asia,26.647,106.63,3299724 +Gujranwala,Pakistan,Asia,32.1567,74.19,2027001 +Gujrat,Pakistan,Asia,32.5739,74.0789,390533 +Gukovo,Russia,Asia,48.05,39.9167,60361 +Gulbene,Latvia,Europe,57.175,26.7564,6715 +Gulfport,United States,North America,30.4274,-89.0704,231754 +Guliston,Uzbekistan,Asia,40.4833,68.7833,90398 +Gulkevichi,Russia,Asia,45.3594,40.7072,33175 +Gulou,China,Asia,26.0865,119.298,2606000 +Gulu,Uganda,Africa,2.7817,32.2992,152276 +Guma,China,Asia,37.6168,78.2809,28163 +Gummersbach,Germany,Europe,51.0333,7.5667,51845 +Gunungsitoli,Indonesia,Asia,1.2833,97.6167,137104 +Guozhen,China,Asia,34.3609,107.1751,3321853 +Gurinhém,Brazil,South America,-7.1239,-35.4244,14127 +Gurlan,Uzbekistan,Asia,41.85,60.4,27600 +Gurupi,Brazil,South America,-11.7289,-49.0689,65034 +Gurupá,Brazil,South America,-1.405,-51.64,23098 +Guryevsk,Russia,Asia,54.7833,20.6167,27751 +Guryongpo,"Korea, South",Asia,35.9833,129.55,9099 +Gusau,Nigeria,Africa,12.15,6.6667,383162 +Gusev,Russia,Asia,54.5833,22.2,28820 +Gusinje,Montenegro,Europe,42.5619,19.8339,1673 +Gustavia,Saint Barthelemy,North America,17.8979,-62.8506,2300 +Gustavsberg,Sweden,Europe,59.3167,18.3833,11333 +Gus’-Khrustal’nyy,Russia,Asia,55.6167,40.6833,51552 +Gutao,China,Asia,37.2,112.15,50000 +Guáimaro,Cuba,North America,21.0589,-77.3478,36114 +Guática,Colombia,South America,5.3167,-75.8,12423 +Guéckédou,Guinea,Africa,8.5667,-10.1333,221715 +Guéret,France,Europe,46.1714,1.8692,12814 +Gvardeysk,Russia,Asia,54.6667,21.0833,13962 +Gwadar,Pakistan,Asia,25.1264,62.3225,70852 +Gwagwalada,Nigeria,Africa,8.9392,7.0758,346000 +Gwanda,Zimbabwe,Africa,-20.9389,29.0186,26700 +Gwangju,"Korea, South",Asia,35.1653,126.8486,1490092 +Gwangyang,"Korea, South",Asia,34.9333,127.6833,152160 +Gweru,Zimbabwe,Africa,-19.4614,29.8022,158200 +Gwoza,Nigeria,Africa,11.0861,13.6914,388600 +Gyangzê,China,Asia,28.9148,89.6045,11039 +Gyomaendrőd,Hungary,Europe,46.9361,20.8233,12003 +Gyula,Hungary,Europe,46.65,21.2828,27834 +Gyumri,Armenia,Europe,40.7894,43.8475,121976 +Gyál,Hungary,Europe,47.3822,19.2136,24376 +Gyêgu,China,Asia,33.0166,96.7333,23000 +Gyöngyös,Hungary,Europe,47.7833,19.9333,27645 +Győr,Hungary,Europe,47.6842,17.6344,246159 +Gárdony,Hungary,Europe,47.1973,18.6091,13750 +Gázi,Greece,Europe,35.325,25.0667,20918 +Gänserndorf,Austria,Europe,48.3406,16.7175,11404 +Gävle,Sweden,Europe,60.6748,17.1444,86533 +Génova,Guatemala,North America,14.6167,-91.8333,46294 +Gérakas,Greece,Europe,38.0333,23.85,29939 +Gómez Palacio,Mexico,North America,25.5611,-103.4983,327985 +Góra,Poland,Europe,51.6667,16.55,11173 +Gödöllő,Hungary,Europe,47.6,19.3667,32524 +Göksun,Turkey,Europe,38.0211,36.4917,50676 +Gökçeada,Turkey,Europe,40.1608,25.8444,9783 +Gökçebey,Turkey,Europe,41.3081,32.1417,8673 +Gölbaşı,Turkey,Europe,39.7953,32.8028,150047 +Gölcük,Turkey,Europe,40.7172,29.8228,175940 +Gölhisar,Turkey,Europe,37.1408,29.5075,16118 +Gölköy,Turkey,Europe,40.6875,37.6178,25960 +Gölmarmara,Turkey,Europe,38.7167,27.9167,15193 +Gölyaka,Turkey,Europe,40.7769,30.9958,10516 +Gömeç,Turkey,Europe,39.3911,26.84,16880 +Gönen,Turkey,Europe,40.1014,27.6514,74871 +Göppingen,Germany,Europe,48.7025,9.6528,59300 +Gördes,Turkey,Europe,38.9328,28.2903,26458 +Görele,Turkey,Europe,41.0333,39.0,18725 +Görlitz,Germany,Europe,51.1528,14.9872,56694 +Görükle,Turkey,Europe,40.2333,28.8333,35882 +Göttingen,Germany,Europe,51.5339,9.9356,120261 +Göygöl,Azerbaijan,Europe,40.5858,46.3189,37280 +Göyçay,Azerbaijan,Europe,40.6553,47.7389,36200 +Güigüe,Venezuela,South America,10.0833,-67.7833,60000 +Güines,Cuba,North America,22.8475,-82.0236,66892 +Güira de Melena,Cuba,North America,22.8019,-82.5047,37838 +Gülchö,Kyrgyzstan,Asia,40.3138,73.4442,13755 +Gülnar,Turkey,Europe,36.3389,33.3986,27889 +Gülşehir,Turkey,Europe,38.7464,34.6206,12474 +Gümüşhacıköy,Turkey,Europe,40.8667,35.2167,14582 +Gümüşhane,Turkey,Europe,40.4597,39.4778,39214 +Gümüşova,Turkey,Europe,40.85,30.9333,9408 +Güney,Turkey,Europe,38.1544,29.0678,9448 +Güneysu,Turkey,Europe,40.9781,40.6136,8722 +Güneysınır,Turkey,Europe,37.2981,32.7211,9266 +Günzburg,Germany,Europe,48.4527,10.2713,21865 +Gürgentepe,Turkey,Europe,40.7883,37.6017,12617 +Güroymak,Turkey,Europe,38.5769,42.0211,25724 +Gürpınar,Turkey,Europe,38.3269,43.4133,31865 +Gürsu,Turkey,Europe,40.2144,29.1931,102601 +Gürün,Turkey,Europe,38.7225,37.2775,10657 +Güstrow,Germany,Europe,53.7939,12.1764,29582 +Gütersloh,Germany,Europe,51.9,8.3833,102464 +Gālīkesh,Iran,Asia,37.2728,55.4328,23394 +Għajnsielem,Malta,Europe,36.0269,14.2903,3200 +Għarb,Malta,Europe,36.0611,14.2092,1539 +Għargħur,Malta,Europe,35.9241,14.4534,2768 +Għasri,Malta,Europe,36.0583,14.2278,424 +Għaxaq,Malta,Europe,35.8483,14.5172,4722 +Gīlān-e Gharb,Iran,Asia,34.1406,45.9183,22331 +Głogów,Poland,Europe,51.6589,16.0803,64261 +Głubczyce,Poland,Europe,50.2011,17.8247,12100 +Gżira,Malta,Europe,35.905,14.4944,8029 +Gədəbəy,Azerbaijan,Europe,40.5706,45.8123,9161 +Gəncə,Azerbaijan,Europe,40.6828,46.3606,331400 +G‘uzor,Uzbekistan,Asia,38.6208,66.2481,24500 +G’allaorol Shahri,Uzbekistan,Asia,40.0214,67.5975,24700 +G’ijduvon Shahri,Uzbekistan,Asia,40.1,64.6667,43400 +G’oliblar Qishlog’i,Uzbekistan,Asia,40.4953,67.8778,12300 +Haaksbergen,Netherlands,Europe,52.1547,6.7419,24229 +Haapsalu,Estonia,Europe,58.9394,23.5408,9375 +Haaren,Netherlands,Europe,51.6017,5.2228,14195 +Haarlem,Netherlands,Europe,52.3833,4.6333,162543 +Habo,Sweden,Europe,57.9066,14.0856,9499 +Hacarí,Colombia,South America,8.3167,-73.15,9745 +Hacılar,Turkey,Europe,38.6497,35.4503,12465 +Hacıqabul,Azerbaijan,Europe,40.0387,48.9429,27536 +Haderslev,Denmark,Europe,55.2524,9.4893,22182 +Hadim,Turkey,Europe,36.9883,32.4569,10999 +Hadžići,Bosnia and Herzegovina,Europe,43.8236,18.2211,23891 +Haeju,"Korea, North",Asia,38.0333,125.7167,241599 +Haeryong,"Korea, South",Asia,34.9131,127.5394,46994 +Hafizabad,Pakistan,Asia,32.0714,73.6878,318621 +Haftkel,Iran,Asia,31.4444,49.5314,15802 +Hagen,Germany,Europe,51.3667,7.4833,190490 +Hagerstown,United States,North America,39.6401,-77.7217,206889 +Hagfors,Sweden,Europe,60.0333,13.65,12125 +Haguenau,France,Europe,48.82,7.79,36070 +Hagåtña,Guam,Oceania,13.4745,144.7504,1051 +Haicheng,China,Asia,40.8824,122.6852,1067905 +Haifa,Israel,Asia,32.8192,34.9992,600000 +Haikou,China,Asia,20.0186,110.3488,2250000 +Hailin,China,Asia,44.594,129.38,292755 +Hailun,China,Asia,47.4667,126.9667,480216 +Haiphong,Vietnam,Asia,20.8651,106.6838,2310280 +Haisyn,Ukraine,Europe,48.8103,29.3842,25698 +Haivoron,Ukraine,Europe,48.3333,29.8667,14010 +Hai’an,China,Asia,32.535,120.563,874334 +Hajdúböszörmény,Hungary,Europe,47.6667,21.5167,29310 +Hajdúhadház,Hungary,Europe,47.6833,21.6667,13073 +Hajdúnánás,Hungary,Europe,47.85,21.4333,16087 +Hajdúszoboszló,Hungary,Europe,47.45,21.3833,23918 +Hajnówka,Poland,Europe,52.7333,23.5667,19667 +Hajīn,Syria,Asia,34.6951,40.8314,37935 +Hakha,Burma,Asia,22.6428,93.6096,20000 +Hakkari,Turkey,Europe,37.577,43.739,58470 +Halachó,Mexico,North America,20.4764,-90.0819,9412 +Halba,Lebanon,Asia,34.5506,36.0781,4730 +Halberstadt,Germany,Europe,51.8958,11.0467,40069 +Halden,Norway,Europe,59.15,11.3833,31387 +Haldensleben,Germany,Europe,52.2833,11.4167,19188 +Half Way Tree,Jamaica,North America,18.0106,-76.7847,96494 +Halfeti,Turkey,Europe,37.2289,37.9457,41662 +Halifax,Canada,North America,44.6475,-63.5906,439819 +Halle,Germany,Europe,51.4828,11.9697,242172 +Hallein,Austria,Europe,47.6831,13.0969,21150 +Hallstahammar,Sweden,Europe,59.6167,16.2167,15200 +Halmstad,Sweden,Europe,56.6718,12.8556,71422 +Hamadān,Iran,Asia,34.8064,48.5161,554406 +Hamar,Norway,Europe,60.7944,11.0678,27947 +Hamburg,Germany,Europe,53.55,10.0,2496600 +Hameln,Germany,Europe,52.1,9.3667,57916 +Hamhŭng,"Korea, North",Asia,39.9167,127.5333,768551 +Hamilton,Canada,North America,43.2567,-79.8692,729560 +Hamina,Finland,Europe,60.5667,27.2,19534 +Hamm,Germany,Europe,51.6833,7.8167,180761 +Hammerfest,Norway,Europe,70.6634,23.6821,8073 +Hampton,United States,North America,37.0551,-76.363,137334 +Hanau,Germany,Europe,50.1328,8.9169,103184 +Handan,China,Asia,36.601,114.487,2708015 +Hangu,Pakistan,Asia,33.5333,71.0667,39766 +Hangzhou,China,Asia,30.267,120.153,9523000 +Hani,Turkey,Europe,38.4136,40.3925,32519 +Hani i Elezit,Kosovo,Europe,42.15,21.2967,9403 +Hannover,Germany,Europe,52.3667,9.7167,548186 +Hannut,Belgium,Europe,50.672,5.0781,16435 +Hanoi,Vietnam,Asia,21.0,105.85,8587100 +Hantsavichy,Belarus,Europe,52.75,26.4333,13355 +Haomen,China,Asia,37.3619,101.5871,36515 +Haqqulobod,Uzbekistan,Asia,40.9061,72.1194,27000 +Haradok,Belarus,Europe,55.4667,30.0,11533 +Harar,Ethiopia,Africa,9.3111,42.1278,129000 +Harare,Zimbabwe,Africa,-17.8292,31.0522,1558823 +Harbin,China,Asia,45.7576,126.6409,3830000 +Harbiye,Turkey,Europe,36.145,36.1411,27057 +Hardenberg,Netherlands,Europe,52.5758,6.6194,59689 +Harderwijk,Netherlands,Europe,52.3506,5.6172,50000 +Hardinxveld-Giessendam,Netherlands,Europe,51.83,4.87,18413 +Haren,Netherlands,Europe,53.1721,6.6093,17950 +Hargeysa,Somalia,Africa,9.56,44.065,1200000 +Harhorin,Mongolia,Asia,47.2,102.8333,14765 +Haripur,Pakistan,Asia,33.9942,72.9333,49318 +Harlingen,United States,North America,26.1916,-97.6977,115767 +Harper,Liberia,Africa,4.3667,-7.7167,17837 +Harran,Turkey,Europe,36.8708,39.025,96072 +Harrisburg,United States,North America,40.2752,-76.8843,499494 +Harrislee,Germany,Europe,54.7972,9.3764,11733 +Harstad,Norway,Europe,68.7989,16.5314,25148 +Harsīn,Iran,Asia,34.2706,47.5803,44146 +Hartford,United States,North America,41.7661,-72.6834,969171 +Hashtgerd,Iran,Asia,35.9628,50.6794,55640 +Hashtpar,Iran,Asia,37.7975,48.9042,54178 +Hashtrūd,Iran,Asia,37.4758,47.0497,20572 +Haskovo,Bulgaria,Europe,41.933,25.567,94156 +Hasköy,Turkey,Europe,38.6822,41.6894,13057 +Haslev,Denmark,Europe,55.3179,11.974,12280 +Hassa,Turkey,Europe,36.7994,36.5178,56675 +Hasselt,Belgium,Europe,50.9304,5.3383,77651 +Hat Yai,Thailand,Asia,7.0167,100.4667,406513 +Hatay,Turkey,Europe,36.2025,36.1606,399045 +Hatillo de Loba,Colombia,South America,8.9586,-74.0808,12270 +Hato Mayor,Dominican Republic,North America,18.767,-69.267,61517 +Hatonuevo,Colombia,South America,11.0694,-72.7669,23911 +Hattem,Netherlands,Europe,52.4744,6.0697,12228 +Hatvan,Hungary,Europe,47.6681,19.6697,19943 +Haugesund,Norway,Europe,59.4164,5.27,45040 +Havana,Cuba,North America,23.1367,-82.3589,2089532 +Havran,Turkey,Europe,39.5583,27.0983,28058 +Havsa,Turkey,Europe,41.5492,26.8217,8702 +Havza,Turkey,Europe,40.9667,35.6667,38492 +Haymana,Turkey,Europe,39.4311,32.4956,26016 +Hayrabolu,Turkey,Europe,41.2131,27.1,30521 +Hayward,United States,North America,37.6328,-122.0766,159201 +Hazorasp,Uzbekistan,Asia,41.3167,61.0667,18800 +Hazro,Turkey,Europe,38.2561,40.7831,16093 +Haßfurt,Germany,Europe,50.0167,10.5,13982 +Hecelchakán,Mexico,North America,20.1667,-90.1333,10285 +Hechi,China,Asia,24.693,108.085,3417945 +Hedensted,Denmark,Europe,55.7701,9.6967,12680 +Heemskerk,Netherlands,Europe,52.5167,4.6667,39191 +Heemstede,Netherlands,Europe,52.35,4.6167,27545 +Heerde,Netherlands,Europe,52.3833,6.0333,18776 +Heerenveen,Netherlands,Europe,52.95,5.9333,28497 +Heerhugowaard,Netherlands,Europe,52.6667,4.8333,58387 +Heerlen,Netherlands,Europe,50.8833,5.9833,86936 +Heesch,Netherlands,Europe,51.7314,5.53,29728 +Heeze,Netherlands,Europe,51.3828,5.5715,9945 +Hefei,China,Asia,31.8206,117.2273,4216940 +Hegang,China,Asia,47.3501,130.298,891271 +Heide,Germany,Europe,54.1961,9.0933,22467 +Heidelberg,Germany,Europe,49.4167,8.7167,162960 +Heidenheim,Germany,Europe,48.6761,10.1544,49895 +Heilbad Heiligenstadt,Germany,Europe,51.3789,10.1386,17260 +Heilbronn,Germany,Europe,49.15,9.2167,130093 +Heiloo,Netherlands,Europe,52.6,4.7167,24144 +Heinola,Finland,Europe,61.2,26.0333,17953 +Heinsberg,Germany,Europe,51.0631,6.0964,43620 +Hejian,China,Asia,38.4466,116.0995,795198 +Hekou,China,Asia,22.5385,104.0111,44998 +Helena,United States,North America,46.5965,-112.0202,33126 +Helixi,China,Asia,30.6267,118.9861,376857 +Heliópolis,Brazil,South America,-10.6828,-38.2858,12309 +Hellevoetsluis,Netherlands,Europe,51.8333,4.1333,40312 +Helmond,Netherlands,Europe,51.4797,5.6556,92627 +Helmstedt,Germany,Europe,52.2281,11.0106,25633 +Helong,China,Asia,42.5436,129.0028,117087 +Helsingborg,Sweden,Europe,56.0424,12.721,116029 +Helsinge,Denmark,Europe,56.0197,12.199,8906 +Helsingør,Denmark,Europe,56.0294,12.5863,47563 +Helsinki,Finland,Europe,60.1708,24.9375,1360075 +Hemet,United States,North America,33.7341,-116.9969,176009 +Hempstead,United States,North America,40.6629,-73.6089,789177 +Hendek,Turkey,Europe,40.805,30.7492,90153 +Henderson,United States,North America,36.0133,-115.0381,324523 +Hendrik-Ido-Ambacht,Netherlands,Europe,51.85,4.63,31258 +Hendījān,Iran,Asia,30.2383,49.7075,29015 +Hengelo,Netherlands,Europe,52.2656,6.7931,81049 +Hengshui,China,Asia,37.739,115.669,4212933 +Hengzhou,China,Asia,22.6799,109.2614,896099 +Henichesk,Ukraine,Europe,46.1667,34.8,18889 +Heppenheim,Germany,Europe,49.6431,8.6389,27610 +Herceg Novi,Montenegro,Europe,42.453,18.5312,19536 +Herculândia,Brazil,South America,-22.0036,-50.3853,9278 +Heredia,Costa Rica,North America,9.9985,-84.1169,3146 +Herford,Germany,Europe,52.1333,8.6833,67265 +Herisau,Switzerland,Europe,47.3851,9.2786,15744 +Hermosillo,Mexico,North America,29.0989,-110.9542,855563 +Herne,Germany,Europe,51.55,7.2167,157896 +Herning,Denmark,Europe,56.1333,8.9833,51193 +Heroica Caborca,Mexico,North America,30.7167,-112.15,67604 +Heroica Ciudad de Tlaxiaco,Mexico,North America,17.2077,-97.6798,40123 +Heroica Guaymas,Mexico,North America,27.9183,-110.8989,117253 +Heroica Matamoros,Mexico,North America,25.8797,-97.5042,541979 +Heroica Nogales,Mexico,North America,31.3186,-110.9458,261137 +Herzberg,Germany,Europe,51.6833,13.2333,8678 +Herāt,Afghanistan,Asia,34.3419,62.2031,556205 +Herīs,Iran,Asia,38.2475,47.1164,10515 +Heshan,China,Asia,23.8,108.8833,98938 +Hesperia,United States,North America,34.3975,-117.3147,100141 +Heunghae,"Korea, South",Asia,36.1167,129.35,34328 +Heves,Hungary,Europe,47.6,20.2833,9863 +Heydərabad,Azerbaijan,Europe,39.7229,44.8485,2000 +Heyin,China,Asia,36.0451,101.4242,7642 +Heyuan,China,Asia,23.7443,114.7002,2837686 +Heze,China,Asia,35.2343,115.4796,8287693 +Hezhou,China,Asia,24.4042,111.5672,2007858 +Hezuo,China,Asia,35.0002,102.9103,112173 +Heṭauḍā,Nepal,Asia,27.4167,85.0333,84000 +Hialeah,United States,North America,25.8696,-80.3045,221901 +Hickory,United States,North America,35.741,-81.3223,200500 +Hidalgotitlán,Mexico,North America,17.7833,-94.6333,19587 +Hidrolândia,Brazil,South America,-4.4078,-40.4378,17855 +High Point,United States,North America,35.991,-79.9937,171342 +Highlands Ranch,United States,North America,39.5419,-104.9708,101437 +Higuerote,Venezuela,South America,10.4833,-66.1,25000 +Higüey,Dominican Republic,North America,18.6181,-68.7111,251243 +Hildburghausen,Germany,Europe,50.4167,10.75,11682 +Hildesheim,Germany,Europe,52.15,9.95,102325 +Hillegom,Netherlands,Europe,52.2833,4.5833,22197 +Hillerød,Denmark,Europe,55.9333,12.3167,36043 +Hillsboro,United States,North America,45.5273,-122.936,107050 +Hilvan,Turkey,Europe,37.5886,38.9556,42218 +Hilvarenbeek,Netherlands,Europe,51.4861,5.1367,15698 +Hilversum,Netherlands,Europe,52.2333,5.1667,91235 +Hinatuan,Philippines,Asia,8.3661,126.3361,43841 +Hinche,Haiti,North America,19.143,-72.004,102745 +Hinwil,Switzerland,Europe,47.3033,8.8444,11179 +Hirado,Japan,Asia,33.3681,129.5536,28822 +Hiroshima,Japan,Asia,34.3914,132.4519,1198021 +Hisor,Tajikistan,Asia,38.5264,68.5381,308100 +Hithadhoo,Maldives,Asia,-0.6094,73.0899,10398 +Hizan,Turkey,Europe,38.2256,42.4269,12409 +Hjørring,Denmark,Europe,57.4663,9.9889,25917 +Hlobyne,Ukraine,Europe,49.3903,33.2503,8955 +Hlohovec,Slovakia,Europe,48.4311,17.8031,20556 +Hlyboka,Ukraine,Europe,48.0833,25.9333,9226 +Hlybokaye,Belarus,Europe,55.1333,27.6833,17746 +Ho,Ghana,Africa,6.6004,0.47,83715 +Ho Chi Minh City,Vietnam,Asia,10.7756,106.7019,15136000 +Hobart,Australia,Oceania,-42.8806,147.325,197451 +Hobro,Denmark,Europe,56.6381,9.7953,12191 +Hobyo,Somalia,Africa,5.3505,48.5268,11800 +Hodoš,Slovenia,Europe,46.8233,16.3342,280 +Hoeryŏng,"Korea, North",Asia,42.4333,129.75,153532 +Hof,Germany,Europe,50.3167,11.9167,46963 +Hofheim,Germany,Europe,50.0833,8.45,40412 +Hoh Ereg,China,Asia,41.0955,111.4408,23776 +Hohhot,China,Asia,40.842,111.749,2866615 +Hoima,Uganda,Africa,1.4319,31.3525,122700 +Hola,Kenya,Africa,-1.5,40.03,6931 +Hola Prystan,Ukraine,Europe,46.5167,32.5167,13544 +Holbæk,Denmark,Europe,55.7135,11.7169,29960 +Holguín,Cuba,North America,20.8872,-76.2631,350191 +Hollabrunn,Austria,Europe,48.5667,16.1,11681 +Holland,United States,North America,42.7677,-86.0984,110905 +Hollola,Finland,Europe,60.9867,25.5167,22885 +Hollywood,United States,North America,26.0293,-80.1679,152935 +Holstebro,Denmark,Europe,56.3595,8.6208,37022 +Holzminden,Germany,Europe,51.8297,9.4483,20217 +Homa Bay,Kenya,Africa,-0.5167,34.45,32174 +Homburg,Germany,Europe,49.3167,7.3333,42498 +Homs,Syria,Asia,34.7319,36.7181,775404 +Homyel’,Belarus,Europe,52.4453,30.9842,501102 +Honaz,Turkey,Europe,37.7578,29.2642,34074 +Honda,Colombia,South America,5.2042,-74.7417,23830 +Hong,Nigeria,Africa,10.2317,12.9303,260900 +Hong Kong,Hong Kong,Asia,22.3,114.2,7450000 +Hongliuwan,China,Asia,39.6348,94.3386,10079 +Hongshui,China,Asia,38.4246,100.7926,19718 +Hong’an,China,Asia,47.21,123.61,289999 +Honiara,Solomon Islands,Oceania,-9.4333,159.95,84520 +Honolulu,United States,North America,21.3294,-157.846,346323 +Hoofddorp,Netherlands,Europe,52.3061,4.6907,77885 +Hoogeveen,Netherlands,Europe,52.7167,6.4667,55603 +Hoogezand,Netherlands,Europe,53.1572,6.7533,22080 +Hoorn,Netherlands,Europe,52.65,5.0667,75645 +Hopa,Turkey,Europe,41.3903,41.4194,23846 +Horad Smalyavichy,Belarus,Europe,54.1,28.0667,22679 +Horasan,Turkey,Europe,40.0397,42.1711,36519 +Horizontina,Brazil,South America,-27.6258,-54.3078,18851 +Horjul,Slovenia,Europe,46.023,14.2981,1169 +Horki,Belarus,Europe,54.2667,30.9833,28961 +Horlivka,Ukraine,Europe,48.3,38.05,239828 +Horodenka,Ukraine,Europe,48.6675,25.5003,8812 +Horodyshche,Ukraine,Europe,49.2925,31.4581,13062 +Horsens,Denmark,Europe,55.8657,9.8735,63162 +Horst,Netherlands,Europe,51.4514,6.0536,13080 +Hortolândia,Brazil,South America,-22.8583,-47.22,215819 +Hosa’ina,Ethiopia,Africa,7.55,37.85,69995 +Hostotipaquillo,Mexico,North America,21.2167,-103.85,8732 +Hotan,China,Asia,37.1167,79.9167,322300 +Houma,United States,North America,29.58,-90.7059,147841 +Houndé,Burkina Faso,Africa,11.5,-3.5167,87154 +Houston,United States,North America,29.786,-95.3885,6046392 +Houten,Netherlands,Europe,52.0261,5.1728,50223 +Hovd,Mongolia,Asia,48.0042,91.6406,26023 +Hoveyzeh,Iran,Asia,31.4606,48.0781,19481 +Hoxut,China,Asia,42.2552,86.8607,19520 +Hpa-An,Burma,Asia,16.8906,97.6333,421525 +Hradec Králové,Czechia,Europe,50.2092,15.8322,93906 +Hrastnik,Slovenia,Europe,46.1479,15.1528,4962 +Hrazdan,Armenia,Europe,40.5,44.7667,52283 +Hrib-Loški Potok,Slovenia,Europe,45.7015,14.5901,353 +Hrodna,Belarus,Europe,53.6667,23.8333,361115 +Hrubieszów,Poland,Europe,50.8167,23.8833,16832 +Hsinchu,Taiwan,Asia,24.8167,120.9833,453536 +Hua Hin,Thailand,Asia,12.5686,99.9578,65983 +Huacho,Peru,South America,-11.1067,-77.605,200585 +Huai Yot,Thailand,Asia,7.7894,99.6347,12337 +Huaiyang,China,Asia,37.7701,114.5312,50859 +Hualien,Taiwan,Asia,23.9722,121.6064,99458 +Huamantla,Mexico,North America,19.3133,-97.9228,51996 +Huambo,Angola,Africa,-12.7767,15.7347,713134 +Huancavelica,Peru,South America,-12.7864,-74.9756,49570 +Huancayo,Peru,South America,-12.0667,-75.2167,378203 +Huandacareo,Mexico,North America,19.9906,-101.275,11592 +Huanggang,China,Asia,30.4537,114.8724,5882719 +Huanghua,China,Asia,38.372,117.33,484313 +Huangshan,China,Asia,29.7149,118.3376,1330565 +Huangshi,China,Asia,30.2011,115.039,2469079 +Huangzhai,China,Asia,38.0751,112.6595,22233 +Huanímaro,Mexico,North America,20.3675,-101.4969,20117 +Huaquechula,Mexico,North America,18.7667,-98.55,26114 +Huaraz,Peru,South America,-9.5333,-77.5333,118836 +Huasca de Ocampo,Mexico,North America,20.2028,-98.5758,15201 +Huatabampo,Mexico,North America,27.05,-109.4167,30324 +Huatusco,Mexico,North America,19.1489,-96.9661,60674 +Huauchinango,Mexico,North America,20.1767,-98.0528,58957 +Huazangsi,China,Asia,36.9836,103.1265,51603 +Huazhou,China,Asia,32.688,112.087,1247807 +Hudiksvall,Sweden,Europe,61.729,17.1121,15015 +Huehuetenango,Guatemala,North America,15.3147,-91.4761,136639 +Huehuetla,Mexico,North America,20.1075,-97.6233,18803 +Huehuetoca,Mexico,North America,19.8342,-99.2033,59721 +Huejotzingo,Mexico,North America,19.1619,-98.4064,59822 +Huejuquilla el Alto,Mexico,North America,22.6256,-103.8964,10015 +Huejúcar,Mexico,North America,22.3591,-103.2108,22083 +Huelva,Spain,Europe,37.25,-6.95,143290 +Huesca,Spain,Europe,42.1333,-0.4167,54704 +Hueyapan de Ocampo,Mexico,North America,18.15,-95.15,43309 +Hueyotlipan,Mexico,North America,18.9,-97.85,8511 +Huichang,China,Asia,34.9073,112.7914,334213 +Huili Chengguanzhen,China,Asia,26.6333,102.25,390531 +Huilong,China,Asia,31.87,121.703,967313 +Huishi,China,Asia,35.6918,105.0531,68958 +Huittinen,Finland,Europe,61.175,22.7,9646 +Huitzilan,Mexico,North America,19.9667,-97.6833,15096 +Huitán,Guatemala,North America,15.1,-91.6167,14839 +Huité,Guatemala,North America,14.9333,-89.7167,12021 +Huixcolotla,Mexico,North America,18.9147,-97.7736,12148 +Huixquilucan,Mexico,North America,19.3611,-99.3508,284965 +Huixtla,Mexico,North America,15.1386,-92.4647,32109 +Huizen,Netherlands,Europe,52.3,5.25,41090 +Huizhou,China,Asia,23.112,114.416,2509243 +Hulbuk,Tajikistan,Asia,37.805,69.6464,24500 +Hulin,China,Asia,45.7667,132.9333,267870 +Hulst,Netherlands,Europe,51.28,4.0528,27575 +Huludao,China,Asia,40.711,120.837,2434194 +Humahuaca,Argentina,South America,-23.2,-65.35,11369 +Humaitá,Brazil,South America,-7.5061,-63.0208,57473 +Humberto de Campos,Brazil,South America,-2.5983,-43.4611,28932 +Humenné,Slovakia,Europe,48.9306,21.9122,31359 +Hunchun,China,Asia,42.863,130.366,225454 +Hunedoara,Romania,Europe,45.7697,22.9203,50457 +Huntington,United States,North America,40.8521,-73.3823,203691 +Huntington Beach,United States,North America,33.696,-118.0018,196010 +Huntsville,United States,North America,34.6981,-86.6412,359389 +Hunucmá,Mexico,North America,21.0153,-89.8744,24910 +Huolu,China,Asia,38.0874,114.3159,78250 +Huquan,China,Asia,39.7603,114.2834,66271 +Hurlingham,Argentina,South America,-34.6,-58.6333,176505 +Husum,Germany,Europe,54.4667,9.05,23814 +Huy,Belgium,Europe,50.5201,5.2375,21293 +Huzhou,China,Asia,30.8925,120.0875,1558826 +Huánuco,Peru,South America,-9.9295,-76.2397,196627 +Huế,Vietnam,Asia,16.4619,107.5955,652572 +Hvalba,Faroe Islands,Europe,61.6,-6.9556,713 +Hvannasund,Faroe Islands,Europe,62.2833,-6.5167,414 +Hyderabad City,Pakistan,Asia,25.3792,68.3683,1732693 +Hyderābād,India,Asia,17.3617,78.4747,10494000 +Hyesan,"Korea, North",Asia,41.4,128.1833,192680 +Hyvinkää,Finland,Europe,60.6333,24.85,46925 +Hà Giang,Vietnam,Asia,22.8333,104.9833,55559 +Hà Tiên,Vietnam,Asia,10.3833,104.4833,100560 +Hà Tĩnh,Vietnam,Asia,18.3333,105.9,380000 +Hàng Trạm,Vietnam,Asia,20.3944,105.6222,11503 +Hämeenkyrö,Finland,Europe,61.6333,23.2,10319 +Hämeenlinna,Finland,Europe,60.9944,24.4667,68288 +Härnösand,Sweden,Europe,62.6323,17.9379,18236 +Hässleholm,Sweden,Europe,56.1568,13.7702,18500 +Hînceşti,Moldova,Europe,46.8258,28.5936,12491 +Hòa Bình,Vietnam,Asia,20.8133,105.3383,234567 +Hòa Thành,Vietnam,Asia,11.2831,106.1297,152339 +Hódmezővásárhely,Hungary,Europe,46.4303,20.3189,41781 +Höganäs,Sweden,Europe,56.196,12.5769,16274 +Höshööt,Mongolia,Asia,48.9408,89.1383,8744 +Höxter,Germany,Europe,51.7667,9.3667,28749 +Höör,Sweden,Europe,55.9333,13.5333,12976 +Hønefoss,Norway,Europe,60.1667,10.3,16547 +Hørsholm,Denmark,Europe,55.881,12.5011,47836 +Hüyük,Turkey,Europe,37.9519,31.5958,15144 +Hīt,Iraq,Asia,33.6417,42.825,66700 +Hıdırbey,Turkey,Europe,36.203,36.129,40226 +Hınıs,Turkey,Europe,39.3586,41.7003,24680 +Hūn,Libya,Africa,29.1268,15.9477,18878 +Hưng Yên,Vietnam,Asia,20.6464,106.0511,118646 +Hương Canh,Vietnam,Asia,21.2833,105.65,16341 +Hương Trà,Vietnam,Asia,16.4675,107.5181,72677 +Hạ Long,Vietnam,Asia,20.95,107.0667,172915 +Hải Dương,Vietnam,Asia,20.9397,106.3306,241373 +I-n-Salah,Algeria,Africa,27.195,2.4833,32518 +IJmuiden,Netherlands,Europe,52.4586,4.6194,48320 +IJsselstein,Netherlands,Europe,52.02,5.0422,33819 +Iacanga,Brazil,South America,-21.89,-49.0247,11858 +Iaciara,Brazil,South America,-14.0958,-46.6319,10584 +Ialoveni,Moldova,Europe,46.9431,28.7778,12515 +Iapu,Brazil,South America,-19.4369,-42.2178,12030 +Iati,Brazil,South America,-9.0458,-36.8458,17165 +Iaçu,Brazil,South America,-12.7669,-40.2119,24607 +Iaşi,Romania,Europe,47.1622,27.5889,271692 +Iba,Philippines,Asia,15.33,119.98,55581 +Ibadan,Nigeria,Africa,7.3964,3.9167,3552000 +Ibagué,Colombia,South America,4.4333,-75.2333,546003 +Ibaiti,Brazil,South America,-23.8489,-50.1878,28830 +Ibanda,Uganda,Africa,-0.1347,30.495,31316 +Ibaretama,Brazil,South America,-4.8039,-38.7528,11956 +Ibarra,Ecuador,South America,0.3627,-78.1307,131856 +Ibateguara,Brazil,South America,-8.9728,-35.9389,13731 +Ibatiba,Brazil,South America,-20.2364,-41.5092,19210 +Ibaté,Brazil,South America,-21.955,-47.9969,32178 +Ibb,Yemen,Asia,13.9667,44.1667,221472 +Ibiapina,Brazil,South America,-3.9233,-40.8894,25082 +Ibiassucê,Brazil,South America,-14.2589,-42.2569,10429 +Ibicaraí,Brazil,South America,-14.865,-39.5878,21665 +Ibicoara,Brazil,South America,-13.4108,-41.285,20785 +Ibicuitinga,Brazil,South America,-4.9739,-38.6389,11611 +Ibicuí,Brazil,South America,-14.8419,-39.9869,13934 +Ibimirim,Brazil,South America,-8.5408,-37.6903,24340 +Ibipeba,Brazil,South America,-11.6408,-42.0108,16603 +Ibipetuba,Brazil,South America,-11.0089,-44.5189,27390 +Ibipitanga,Brazil,South America,-12.8819,-42.4858,13863 +Ibiporã,Brazil,South America,-23.2692,-51.0481,55131 +Ibiraci,Brazil,South America,-20.4619,-47.1219,10948 +Ibirama,Brazil,South America,-27.0569,-49.5178,19862 +Ibirapitanga,Brazil,South America,-14.1639,-39.3739,22177 +Ibirapuã,Brazil,South America,-17.6878,-40.1089,8896 +Ibirataia,Brazil,South America,-14.0669,-39.6408,18792 +Ibiraçu,Brazil,South America,-19.8319,-40.3697,12591 +Ibirité,Brazil,South America,-20.0219,-44.0589,133044 +Ibirubá,Brazil,South America,-28.6278,-53.09,21583 +Ibirá,Brazil,South America,-21.08,-49.2408,11690 +Ibitiara,Brazil,South America,-12.6519,-42.2178,16699 +Ibitinga,Brazil,South America,-21.7578,-48.8289,60600 +Ibitirama,Brazil,South America,-20.5408,-41.6669,8957 +Ibititá,Brazil,South America,-11.5469,-41.9778,16969 +Ibiá,Brazil,South America,-19.4778,-46.5389,22229 +Ibiúna,Brazil,South America,-23.6564,-47.2225,76432 +Ibotirama,Brazil,South America,-12.185,-43.2208,26309 +Ibrā’,Oman,Asia,22.6906,58.5334,35000 +Ica,Peru,South America,-14.0667,-75.7333,282407 +Icapuí,Brazil,South America,-4.7128,-37.355,21433 +Icaraíma,Brazil,South America,-23.3958,-53.6139,8991 +Icatu,Brazil,South America,-2.7758,-44.0658,24794 +Icononzo,Colombia,South America,4.1833,-74.5333,10801 +Icó,Brazil,South America,-6.4008,-38.8619,62521 +Idah,Nigeria,Africa,7.0978,6.7433,79815 +Idaho Falls,United States,North America,43.4871,-112.0362,111295 +Idanha-a-Nova,Portugal,Europe,39.9197,-7.2367,9716 +Idlib,Syria,Asia,35.9297,36.6317,165000 +Idrija,Slovenia,Europe,46.0025,14.0275,5848 +Ielmo Marinho,Brazil,South America,-5.8239,-35.5528,11615 +Ieper,Belgium,Europe,50.8508,2.885,35039 +Ierápetra,Greece,Europe,35.0117,25.7414,12355 +Ig,Slovenia,Europe,45.9692,14.5394,2240 +Igaci,Brazil,South America,-9.5369,-36.6336,25613 +Iganga,Uganda,Africa,0.615,33.485,53870 +Igaporã,Brazil,South America,-13.7728,-42.7139,15527 +Igarapava,Brazil,South America,-20.0383,-47.7469,29727 +Igarapé,Brazil,South America,-20.07,-44.3019,45847 +Igarapé Grande,Brazil,South America,-4.585,-44.8528,10231 +Igarapé-Açu,Brazil,South America,-1.1269,-47.6178,35797 +Igarapé-Miri,Brazil,South America,-1.975,-48.96,64831 +Igarassu,Brazil,South America,-7.8339,-34.9058,115196 +Igaratinga,Brazil,South America,-19.955,-44.7089,10830 +Igaratá,Brazil,South America,-23.2044,-46.1561,9349 +Igaraçu do Tietê,Brazil,South America,-22.5092,-48.5578,24451 +Igboho,Nigeria,Africa,8.8333,3.75,400000 +Iglesias,Italy,Europe,39.3103,8.5372,25093 +Iglino,Russia,Asia,54.8383,56.4231,16811 +Ignacio de la Llave,Mexico,North America,18.6618,-95.9721,17370 +Ignalina,Lithuania,Europe,55.3406,26.1605,4998 +Igrapiúna,Brazil,South America,-13.8258,-39.1419,13151 +Igreja Nova,Brazil,South America,-10.1253,-36.6619,21372 +Igrejinha,Brazil,South America,-29.5739,-50.79,32808 +Iguaba Grande,Brazil,South America,-22.8389,-42.2289,27920 +Iguala de la Independencia,Mexico,North America,18.345,-99.5383,118468 +Iguape,Brazil,South America,-24.7081,-47.5553,30989 +Iguaraci,Brazil,South America,-7.835,-37.515,12247 +Iguatemi,Brazil,South America,-23.68,-54.5608,13617 +Iguatu,Brazil,South America,-6.3594,-39.2986,103074 +Iguaí,Brazil,South America,-14.7558,-40.0889,21491 +Ihosy,Madagascar,Africa,-22.3996,46.1167,39556 +Ii,Finland,Europe,65.3167,25.3667,9766 +Iisalmi,Finland,Europe,63.5567,27.1883,20618 +Ijebu-Ode,Nigeria,Africa,6.8208,3.9208,394246 +Ijevan,Armenia,Europe,40.8756,45.1492,19200 +Ijuí,Brazil,South America,-28.3878,-53.915,78461 +Ikare,Nigeria,Africa,7.5167,5.75,1099931 +Ikast,Denmark,Europe,56.1367,9.16,16215 +Ikeja,Nigeria,Africa,6.6,3.35,861300 +Iklin,Malta,Europe,35.9042,14.4544,3130 +Ikom,Nigeria,Africa,6.0833,8.6167,79103 +Ikot Abasi,Nigeria,Africa,4.5704,7.56,34911 +Ikot Ekpene,Nigeria,Africa,5.1833,7.7167,143077 +Ilagan,Philippines,Asia,17.1489,121.8894,158218 +Ilamatlán,Mexico,North America,20.7833,-98.45,13474 +Ilanskiy,Russia,Asia,56.2414,96.0581,15945 +Ile-Ife,Nigeria,Africa,7.4833,4.5592,501952 +Iles,Colombia,South America,0.9667,-77.5167,8701 +Ilesa,Nigeria,Africa,7.6167,4.7167,384334 +Ilgın,Turkey,Europe,38.2792,31.9139,53489 +Ilha Solteira,Brazil,South America,-20.4272,-51.3436,26344 +Ilhabela,Brazil,South America,-23.7778,-45.3578,34934 +Ilhota,Brazil,South America,-26.9,-48.8269,17046 +Ilhéus,Brazil,South America,-14.7889,-39.0489,157639 +Iliatenco,Mexico,North America,18.2167,-99.4833,10039 +Ilicínia,Brazil,South America,-20.9358,-45.8328,12741 +Ilidža,Bosnia and Herzegovina,Europe,43.8331,18.3039,66730 +Iligan,Philippines,Asia,8.23,124.25,363115 +Ilijaš,Bosnia and Herzegovina,Europe,43.9583,18.2667,19603 +Ilioúpoli,Greece,Europe,37.9333,23.75,76730 +Ilirska Bistrica,Slovenia,Europe,45.5679,14.2527,4323 +Illapel,Chile,South America,-31.6333,-71.1667,20751 +Illintsi,Ukraine,Europe,49.1056,29.2089,11095 +Illizi,Algeria,Africa,26.505,8.4822,17252 +Ilmajoki,Finland,Europe,62.7333,22.5833,12369 +Ilobu,Nigeria,Africa,7.84,4.486,118097 +Iloilo,Philippines,Asia,10.72,122.57,574000 +Ilorin,Nigeria,Africa,8.5,4.55,847582 +Ilulissat,Greenland,North America,69.2167,-51.1,4737 +Imaculada,Brazil,South America,-7.3897,-37.5092,11848 +Imaruí,Brazil,South America,-28.3408,-48.82,11881 +Imatra,Finland,Europe,61.1833,28.7667,24919 +Imbaú,Brazil,South America,-24.445,-50.7608,14249 +Imbituba,Brazil,South America,-28.24,-48.67,35700 +Imbituva,Brazil,South America,-25.23,-50.6044,29924 +Imdina,Malta,Europe,35.8858,14.4031,292 +Imotski,Croatia,Europe,43.45,17.2167,9153 +Imperatriz,Brazil,South America,-5.5258,-47.4758,273110 +Imperia,Italy,Europe,43.8865,8.0297,42060 +Impfondo,Congo (Brazzaville),Africa,1.6186,18.0622,33911 +Imphāl,India,Asia,24.8074,93.9384,264986 +Imqabba,Malta,Europe,35.8442,14.4669,3315 +Imsida,Malta,Europe,35.8978,14.4894,8545 +Imst,Austria,Europe,47.2394,10.7381,10504 +Imtarfa,Malta,Europe,35.8908,14.3969,2572 +Imías,Cuba,North America,20.0694,-74.6314,9699 +Imām Şāḩib,Afghanistan,Asia,37.1844,68.9144,12655 +Imġarr,Malta,Europe,35.9197,14.3664,3629 +In Guezzam,Algeria,Africa,19.5686,5.7722,7045 +Inajá,Brazil,South America,-8.903,-37.827,13280 +Incheon,"Korea, South",Asia,37.4833,126.6333,2936117 +Indaial,Brazil,South America,-26.8978,-49.2319,71549 +Indaiatuba,Brazil,South America,-23.0903,-47.2181,256223 +Independence,United States,North America,39.0871,-94.3501,121944 +Independência,Brazil,South America,-5.3958,-40.3089,24024 +Indianapolis,United States,North America,39.7771,-86.1458,1740984 +Indiaroba,Brazil,South America,-11.5189,-37.5119,16549 +Indio,United States,North America,33.7346,-116.2346,371926 +Indramayu,Indonesia,Asia,-6.3528,108.3242,122400 +Ingelheim,Germany,Europe,49.9747,8.0564,36390 +Ingeniero Guillermo N. Juárez,Argentina,South America,-23.9,-61.85,6453 +Inglewood,United States,North America,33.9566,-118.3444,105575 +Ingolstadt,Germany,Europe,48.7631,11.425,142308 +Ingá,Brazil,South America,-7.2808,-35.6044,18144 +Inhambane,Mozambique,Africa,-23.865,35.3833,63837 +Inhambupe,Brazil,South America,-11.7839,-38.3528,33790 +Inhangapi,Brazil,South America,-1.43,-47.9169,10325 +Inhapi,Brazil,South America,-9.2214,-37.7486,18392 +Inhapim,Brazil,South America,-19.5489,-42.12,22692 +Inhuma,Brazil,South America,-6.6683,-41.7078,15319 +Inhumas,Brazil,South America,-16.3578,-49.4961,53259 +Innsbruck,Austria,Europe,47.2683,11.3933,130585 +Inongo,Congo (Kinshasa),Africa,-1.95,18.2667,45159 +Inowrocław,Poland,Europe,52.7931,18.2611,69576 +Inta,Russia,Asia,66.0833,60.1333,19372 +Invercargill,New Zealand,Oceania,-46.429,168.362,47625 +Inzá,Colombia,South America,2.55,-76.0667,32582 +Inácio Martins,Brazil,South America,-25.5708,-51.0789,9670 +Inírida,Colombia,South America,3.8653,-67.9239,20279 +Inđija,Serbia,Europe,45.0492,20.0792,47433 +Iowa City,United States,North America,41.6559,-91.5303,129897 +Ioánnina,Greece,Europe,39.6636,20.8522,64896 +Ipaba,Brazil,South America,-19.4139,-42.4189,17136 +Ipala,Guatemala,North America,14.6191,-89.6226,24024 +Ipameri,Brazil,South America,-17.7219,-48.16,25548 +Ipanema,Brazil,South America,-19.8008,-41.7128,19522 +Ipanguaçu,Brazil,South America,-5.4978,-36.855,14131 +Ipaporanga,Brazil,South America,-4.9,-40.7589,11575 +Ipatinga,Brazil,South America,-19.4678,-42.5369,227731 +Ipatovo,Russia,Asia,45.7167,42.0,25682 +Ipaumirim,Brazil,South America,-6.79,-38.7189,12083 +Ipauçu,Brazil,South America,-23.0569,-49.6267,15069 +Ipecaetá,Brazil,South America,-12.3,-39.3078,13709 +Iperó,Brazil,South America,-23.3503,-47.6886,33367 +Ipetumodu,Nigeria,Africa,7.4999,4.4472,135000 +Ipiales,Colombia,South America,0.8303,-77.6444,169864 +Ipiaú,Brazil,South America,-14.1369,-39.7339,40706 +Ipil,Philippines,Asia,7.7822,122.5867,89401 +Ipiranga,Brazil,South America,-25.0239,-50.5839,14142 +Ipiranga do Piauí,Brazil,South America,-6.8278,-41.7408,9420 +Ipirá,Brazil,South America,-12.1578,-39.7369,56876 +Ipixuna,Brazil,South America,-7.0508,-71.695,24311 +Ipoh,Malaysia,Asia,4.6,101.065,866772 +Ipojuca,Brazil,South America,-8.3989,-35.0639,97669 +Ipokia,Nigeria,Africa,6.5333,2.85,150426 +Iporá,Brazil,South America,-16.4419,-51.1178,35684 +Iporã,Brazil,South America,-24.0028,-53.7039,15746 +Ipu,Brazil,South America,-4.3257,-40.7103,26678 +Ipubi,Brazil,South America,-7.6519,-40.1489,29009 +Ipueiras,Brazil,South America,-4.5419,-40.7189,36798 +Ipuiúna,Brazil,South America,-22.0989,-46.19,9135 +Ipupiara,Brazil,South America,-11.82,-42.6139,9935 +Ipuã,Brazil,South America,-20.4381,-48.0122,15567 +Iqaluit,Canada,North America,63.7598,-68.5107,7740 +Iquique,Chile,South America,-20.2167,-70.15,188003 +Iquitos,Peru,South America,-3.75,-73.25,377609 +Iracema,Brazil,South America,-5.8167,-38.3,14001 +Iracemápolis,Brazil,South America,-22.5808,-47.5189,21967 +Iraci,Brazil,South America,-8.8078,-35.9519,13113 +Iracoubo,French Guiana,South America,5.4804,-53.22,1536 +Iramaia,Brazil,South America,-13.2858,-40.9508,10752 +Iranduba,Brazil,South America,-3.285,-60.1886,32303 +Irani,Brazil,South America,-27.025,-51.9019,10195 +Irapuato,Mexico,North America,20.6667,-101.35,592953 +Iraquara,Brazil,South America,-12.2489,-41.6189,23879 +Irará,Brazil,South America,-12.05,-38.7667,29173 +Irati,Brazil,South America,-25.4669,-50.6508,59250 +Irauçuba,Brazil,South America,-3.7458,-39.7828,23915 +Irbid,Jordan,Asia,32.55,35.85,569068 +Irbit,Russia,Asia,57.6667,63.0667,36587 +Irecê,Brazil,South America,-11.3042,-41.8558,73524 +Iretama,Brazil,South America,-24.4239,-52.1058,10684 +Irig,Serbia,Europe,45.1011,19.8583,4848 +Iringa,Tanzania,Africa,-7.77,35.69,202490 +Irituia,Brazil,South America,-1.7689,-47.4389,30955 +Irkutsk,Russia,Asia,52.2892,104.28,623736 +Irshava,Ukraine,Europe,48.3,23.05,9163 +Irupi,Brazil,South America,-20.345,-41.6408,10354 +Irvine,United States,North America,33.6772,-117.7738,308160 +Irving,United States,North America,32.8583,-96.9702,255036 +Irákleia,Greece,Europe,41.1833,23.2833,9946 +Irákleio,Greece,Europe,35.3403,25.1344,211370 +Isangel,Vanuatu,Oceania,-19.53,169.28,1200 +Isanlu,Nigeria,Africa,8.1667,5.8,200000 +Iscuandé,Colombia,South America,2.4444,-77.9717,8989 +Iseyin,Nigeria,Africa,7.9667,3.6,236000 +Isfana,Kyrgyzstan,Asia,39.8389,69.5276,34219 +Isfara,Tajikistan,Asia,40.1167,70.6333,274000 +Ishim,Russia,Asia,56.1167,69.5,67614 +Ishimbay,Russia,Asia,53.45,56.0333,64041 +Ishtixon Shahri,Uzbekistan,Asia,39.9664,66.4861,16000 +Isingiro,Uganda,Africa,-0.795,30.8153,34300 +Isiolo,Kenya,Africa,0.35,37.5833,45989 +Isiro,Congo (Kinshasa),Africa,2.7833,27.6167,182900 +Isla,Mexico,North America,18.0333,-95.5333,43349 +Isla Mujeres,Mexico,North America,21.2333,-86.7333,12642 +Isla Ratón,Venezuela,South America,5.1311,-67.8106,12612 +Islamabad,Pakistan,Asia,33.6931,73.0639,1014825 +Islip,United States,North America,40.7385,-73.1888,338784 +Ismailia,Egypt,Africa,30.5833,32.2667,429465 +Isnos,Colombia,South America,1.9333,-76.2333,28530 +Isparta,Turkey,Europe,37.7647,30.5567,247580 +Issoire,France,Europe,45.545,3.2497,15078 +Issoudun,France,Europe,46.9608,1.9944,10953 +Istanbul,Turkey,Europe,41.0136,28.955,14441000 +Istaravshan,Tajikistan,Asia,39.9108,69.0064,273500 +Istmina,Colombia,South America,5.1667,-76.6833,25981 +Istog,Kosovo,Europe,42.7808,20.4875,39289 +Istra,Russia,Asia,55.9167,36.8667,34971 +Istres,France,Europe,43.5151,4.9895,44044 +Isulan,Philippines,Asia,6.63,124.6,97490 +Itabaiana,Brazil,South America,-10.685,-37.4253,96142 +Itabaianinha,Brazil,South America,-11.2739,-37.79,40678 +Itabela,Brazil,South America,-16.575,-39.5528,28165 +Itaberaba,Brazil,South America,-12.5278,-40.3069,65073 +Itaberaí,Brazil,South America,-16.02,-49.81,44734 +Itaberá,Brazil,South America,-23.8619,-49.1372,18015 +Itabira,Brazil,South America,-19.6189,-43.2269,113343 +Itabirinha de Mantena,Brazil,South America,-18.5658,-41.2328,10362 +Itabirito,Brazil,South America,-20.2528,-43.8008,37901 +Itaboraí,Brazil,South America,-22.7475,-42.8558,224267 +Itabuna,Brazil,South America,-14.7858,-39.28,186708 +Itacarambi,Brazil,South America,-15.1019,-44.0919,17208 +Itacaré,Brazil,South America,-14.2778,-38.9969,18120 +Itacoatiara,Brazil,South America,-3.1428,-58.4439,103598 +Itaeté,Brazil,South America,-12.9858,-40.9728,13472 +Itagi,Brazil,South America,-14.1628,-40.0058,13803 +Itaguara,Brazil,South America,-20.3919,-44.4878,13846 +Itaguaçu,Brazil,South America,-19.8019,-40.8558,13589 +Itagüí,Colombia,South America,6.1667,-75.6167,276744 +Itainópolis,Brazil,South America,-7.4469,-41.4783,11551 +Itaipé,Brazil,South America,-17.4019,-41.6689,10463 +Itaitinga,Brazil,South America,-3.9694,-38.5281,38325 +Itaituba,Brazil,South America,-4.2758,-55.9839,94750 +Itaiópolis,Brazil,South America,-26.3358,-49.9058,22051 +Itajaí,Brazil,South America,-26.9078,-48.6619,264054 +Itajibá,Brazil,South America,-14.2839,-39.8428,15310 +Itajobi,Brazil,South America,-21.3178,-49.0539,16989 +Itajubá,Brazil,South America,-22.4258,-45.4528,93073 +Itajuípe,Brazil,South America,-14.6778,-39.375,18781 +Italva,Brazil,South America,-21.4208,-41.6908,14073 +Itamaracá,Brazil,South America,-7.7514,-34.8276,26672 +Itamaraju,Brazil,South America,-17.0392,-39.5311,64455 +Itamarandiba,Brazil,South America,-17.8569,-42.8589,32948 +Itamarati,Brazil,South America,-6.425,-68.2528,10937 +Itambacuri,Brazil,South America,-18.0364,-41.683,21042 +Itambé,Brazil,South America,-15.245,-40.6239,24394 +Itamogi,Brazil,South America,-21.0778,-47.0478,10770 +Itamonte,Brazil,South America,-22.2839,-44.87,14786 +Itanhandu,Brazil,South America,-22.2958,-44.935,15236 +Itanhaém,Brazil,South America,-24.1831,-46.7889,103102 +Itanhomi,Brazil,South America,-19.1719,-41.865,11128 +Itanhém,Brazil,South America,-17.1658,-40.33,17813 +Itaocara,Brazil,South America,-21.6789,-42.0819,22919 +Itapaci,Brazil,South America,-14.9508,-49.5489,21087 +Itapagipe,Brazil,South America,-19.9089,-49.3808,13690 +Itapagé,Brazil,South America,-3.6867,-39.5861,53067 +Itaparica,Brazil,South America,-12.8878,-38.6789,19789 +Itapebi,Brazil,South America,-15.9508,-39.5339,9174 +Itapecerica,Brazil,South America,-20.4728,-45.1258,21046 +Itapecerica da Serra,Brazil,South America,-23.7172,-46.8494,167236 +Itapecuru Mirim,Brazil,South America,-3.3928,-44.3589,42772 +Itapema,Brazil,South America,-27.0903,-48.6114,67338 +Itaperuna,Brazil,South America,-21.205,-41.8878,101041 +Itaperuçu,Brazil,South America,-25.2212,-49.3458,31217 +Itapetim,Brazil,South America,-7.3778,-37.19,13492 +Itapetinga,Brazil,South America,-15.2489,-40.2478,65897 +Itapetininga,Brazil,South America,-23.5917,-48.0531,157016 +Itapeva,Brazil,South America,-23.9819,-48.8758,89728 +Itapevi,Brazil,South America,-23.5489,-46.9342,223404 +Itapicuru,Brazil,South America,-11.3169,-38.2328,31679 +Itapipoca,Brazil,South America,-3.4939,-39.5789,131123 +Itapira,Brazil,South America,-22.4361,-46.8217,75234 +Itapiranga,Brazil,South America,-27.1689,-53.7119,16638 +Itapissuma,Brazil,South America,-7.7764,-34.8919,27749 +Itapitanga,Brazil,South America,-14.4228,-39.565,10279 +Itapiúna,Brazil,South America,-4.5639,-38.9219,17841 +Itaporanga,Brazil,South America,-7.3039,-38.15,23940 +Itaporanga d’Ajuda,Brazil,South America,-10.9978,-37.3108,34411 +Itapororoca,Brazil,South America,-6.83,-35.2469,18382 +Itaporã,Brazil,South America,-22.0789,-54.7889,24137 +Itapuranga,Brazil,South America,-15.5619,-49.9489,26113 +Itapuí,Brazil,South America,-22.2333,-48.7192,13328 +Itapé,Brazil,South America,-14.8978,-39.4208,10341 +Itaquaquecetuba,Brazil,South America,-23.4864,-46.3486,375011 +Itaqui,Brazil,South America,-29.1253,-56.5531,37489 +Itaquitinga,Brazil,South America,-7.6678,-35.1019,16554 +Itarana,Brazil,South America,-19.8739,-40.875,10597 +Itarantim,Brazil,South America,-15.66,-40.0658,17052 +Itararé,Brazil,South America,-24.1125,-49.3317,50105 +Itarema,Brazil,South America,-2.92,-39.915,30347 +Itariri,Brazil,South America,-24.2888,-47.1332,15528 +Itatiaia,Brazil,South America,-22.4914,-44.5592,30908 +Itatiaiuçu,Brazil,South America,-20.1969,-44.4208,12966 +Itatiba,Brazil,South America,-23.0058,-46.8389,122581 +Itatim,Brazil,South America,-12.7119,-39.6978,15737 +Itatinga,Brazil,South America,-23.1017,-48.6158,19738 +Itatira,Brazil,South America,-4.6072,-39.6541,20424 +Itatuba,Brazil,South America,-7.375,-35.6283,11016 +Itaueira,Brazil,South America,-7.6028,-43.0258,10323 +Itaí,Brazil,South America,-23.4178,-49.0906,27382 +Itaíba,Brazil,South America,-8.9478,-37.4228,32650 +Itaú de Minas,Brazil,South America,-20.7389,-46.7519,14406 +Itaúna,Brazil,South America,-20.075,-44.5758,97669 +Iten,Kenya,Africa,0.6703,35.5081,42312 +Itinga,Brazil,South America,-16.6128,-41.765,13745 +Itiquira,Brazil,South America,-17.2089,-54.15,12236 +Itirapina,Brazil,South America,-22.2528,-47.8228,17160 +Itiruçu,Brazil,South America,-13.5319,-40.15,10999 +Itiúba,Brazil,South America,-10.6908,-39.8528,33872 +Itororó,Brazil,South America,-15.1169,-40.07,16617 +Itu,Brazil,South America,-23.2642,-47.2992,167095 +Ituango,Colombia,South America,7.1717,-75.7642,23784 +Ituaçu,Brazil,South America,-13.8128,-41.2969,17914 +Ituberá,Brazil,South America,-13.7319,-39.1489,21902 +Ituiutaba,Brazil,South America,-18.9689,-49.465,102217 +Itumbiara,Brazil,South America,-18.42,-49.2178,107970 +Itupeva,Brazil,South America,-23.1531,-47.0578,62813 +Itupiranga,Brazil,South America,-5.135,-49.3269,49655 +Ituporanga,Brazil,South America,-27.4139,-49.6008,26525 +Iturama,Brazil,South America,-19.7278,-50.1958,38295 +Ituzaingó,Argentina,South America,-34.6667,-58.6667,129510 +Itzehoe,Germany,Europe,53.925,9.5164,32319 +Itápolis,Brazil,South America,-21.5958,-48.8128,39493 +Itānagar,India,Asia,27.1,93.62,59490 +Iuiú,Brazil,South America,-14.4139,-43.5539,11118 +Ivaiporã,Brazil,South America,-24.2478,-51.685,32720 +Ivanava,Belarus,Europe,52.1333,25.55,16172 +Ivanec,Croatia,Europe,46.2167,16.1167,12723 +Ivanić-Grad,Croatia,Europe,45.7081,16.3947,12982 +Ivanjica,Serbia,Europe,43.5811,20.2297,27767 +Ivano-Frankivsk,Ukraine,Europe,48.9228,24.7106,238196 +Ivanovka,Kyrgyzstan,Asia,42.8878,75.085,17513 +Ivanovo,Russia,Asia,56.9967,40.9819,361644 +Ivančna Gorica,Slovenia,Europe,45.9374,14.8029,2205 +Ivatsevichy,Belarus,Europe,52.7167,25.3333,22377 +Ivaí,Brazil,South America,-25.0108,-50.8589,13229 +Ivdel,Russia,Asia,60.6833,60.4167,14041 +Ivoti,Brazil,South America,-29.5908,-51.1608,22983 +Iwo,Nigeria,Africa,7.6333,4.1833,191377 +Ixchiguán,Guatemala,North America,15.1675,-91.9319,24926 +Ixhuatlancillo,Mexico,North America,18.9,-97.15,24896 +Ixhuatlán de Madero,Mexico,North America,20.6833,-98.0167,54132 +Ixhuatlán del Sureste,Mexico,North America,18.017,-94.38,15800 +Ixmiquilpan,Mexico,North America,20.4819,-99.2181,98654 +Ixtaczoquitlán,Mexico,North America,18.85,-97.0667,68823 +Ixtahuacán,Guatemala,North America,15.4167,-91.7667,56511 +Ixtapaluca,Mexico,North America,19.3186,-98.8822,542211 +Ixtapan de la Sal,Mexico,North America,18.8333,-99.6833,33541 +Ixtlahuaca,Mexico,North America,19.5689,-99.7669,153184 +Ixtlahuacán de los Membrillos,Mexico,North America,20.35,-103.1833,67969 +Ixtlahuacán del Río,Mexico,North America,20.8667,-103.25,19070 +Izhevsk,Russia,Asia,56.8333,53.1833,618776 +Izium,Ukraine,Europe,49.2128,37.2569,44979 +Izmail,Ukraine,Europe,45.3517,28.8364,69932 +Izobil’nyy,Russia,Asia,45.3667,41.7167,38177 +Izola,Slovenia,Europe,45.5344,13.6675,11682 +Izra‘,Syria,Asia,32.8671,36.2507,19158 +Iztapa,Guatemala,North America,13.9331,-90.7075,20401 +Izúcar de Matamoros,Mexico,North America,18.6,-98.4667,69413 +Içara,Brazil,South America,-28.7133,-49.3,57247 +Iúna,Brazil,South America,-20.3458,-41.5358,28590 +Iğdır,Turkey,Europe,39.9208,44.0444,101700 +Iława,Poland,Europe,53.5964,19.5656,32664 +I‘zāz,Syria,Asia,36.5866,37.0463,31534 +Jablah,Syria,Asia,35.3621,35.9276,75505 +Jablanica,Bosnia and Herzegovina,Europe,43.662,17.7618,10580 +Jaboatão dos Guararapes,Brazil,South America,-8.1128,-35.015,581556 +Jaboticatubas,Brazil,South America,-19.5139,-43.745,20406 +Jabuticabal,Brazil,South America,-21.255,-48.3219,71821 +Jacaltenango,Guatemala,North America,15.6667,-91.7333,44605 +Jacaraci,Brazil,South America,-14.85,-42.4328,14436 +Jacaraú,Brazil,South America,-6.6122,-35.2928,14450 +Jacareacanga,Brazil,South America,-6.2222,-57.7528,7590 +Jacareí,Brazil,South America,-23.305,-45.9658,240275 +Jacarèzinho,Brazil,South America,-23.1606,-49.9694,39322 +Jacaré,Brazil,South America,-18.9058,-40.0758,19539 +Jaciara,Brazil,South America,-15.965,-54.9678,28569 +Jacinto,Brazil,South America,-16.1439,-40.2928,11042 +Jacinto Machado,Brazil,South America,-28.9969,-49.7639,10624 +Jackson,United States,North America,32.3157,-90.2125,331332 +Jacksonville,United States,North America,30.3322,-81.6749,1303156 +Jacmel,Haiti,North America,18.235,-72.537,137966 +Jacobabad,Pakistan,Asia,28.2769,68.4514,200815 +Jacobina,Brazil,South America,-11.1808,-40.5178,82590 +Jacqueville,Côte d’Ivoire,Africa,5.2,-4.4167,49694 +Jacundá,Brazil,South America,-4.4508,-49.1158,37707 +Jacupiranga,Brazil,South America,-24.6925,-48.0022,16097 +Jacutinga,Brazil,South America,-22.2858,-46.6119,25525 +Jaffna,Sri Lanka,Asia,9.6647,80.0167,88138 +Jagodina,Serbia,Europe,43.975,21.2564,34892 +Jaguapitã,Brazil,South America,-23.1128,-51.5319,15122 +Jaguaquara,Brazil,South America,-13.5308,-39.9708,45964 +Jaguarari,Brazil,South America,-10.26,-40.1958,32703 +Jaguaretama,Brazil,South America,-5.6128,-38.7669,17232 +Jaguari,Brazil,South America,-29.4969,-54.69,10579 +Jaguariaíva,Brazil,South America,-24.2508,-49.7058,35141 +Jaguaribara,Brazil,South America,-5.6578,-38.62,10356 +Jaguaribe,Brazil,South America,-5.8908,-38.6219,33726 +Jaguaripe,Brazil,South America,-13.1128,-38.8958,17659 +Jaguariúna,Brazil,South America,-22.68,-46.99,58722 +Jaguaruana,Brazil,South America,-4.8339,-37.7808,31701 +Jaguaruna,Brazil,South America,-28.615,-49.0258,20375 +Jaguarão,Brazil,South America,-32.5658,-53.3758,26603 +Jagüey Grande,Cuba,North America,22.5292,-81.1325,87771 +Jahrom,Iran,Asia,28.5147,53.5731,141634 +Jaicós,Brazil,South America,-7.3592,-41.1378,19170 +Jaipur,India,Asia,26.9,75.8,3073350 +Jajce,Bosnia and Herzegovina,Europe,44.3417,17.2694,12000 +Jakar,Bhutan,Asia,27.55,90.7333,6243 +Jakarta,Indonesia,Asia,-6.175,106.8275,33756000 +Jakobstad,Finland,Europe,63.6667,22.7,19475 +Jalacingo,Mexico,North America,19.6,-97.2167,44488 +Jalal-Abad,Kyrgyzstan,Asia,40.9375,72.9861,89004 +Jalapa,Guatemala,North America,14.6333,-89.9833,192676 +Jales,Brazil,South America,-20.2689,-50.5458,48776 +Jalingo,Nigeria,Africa,8.9,11.3667,660213 +Jalpa de Méndez,Mexico,North America,18.1764,-93.0631,16679 +Jalpan,Mexico,North America,21.2167,-99.4725,27343 +Jalpatagua,Guatemala,North America,14.1333,-90.0167,31601 +Jaltenango,Mexico,North America,15.8725,-92.725,11875 +Jaltenco,Mexico,North America,19.7511,-99.0931,26359 +Jalālābād,Afghanistan,Asia,34.4342,70.4478,263312 +Jam,Iran,Asia,27.8211,52.3306,31436 +Jamapa,Mexico,North America,19.0411,-96.2411,11132 +Jamay,Mexico,North America,20.29,-102.71,24753 +Jambaló,Colombia,South America,2.85,-76.3167,16353 +Jambi,Indonesia,Asia,-1.59,103.61,612162 +Jamestown,"Saint Helena, Ascension, and Tristan da Cunha",Africa,-15.9251,-5.7179,714 +Jamiltepec,Mexico,North America,16.2833,-97.8167,19201 +Jamjamāl,Iraq,Asia,35.5333,44.8342,65300 +Jammu,India,Asia,32.73,74.87,502197 +Jamshoro,Pakistan,Asia,25.4244,68.2811,80000 +Jamundí,Colombia,South America,3.2667,-76.55,130114 +Janakkala,Finland,Europe,60.9167,24.65,16123 +Janakpur,Nepal,Asia,26.7122,85.9217,97776 +Janaúba,Brazil,South America,-15.8025,-43.3089,66803 +Jandaia do Sul,Brazil,South America,-23.6028,-51.6428,21408 +Jandaíra,Brazil,South America,-11.5639,-37.7839,9285 +Jandira,Brazil,South America,-23.5278,-46.9028,126356 +Janjanbureh,"Gambia, The",Africa,13.5341,-14.7662,3584 +Januária,Brazil,South America,-15.4886,-44.36,67852 +Januário Cicco,Brazil,South America,-6.1578,-35.6008,9051 +Janów Lubelski,Poland,Europe,50.7167,22.4167,11308 +Japaratinga,Brazil,South America,-9.2544,-35.2578,9219 +Japaratuba,Brazil,South America,-10.5933,-36.9403,16864 +Japeri,Brazil,South America,-22.6431,-43.6533,105548 +Japoatã,Brazil,South America,-10.3467,-36.8011,12938 +Japurá,Brazil,South America,-23.47,-52.5528,9144 +Jaqueira,Brazil,South America,-8.7269,-35.7928,10247 +Jaqué,Panama,North America,7.519,-78.166,2386 +Jaraguá,Brazil,South America,-15.7569,-49.3339,45223 +Jaraguá do Sul,Brazil,South America,-26.4858,-49.0669,182660 +Jaral del Progreso,Mexico,North America,20.3667,-101.0667,36584 +Jaramānā,Syria,Asia,33.4862,36.3461,114363 +Jarash,Jordan,Asia,32.2806,35.8972,50745 +Jardim,Brazil,South America,-7.5819,-39.2978,27411 +Jardim Alegre,Brazil,South America,-24.1789,-51.6919,12004 +Jardim de Piranhas,Brazil,South America,-6.3789,-37.3519,13977 +Jardim do Seridó,Brazil,South America,-6.5839,-36.7739,11655 +Jardinópolis,Brazil,South America,-21.0178,-47.7639,41799 +Jardín,Colombia,South America,5.5986,-75.8194,13748 +Jarinu,Brazil,South America,-23.1014,-46.7283,30617 +Jarocin,Poland,Europe,51.9667,17.5,26399 +Jarosław,Poland,Europe,50.0186,22.6797,36303 +Jaru,Brazil,South America,-10.4389,-62.4664,50591 +Jaruco,Cuba,North America,23.0428,-82.0094,24162 +Jarābulus,Syria,Asia,36.8175,38.0111,11570 +Jastrebarsko,Croatia,Europe,45.6667,15.65,14562 +Jastrzębie-Zdrój,Poland,Europe,49.95,18.5833,82788 +Jasło,Poland,Europe,49.7478,21.4714,33830 +Jataìzinho,Brazil,South America,-23.2539,-50.98,11813 +Jataí,Brazil,South America,-17.8808,-51.7139,105729 +Jataúba,Brazil,South America,-7.99,-36.4958,15843 +Jatibonico,Cuba,North America,21.9464,-79.1675,42959 +Javānrūd,Iran,Asia,34.8078,46.4939,54354 +Jawhar,Somalia,Africa,2.7833,45.5,111308 +Jawor,Poland,Europe,51.05,16.2,21494 +Jaworzno,Poland,Europe,50.2044,19.27,88998 +Jayapura,Indonesia,Asia,-2.533,140.717,413283 +Jayrūd,Syria,Asia,33.8071,36.7386,39903 +Jaén,Spain,Europe,37.7667,-3.7711,112074 +Jaú,Brazil,South America,-22.2958,-48.5578,133497 +Ja‘ār,Yemen,Asia,13.2231,45.3056,29495 +Jbaïl,Lebanon,Asia,34.1236,35.6519,40000 +Jefferson City,United States,North America,38.5676,-92.1759,42565 +Jegunovce,North Macedonia,Europe,42.0731,21.1231,846 +Jeju,"Korea, South",Asia,33.513,126.523,435413 +Jelenia Góra,Poland,Europe,50.9033,15.7344,77305 +Jelgava,Latvia,Europe,56.6522,23.7244,54701 +Jember,Indonesia,Asia,-8.1727,113.6873,298585 +Jena,Germany,Europe,50.9272,11.5861,110791 +Jendouba,Tunisia,Africa,36.4833,8.7833,47551 +Jeonju,"Korea, South",Asia,35.8167,127.15,341545 +Jepara,Indonesia,Asia,-6.5333,110.6667,81920 +Jequeri,Brazil,South America,-20.4558,-42.6658,12419 +Jequitinhonha,Brazil,South America,-16.4339,-41.0028,22902 +Jequié,Brazil,South America,-13.8578,-40.0839,147202 +Jeremoabo,Brazil,South America,-10.0669,-38.35,37626 +Jerez,Guatemala,North America,14.1,-89.75,31601 +Jerez de García Salinas,Mexico,North America,22.6481,-102.9903,57610 +Jericoacoara,Brazil,South America,-2.8,-40.4833,25555 +Jericó,Colombia,South America,5.7833,-75.7833,12103 +Jersey City,United States,North America,40.7184,-74.0686,289691 +Jerusalem,Israel,Asia,31.7789,35.2256,936425 +Jerônimo Monteiro,Brazil,South America,-20.7894,-41.395,12265 +Jesenice,Slovenia,Europe,46.4366,14.0602,13702 +Jesús María,Mexico,North America,21.9667,-102.35,43012 +Jesús Menéndez,Cuba,North America,21.1633,-76.4775,46729 +Jever,Germany,Europe,53.5744,7.9008,14913 +Jhang City,Pakistan,Asia,31.2694,72.3161,414131 +Jhelum,Pakistan,Asia,32.9425,73.7256,190425 +Ji-Paraná,Brazil,South America,-10.8853,-61.9517,106800 +Jiaji,China,Asia,19.2431,110.4642,515700 +Jiamusi,China,Asia,46.8,130.319,2156505 +Jiancheng,China,Asia,30.38,104.53,1045900 +Jiangguanchi,China,Asia,34.037,113.852,4307488 +Jiangmen,China,Asia,22.5789,113.0815,4630300 +Jiangna,China,Asia,23.6092,104.3731,119665 +Jiannan,China,Asia,31.3333,104.2,439958 +Jianshe,China,Asia,34.2137,113.7629,687130 +Jiantang,China,Asia,27.8333,99.7,95163 +Jian’ou,China,Asia,27.0333,118.3167,434451 +Jiaoxiyakou,China,Asia,26.1283,102.45,14342 +Jiaxing,China,Asia,30.747,120.756,1518654 +Jiayuguan,China,Asia,39.7732,98.2882,312663 +Jiblah,Yemen,Asia,13.9167,44.15,15431 +Jieyang,China,Asia,23.551,116.3727,5577814 +Jiguaní,Cuba,North America,20.3731,-76.4222,58982 +Jihlava,Czechia,Europe,49.4003,15.5906,53986 +Jijel,Algeria,Africa,36.8167,5.75,131513 +Jijiga,Ethiopia,Africa,9.35,42.8,125876 +Jilikŭl,Tajikistan,Asia,37.4928,68.5286,25382 +Jilotepec,Mexico,North America,19.9519,-99.5328,87927 +Jimaguayú,Cuba,North America,21.2422,-77.8263,19687 +Jimaní,Dominican Republic,North America,18.4833,-71.85,11414 +Jiménez,Mexico,North America,27.13,-104.9067,48859 +Jinan,China,Asia,36.6702,117.0207,5606374 +Jinbi,China,Asia,25.7086,101.3368,93281 +Jinchang,China,Asia,38.5214,102.188,438026 +Jincheng,China,Asia,35.491,112.852,2194545 +Jindayris,Syria,Asia,36.395,36.6889,13661 +Jingcheng,China,Asia,32.009,120.263,663408 +Jingdezhen,China,Asia,29.2917,117.1986,1618979 +Jinghai,China,Asia,38.9294,116.9169,154325 +Jinghong,China,Asia,22.009,100.797,642737 +Jingping,China,Asia,39.5189,112.2837,101297 +Jingzhou,China,Asia,37.6911,116.2672,72806 +Jinhe,China,Asia,22.7667,103.25,85350 +Jinhua,China,Asia,29.079,119.647,1463990 +Jinja,Uganda,Africa,0.4233,33.2039,300000 +Jinjiang,China,Asia,19.7288,110.0085,142948 +Jinotega,Nicaragua,North America,13.0884,-85.9994,99382 +Jinotepe,Nicaragua,North America,11.85,-86.2,42109 +Jinshan,China,Asia,25.1475,101.9764,366512 +Jinshi,China,Asia,29.5,111.908,212739 +Jinzhong,China,Asia,26.3504,103.4167,5170 +Jin’an,China,Asia,32.6394,103.5806,12361 +Jin’e,China,Asia,29.3394,105.2875,568900 +Jipijapa,Ecuador,South America,-1.3486,-80.5786,45382 +Jiquipilas,Mexico,North America,16.6683,-93.6444,11596 +Jiquiriçá,Brazil,South America,-13.2569,-39.5719,13629 +Jiquílpan de Juárez,Mexico,North America,19.59,-102.43,24662 +Jishi,China,Asia,35.8461,102.4817,33022 +Jisr ash Shughūr,Syria,Asia,35.8142,36.3198,44322 +Jitaúna,Brazil,South America,-14.0189,-39.8889,14355 +Jiujiang,China,Asia,29.661,115.954,4600276 +Jiutepec,Mexico,North America,18.8833,-99.1667,215357 +Jixi,China,Asia,45.295,130.969,1502060 +Jizhou,China,Asia,37.5455,115.5663,124716 +Jizzax,Uzbekistan,Asia,40.1158,67.8422,202200 +Ji’an,China,Asia,27.0912,114.9668,4469176 +Ji’an Shi,China,Asia,41.1253,126.194,169220 +Joanópolis,Brazil,South America,-22.9303,-46.2756,12725 +Joaquim Gomes,Brazil,South America,-9.1328,-35.7478,17150 +Joaquim Nabuco,Brazil,South America,-8.6239,-35.5328,13269 +Joaquim Pires,Brazil,South America,-3.5078,-42.1978,13886 +Joaquim Távora,Brazil,South America,-23.4989,-49.905,11945 +Joaquín V. González,Argentina,South America,-25.0833,-64.1833,21045 +Joaçaba,Brazil,South America,-27.1778,-51.505,30146 +Joaíma,Brazil,South America,-16.6542,-41.0306,14941 +Jobabo,Cuba,North America,20.9075,-77.2819,47350 +Jocotenango,Guatemala,North America,14.5819,-90.7436,23492 +Jocotepec,Mexico,North America,20.2863,-103.4304,42164 +Jocotitlán,Mexico,North America,19.7072,-99.7867,65291 +Jocotán,Guatemala,North America,14.8167,-89.3833,75578 +Joensuu,Finland,Europe,62.6,29.75,78175 +Joghtāy,Iran,Asia,36.6383,57.0717,9268 +Johannesburg,South Africa,Africa,-26.2044,28.0456,7860781 +Johnson City,United States,North America,36.3406,-82.3806,129818 +Johor Bahru,Malaysia,Asia,1.482,103.7281,497097 +Joinvile,Brazil,South America,-26.3039,-48.8458,429604 +Jolfā,Iran,Asia,38.9386,45.6253,8810 +Joliet,United States,North America,41.5188,-88.1499,149785 +Jolo,Philippines,Asia,6.0,121.0,137266 +Jomboy Shahri,Uzbekistan,Asia,39.6989,67.0933,17400 +Jonava,Lithuania,Europe,55.0722,24.2806,27134 +Jondor Shaharchasi,Uzbekistan,Asia,39.7333,64.1833,8700 +Joniškis,Lithuania,Europe,56.2333,23.6167,8466 +Jonuta,Mexico,North America,18.0833,-92.1333,30798 +Joquicingo,Mexico,North America,19.0556,-99.5467,11042 +Jordan,Philippines,Asia,10.6,122.6,39566 +Jordânia,Brazil,South America,-15.9,-40.1778,10304 +Jos,Nigeria,Africa,9.9167,8.8903,622802 +José Bonifácio,Brazil,South America,-21.0528,-49.6878,36633 +José C. Paz,Argentina,South America,-34.5167,-58.7667,216637 +José Cardel,Mexico,North America,19.3667,-96.3667,19341 +José María Ezeiza,Argentina,South America,-34.8333,-58.5167,160219 +José María Morelos,Mexico,North America,19.75,-88.7,11750 +José de Freitas,Brazil,South America,-4.7558,-42.5758,42559 +Joubb Jannîne,Lebanon,Asia,33.6333,35.7833,14728 +Joure,Netherlands,Europe,52.965,5.8,13090 +Jovellanos,Cuba,North America,22.8106,-81.1981,58685 +Joyabaj,Guatemala,North America,14.995,-90.8075,107634 +João Alfredo,Brazil,South America,-7.8558,-35.5878,27725 +João Câmara,Brazil,South America,-5.5378,-35.82,33290 +João Lisboa,Brazil,South America,-5.4478,-47.4008,24709 +João Monlevade,Brazil,South America,-19.81,-43.1736,73610 +João Neiva,Brazil,South America,-19.7578,-40.3858,14079 +João Pessoa,Brazil,South America,-7.12,-34.88,833932 +João Pinheiro,Brazil,South America,-17.7442,-46.1739,46801 +João Teves,Cabo Verde,Africa,15.0667,-23.5917,1699 +Joünié,Lebanon,Asia,33.9697,35.6156,102221 +Juan Aldama,Mexico,North America,24.2911,-103.3939,14162 +Juan Rodríguez Clara,Mexico,North America,18.0,-95.4,38895 +Juan de Acosta,Colombia,South America,10.8333,-75.0333,14184 +Juanacatlán,Mexico,North America,20.5,-103.1667,30855 +Juangriego,Venezuela,South America,11.0833,-63.9667,28256 +Juara,Brazil,South America,-11.255,-57.52,34906 +Juatuba,Brazil,South America,-19.9519,-44.3428,30716 +Juazeirinho,Brazil,South America,-7.0678,-36.5778,17007 +Juazeiro,Brazil,South America,-9.4139,-40.5028,174567 +Juazeiro do Norte,Brazil,South America,-7.2128,-39.315,286120 +Juba,South Sudan,Africa,4.83,31.58,459342 +Jucati,Brazil,South America,-8.7058,-36.4889,11517 +Juchipila,Mexico,North America,21.4103,-103.1175,12251 +Juchique de Ferrer,Mexico,North America,19.8333,-96.7,16360 +Juchitepec,Mexico,North America,19.0997,-98.8792,29436 +Juchitán de Zaragoza,Mexico,North America,16.4333,-95.0167,74825 +Jucurutu,Brazil,South America,-6.0339,-37.02,17793 +Jucuruçu,Brazil,South America,-16.8428,-40.1589,9655 +Jucás,Brazil,South America,-6.525,-39.5278,23922 +Judenburg,Austria,Europe,47.1725,14.6603,9614 +Juigalpa,Nicaragua,North America,12.1,-85.3667,53798 +Juiz de Fora,Brazil,South America,-21.7642,-43.3503,540756 +Julu,China,Asia,37.2075,115.0233,90311 +Juma Shahri,Uzbekistan,Asia,39.7161,66.6642,21500 +Jundiaí,Brazil,South America,-23.1864,-46.8842,401896 +Juneau,United States,North America,58.4546,-134.1739,31969 +Junik,Kosovo,Europe,42.4758,20.2772,6084 +Junqueiro,Brazil,South America,-9.9253,-36.4758,24722 +Junqueirópolis,Brazil,South America,-21.5147,-51.4336,20831 +Junín,Argentina,South America,-34.5833,-60.95,85420 +Junín de los Andes,Argentina,South America,-39.9167,-71.0667,18716 +Jupi,Brazil,South America,-8.7119,-36.415,15329 +Juquitiba,Brazil,South America,-23.9319,-47.0686,30642 +Juquiá,Brazil,South America,-24.3208,-47.6347,19359 +Juradó,Colombia,South America,7.1114,-77.7714,3239 +Jurbarkas,Lithuania,Europe,55.0833,22.7667,10571 +Jurema,Brazil,South America,-8.7178,-36.1358,13648 +Juripiranga,Brazil,South America,-7.3728,-35.2378,10012 +Jurovski Dol,Slovenia,Europe,46.6064,15.7847,361 +Juru,Brazil,South America,-7.5369,-37.8186,9849 +Juruaia,Brazil,South America,-21.2528,-46.5769,11084 +Jurupa Valley,United States,North America,34.001,-117.4706,106244 +Juruá,Brazil,South America,-3.4808,-66.0689,10742 +Juršinci,Slovenia,Europe,46.4847,15.9714,381 +Jussara,Brazil,South America,-15.865,-50.8678,18266 +Jutaí,Brazil,South America,-2.7469,-66.7669,25172 +Jutiapa,Guatemala,North America,14.2828,-89.8925,167049 +Juticalpa,Honduras,North America,14.6672,-86.2196,124828 +Juventino Rosas,Mexico,North America,20.65,-101.0,42264 +Juárez,Mexico,North America,31.745,-106.485,1321004 +Juína,Brazil,South America,-11.3778,-58.7408,45869 +Jwaneng,Botswana,Africa,-24.6017,24.7281,18784 +Jyväskylä,Finland,Europe,62.2417,25.7417,147856 +Jáltipan de Morelos,Mexico,North America,17.9703,-94.7144,41644 +Jászberény,Hungary,Europe,47.5,19.9167,25844 +Jämsä,Finland,Europe,61.8667,25.1833,19182 +Järvenpää,Finland,Europe,60.475,25.0917,46490 +Jérémie,Haiti,North America,18.6339,-74.1184,30917 +Jõgeva,Estonia,Europe,58.7469,26.4,4851 +Jõhvi,Estonia,Europe,59.3575,27.4269,10482 +Jönköping,Sweden,Europe,57.7713,14.165,112766 +Júlio de Castilhos,Brazil,South America,-29.2269,-53.6819,18226 +Jājarm,Iran,Asia,36.95,56.38,19580 +Jāsim,Syria,Asia,32.9912,36.0609,31683 +Jāsk,Iran,Asia,25.6531,57.7817,16860 +Jāzān,Saudi Arabia,Asia,16.8892,42.5611,127743 +Jēkabpils,Latvia,Europe,56.4975,25.8664,21150 +Jędrzejów,Poland,Europe,50.6333,20.3,14592 +Jīroft,Iran,Asia,28.6797,57.7447,130429 +Jūrmala,Latvia,Europe,56.9665,23.7221,52154 +Kaabong,Uganda,Africa,3.52,34.12,13400 +Kaarina,Finland,Europe,60.4,22.3667,36389 +Kaatsheuvel,Netherlands,Europe,51.6667,5.0667,16600 +Kabale,Uganda,Africa,-1.25,29.99,53200 +Kabasalan,Philippines,Asia,7.7968,122.7627,46884 +Kabataş,Turkey,Europe,40.75,37.45,10119 +Kaberamaido,Uganda,Africa,1.7667,33.1522,3400 +Kabinda,Congo (Kinshasa),Africa,-6.13,24.48,572000 +Kabugao,Philippines,Asia,18.0239,121.1833,16215 +Kabul,Afghanistan,Asia,34.5253,69.1783,4273156 +Kabwe,Zambia,Africa,-14.4333,28.45,288598 +Kabūdarāhang,Iran,Asia,35.2069,48.7228,19216 +Kachkanar,Russia,Asia,58.7,59.4833,36910 +Kadiolo,Mali,Africa,10.55,-5.7667,52932 +Kadirli,Turkey,Europe,37.3697,36.1,98469 +Kadugli,Sudan,Africa,11.01,29.7,92674 +Kaduna,Nigeria,Africa,10.5167,7.4333,760084 +Kadınhanı,Turkey,Europe,38.2397,32.2114,31206 +Kaeng Khoi,Thailand,Asia,14.5862,100.9976,12752 +Kaeng Khro,Thailand,Asia,16.1086,102.2581,9012 +Kaesŏng,"Korea, North",Asia,37.9667,126.55,192578 +Kaffrine,Senegal,Africa,14.1016,-15.5467,39357 +Kafr Baţnā,Syria,Asia,33.5117,36.3727,22535 +Kafr Nubl,Syria,Asia,35.6138,36.5645,30000 +Kafr Takhārīm,Syria,Asia,36.1168,36.5152,10084 +Kafr Zaytā,Syria,Asia,35.3743,36.6033,17052 +Kafr ash Shaykh,Egypt,Africa,31.1,30.95,147393 +Kaga Bandoro,Central African Republic,Africa,7.0,19.1833,24661 +Kagadi,Uganda,Africa,0.9411,30.8108,22813 +Kagoshima,Japan,Asia,31.5969,130.5572,593754 +Kaharlyk,Ukraine,Europe,49.8522,30.8092,13133 +Kahnūj,Iran,Asia,27.9406,57.6992,52624 +Kahramanmaraş,Turkey,Europe,37.5833,36.9333,571266 +Kaihua,China,Asia,23.387,104.233,623772 +Kailua,United States,North America,21.392,-157.7396,115634 +Kaimana,Indonesia,Asia,-3.6444,133.6953,44332 +Kairouan,Tunisia,Africa,35.6772,10.1008,210313 +Kaisarianí,Greece,Europe,37.9683,23.7583,26269 +Kaiserslautern,Germany,Europe,49.4447,7.7689,101486 +Kaiwen,China,Asia,27.1548,99.8371,13400 +Kaiyuan,China,Asia,36.0193,113.86,950939 +Kaiyun,China,Asia,27.2399,112.8639,110964 +Kaišiadorys,Lithuania,Europe,54.8653,24.4682,8334 +Kajaani,Finland,Europe,64.2311,27.7194,36513 +Kaji,China,Asia,26.0285,102.7888,13005 +Kajiado,Kenya,Africa,-1.85,36.7833,24678 +Kakamega,Kenya,Africa,0.2833,34.75,91778 +Kakata,Liberia,Africa,6.53,-10.3517,33945 +Kakhovka,Ukraine,Europe,46.7978,33.475,34749 +Kalabahi,Indonesia,Asia,-8.2167,124.5167,19210 +Kalach-na-Donu,Russia,Asia,48.7,43.5167,24277 +Kalajoki,Finland,Europe,64.2667,23.95,12372 +Kalamariá,Greece,Europe,40.5833,22.95,92248 +Kalamazoo,United States,North America,42.2749,-85.5882,204408 +Kalampáka,Greece,Europe,39.7081,21.6284,11492 +Kalamáta,Greece,Europe,37.0378,22.1111,37781 +Kalanchak,Ukraine,Europe,46.2522,33.2906,8977 +Kalangala,Uganda,Africa,-0.3214,32.2919,5200 +Kalasin,Thailand,Asia,16.4342,103.5092,34429 +Kalat,Pakistan,Asia,29.03,66.589,22879 +Kale,Turkey,Europe,37.4392,28.8453,19202 +Kalecik,Turkey,Europe,40.0972,33.4083,12794 +Kalemie,Congo (Kinshasa),Africa,-5.9128,29.1906,146974 +Kaleybar,Iran,Asia,38.8664,47.0406,9324 +Kalibo,Philippines,Asia,11.7072,122.37,89127 +Kaliningrad,Russia,Asia,54.7003,20.4531,489584 +Kalinkavichy,Belarus,Europe,52.125,29.3333,36941 +Kaliro,Uganda,Africa,0.8944,33.4994,16796 +Kalisz,Poland,Europe,51.7575,18.08,95905 +Kalkara,Malta,Europe,35.8892,14.5294,3014 +Kallithéa,Greece,Europe,37.95,23.7,97616 +Kalmar,Sweden,Europe,56.6643,16.3656,42622 +Kalmiuske,Ukraine,Europe,47.6667,38.0667,11422 +Kalocsa,Hungary,Europe,46.5333,18.9856,14433 +Kaluga,Russia,Asia,54.55,36.2833,331842 +Kalundborg,Denmark,Europe,55.682,11.1004,16486 +Kalush,Ukraine,Europe,49.0442,24.3597,65088 +Kalvarija,Lithuania,Europe,54.4147,23.2281,4025 +Kalyazin,Russia,Asia,57.2333,37.8833,12621 +Kalynivka,Ukraine,Europe,49.4472,28.5231,18492 +Kalávryta,Greece,Europe,38.0333,22.1167,9223 +Kalývia Thorikoú,Greece,Europe,37.8333,23.9167,15486 +Kalāleh,Iran,Asia,37.3789,55.4889,36176 +Kalār,Iraq,Asia,34.6281,45.3185,250000 +Kalārdasht,Iran,Asia,36.5056,51.1589,13401 +Kamalasai,Thailand,Asia,16.3384,103.5756,9942 +Kaman,Turkey,Europe,39.3575,33.7239,21340 +Kambarka,Russia,Asia,56.2667,54.2167,10080 +Kamenicë,Kosovo,Europe,42.5781,21.5803,36085 +Kamensk-Shakhtinskiy,Russia,Asia,48.3206,40.2611,83286 +Kamensk-Ural’skiy,Russia,Asia,56.4,61.9333,161085 +Kameshkovo,Russia,Asia,56.35,41.0,12028 +Kamianets-Podilskyi,Ukraine,Europe,48.6833,26.5833,96896 +Kamianka-Dniprovska,Ukraine,Europe,47.4628,34.4061,12117 +Kamianske,Ukraine,Europe,48.5167,34.6133,226845 +Kamienna Góra,Poland,Europe,50.7833,16.0333,17998 +Kamień Pomorski,Poland,Europe,53.97,14.7725,8564 +Kamin-Kashyrskyi,Ukraine,Europe,51.62,24.9653,11713 +Kamina,Congo (Kinshasa),Africa,-8.7386,24.9906,156761 +Kamnik,Slovenia,Europe,46.2257,14.612,13800 +Kampala,Uganda,Africa,0.3136,32.5811,1680600 +Kampen,Netherlands,Europe,52.55,5.9,54474 +Kamphaeng Phet,Thailand,Asia,16.4811,99.5222,28817 +Kampong Cham,Cambodia,Asia,12.0,105.45,118242 +Kampong Chhnang,Cambodia,Asia,12.25,104.6667,41700 +Kampong Speu,Cambodia,Asia,11.452,104.519,33231 +Kampong Thom,Cambodia,Asia,12.7,104.9,31871 +Kampong Trach,Cambodia,Asia,10.5585,104.4675,83061 +Kampot,Cambodia,Asia,10.6,104.1667,39186 +Kamuli,Uganda,Africa,0.945,33.125,67800 +Kamwenge,Uganda,Africa,0.1861,30.4539,19240 +Kamyshin,Russia,Asia,50.0833,45.4,105187 +Kamyshlov,Russia,Asia,56.8333,62.7167,26933 +Kamyzyak,Russia,Asia,46.1167,48.0833,16154 +Kanal,Slovenia,Europe,46.088,13.6347,1142 +Kananga,Congo (Kinshasa),Africa,-5.897,22.4488,1971704 +Kanasín,Mexico,North America,20.9344,-89.5578,139753 +Kanata,Canada,North America,45.3333,-75.9,137118 +Kanazawa,Japan,Asia,36.5611,136.6564,462479 +Kanchanaburi,Thailand,Asia,14.0194,99.5311,25651 +Kanchanadit,Thailand,Asia,9.1661,99.4701,9492 +Kandahār,Afghanistan,Asia,31.62,65.7158,614254 +Kandalaksha,Russia,Asia,67.1569,32.4117,28438 +Kandangan,Indonesia,Asia,-2.7833,115.2667,49604 +Kandi,Benin,Africa,11.1286,2.9369,177683 +Kandy,Sri Lanka,Asia,7.2931,80.635,120087 +Kandıra,Turkey,Europe,41.0722,30.1611,52874 +Kangaba,Mali,Africa,11.9333,-8.4167,17232 +Kangal,Turkey,Europe,39.2367,37.3886,8948 +Kangar,Malaysia,Asia,6.433,100.19,48898 +Kangasala,Finland,Europe,61.4667,24.0667,33601 +Kangbao,China,Asia,41.8511,114.6176,63779 +Kanggye,"Korea, North",Asia,40.9667,126.6,251971 +Kangān,Iran,Asia,27.8392,52.0608,60187 +Kanifing,"Gambia, The",Africa,13.4475,-16.6725,382096 +Kanjiža,Serbia,Europe,46.0667,20.05,9871 +Kankaanpää,Finland,Europe,61.8,22.4,12394 +Kankan,Guinea,Africa,10.3833,-9.3,198013 +Kano,Nigeria,Africa,12.0,8.5167,4224966 +Kanoni,Uganda,Africa,0.1728,31.9058,12439 +Kansas City,United States,North America,39.1238,-94.5541,1686807 +Kansk,Russia,Asia,56.2,95.7167,86816 +Kant,Kyrgyzstan,Asia,42.8833,74.85,23249 +Kantang,Thailand,Asia,7.4054,99.5156,12533 +Kantharalak,Thailand,Asia,14.6406,104.6499,19392 +Kanungu,Uganda,Africa,-0.8969,29.7756,15056 +Kanye,Botswana,Africa,-24.9833,25.35,48028 +Kaohsiung,Taiwan,Asia,22.615,120.2975,2737660 +Kaolack,Senegal,Africa,14.1389,-16.0764,172305 +Kapaklı,Turkey,Europe,41.3333,27.9667,137514 +Kapan,Armenia,Europe,39.2011,46.415,43190 +Kapchorwa,Uganda,Africa,1.4,34.45,12900 +Kapelle,Netherlands,Europe,51.5,3.95,12882 +Kaposvár,Hungary,Europe,46.3667,17.7833,58830 +Kappeln,Germany,Europe,54.6614,9.9311,8683 +Kapsabet,Kenya,Africa,0.2,35.1,17918 +Kapuvár,Hungary,Europe,47.6,17.0333,10024 +Kapyl,Belarus,Europe,53.15,27.0917,9985 +Kara-Bak,Kyrgyzstan,Asia,40.1583,70.8167,8923 +Kara-Balta,Kyrgyzstan,Asia,42.8306,73.8857,48278 +Kara-Kulja,Kyrgyzstan,Asia,40.6322,73.5925,15616 +Kara-Suu,Kyrgyzstan,Asia,40.7,72.8833,20862 +Karabash,Russia,Asia,55.4833,60.2167,10339 +Karabulak,Russia,Asia,43.3167,44.9167,44323 +Karaburun,Turkey,Europe,38.6333,26.5167,12200 +Karabük,Turkey,Europe,41.1986,32.6264,125403 +Karacabey,Turkey,Europe,40.2167,28.35,84907 +Karacasu,Turkey,Europe,37.7308,28.6061,17620 +Karachayevsk,Russia,Asia,43.7731,41.9169,23728 +Karachev,Russia,Asia,53.1167,34.9667,17449 +Karachi,Pakistan,Asia,24.86,67.01,20249000 +Karaisalı,Turkey,Europe,37.2611,35.06,22042 +Karaj,Iran,Asia,35.8292,50.9675,1973470 +Karak,Pakistan,Asia,33.1167,71.1,35844 +Karakax,China,Asia,37.2714,79.7267,66541 +Karakol,Kyrgyzstan,Asia,42.4906,78.3936,85588 +Karakoçan,Turkey,Europe,38.95,40.0333,14811 +Karaköprü,Turkey,Europe,37.1847,38.7908,265035 +Karaman,Turkey,Europe,37.1819,33.2181,175390 +Karamay,China,Asia,45.5799,84.8892,487000 +Karamürsel,Turkey,Europe,40.6892,29.615,59676 +Karanganyar,Indonesia,Asia,-7.6033,110.9778,86402 +Karapürçek,Turkey,Europe,40.6425,30.5386,13339 +Karapınar,Turkey,Europe,37.7147,33.5508,50323 +Karasu,Turkey,Europe,41.1036,30.6936,70600 +Karataş,Turkey,Europe,36.5625,35.3803,23499 +Karawang,Indonesia,Asia,-6.3125,107.295,307880 +Karayazı,Turkey,Europe,39.7039,42.1486,25543 +Karaçoban,Turkey,Europe,39.3508,42.1119,22250 +Karbalā’,Iraq,Asia,32.6167,44.0333,690100 +Karbinci,North Macedonia,Europe,41.8167,22.2375,574 +Karcag,Hungary,Europe,47.3167,20.9333,18270 +Kardzhali,Bulgaria,Europe,41.65,25.367,51841 +Kardítsa,Greece,Europe,39.3667,21.9167,55979 +Kargilik,China,Asia,37.885,77.4131,75730 +Kargopol,Russia,Asia,61.5,38.9333,8737 +Karkamış,Turkey,Europe,36.8339,37.9969,9379 +Karkkila,Finland,Europe,60.5333,24.2167,8581 +Karlovy Vary,Czechia,Europe,50.2306,12.8725,49353 +Karlskoga,Sweden,Europe,59.3333,14.5167,27562 +Karlskrona,Sweden,Europe,56.1611,15.5881,66675 +Karlsruhe,Germany,Europe,49.0092,8.404,309964 +Karlstad,Sweden,Europe,59.3671,13.4999,65856 +Karlıova,Turkey,Europe,39.2992,41.0142,9016 +Karmaskaly,Russia,Asia,54.3694,56.1778,8540 +Karonga,Malawi,Africa,-9.9329,33.9333,61609 +Karpinsk,Russia,Asia,59.7667,59.9833,25396 +Karpuzlu,Turkey,Europe,37.5592,27.8344,10590 +Kars,Turkey,Europe,40.6078,43.0958,91450 +Kartal,Turkey,Europe,40.8872,29.19,483418 +Kartaly,Russia,Asia,53.05,60.65,13700 +Kartuzy,Poland,Europe,54.3333,18.2,14204 +Karukh,Afghanistan,Asia,34.4922,62.5922,17484 +Karuzi,Burundi,Africa,-3.1,30.1667,10317 +Karşıyaka,Turkey,Europe,38.4594,27.1153,346264 +Kasama,Zambia,Africa,-10.2117,31.1783,101845 +Kasane,Botswana,Africa,-17.7983,25.1536,9008 +Kasese,Uganda,Africa,0.1867,30.0881,115400 +Kaset Wisai,Thailand,Asia,15.6556,103.5836,11217 +Kashgar,China,Asia,39.4681,75.9938,920000 +Kashin,Russia,Asia,57.35,37.6167,14113 +Kasimov,Russia,Asia,54.9333,41.4,27821 +Kasli,Russia,Asia,55.9,60.7667,15006 +Kaspi,Georgia,Europe,41.9244,44.4189,12708 +Kaspiyskiy,Russia,Asia,45.3908,47.3658,13919 +Kassala,Sudan,Africa,15.45,36.4,536009 +Kassel,Germany,Europe,51.3158,9.4979,204687 +Kasserine,Tunisia,Africa,35.1667,8.8333,108794 +Kastamonu,Turkey,Europe,41.3764,33.7764,125622 +Kastav,Croatia,Europe,45.3726,14.349,10202 +Kastoriá,Greece,Europe,40.5167,21.2667,12548 +Kastsyukovichy,Belarus,Europe,53.3333,32.05,14870 +Kasungu,Malawi,Africa,-13.0364,33.4822,58653 +Kasur,Pakistan,Asia,31.1167,74.45,314617 +Katakwi,Uganda,Africa,1.915,33.955,9100 +Katav-Ivanovsk,Russia,Asia,54.75,58.2167,16732 +Kateríni,Greece,Europe,40.2667,22.5,59189 +Kathmandu,Nepal,Asia,27.71,85.32,845767 +Kathu,Thailand,Asia,7.909,98.3332,26078 +Kati,Mali,Africa,12.7504,-8.08,66895 +Katima Mulilo,Namibia,Africa,-17.5,24.2667,28699 +Katiola,Côte d’Ivoire,Africa,8.1333,-5.1,90641 +Katowice,Poland,Europe,50.2625,19.0217,285711 +Katrineholm,Sweden,Europe,59.0,16.2,24271 +Katsina,Nigeria,Africa,12.25,7.5,318459 +Kattaqo’rg’on Shahri,Uzbekistan,Asia,39.8989,66.2561,90600 +Katwijk,Netherlands,Europe,52.2,4.4167,66607 +Kaufbeuren,Germany,Europe,47.88,10.6225,45792 +Kauhajoki,Finland,Europe,62.4319,22.1794,12618 +Kauhava,Finland,Europe,63.1,23.0667,15165 +Kaunas,Lithuania,Europe,54.9,23.9333,305120 +Kauniainen,Finland,Europe,60.2167,24.7333,10270 +Kaura Namoda,Nigeria,Africa,12.6,6.5897,281367 +Kavadarci,North Macedonia,Europe,41.4328,22.0117,6228 +Kavak,Turkey,Europe,41.0736,36.0403,22747 +Kavaklıdere,Turkey,Europe,37.4469,28.3633,10909 +Kavalerovo,Russia,Asia,44.2702,135.0498,18657 +Kavaratti,India,Asia,10.5626,72.6369,11473 +Kavarna,Bulgaria,Europe,43.4361,28.3394,11202 +Kavieng,Papua New Guinea,Oceania,-2.5667,150.8,19849 +Kavála,Greece,Europe,40.9333,24.4,65857 +Kavār,Iran,Asia,29.2017,52.6917,31711 +Kaya,Burkina Faso,Africa,13.0833,-1.0833,122949 +Kayanza,Burundi,Africa,-2.9167,29.6167,21767 +Kayapınar,Turkey,Europe,37.95,40.1667,419513 +Kayes,Mali,Africa,14.4497,-11.4367,127368 +Kaynarca,Turkey,Europe,41.0308,30.3075,24483 +Kaynaşlı,Turkey,Europe,40.7667,31.3167,10176 +Kayseri,Turkey,Europe,38.7225,35.4875,1434357 +Kayunga,Uganda,Africa,0.7033,32.9036,30000 +Kayyngdy,Kyrgyzstan,Asia,42.8247,73.6758,9561 +Kazan,Russia,Asia,55.7964,49.1089,1259173 +Kazarman,Kyrgyzstan,Asia,41.4045,74.0283,11191 +Kazincbarcika,Hungary,Europe,48.2531,20.6456,23990 +Kazlų Rūda,Lithuania,Europe,54.7492,23.4865,5507 +Kazo,Uganda,Africa,-0.0528,30.7569,10200 +Kaçanik,Kosovo,Europe,42.2319,21.2594,33409 +Kaédi,Mauritania,Africa,16.1503,-13.5037,49152 +Kağızman,Turkey,Europe,40.1583,43.1342,21130 +Kaş,Turkey,Europe,36.202,29.638,58600 +Kebili,Tunisia,Africa,33.705,8.965,28081 +Kecskemét,Hungary,Europe,46.9075,19.6917,109450 +Keelung,Taiwan,Asia,25.1333,121.7333,362487 +Keetmanshoop,Namibia,Africa,-26.5833,18.1333,16800 +Kefamenanu,Indonesia,Asia,-9.4467,124.4781,47628 +Keffi,Nigeria,Africa,8.8464,7.8733,85911 +Kegen,Kazakhstan,Asia,43.0197,79.2203,9049 +Kegeyli Shahar,Uzbekistan,Asia,42.7767,59.6078,12400 +Keila,Estonia,Europe,59.3086,24.4225,10499 +Keles,Turkey,Europe,39.9136,29.2294,10955 +Kelheim,Germany,Europe,48.9167,11.8667,17094 +Kelkit,Turkey,Europe,40.1286,39.4383,21316 +Kelmė,Lithuania,Europe,55.6333,22.9333,7512 +Kelowna,Canada,North America,49.8881,-119.4956,181380 +Kem,Russia,Asia,64.95,34.5833,9712 +Kemalpaşa,Turkey,Europe,38.4278,27.4161,114250 +Kemer,Turkey,Europe,36.6,30.55,49383 +Kemerovo,Russia,Asia,55.3667,86.0667,544600 +Kemi,Finland,Europe,65.7361,24.5636,19371 +Kemijärvi,Finland,Europe,66.7167,27.4333,7030 +Kemin,Kyrgyzstan,Asia,42.7846,75.6892,10354 +Kempele,Finland,Europe,64.9125,25.5083,19514 +Kempten,Germany,Europe,47.7333,10.3167,70056 +Kendari,Indonesia,Asia,-3.9907,122.5086,331013 +Kenema,Sierra Leone,Africa,7.8833,-11.1833,188463 +Kenge,Congo (Kinshasa),Africa,-4.8056,17.0417,44743 +Kennewick,United States,North America,46.1978,-119.1732,260091 +Kenosha,United States,North America,42.5865,-87.8773,125807 +Kent,United States,North America,47.3887,-122.2128,135015 +Kentaū,Kazakhstan,Asia,43.5167,68.5167,81484 +Kepsut,Turkey,Europe,39.6889,28.1522,21825 +Keratsíni,Greece,Europe,37.9667,23.6167,75721 +Kerava,Finland,Europe,60.4,25.1,38211 +Kerben,Kyrgyzstan,Asia,41.5,71.75,14141 +Kerch,Ukraine,Europe,45.3619,36.4711,149566 +Kerema,Papua New Guinea,Oceania,-7.927,145.838,6551 +Keren,Eritrea,Africa,15.7778,38.4581,146500 +Kerewan,"Gambia, The",Africa,13.5,-16.0833,2751 +Kericho,Kenya,Africa,-0.3692,35.2839,30023 +Kerkrade,Netherlands,Europe,50.8667,6.0667,45442 +Kermān,Iran,Asia,30.2625,57.0575,738374 +Kermānshāh,Iran,Asia,34.3369,47.0911,946651 +Kerugoya,Kenya,Africa,-0.4989,37.2803,14056 +Kerċem,Malta,Europe,36.0406,14.2297,1938 +Keshan,China,Asia,48.0263,125.8659,72403 +Keskin,Turkey,Europe,39.6731,33.6136,9183 +Keszthely,Hungary,Europe,46.7675,17.2463,17849 +Ketapang,Indonesia,Asia,-1.85,109.9833,92755 +Keuruu,Finland,Europe,62.2583,24.7083,9184 +Keçiören,Turkey,Europe,40.0,32.8667,939279 +Keşan,Turkey,Europe,40.85,26.6333,83874 +Keşap,Turkey,Europe,40.9167,38.5167,9227 +Kežmarok,Slovakia,Europe,49.1383,20.4292,15304 +Khabarovsk,Russia,Asia,48.4833,135.0833,615570 +Khairpur Mir’s,Pakistan,Asia,27.5333,68.7667,144949 +Khalkhāl,Iran,Asia,37.6147,48.5314,38521 +Khamir,Yemen,Asia,15.9889,43.9589,18645 +Khandyga,Russia,Asia,62.666,135.6,6796 +Khanewal,Pakistan,Asia,30.3,71.9333,281890 +Khanty-Mansiysk,Russia,Asia,61.0,69.0,105995 +Khao Yoi,Thailand,Asia,13.2402,99.8243,15416 +Kharabali,Russia,Asia,47.405,47.2556,18514 +Kharan,Pakistan,Asia,28.5833,65.4167,80806 +Kharkiv,Ukraine,Europe,49.9925,36.2311,1421125 +Khartoum,Sudan,Africa,15.6031,32.5265,7869000 +Khasavyurt,Russia,Asia,43.25,46.5833,155144 +Khashuri,Georgia,Europe,41.9975,43.5986,24601 +Khaşab,Oman,Asia,26.1833,56.25,17777 +Khe Sanh,Vietnam,Asia,16.6193,106.7305,13927 +Khenchela,Algeria,Africa,35.4167,7.1333,108580 +Kherson,Ukraine,Europe,46.6425,32.625,279131 +Kherāmeh,Iran,Asia,29.4992,53.3131,18477 +Khimki,Russia,Asia,55.8892,37.445,256684 +Khirbat Ghazālah,Syria,Asia,32.7338,36.2046,16240 +Khlong Luang,Thailand,Asia,14.0647,100.6458,56128 +Khlung,Thailand,Asia,12.4547,102.2214,10835 +Khmelnytskyi,Ukraine,Europe,49.4167,27.0,274452 +Khmilnyk,Ukraine,Europe,49.5569,27.9572,26916 +Kholmsk,Russia,Asia,47.05,142.05,24884 +Khomeyn,Iran,Asia,33.6422,50.0789,72882 +Khomeynī Shahr,Iran,Asia,32.6803,51.5361,247128 +Khon Kaen,Thailand,Asia,16.4333,102.8333,412758 +Khonj,Iran,Asia,27.8897,53.4308,19217 +Khorol,Ukraine,Europe,49.7667,33.2667,12540 +Khorramdarreh,Iran,Asia,36.2061,49.2003,55368 +Khorramshahr,Iran,Asia,30.4394,48.1817,133097 +Khorramābād,Iran,Asia,33.4875,48.3561,200000 +Khorugh,Tajikistan,Asia,37.4833,71.55,30500 +Khotyn,Ukraine,Europe,48.5069,26.4903,18426 +Khovaling,Tajikistan,Asia,38.3386,69.9728,10601 +Khowrmūj,Iran,Asia,28.6572,51.3811,40722 +Khowy,Iran,Asia,38.5464,44.9542,198845 +Khoyniki,Belarus,Europe,51.8892,29.9644,13167 +Khromtaū,Kazakhstan,Asia,50.2503,58.4347,21156 +Khulm,Afghanistan,Asia,36.6833,67.6833,28078 +Khulna,Bangladesh,Asia,22.8167,89.55,928891 +Khushab,Pakistan,Asia,32.2986,72.3508,110868 +Khust,Ukraine,Europe,48.1814,23.2978,28039 +Khutubi,China,Asia,44.1874,86.8946,43127 +Khvāf,Iran,Asia,34.5711,60.1439,33189 +Khvānsār,Iran,Asia,33.2208,50.3153,20490 +Khān Shaykhūn,Syria,Asia,35.4421,36.6509,55843 +Khānaqīn,Iraq,Asia,34.3483,45.3906,11791 +Khānābād,Afghanistan,Asia,36.6831,69.1636,26803 +Khāsh,Iran,Asia,28.22,61.2114,56584 +Khōst,Afghanistan,Asia,33.3331,69.9169,160214 +Khŭjand,Tajikistan,Asia,40.2833,69.6333,770000 +Kiambu,Kenya,Africa,-1.1714,36.8356,147870 +Kibaha,Tanzania,Africa,-6.7667,38.9167,265360 +Kibiito,Uganda,Africa,0.4775,30.1925,12984 +Kiboga,Uganda,Africa,0.92,31.76,22400 +Kibuku,Uganda,Africa,1.0375,33.84,25000 +Kibuye,Rwanda,Africa,-2.0617,29.3483,12325 +Kidal,Mali,Africa,18.4333,1.4,15086 +Kidapawan,Philippines,Asia,7.0083,125.0894,160791 +Kidričevo,Slovenia,Europe,46.4036,15.7911,1257 +Kiel,Germany,Europe,54.3233,10.1394,248873 +Kielce,Poland,Europe,50.8742,20.6333,186894 +Kiffa,Mauritania,Africa,16.6164,-11.4044,110714 +Kifisiá,Greece,Europe,38.0833,23.8167,47332 +Kifrī,Iraq,Asia,34.6896,44.9606,32870 +Kigali,Rwanda,Africa,-1.9536,30.0606,1156663 +Kigoma,Tanzania,Africa,-4.8833,29.6333,232388 +Kikinda,Serbia,Europe,45.8333,20.45,28743 +Kikube,Uganda,Africa,1.3328,31.2075,18100 +Kiliia,Ukraine,Europe,45.45,29.2667,18745 +Kilimli,Turkey,Europe,41.4833,31.8333,19989 +Kilis,Turkey,Europe,36.7167,37.1167,112187 +Kilkenny,Ireland,Europe,52.6477,-7.2561,22179 +Kilkís,Greece,Europe,40.9833,22.8667,12276 +Killeen,United States,North America,31.0753,-97.7297,261099 +Kimbe,Papua New Guinea,Oceania,-5.55,150.143,27191 +Kimberley,South Africa,Africa,-28.7383,24.7639,96977 +Kimovsk,Russia,Asia,53.9667,38.5333,26475 +Kimry,Russia,Asia,56.8667,37.35,40875 +Kindia,Guinea,Africa,10.0497,-12.8542,181126 +Kindu,Congo (Kinshasa),Africa,-2.95,25.95,172321 +Kineshma,Russia,Asia,57.4333,42.1333,77694 +Kingisepp,Russia,Asia,59.3667,28.6167,48807 +Kingston,Jamaica,North America,17.9714,-76.7931,580000 +Kingstown,Saint Vincent and the Grenadines,North America,13.1578,-61.225,12909 +Kinh Môn,Vietnam,Asia,21.033,106.5,203638 +Kinkala,Congo (Brazzaville),Africa,-4.3567,14.7589,12102 +Kinna,Sweden,Europe,57.4954,12.6805,15507 +Kinshasa,Congo (Kinshasa),Africa,-4.3219,15.3119,12836000 +Kirakira,Solomon Islands,Oceania,-10.4544,161.9205,3500 +Kiraz,Turkey,Europe,38.2306,28.2044,43510 +Kirensk,Russia,Asia,57.7833,108.0833,900 +Kireyevsk,Russia,Asia,53.9333,37.9333,25560 +Kirishi,Russia,Asia,59.45,32.0167,49631 +Kirkenes,Norway,Europe,69.7271,30.0451,3531 +Kirkkonummi,Finland,Europe,60.1167,24.4333,41154 +Kirkop,Malta,Europe,35.8419,14.485,2191 +Kirkuk,Iraq,Asia,35.4667,44.4,975000 +Kirov,Russia,Asia,58.6,49.6833,471754 +Kirovgrad,Russia,Asia,57.435,60.0561,18497 +Kirovo-Chepetsk,Russia,Asia,58.55,50.0,71498 +Kirovsk,Russia,Asia,59.8667,30.9833,26986 +Kirs,Russia,Asia,59.35,52.25,3171 +Kirsanov,Russia,Asia,52.65,42.7333,16164 +Kiruhura,Uganda,Africa,-0.21,30.83,6900 +Kiruna,Sweden,Europe,67.85,20.2166,26703 +Kirundo,Burundi,Africa,-2.59,30.09,10024 +Kiryandongo,Uganda,Africa,1.9525,32.1389,31610 +Kirzhach,Russia,Asia,56.15,38.8667,27318 +Kisangani,Congo (Kinshasa),Africa,0.5167,25.2,1081000 +Kisaran,Indonesia,Asia,2.9833,99.6125,147639 +Kiseljak,Bosnia and Herzegovina,Europe,43.95,18.0833,20722 +Kishkeneköl,Kazakhstan,Asia,53.6394,72.3439,7421 +Kisi,Nigeria,Africa,9.0833,3.85,155510 +Kisii,Kenya,Africa,-0.6698,34.7675,37531 +Kiskunfélegyháza,Hungary,Europe,46.705,19.85,28562 +Kiskunhalas,Hungary,Europe,46.4319,19.4883,26009 +Kiskunmajsa,Hungary,Europe,46.4922,19.7367,11127 +Kiskőrös,Hungary,Europe,46.6203,19.2836,13173 +Kislovodsk,Russia,Asia,43.9167,42.7167,126423 +Kismaayo,Somalia,Africa,-0.3603,42.5489,166667 +Kisoro,Uganda,Africa,-1.285,29.685,17561 +Kissidougou,Guinea,Africa,9.1905,-10.12,66815 +Kissimmee,United States,North America,28.3041,-81.4169,442790 +Kisumu,Kenya,Africa,-0.1,34.75,409928 +Kisvárda,Hungary,Europe,48.2264,22.0844,14888 +Kita,Mali,Africa,13.0504,-9.4833,46435 +Kitale,Kenya,Africa,1.0167,35.0,63245 +Kitchener,Canada,North America,43.4186,-80.4728,522888 +Kitee,Finland,Europe,62.1,30.1333,9689 +Kitgum,Uganda,Africa,3.2889,32.8778,44604 +Kitui,Kenya,Africa,-1.3667,38.0167,29062 +Kitzingen,Germany,Europe,49.7333,10.1667,23377 +Kizel,Russia,Asia,59.05,57.6667,15319 +Kizilyurt,Russia,Asia,43.2167,46.8667,38335 +Kizlyar,Russia,Asia,43.85,46.7167,49999 +Kiáto,Greece,Europe,38.0117,22.7467,9274 +Kiên Lương,Vietnam,Asia,10.2856,104.6444,24287 +Kičevo,North Macedonia,Europe,41.5142,20.9631,7280 +Kiến Giang,Vietnam,Asia,17.225,106.7917,10558 +Kladanj,Bosnia and Herzegovina,Europe,44.2267,18.6927,12348 +Kladovo,Serbia,Europe,44.6039,22.6072,20635 +Klaeng,Thailand,Asia,12.7797,101.6483,17764 +Klagenfurt,Austria,Europe,46.6167,14.3,101403 +Klaipėda,Lithuania,Europe,55.7125,21.135,158420 +Klaksvík,Faroe Islands,Europe,62.2375,-6.539,4664 +Kleppe,Norway,Europe,58.7667,5.6667,20615 +Kleve,Germany,Europe,51.79,6.14,53458 +Klimavichy,Belarus,Europe,53.6167,31.95,15020 +Klin,Russia,Asia,56.3333,36.7333,3900 +Klintsy,Russia,Asia,52.75,32.2333,63059 +Klinë,Kosovo,Europe,42.6217,20.5778,38496 +Ključ,Bosnia and Herzegovina,Europe,44.5333,16.7667,18714 +Kllokot,Kosovo,Europe,42.3714,21.3744,2556 +Klosterneuburg,Austria,Europe,48.3042,16.3167,27058 +Kluczbork,Poland,Europe,50.9833,18.2167,22703 +Klyetsk,Belarus,Europe,53.0636,26.6372,11248 +Knezha,Bulgaria,Europe,43.4931,24.0806,10136 +Knin,Croatia,Europe,44.0333,16.1833,11633 +Knić,Serbia,Europe,43.9167,20.7167,14237 +Knjaževac,Serbia,Europe,43.5,22.4333,31491 +Knoxville,United States,North America,35.9692,-83.9496,618226 +Ko Samui,Thailand,Asia,9.5,100.0,70059 +Kobarid,Slovenia,Europe,46.2464,13.578,1085 +Kobeliaky,Ukraine,Europe,49.1333,34.2,9465 +Kobilje,Slovenia,Europe,46.6847,16.3978,542 +Koblenz,Germany,Europe,50.3597,7.5978,115298 +Koboko,Uganda,Africa,3.41,30.96,37825 +Kobryn,Belarus,Europe,52.2167,24.3667,52635 +Kobuleti,Georgia,Europe,41.8214,41.7792,27546 +Kocaali,Turkey,Europe,41.0547,30.8503,22882 +Kocaeli,Turkey,Europe,40.7625,29.9175,363416 +Kocaköy,Turkey,Europe,38.2897,40.5014,15355 +Kocasinan,Turkey,Europe,38.7333,35.4833,409005 +Kochkor,Kyrgyzstan,Asia,42.2155,75.7566,11373 +Kodinsk,Russia,Asia,58.6833,99.1833,13324 +Koforidua,Ghana,Africa,6.1,-0.2667,127334 +Kogon Shahri,Uzbekistan,Asia,39.7275,64.5547,107566 +Koh Kong,Cambodia,Asia,11.6167,102.9833,33591 +Kohat,Pakistan,Asia,33.5833,71.4333,170800 +Kohtla-Järve,Estonia,Europe,59.3978,27.2572,33675 +Kohīma,India,Asia,25.67,94.1,99039 +Kokkola,Finland,Europe,63.8367,23.1333,48299 +Kokologo,Burkina Faso,Africa,12.1833,-1.8833,9704 +Kokopo,Papua New Guinea,Oceania,-4.35,152.2736,20262 +Koktokay,China,Asia,47.0004,89.4666,80000 +Kolašin,Montenegro,Europe,42.8236,19.5225,2725 +Kolbuszowa,Poland,Europe,50.25,21.7667,8710 +Kolchugino,Russia,Asia,56.3167,39.3667,37741 +Kolda,Senegal,Africa,12.8958,-14.9408,62258 +Kolding,Denmark,Europe,55.4929,9.4681,62338 +Kolkāta,India,Asia,22.5675,88.37,21747000 +Kolno,Poland,Europe,53.4106,21.9339,9999 +Kolomna,Russia,Asia,55.0833,38.7833,132247 +Kolomyia,Ukraine,Europe,48.5306,25.0403,60821 +Kolondiéba,Mali,Africa,11.0883,-6.8925,57898 +Kolonia,"Micronesia, Federated States of",Oceania,6.9639,158.2081,6074 +Kolonodale,Indonesia,Asia,-1.9833,121.3333,17997 +Kolpashevo,Russia,Asia,58.3,82.9,20824 +Kolwezi,Congo (Kinshasa),Africa,-10.7167,25.4667,480000 +Kombissiri,Burkina Faso,Africa,12.0667,-1.3333,28617 +Komen,Slovenia,Europe,45.8159,13.7507,642 +Komenda,Slovenia,Europe,46.2073,14.539,896 +Komló,Hungary,Europe,46.1911,18.2611,19679 +Komotiní,Greece,Europe,41.1219,25.4042,38532 +Komsomol’sk-na-Amure,Russia,Asia,50.5667,137.0,235488 +Komárom,Hungary,Europe,47.74,18.1217,20391 +Komījān,Iran,Asia,34.7181,49.325,8776 +Konak,Turkey,Europe,38.4189,27.1285,332277 +Konakovo,Russia,Asia,56.7,36.75,33560 +Kondopoga,Russia,Asia,62.2,34.2833,3833 +Kondrovo,Russia,Asia,54.8,35.9333,15734 +Kong,Côte d’Ivoire,Africa,9.1506,-4.6103,33391 +Kongoussi,Burkina Faso,Africa,13.3333,-1.5333,53641 +Kongsberg,Norway,Europe,59.65,9.65,25090 +Kongsvinger,Norway,Europe,60.1905,11.9977,12034 +Konibodom,Tajikistan,Asia,40.2833,70.4167,219000 +Konin,Poland,Europe,52.2167,18.2667,69858 +Konotop,Ukraine,Europe,51.2167,33.2,83543 +Konstantinovsk,Russia,Asia,47.5667,41.0833,17207 +Kontagora,Nigeria,Africa,10.403,5.473,98763 +Kontiolahti,Finland,Europe,62.7667,29.85,15136 +Konya,Turkey,Europe,37.8667,32.4833,2320241 +Koné,New Caledonia,Oceania,-21.059,164.8518,8144 +Konārak,Iran,Asia,25.3589,60.3992,43258 +Konče,North Macedonia,Europe,41.4958,22.3825,915 +Koper,Slovenia,Europe,45.55,13.7333,25753 +Kopervik,Norway,Europe,59.2833,5.3,11561 +Kopeysk,Russia,Asia,55.1,61.6167,144957 +Koprivnica,Croatia,Europe,46.15,16.8167,28580 +Korablino,Russia,Asia,53.9167,40.0167,10084 +Korbach,Germany,Europe,51.2833,8.8667,24089 +Kord Kūy,Iran,Asia,36.7931,54.1122,39881 +Korenovsk,Russia,Asia,45.4686,39.4519,13284 +Korgan,Turkey,Europe,40.8247,37.3467,27349 +Korgas,China,Asia,44.2125,80.4097,71466 +Korhogo,Côte d’Ivoire,Africa,9.4578,-5.6294,440926 +Koriukivka,Ukraine,Europe,51.7833,32.25,12202 +Korkino,Russia,Asia,54.9,61.4167,36591 +Korkuteli,Turkey,Europe,37.0667,30.2,56285 +Korla,China,Asia,41.7259,86.1746,770000 +Korneuburg,Austria,Europe,48.3453,16.3331,12986 +Koro,Mali,Africa,14.0639,-3.0753,62681 +Korolëv,Russia,Asia,55.9167,37.8167,226007 +Koropí,Greece,Europe,37.9,23.8733,20152 +Korosten,Ukraine,Europe,50.95,28.6333,61496 +Korsholm,Finland,Europe,63.1125,21.6778,19763 +Korsimoro,Burkina Faso,Africa,12.825,-1.0672,18875 +Kortrijk,Belgium,Europe,50.8275,3.2658,77741 +Koryazhma,Russia,Asia,61.3167,47.1333,34002 +Korydallós,Greece,Europe,37.9833,23.65,61248 +Korçë,Albania,Europe,40.6167,20.7667,51152 +Kos,Greece,Europe,36.85,27.2333,8863 +Kosjerić,Serbia,Europe,44.0,19.9167,12090 +Kosonsoy,Uzbekistan,Asia,41.25,71.55,50900 +Kostanjevica na Krki,Slovenia,Europe,45.8463,15.4249,689 +Kostel,Slovenia,Europe,45.5088,14.91,10 +Kostolac,Serbia,Europe,44.7167,21.1667,13637 +Kostomuksha,Russia,Asia,64.5833,30.6,25928 +Kostroma,Russia,Asia,57.7681,40.9269,267481 +Kosum Phisai,Thailand,Asia,16.2486,103.0674,8984 +Koszalin,Poland,Europe,54.2,16.1833,105883 +Kota Bharu,Malaysia,Asia,6.12,102.23,491237 +Kota Kinabalu,Malaysia,Asia,5.98,116.11,452058 +Kotabumi,Indonesia,Asia,-4.8333,104.9,42366 +Kotelnich,Russia,Asia,58.3078,48.3181,157 +Kotelniki,Russia,Asia,55.6617,37.8672,72311 +Kotelnikovo,Russia,Asia,47.6333,43.15,22016 +Kotido,Uganda,Africa,3.0061,34.1125,22900 +Kotka,Finland,Europe,60.4667,26.9417,50477 +Kotlas,Russia,Asia,61.25,46.6333,56122 +Kotli,Pakistan,Asia,33.5156,73.9019,21462 +Kotor,Montenegro,Europe,42.43,18.77,13347 +Kotovo,Russia,Asia,50.3167,44.8,21028 +Kotovsk,Russia,Asia,52.5833,41.5167,26694 +Koudougou,Burkina Faso,Africa,12.25,-2.3667,160207 +Koufália,Greece,Europe,40.7783,22.5767,9462 +Koulamoutou,Gabon,Africa,-1.1333,12.4833,25651 +Koulikoro,Mali,Africa,12.8833,-7.55,23919 +Koumra,Chad,Africa,8.91,17.55,47950 +Koundara,Guinea,Africa,12.48,-13.296,13990 +Koupéla,Burkina Faso,Africa,12.1794,-0.3517,45900 +Kourou,French Guiana,South America,5.16,-52.6499,24029 +Kouroussa,Guinea,Africa,10.653,-9.892,14223 +Koutiala,Mali,Africa,12.3833,-5.4667,137919 +Kouvola,Finland,Europe,60.8681,26.7042,78824 +Kovancılar,Turkey,Europe,38.7217,39.8672,25295 +Kovačica,Serbia,Europe,45.1117,20.6214,5398 +Kovel,Ukraine,Europe,51.2167,24.7167,67575 +Kovin,Serbia,Europe,44.7475,20.9761,33722 +Kovrov,Russia,Asia,56.3683,41.3108,128879 +Kozakai-chō,Japan,Asia,34.8,137.3667,21708 +Kozan,Turkey,Europe,37.45,35.8,132703 +Kozelsk,Russia,Asia,54.0353,35.7767,16603 +Koziatyn,Ukraine,Europe,49.7167,28.8333,22241 +Kozienice,Poland,Europe,51.5833,21.5667,16192 +Kozje,Slovenia,Europe,46.075,15.5603,634 +Kozloduy,Bulgaria,Europe,43.7833,23.7333,11331 +Kozlu,Turkey,Europe,41.4333,31.75,43140 +Kozluk,Turkey,Europe,38.1944,41.4911,27825 +Kozmodemyansk,Russia,Asia,56.3367,46.5711,19355 +Kozova,Ukraine,Europe,49.4319,25.1594,8750 +Kozáni,Greece,Europe,40.3,21.7833,34138 +Koçarlı,Turkey,Europe,37.7608,27.7061,21832 +Kočani,North Macedonia,Europe,41.9167,22.4125,28330 +Kočevje,Slovenia,Europe,45.643,14.8594,8151 +Koło,Poland,Europe,52.2,18.6333,20380 +Kołobrzeg,Poland,Europe,54.1761,15.5761,44340 +Końskie,Poland,Europe,51.2,20.4167,18201 +Kościan,Poland,Europe,52.0833,16.65,24022 +Kościerzyna,Poland,Europe,54.1167,17.9833,23723 +Košice,Slovakia,Europe,48.7167,21.25,229040 +Krabi,Thailand,Asia,8.0592,98.9189,32644 +Kragerø,Norway,Europe,58.8869,9.3469,10413 +Kragujevac,Serbia,Europe,44.0142,20.9394,150623 +Kraków,Poland,Europe,50.0614,19.9372,804237 +Kralendijk,"Bonaire, Sint Eustatius, and Saba",South America,12.1444,-68.2656,10620 +Kraljevo,Serbia,Europe,43.7234,20.687,66688 +Kramatorsk,Ukraine,Europe,48.7392,37.5839,147145 +Kranj,Slovenia,Europe,46.2333,14.3667,37553 +Kranjska Gora,Slovenia,Europe,46.4854,13.7871,1452 +Kranuan,Thailand,Asia,16.7067,103.0788,10169 +Kranídi,Greece,Europe,37.38,23.159,9631 +Krapina,Croatia,Europe,46.1589,15.8744,11530 +Krapkowice,Poland,Europe,50.4667,17.9667,15786 +Krasnodar,Russia,Asia,45.0333,38.9667,1138654 +Krasnogorsk,Russia,Asia,55.8217,37.3386,193127 +Krasnogvardeyskoye,Russia,Asia,45.85,41.5167,13959 +Krasnohrad,Ukraine,Europe,49.3667,35.45,19674 +Krasnokamsk,Russia,Asia,58.0833,55.6833,47968 +Krasnoslobodsk,Russia,Asia,54.4333,43.8,8910 +Krasnotur’insk,Russia,Asia,59.7733,60.1853,55727 +Krasnoufimsk,Russia,Asia,56.6167,57.7722,37307 +Krasnouralsk,Russia,Asia,58.35,60.05,21384 +Krasnousol’skiy,Russia,Asia,53.8947,56.4686,10662 +Krasnovishersk,Russia,Asia,60.4167,57.1,14164 +Krasnoyarsk,Russia,Asia,56.0089,92.8719,1092851 +Krasnoznamensk,Russia,Asia,55.6008,37.0358,25300 +Krasnystaw,Poland,Europe,51.0,23.1667,17519 +Krasnyy Sulin,Russia,Asia,47.8833,40.1,35697 +Krathum Baen,Thailand,Asia,13.6631,100.2572,21904 +Kratie,Cambodia,Asia,12.4881,106.0188,60000 +Kratovo,North Macedonia,Europe,42.0783,22.175,5401 +Kraśnik,Poland,Europe,50.9167,22.2167,32593 +Krefeld,Germany,Europe,51.3333,6.5667,228550 +Kremenchuk,Ukraine,Europe,49.0631,33.4039,215271 +Kremenets,Ukraine,Europe,50.1081,25.7275,20476 +Krems an der Donau,Austria,Europe,48.4167,15.6167,50000 +Kretinga,Lithuania,Europe,55.89,21.2422,17207 +Kreuzlingen,Switzerland,Europe,47.6458,9.1783,21801 +Krimpen aan den IJssel,Netherlands,Europe,51.92,4.6,29410 +Kristiansand,Norway,Europe,58.1467,7.9956,64057 +Kristianstad,Sweden,Europe,56.0337,14.1333,32188 +Kristiansund,Norway,Europe,63.1105,7.7279,18273 +Kristinehamn,Sweden,Europe,59.3333,14.1167,17839 +Kriva Palanka,North Macedonia,Europe,42.2017,22.3317,13481 +Krivogaštani,North Macedonia,Europe,41.3358,21.3331,1870 +Križevci,Croatia,Europe,46.0333,16.5333,18949 +Kronach,Germany,Europe,50.2411,11.3281,16924 +Krong Kep,Cambodia,Asia,10.4833,104.3167,35990 +Kropotkin,Russia,Asia,45.4333,40.5667,74893 +Kropyvnytskyi,Ukraine,Europe,48.5,32.2667,219676 +Krosno,Poland,Europe,49.6833,21.75,45057 +Krosno Odrzańskie,Poland,Europe,52.0333,15.1,10905 +Krotoszyn,Poland,Europe,51.6833,17.4333,28704 +Krupanj,Serbia,Europe,44.3667,19.3667,17295 +Kruševac,Serbia,Europe,43.5833,21.3267,58745 +Kruševo,North Macedonia,Europe,41.37,21.2483,4104 +Krychaw,Belarus,Europe,53.7194,31.7139,23264 +Krymsk,Russia,Asia,44.9233,37.9806,19123 +Kryvyi Rih,Ukraine,Europe,47.91,33.39,603904 +Krāslava,Latvia,Europe,55.8956,27.1644,6854 +Krško,Slovenia,Europe,45.959,15.4922,6852 +Kstovo,Russia,Asia,56.1667,44.1667,61602 +Kuacjok,South Sudan,Africa,8.3028,27.98,78111 +Kuaidamao,China,Asia,41.7283,125.9397,1302778 +Kuala Belait,Brunei,Asia,4.5828,114.1918,4259 +Kuala Kapuas,Indonesia,Asia,-3.0013,114.3916,55573 +Kuala Lumpur,Malaysia,Asia,3.1686,101.698,8911000 +Kuala Pembuang,Indonesia,Asia,-3.3871,112.5434,20413 +Kuala Terengganu,Malaysia,Asia,5.3304,103.12,255109 +Kuantan,Malaysia,Asia,3.83,103.32,607778 +Kuchinarai,Thailand,Asia,16.541,104.05,11753 +Kuching,Malaysia,Asia,1.53,110.33,325132 +Kudahuvadhoo,Maldives,Asia,2.6711,72.8935,3132 +Kudymkar,Russia,Asia,59.0167,54.6667,28630 +Kufstein,Austria,Europe,47.5833,12.1667,19126 +Kuhsān,Afghanistan,Asia,34.65,61.1667,12463 +Kui Buri,Thailand,Asia,12.0828,99.8543,8916 +Kuiju,China,Asia,36.8667,119.4,564501 +Kuje,Nigeria,Africa,8.8822,7.2203,212100 +Kukawa,Nigeria,Africa,12.9167,13.5667,203864 +Kukrahill,Nicaragua,North America,12.25,-83.75,8789 +Kukës,Albania,Europe,42.0833,20.4167,15643 +Kula,Turkey,Europe,38.5458,28.6494,43227 +Kuldīga,Latvia,Europe,56.9667,21.9667,9940 +Kulebaki,Russia,Asia,55.4167,42.5333,32184 +Kulhudhuffushi,Maldives,Asia,6.6225,73.0703,10251 +Kulmbach,Germany,Europe,50.1,11.4333,26052 +Kulp,Turkey,Europe,38.6642,41.0067,34357 +Kulu,Turkey,Europe,39.0892,33.0806,51612 +Kulundu,Kyrgyzstan,Asia,40.1069,69.6431,10170 +Kumamoto,Japan,Asia,32.8031,130.7078,738385 +Kumanovo,North Macedonia,Europe,42.1322,21.7144,70842 +Kumasi,Ghana,Africa,6.7,-1.625,3903480 +Kumertau,Russia,Asia,52.7667,55.7833,57949 +Kumi,Uganda,Africa,1.4608,33.9361,13000 +Kumla,Sweden,Europe,59.1333,15.1333,21640 +Kumlu,Turkey,Europe,36.3728,36.4528,13333 +Kumluca,Turkey,Europe,36.3667,30.2833,73496 +Kumo,Nigeria,Africa,10.0431,11.2183,35712 +Kumru,Turkey,Europe,40.8744,37.2639,28436 +Kumul,China,Asia,42.8193,93.5151,492096 +Kundiawa,Papua New Guinea,Oceania,-6.023,144.96,11553 +Kunduz,Afghanistan,Asia,36.7286,68.8681,268893 +Kungsbacka,Sweden,Europe,57.4833,12.0667,23000 +Kungsängen,Sweden,Europe,59.4833,17.75,9382 +Kungur,Russia,Asia,57.4333,56.9333,62173 +Kungälv,Sweden,Europe,57.8667,11.9667,22768 +Kuningan,Indonesia,Asia,-6.9764,108.4844,113860 +Kunming,China,Asia,25.0464,102.7094,4422686 +Kuopio,Finland,Europe,62.8925,27.6783,124131 +Kupang,Indonesia,Asia,-10.1702,123.6077,466632 +Kupiansk,Ukraine,Europe,49.7167,37.5833,26627 +Kupiškis,Lithuania,Europe,55.8333,24.9667,6178 +Kuqa,China,Asia,41.7156,82.9322,462588 +Kurchatov,Russia,Asia,51.6667,35.65,39167 +Kuressaare,Estonia,Europe,58.25,22.4833,12698 +Kurgan,Russia,Asia,55.4667,65.35,310911 +Kurganinsk,Russia,Asia,44.8667,40.6167,46393 +Kurikka,Finland,Europe,62.6167,22.4,19759 +Kurman,Ukraine,Europe,45.4947,34.2947,11134 +Kursk,Russia,Asia,51.7167,36.1833,436678 +Kurtalan,Turkey,Europe,37.9261,41.7003,36273 +Kurtamysh,Russia,Asia,54.9167,64.4333,1075 +Kurunegala,Sri Lanka,Asia,7.4833,80.3667,30315 +Kuršumlija,Serbia,Europe,43.1408,21.2678,15905 +Kusa,Russia,Asia,55.35,59.45,13300 +Kusapín,Panama,North America,9.18,-81.89,33121 +Kushnarënkovo,Russia,Asia,55.105,55.3478,9870 +Kushva,Russia,Asia,58.2833,59.7333,26891 +Kut Chap,Thailand,Asia,17.4257,102.5669,10259 +Kutaisi,Georgia,Europe,42.2717,42.7056,147900 +Kutina,Croatia,Europe,45.4791,16.7768,19601 +Kutno,Poland,Europe,52.2333,19.3667,42255 +Kuusamo,Finland,Europe,65.9667,29.1833,15019 +Kuvandyk,Russia,Asia,51.4667,57.3667,11200 +Kuvshinovo,Russia,Asia,57.0333,34.1833,9262 +Kuwait City,Kuwait,Asia,29.3697,47.9783,2989000 +Kuyganyor,Uzbekistan,Asia,40.8611,72.3111,12200 +Kuytun,China,Asia,44.4264,84.9032,166261 +Kuyucak,Turkey,Europe,37.9153,28.4592,26111 +Kuzma,Slovenia,Europe,46.8369,16.0833,429 +Kučevo,Serbia,Europe,44.4833,21.6667,15516 +Kuşadası,Turkey,Europe,37.8597,27.2597,130835 +Kwale,Kenya,Africa,-4.1737,39.4521,10063 +Kwidzyn,Poland,Europe,53.7358,18.9308,37768 +Kyegegwa,Uganda,Africa,0.4803,31.0547,29400 +Kyenjojo,Uganda,Africa,0.61,30.6442,23467 +Kyiv,Ukraine,Europe,50.45,30.5233,2952301 +Kyrenia,Cyprus,Europe,35.3403,33.3192,33207 +Kyshtym,Russia,Asia,55.7,60.55,35325 +Kysucké Nové Mesto,Slovakia,Europe,49.3,18.7833,14660 +Kyustendil,Bulgaria,Europe,42.2797,22.687,43534 +Kyzyl,Russia,Asia,51.7167,94.45,116015 +Kyzyl-Adyr,Kyrgyzstan,Asia,42.62,71.59,13612 +Kyzyl-Suu,Kyrgyzstan,Asia,42.3421,78.0064,15464 +Kyōto,Japan,Asia,35.0117,135.7683,1463723 +Kálymnos,Greece,Europe,36.99,26.985,9683 +Kâhta,Turkey,Europe,37.7803,38.6217,86232 +Kärdla,Estonia,Europe,58.9981,22.7467,3160 +Kävlinge,Sweden,Europe,55.8,13.1,32341 +Kédougou,Senegal,Africa,12.5556,-12.1807,11216 +Kérkyra,Greece,Europe,39.6239,19.9214,23541 +Kérouané,Guinea,Africa,9.2704,-9.02,15406 +Kórinthos,Greece,Europe,37.9386,22.9272,30816 +Kökshetaū,Kazakhstan,Asia,53.2833,69.3833,193492 +Köping,Sweden,Europe,59.5141,15.9915,17743 +Köprübaşı,Turkey,Europe,38.7497,28.4047,12730 +Köprüköy,Turkey,Europe,39.9756,41.8711,14204 +Körfez,Turkey,Europe,40.7833,29.7333,178048 +Körmend,Hungary,Europe,47.011,16.606,10177 +Köyceğiz,Turkey,Europe,36.9714,28.6889,39242 +Köşk,Turkey,Europe,37.8533,28.0517,28235 +Køge,Denmark,Europe,55.46,12.1834,38588 +Künzelsau,Germany,Europe,49.2833,9.6833,16436 +Kürdəmir,Azerbaijan,Europe,40.3383,48.1608,15385 +Küssnacht,Switzerland,Europe,47.0828,8.4408,13084 +Kütahya,Turkey,Europe,39.4167,29.9833,263863 +Küçükkuyu,Turkey,Europe,39.55,26.6167,10604 +Küçükçekmece,Turkey,Europe,41.0,28.8,326452 +Kāshmar,Iran,Asia,35.2447,58.4608,102282 +Kāshān,Iran,Asia,33.9825,51.4325,304487 +Kāzerūn,Iran,Asia,29.6156,51.6567,96683 +Kėdainiai,Lithuania,Europe,55.2833,23.9667,23447 +Kędzierzyn-Koźle,Poland,Europe,50.35,18.2,56931 +Kępno,Poland,Europe,51.2833,17.9833,13951 +Kętrzyn,Poland,Europe,54.0833,21.3833,26174 +Kınık,Turkey,Europe,39.0939,27.3767,28694 +Kırkağaç,Turkey,Europe,39.1056,27.6733,37645 +Kırklareli,Turkey,Europe,41.7347,27.2253,85493 +Kırıkhan,Turkey,Europe,36.4975,36.3608,121028 +Kırıkkale,Turkey,Europe,39.8417,33.5139,186960 +Kırşehir,Turkey,Europe,39.1456,34.1608,150700 +Kızılcahamam,Turkey,Europe,40.4697,32.6506,26872 +Kızıltepe,Turkey,Europe,37.1939,40.5861,267151 +Kłobuck,Poland,Europe,50.9167,18.9333,12513 +Kłodzko,Poland,Europe,50.4386,16.655,25717 +Kōbe,Japan,Asia,34.69,135.1956,1521707 +Kōchi,Japan,Asia,33.5589,133.5314,325535 +Kōfu,Japan,Asia,35.6621,138.5682,187144 +Kőszeg,Hungary,Europe,47.3819,16.5522,11757 +Kūhbanān,Iran,Asia,31.41,56.2825,10761 +Kūhdasht,Iran,Asia,33.5294,47.6092,89091 +Kūt-e ‘Abdollāh,Iran,Asia,31.2447,48.6594,56252 +Kūysinjaq,Iraq,Asia,36.0828,44.6286,19878 +Kŭlob,Tajikistan,Asia,37.9092,69.7819,214700 +Kəlbəcər,Azerbaijan,Europe,40.1098,46.0445,600 +Kỳ Anh,Vietnam,Asia,18.0678,106.2967,120518 +La Apartada,Colombia,South America,8.1006,-75.3825,15204 +La Argentina,Colombia,South America,2.1961,-75.98,12475 +La Asunción,Venezuela,South America,11.0333,-63.8628,28513 +La Banda,Argentina,South America,-27.7333,-64.25,106441 +La Barca,Mexico,North America,20.2833,-102.5667,67937 +La Belleza,Colombia,South America,5.8614,-73.9683,8596 +La Blanca,Guatemala,North America,14.5791,-92.1414,34307 +La Calera,Colombia,South America,4.7531,-73.92,28908 +La Carlota,Argentina,South America,-33.4333,-63.3,12537 +La Ceiba,Honduras,North America,15.7833,-86.7918,197267 +La Ceja,Colombia,South America,6.0306,-75.4317,64889 +La Celia,Colombia,South America,5.0019,-76.0039,9660 +La Chaux-de-Fonds,Switzerland,Europe,47.1028,6.83,38965 +La Chorrera,Panama,North America,8.8792,-79.7822,68896 +La Colonia Tovar,Venezuela,South America,10.4056,-67.2894,21000 +La Concepción,Nicaragua,North America,11.9333,-86.1833,31950 +La Cruz,Colombia,South America,1.6047,-76.9742,16674 +La Cruz de Río Grande,Nicaragua,North America,13.1167,-84.1833,23284 +La Cumbre,Colombia,South America,3.0,38.0,11512 +La Democracia,Guatemala,North America,15.6333,-91.8667,61486 +La Dorada,Colombia,South America,5.4538,-74.6647,72925 +La Esmeralda,Venezuela,South America,3.1739,-65.5467,20030 +La Esperanza,Guatemala,North America,14.8667,-91.5667,24948 +La Estrella,Colombia,South America,6.1667,-75.6667,71545 +La Florida,Colombia,South America,1.3017,-77.4106,9047 +La Flèche,France,Europe,47.6959,-0.0747,15081 +La Gloria,Colombia,South America,8.5833,-73.5833,14989 +La Gomera,Guatemala,North America,14.0833,-91.05,53211 +La Grita,Venezuela,South America,8.1333,-71.9833,90000 +La Guaira,Venezuela,South America,10.6,-66.9331,203520 +La Huacana,Mexico,North America,18.9625,-101.8069,34243 +La Huerta,Mexico,North America,19.4844,-104.6438,23258 +La Independencia,Mexico,North America,16.2526,-92.0237,46409 +La Jagua de Ibirico,Colombia,South America,9.5667,-73.3333,46722 +La Leonesa,Argentina,South America,-27.05,-58.7167,10067 +La Libertad,Guatemala,North America,16.7861,-90.1167,86171 +La Ligua,Chile,South America,-32.45,-71.2167,19127 +La Macarena,Colombia,South America,2.1828,-73.7847,36781 +La Magdalena Tlaltelulco,Mexico,North America,19.2667,-98.2,15046 +La Maná,Ecuador,South America,-0.93,-79.22,31740 +La Massana,Andorra,Europe,42.5444,1.5144,11591 +La Mesa,Colombia,South America,4.6303,-74.4625,33241 +La Montañita,Colombia,South America,1.5833,-75.25,15725 +La Máquina,Guatemala,North America,14.3,-91.5667,26062 +La Palma,Cuba,North America,22.7472,-83.5525,35346 +La Paz,Bolivia,South America,-16.4958,-68.1333,321073 +La Paz Centro,Nicaragua,North America,12.34,-86.6753,28118 +La Piedad,Mexico,North America,20.3333,-102.0167,83323 +La Plata,Argentina,South America,-34.9211,-57.9544,904789 +La Primavera,Colombia,South America,5.4906,-70.4092,17626 +La Quiaca,Argentina,South America,-22.1042,-65.5972,16874 +La Reforma,Guatemala,North America,14.8,-91.8167,23717 +La Resolana,Mexico,North America,19.3425,-104.2878,20548 +La Rioja,Argentina,South America,-29.4125,-66.8542,180995 +La Roche-sur-Yon,France,Europe,46.6705,-1.426,54699 +La Rochelle,France,Europe,46.16,-1.15,79961 +La Romana,Dominican Republic,North America,18.43,-68.97,139238 +La Serena,Chile,South America,-29.9,-71.25,195382 +La Sierpe,Cuba,North America,21.7606,-79.2433,16573 +La Sierra,Colombia,South America,2.25,-76.8333,9935 +La Spezia,Italy,Europe,44.108,9.8289,92119 +La Tebaida,Colombia,South America,4.45,-75.8,46323 +La Trinidad,Philippines,Asia,16.46,120.59,137404 +La Trinitaria,Mexico,North America,16.1203,-92.0408,11055 +La Troncal,Ecuador,South America,-2.4,-79.33,39600 +La Unión,Colombia,South America,3.6539,-76.5722,40339 +La Vega,Dominican Republic,North America,19.22,-70.53,220279 +La Victoria,Venezuela,South America,10.2278,-67.3336,143468 +La Virginia,Colombia,South America,4.9167,-75.8333,32330 +Labin,Croatia,Europe,45.0833,14.1167,10424 +Labinsk,Russia,Asia,44.6333,40.7333,28831 +Laboulaye,Argentina,South America,-34.1167,-63.4,20534 +Labuan,Malaysia,Asia,5.2803,115.2475,95120 +Labuleng,China,Asia,35.1973,102.5187,22970 +Labé,Guinea,Africa,11.3167,-12.2833,107695 +Lachen,Switzerland,Europe,47.1911,8.8567,8754 +Ladainha,Brazil,South America,-17.6319,-41.7378,14383 +Ladário,Brazil,South America,-19.005,-57.6019,21522 +Lae,Papua New Guinea,Oceania,-6.7303,147.0008,100677 +Lafayette,United States,North America,30.2082,-92.0325,231548 +Lafia,Nigeria,Africa,8.4917,8.5167,330712 +Lagarto,Brazil,South America,-10.9172,-37.65,105221 +Lagawe,Philippines,Asia,16.7975,121.1228,18876 +Laghouat,Algeria,Africa,33.8028,2.875,134372 +Lagkadás,Greece,Europe,40.75,23.0667,37022 +Lagoa,Portugal,Europe,37.1347,-8.4528,22975 +Lagoa Dourada,Brazil,South America,-20.9139,-44.0778,12769 +Lagoa Formosa,Brazil,South America,-18.7789,-46.4078,18904 +Lagoa Grande,Brazil,South America,-8.9969,-40.2719,24088 +Lagoa Real,Brazil,South America,-14.035,-42.1419,14105 +Lagoa Santa,Brazil,South America,-19.6357,-43.8966,75145 +Lagoa Sêca,Brazil,South America,-7.1708,-35.8536,27617 +Lagoa Vermelha,Brazil,South America,-28.2089,-51.5258,27659 +Lagoa da Canoa,Brazil,South America,-9.83,-36.7378,18457 +Lagoa da Confusão,Brazil,South America,-10.7939,-49.6239,15288 +Lagoa da Prata,Brazil,South America,-20.0225,-45.5436,52711 +Lagoa de Itaenga,Brazil,South America,-7.9358,-35.29,19003 +Lagoa do Carro,Brazil,South America,-7.845,-35.32,17981 +Lagoa do Mato,Brazil,South America,-6.0469,-43.5258,10572 +Lagoa do Ouro,Brazil,South America,-9.1269,-36.4589,11933 +Lagoa dos Gatos,Brazil,South America,-8.6578,-35.9,14076 +Lagos,Nigeria,Africa,6.455,3.3841,16637000 +Lagos de Moreno,Mexico,North America,21.35,-101.9167,172402 +Laguna,Brazil,South America,-28.4828,-48.7808,42785 +Lahat,Indonesia,Asia,-3.8,103.5333,65906 +Lahore,Pakistan,Asia,31.5497,74.3436,12306000 +Lahoysk,Belarus,Europe,54.2,27.85,15565 +Lahti,Finland,Europe,60.9833,25.65,120809 +Lai Châu,Vietnam,Asia,22.3992,103.4392,42973 +Laibin,China,Asia,23.7501,109.2212,2074611 +Laiyuan,China,Asia,39.3478,114.6808,75701 +Lajas,Cuba,North America,22.4164,-80.2906,22602 +Laje,Brazil,South America,-13.1819,-39.425,21052 +Lajeado,Brazil,South America,-29.4669,-51.9614,97432 +Lajedo,Brazil,South America,-8.6639,-36.32,32209 +Lajes,Brazil,South America,-27.8158,-50.3258,164981 +Lajinha,Brazil,South America,-20.1508,-41.6228,20835 +Lajkovac,Serbia,Europe,44.3667,20.1667,15475 +Lakatoro,Vanuatu,Oceania,-16.1069,167.4208,705 +Lake Charles,United States,North America,30.201,-93.2111,145110 +Lakeland,United States,North America,28.0557,-81.9545,290762 +Lakewood,United States,North America,39.6977,-105.1172,156309 +Lam Luk Ka,Thailand,Asia,13.9323,100.7494,15881 +Lambari,Brazil,South America,-21.9758,-45.35,20414 +Lambaréné,Gabon,Africa,-0.6883,10.2319,38775 +Lamego,Portugal,Europe,41.0975,-7.81,26691 +Lampang,Thailand,Asia,18.3,99.5,58074 +Lamphun,Thailand,Asia,18.5864,99.0119,12595 +Lamu,Kenya,Africa,-2.262,40.9197,25385 +Lamía,Greece,Europe,38.9,22.4333,47529 +Lancaster,United States,North America,40.042,-76.3012,408023 +Landau,Germany,Europe,49.2,8.1167,48341 +Landeh,Iran,Asia,30.9808,50.4214,12772 +Landgraaf,Netherlands,Europe,50.9083,6.0297,37262 +Landsberg,Germany,Europe,48.0478,10.8989,29739 +Landshut,Germany,Europe,48.5397,12.1508,75272 +Landskrona,Sweden,Europe,55.8667,12.8333,33372 +Landsmeer,Netherlands,Europe,52.4333,4.9167,11565 +Landázuri,Colombia,South America,6.2181,-73.8114,15395 +Lang Suan,Thailand,Asia,9.95,99.0667,11250 +Langarūd,Iran,Asia,37.1911,50.1556,79445 +Langfang,China,Asia,39.5383,116.6835,4358839 +Langnau,Switzerland,Europe,46.9433,7.7853,9366 +Langzhong,China,Asia,31.558,106.005,622667 +Lannion,France,Europe,48.7333,-3.4542,20525 +Lanquín,Guatemala,North America,15.5667,-89.9667,26551 +Lansing,United States,North America,42.7142,-84.5601,318001 +Lanxi,China,Asia,46.2664,126.276,72528 +Lanyi,China,Asia,38.7048,111.5601,38431 +Lanzhou,China,Asia,36.0606,103.8268,3067141 +Lanús,Argentina,South America,-34.7,-58.4,459263 +Laoag,Philippines,Asia,18.1978,120.5936,111651 +Laojiezi,China,Asia,26.86,103.1306,27580 +Laon,France,Europe,49.5639,3.6244,24066 +Lapa,Brazil,South America,-25.77,-49.7158,45003 +Lapinlahti,Finland,Europe,63.3667,27.3833,8975 +Lapovo,Serbia,Europe,44.1833,21.1,7143 +Lappeenranta,Finland,Europe,61.0667,28.1833,72959 +Lapseki,Turkey,Europe,40.3439,26.6836,14984 +Lapu-Lapu City,Philippines,Asia,10.3127,123.9488,497604 +Lapua,Finland,Europe,62.97,23.0083,14024 +Lapão,Brazil,South America,-11.3828,-41.8319,24727 +Laranja da Terra,Brazil,South America,-19.8989,-41.0567,10826 +Laranjal Paulista,Brazil,South America,-23.0118,-47.8657,26261 +Laranjeiras,Brazil,South America,-10.8061,-37.1717,23975 +Laranjeiras do Sul,Brazil,South America,-25.4078,-52.4158,32227 +Larantuka,Indonesia,Asia,-8.3133,123.0042,41642 +Laredo,United States,North America,27.5625,-99.4874,255949 +Laren,Netherlands,Europe,52.25,5.2333,11400 +Larkana,Pakistan,Asia,27.5583,68.2111,490508 +Larnaca,Cyprus,Europe,34.9167,33.6333,90419 +Las Breñas,Argentina,South America,-27.0667,-61.0667,22953 +Las Cruces,United States,North America,32.3265,-106.7893,142432 +Las Flores,Argentina,South America,-36.0139,-59.1,26651 +Las Heras,Argentina,South America,-32.85,-68.8167,189067 +Las Lajas,Argentina,South America,-38.6,-70.3,6215 +Las Margaritas,Mexico,North America,16.3153,-91.9817,24326 +Las Mercedes,Venezuela,South America,9.1104,-66.3961,33025 +Las Palmas,Spain,Europe,28.1258,-15.4353,635000 +Las Rosas,Mexico,North America,16.3667,-92.35,28829 +Las Tablas,Panama,North America,7.7667,-80.2833,9255 +Las Tunas,Cuba,North America,20.9667,-76.95,202105 +Las Vegas,United States,North America,36.2333,-115.2654,2256509 +Las Vigas de Ramírez,Mexico,North America,19.6369,-97.1,20300 +Lashkar Gāh,Afghanistan,Asia,31.5831,64.3692,276831 +Lata,Solomon Islands,Oceania,-10.738,165.8567,553 +Latacunga,Ecuador,South America,-0.9319,-78.6161,77267 +Latakia,Syria,Asia,35.54,35.78,700000 +Latina,Italy,Europe,41.4672,12.9036,127564 +Lauf,Germany,Europe,49.5103,11.2772,26413 +Laukaa,Finland,Europe,62.4167,25.95,18762 +Lauro Müller,Brazil,South America,-28.3928,-49.3969,14381 +Lauro de Freitas,Brazil,South America,-12.8939,-38.3269,113543 +Lausanne,Switzerland,Europe,46.52,6.6333,141418 +Lauterbach,Germany,Europe,50.6378,9.3944,13883 +Laval,Canada,North America,45.5833,-73.75,438366 +Lavras,Brazil,South America,-21.245,-45.0,104761 +Lavras da Mangabeira,Brazil,South America,-6.7528,-38.9719,30802 +Lavrentiya,Russia,Asia,65.5842,-170.9889,809 +Lavínia,Brazil,South America,-21.1683,-51.0397,12285 +Lazarevac,Serbia,Europe,44.3828,20.2583,55146 +Lazdijai,Lithuania,Europe,54.2347,23.5167,3895 +Laçın,Azerbaijan,Europe,39.6408,46.5469,2190 +Laï,Chad,Africa,9.4,16.3,20428 +Laško,Slovenia,Europe,46.1563,15.2386,3288 +Laḩij,Yemen,Asia,13.05,44.8833,30661 +Le Havre,France,Europe,49.49,0.1,166462 +Le Mans,France,Europe,48.0077,0.1984,145182 +Le Puy-en-Velay,France,Europe,45.0444,3.8847,18989 +Le Raincy,France,Europe,48.9,2.5167,14778 +League City,United States,North America,29.4874,-95.1087,114885 +Leandro N. Alem,Argentina,South America,-27.6,-55.3333,25404 +Leava,Wallis and Futuna,Oceania,-14.2933,-178.1583,322 +Lebane,Serbia,Europe,42.9167,21.7333,22000 +Lebedinovka,Kyrgyzstan,Asia,42.88,74.68,20709 +Lebon Régis,Brazil,South America,-26.9289,-50.695,11472 +Lebrija,Colombia,South America,7.1131,-73.2189,42000 +Lebu,Chile,South America,-37.6,-73.6667,23722 +Lecce,Italy,Europe,40.352,18.1691,94783 +Lecco,Italy,Europe,45.8533,9.3905,46871 +Lechang,China,Asia,35.6415,111.4633,64587 +Lecheng,China,Asia,25.1333,113.3333,383498 +Lecherías,Venezuela,South America,10.1917,-64.6921,36450 +Lee's Summit,United States,North America,38.9171,-94.3816,102583 +Leek,Netherlands,Europe,53.1616,6.3847,11175 +Leer,Germany,Europe,53.2308,7.4528,35163 +Leesburg,United States,North America,28.7672,-81.8962,161515 +Leeuwarden,Netherlands,Europe,53.2,5.7833,124481 +Lefkáda,Greece,Europe,38.7167,20.65,22652 +Legazpi City,Philippines,Asia,13.13,123.73,209533 +Legionowo,Poland,Europe,52.4,20.8833,53216 +Legnica,Poland,Europe,51.2083,16.1603,94878 +Lehigh Acres,United States,North America,26.612,-81.6388,127391 +Leibnitz,Austria,Europe,46.7831,15.545,13014 +Leiden,Netherlands,Europe,52.16,4.49,124093 +Leiderdorp,Netherlands,Europe,52.1667,4.5333,27377 +Leidschendam,Netherlands,Europe,52.0833,4.4,34660 +Leipzig,Germany,Europe,51.34,12.375,619879 +Leiria,Portugal,Europe,39.7431,-8.8069,126897 +Leiyang,China,Asia,26.4223,112.8598,1140675 +Leizhou,China,Asia,20.9143,110.0967,1321091 +Lejanías,Colombia,South America,3.5268,-74.0232,10576 +Leling,China,Asia,37.73,117.232,559166 +Lelydorp,Suriname,South America,5.6967,-55.2167,18663 +Lelystad,Netherlands,Europe,52.5167,5.4833,83033 +Leme,Brazil,South America,-22.1858,-47.39,80757 +Lemmer,Netherlands,Europe,52.8437,5.7093,10395 +Lempäälä,Finland,Europe,61.3167,23.75,24711 +Lenart v Slovenskih Goricah,Slovenia,Europe,46.5755,15.8306,3285 +Lendava,Slovenia,Europe,46.5662,16.4499,2954 +Lengir,Kazakhstan,Asia,42.1819,69.8878,25298 +Lenguazaque,Colombia,South America,5.3069,-73.7061,10268 +Leninsk,Russia,Asia,48.7,45.2167,13391 +Lens,France,Europe,50.4322,2.8333,32697 +Lensk,Russia,Asia,60.7333,114.9167,21181 +Lençóis,Brazil,South America,-12.5628,-41.39,11586 +Lençóis Paulista,Brazil,South America,-22.5986,-48.8003,66131 +Leoben,Austria,Europe,47.3817,15.0972,24645 +Leominster,United States,North America,42.5209,-71.7717,113462 +Leopoldina,Brazil,South America,-21.5319,-42.6428,51145 +Leopoldo de Bulhões,Brazil,South America,-16.6189,-48.7439,8745 +Leova,Moldova,Europe,46.4786,28.2553,7443 +Leping,China,Asia,28.978,117.152,753787 +Leposaviq,Kosovo,Europe,43.1039,20.8028,18600 +Leppävirta,Finland,Europe,62.5,27.7833,9049 +Leramatang,Indonesia,Asia,-8.4,123.57,40200 +Lerdo de Tejada,Mexico,North America,18.6239,-95.5142,19606 +Leribe,Lesotho,Africa,-28.8734,28.0416,38558 +Lerik,Azerbaijan,Europe,38.7753,48.4153,7301 +Lerma,Mexico,North America,19.2847,-99.5119,105578 +Lermontov,Russia,Asia,44.1167,42.9833,22887 +Lerum,Sweden,Europe,57.7667,12.2667,16855 +Les Cayes,Haiti,North America,18.2,-73.75,86780 +Les Sables-d’Olonne,France,Europe,46.4972,-1.7833,14545 +Leshou,China,Asia,38.1822,116.1108,88069 +Leskovac,Serbia,Europe,42.9981,21.9461,20913 +Lesnoy,Russia,Asia,58.6333,59.7833,48231 +Lesosibirsk,Russia,Asia,58.2358,92.4828,55730 +Lesozavodsk,Russia,Asia,45.4667,133.4,34967 +Leszno,Poland,Europe,51.8458,16.5806,61791 +Lethem,Guyana,South America,3.3833,-59.8,3000 +Leticia,Colombia,South America,-4.2167,-69.9333,33503 +Leulumoega,Samoa,Oceania,-13.8167,-171.9167,1184 +Leusden,Netherlands,Europe,52.1331,5.4297,30544 +Levakant,Tajikistan,Asia,37.8667,68.9167,48300 +Levanger,Norway,Europe,63.7464,11.2996,10333 +Leverkusen,Germany,Europe,51.0333,6.9833,166414 +Levice,Slovakia,Europe,48.2136,18.6069,31974 +Levoča,Slovakia,Europe,49.0228,20.5906,14256 +Lewisville,United States,North America,33.0454,-96.9815,128313 +Lewoleba,Indonesia,Asia,-8.3833,123.4,30151 +Lexington,United States,North America,38.0423,-84.4587,321122 +Lezhë,Albania,Europe,41.7819,19.6444,15510 +León,Nicaragua,North America,12.4333,-86.8867,176171 +León de los Aldama,Mexico,North America,21.1167,-101.6833,1579803 +Leżajsk,Poland,Europe,50.2667,22.4333,13164 +Lgov,Russia,Asia,51.6667,35.2667,16779 +Lhasa,China,Asia,29.6534,91.1719,867891 +Liancheng,China,Asia,24.0519,105.075,160961 +Lianga,Philippines,Asia,8.633,126.0932,33869 +Lianhe,China,Asia,47.1314,129.2906,121367 +Lianran,China,Asia,24.919,102.478,483753 +Lianshan,China,Asia,40.7523,120.828,2426000 +Lianyuan,China,Asia,27.692,111.664,862099 +Lianzhou,China,Asia,24.781,112.377,377220 +Liaocheng,China,Asia,36.4559,115.9852,5789863 +Liaoyuan,China,Asia,42.888,125.1447,996903 +Liberec,Czechia,Europe,50.7667,15.0667,107982 +Liberia,Costa Rica,North America,10.6333,-85.4333,53382 +Libertador General San Martín,Argentina,South America,-23.8,-64.7833,46642 +Libourne,France,Europe,44.92,-0.24,24668 +Libreville,Gabon,Africa,0.3901,9.4544,797003 +Lice,Turkey,Europe,38.45,40.65,24364 +Licheng,China,Asia,31.4306,119.4788,749522 +Lichinga,Mozambique,Africa,-13.3,35.2456,142253 +Lichtenfels,Germany,Europe,50.1333,11.0333,20403 +Licínio de Almeida,Brazil,South America,-14.6819,-42.5078,11834 +Lidingö,Sweden,Europe,59.363,18.151,48377 +Lidköping,Sweden,Europe,58.5,13.1667,40000 +Lidzbark Warmiński,Poland,Europe,54.1167,20.5833,14878 +Lieksa,Finland,Europe,63.3167,30.0167,10228 +Lienz,Austria,Europe,46.8297,12.7697,11935 +Liepāja,Latvia,Europe,56.5083,21.0111,66680 +Lier,Norway,Europe,59.8675,10.2142,28167 +Liestal,Switzerland,Europe,47.4839,7.735,14390 +Lieto,Finland,Europe,60.505,22.4583,20637 +Lifford,Ireland,Europe,54.8356,-7.4779,1613 +Lija,Malta,Europe,35.9014,14.4472,3070 +Likhoslavl,Russia,Asia,57.1333,35.4833,11017 +Lille,France,Europe,50.6278,3.0583,238695 +Lillehammer,Norway,Europe,61.1161,10.4578,20580 +Lilongwe,Malawi,Africa,-13.9669,33.7873,989318 +Lima,Peru,South America,-12.06,-77.0375,10320000 +Lima Duarte,Brazil,South America,-21.8428,-43.7928,17221 +Limanowa,Poland,Europe,49.7006,20.4267,14635 +Limassol,Cyprus,Europe,34.6747,33.0442,235056 +Limbaži,Latvia,Europe,57.5147,24.7131,6613 +Limburg,Germany,Europe,50.3833,8.0667,36506 +Limeira,Brazil,South America,-22.5647,-47.4017,249046 +Limeira d’Oeste,Brazil,South America,-19.5508,-50.5808,8687 +Limerick,Ireland,Europe,52.6653,-8.6238,62702 +Liminka,Finland,Europe,64.8083,25.4167,10256 +Limoeiro,Brazil,South America,-7.875,-35.45,56510 +Limoeiro de Anadia,Brazil,South America,-9.7406,-36.5028,28953 +Limoeiro do Ajuru,Brazil,South America,-1.895,-49.3808,29569 +Limoeiro do Norte,Brazil,South America,-5.1458,-38.0978,59560 +Limoges,France,Europe,45.8353,1.2625,129754 +Limonar,Cuba,North America,22.9561,-81.4086,25421 +Limoux,France,Europe,43.0578,2.2192,10339 +Linares,Chile,South America,-35.85,-71.6,73602 +Lincoln,United States,North America,40.8099,-96.6784,293055 +Linden,Guyana,South America,6.0,-58.3,28674 +Lindesberg,Sweden,Europe,59.6024,15.213,9672 +Lindi,Tanzania,Africa,-9.9969,39.7144,95096 +Lindong,China,Asia,43.9681,119.3756,68000 +Lingayen,Philippines,Asia,16.0167,120.2333,107728 +Lingbao Chengguanzhen,China,Asia,34.5176,110.8942,736300 +Lingquan,China,Asia,36.9985,110.8417,41996 +Lingshou,China,Asia,38.3064,114.3783,62713 +Linhares,Brazil,South America,-19.3987,-40.0651,166786 +Linjiang,China,Asia,41.8167,126.9167,121616 +Linkou,China,Asia,45.2819,130.2519,77754 +Linköping,Sweden,Europe,58.4094,15.6257,116851 +Linquan,China,Asia,37.9513,110.9877,91670 +Lins,Brazil,South America,-21.6786,-49.7425,76092 +Linxi,China,Asia,43.5171,118.0333,679 +Linxia Chengguanzhen,China,Asia,35.6047,103.2422,355968 +Linz,Austria,Europe,48.3058,14.2864,211944 +Lin’an,China,Asia,23.6025,102.8309,232160 +Liperi,Finland,Europe,62.5333,29.3833,11969 +Lipetsk,Russia,Asia,52.6167,39.6,496403 +Lipjan,Kosovo,Europe,42.5217,21.1258,77605 +Lipno,Poland,Europe,52.85,19.1667,13752 +Liptovský Mikuláš,Slovakia,Europe,49.0811,19.6181,30522 +Liquiçá,Timor-Leste,Asia,-8.5875,125.3419,5152 +Lira,Uganda,Africa,2.2472,32.9,99059 +Lisala,Congo (Kinshasa),Africa,2.1486,21.5136,79235 +Lisbon,Portugal,Europe,38.7122,-9.134,548703 +Lishui,China,Asia,28.468,119.923,2116957 +Lisieux,France,Europe,49.15,0.23,19540 +Lisse,Netherlands,Europe,52.25,4.55,22982 +Litija,Slovenia,Europe,46.0667,14.8167,6688 +Little Rock,United States,North America,34.7256,-92.3577,467846 +Liubashivka,Ukraine,Europe,47.8358,30.2642,11500 +Liuhe,China,Asia,42.2669,125.7404,68938 +Liujiaxia,China,Asia,35.9423,103.3146,48505 +Liulin,China,Asia,34.5891,103.5081,17731 +Liuzhou,China,Asia,24.3278,109.4278,4041700 +Livadeiá,Greece,Europe,38.4333,22.8667,29379 +Livno,Bosnia and Herzegovina,Europe,43.8269,17.0081,9045 +Livny,Russia,Asia,52.4239,37.5997,42928 +Livorno,Italy,Europe,43.55,10.3167,152914 +Livramento de Nossa Senhora,Brazil,South America,-13.6428,-41.8408,38025 +Liège,Belgium,Europe,50.6397,5.5706,195278 +Ljubljana,Slovenia,Europe,46.0514,14.5061,284293 +Ljubno,Slovenia,Europe,46.3333,14.8333,1094 +Ljubuški,Bosnia and Herzegovina,Europe,43.1981,17.5467,28184 +Ljungby,Sweden,Europe,56.8333,13.9333,15205 +Ljutomer,Slovenia,Europe,46.5208,16.1975,3244 +Lleida,Spain,Europe,41.6167,0.6333,144739 +Lloró,Colombia,South America,5.5,-76.5333,11197 +Loanda,Brazil,South America,-22.9228,-53.1369,23225 +Lobamba,Eswatini,Africa,-26.4667,31.2,5800 +Lobatse,Botswana,Africa,-25.2167,25.6667,26900 +Lobería,Argentina,South America,-38.1333,-58.7667,12199 +Lobos,Argentina,South America,-35.1833,-59.0833,31190 +Lochem,Netherlands,Europe,52.1606,6.4158,33948 +Lodeynoye Pole,Russia,Asia,60.7167,33.55,18444 +Lodhran,Pakistan,Asia,29.5333,71.6333,144512 +Lodi,Italy,Europe,45.3167,9.5,44709 +Lodwar,Kenya,Africa,3.1167,35.6,16981 +Loei,Thailand,Asia,17.4853,101.7303,21013 +Logan,United States,North America,41.7399,-111.8422,122915 +Logatec,Slovenia,Europe,45.9167,14.2297,9091 +Logroño,Spain,Europe,42.465,-2.4456,151164 +Lohja,Finland,Europe,60.25,24.0667,45645 +Loikaw,Burma,Asia,19.6742,97.2092,11000 +Loimaa,Finland,Europe,60.85,23.0583,15423 +Loja,Ecuador,South America,-3.9833,-79.2,203496 +Lokhvytsya,Ukraine,Europe,50.3598,33.2723,11014 +Lokoja,Nigeria,Africa,7.8019,6.7442,692050 +Lokossa,Benin,Africa,6.6333,1.7167,106081 +Lom,Bulgaria,Europe,43.8256,23.2375,21425 +Lom Sak,Thailand,Asia,16.7798,101.2422,11685 +Lomas de Sargentillo,Ecuador,South America,-1.8833,-80.0833,16603 +Lomas de Zamora,Argentina,South America,-34.7667,-58.4,112580 +Lomma,Sweden,Europe,55.6833,13.0833,13772 +Lomé,Togo,Africa,6.1308,1.2153,1500000 +London,United Kingdom,Europe,51.5072,-0.1275,11262000 +Londrina,Brazil,South America,-23.31,-51.1628,447065 +Long Beach,United States,North America,33.7977,-118.167,458491 +Long Khánh,Vietnam,Asia,10.917,107.167,151467 +Long Mỹ,Vietnam,Asia,9.6814,105.5708,71963 +Long Thành,Vietnam,Asia,10.8667,106.9167,27084 +Long Xuyên,Vietnam,Asia,10.3736,105.4458,278658 +Longchuan,China,Asia,25.1615,101.2942,84955 +Longford,Ireland,Europe,53.727,-7.7998,10952 +Longhua,China,Asia,41.3246,117.7344,85697 +Longjiang,China,Asia,47.3404,123.18,106384 +Longjing,China,Asia,42.7667,129.4333,129286 +Longmont,United States,North America,40.1686,-105.1005,102183 +Longquan,China,Asia,40.3703,113.7483,95849 +Longueuil,Canada,North America,45.5333,-73.5167,254483 +Longview,United States,North America,32.5193,-94.7622,112687 +Longxing,China,Asia,35.6091,111.2304,56381 +Longyan,China,Asia,25.076,117.017,2640000 +Longyearbyen,Svalbard,Europe,78.2167,15.6333,1753 +Longzhou,China,Asia,38.4348,114.5465,70614 +Lons-le-Saunier,France,Europe,46.6744,5.5539,16942 +Lontra,Brazil,South America,-15.9073,-44.3038,8790 +Lontras,Brazil,South America,-27.1658,-49.5419,12873 +Lop Buri,Thailand,Asia,14.8,100.6269,58000 +Lopare,Bosnia and Herzegovina,Europe,44.635,18.8433,15357 +Lopik,Netherlands,Europe,51.9722,4.9464,14456 +Lorain,United States,North America,41.4409,-82.184,203802 +Lordegān,Iran,Asia,31.5094,50.8344,40528 +Lorena,Brazil,South America,-22.7308,-45.1247,87178 +Lorengau,Papua New Guinea,Oceania,-2.0208,147.2667,6313 +Loreto,Mexico,North America,22.2667,-101.9833,43411 +Lorica,Colombia,South America,9.2419,-75.816,52771 +Lorient,France,Europe,47.75,-3.36,58202 +Los Amates,Guatemala,North America,15.2667,-89.1,65475 +Los Andes,Chile,South America,-32.8333,-70.6167,59388 +Los Angeles,United States,North America,34.1141,-118.4068,11885717 +Los Arabos,Cuba,North America,22.74,-80.7161,23111 +Los Córdobas,Colombia,South America,8.9,-76.3667,26705 +Los Guayos,Venezuela,South America,10.1833,-67.9333,130345 +Los Mochis,Mexico,North America,25.7835,-108.9937,298009 +Los Palacios,Cuba,North America,22.5822,-83.2489,38731 +Los Palmitos,Colombia,South America,9.3811,-75.2714,21831 +Los Patios,Colombia,South America,7.8333,-72.5167,94847 +Los Polvorines,Argentina,South America,-34.5,-58.7,60867 +Los Reyes de Juárez,Mexico,North America,18.9458,-97.8058,27454 +Los Santos,Colombia,South America,6.9167,-73.0833,12648 +Los Teques,Venezuela,South America,10.3333,-67.0417,252242 +Los Ángeles,Chile,South America,-37.4667,-72.35,143023 +Losino-Petrovskiy,Russia,Asia,55.8744,38.2017,29143 +Losser,Netherlands,Europe,52.2617,7.0044,22888 +Louang Namtha,Laos,Asia,20.95,101.4,54100 +Louangphabang,Laos,Asia,19.89,102.1347,53792 +Loufan,China,Asia,38.0694,111.7911,42520 +Louga,Senegal,Africa,15.6167,-16.2167,82884 +Louisville,United States,North America,38.1663,-85.6485,965005 +Loulé,Portugal,Europe,37.144,-8.0235,70622 +Loures,Portugal,Europe,38.8309,-9.1684,201632 +Lourinhã,Portugal,Europe,39.2425,-9.3122,25735 +Lousada,Portugal,Europe,41.2833,-8.2833,47387 +Lousã,Portugal,Europe,40.1125,-8.2469,17604 +Louveira,Brazil,South America,-23.0864,-46.9506,49993 +Lovech,Bulgaria,Europe,43.1348,24.7115,31899 +Loviisa,Finland,Europe,60.4583,26.2333,14557 +Lovrenc na Pohorju,Slovenia,Europe,46.5381,15.3844,1992 +Lowell,United States,North America,42.6389,-71.3217,114799 +Loyish Shaharchasi,Uzbekistan,Asia,39.8839,66.7481,12769 +Loznica,Serbia,Europe,44.5333,19.2258,86413 +Lozova,Ukraine,Europe,48.8833,36.3833,53126 +Lozovo,North Macedonia,Europe,41.7817,21.9025,521 +Luancheng,China,Asia,37.9003,114.6483,84068 +Luanda,Angola,Africa,-8.8383,13.2344,9051000 +Luanzhou,China,Asia,39.741,118.704,520102 +Luba,Equatorial Guinea,Africa,3.45,8.55,7739 +Lubaczów,Poland,Europe,50.1667,23.1167,11619 +Lubango,Angola,Africa,-14.9167,13.5,776249 +Lubartów,Poland,Europe,51.4667,22.6,20494 +Lubań,Poland,Europe,51.1181,15.2889,20096 +Lubbock,United States,North America,33.5657,-101.8879,283024 +Lubin,Poland,Europe,51.3972,16.2056,70016 +Lublin,Poland,Europe,51.25,22.5667,334681 +Lubliniec,Poland,Europe,50.6833,18.6833,23427 +Lubny,Ukraine,Europe,50.0167,33.0,44089 +Lubuk Sikaping,Indonesia,Asia,0.15,100.1667,53150 +Lubumbashi,Congo (Kinshasa),Africa,-11.6647,27.4794,1786397 +Lucas do Rio Verde,Brazil,South America,-13.05,-55.9108,83798 +Lucca,Italy,Europe,43.85,10.5167,88798 +Lucea,Jamaica,North America,18.45,-78.1833,6002 +Lucena,Philippines,Asia,13.93,121.62,278924 +Lucerne,Switzerland,Europe,47.0523,8.3059,81691 +Lucheng,China,Asia,29.9985,101.9569,126785 +Luckenwalde,Germany,Europe,52.0833,13.1667,21000 +Lucknow,India,Asia,26.85,80.95,3382000 +Lucélia,Brazil,South America,-21.7203,-51.0189,21196 +Ludvika,Sweden,Europe,60.15,15.1833,14498 +Ludwigsburg,Germany,Europe,48.8975,9.1922,94859 +Ludwigshafen,Germany,Europe,49.4811,8.4353,176110 +Ludza,Latvia,Europe,56.5439,27.7211,7524 +Luena,Angola,Africa,-11.7918,19.9062,357413 +Lufilufi,Samoa,Oceania,-13.85,-171.5833,949 +Luga,Russia,Asia,58.7333,29.8167,37015 +Luganville,Vanuatu,Oceania,-15.5333,167.1667,18062 +Lugo,Spain,Europe,43.0167,-7.55,99482 +Luhansk,Ukraine,Europe,48.5678,39.3031,397677 +Luján,Argentina,South America,-34.5667,-59.1,106899 +Luján de Cuyo,Argentina,South America,-32.998,-68.873,73058 +Lukavac,Bosnia and Herzegovina,Europe,44.5333,18.5333,46731 +Lukovica,Slovenia,Europe,46.1686,14.6892,438 +Luleå,Sweden,Europe,65.5838,22.1915,49646 +Lumphat,Cambodia,Asia,13.507,106.981,19205 +Luninyets,Belarus,Europe,52.25,26.8,23592 +Lunéville,France,Europe,48.5894,6.5017,17920 +Luocheng,China,Asia,22.7689,111.5697,936931 +Luofeng,China,Asia,37.359,120.396,543127 +Luohe,China,Asia,33.5804,114.0166,2367490 +Luoyang,China,Asia,34.6197,112.4539,2372571 +Lupon,Philippines,Asia,6.8969,126.0117,66979 +Luqa,Malta,Europe,35.8597,14.4892,5945 +Luruaco,Colombia,South America,10.6083,-75.1417,22878 +Lusaka,Zambia,Africa,-15.4167,28.2833,1747152 +Lusambo,Congo (Kinshasa),Africa,-4.9729,23.4368,41416 +Lushar,China,Asia,36.4842,101.5633,67153 +Lutsk,Ukraine,Europe,50.75,25.3358,220986 +Luwero,Uganda,Africa,0.8331,32.4994,49100 +Luwuk,Indonesia,Asia,-0.9396,122.79,47778 +Luxembourg,Luxembourg,Europe,49.6117,6.1319,132780 +Luxor,Egypt,Africa,25.6967,32.6444,202232 +Luz,Brazil,South America,-19.8008,-45.6858,18257 +Luzhang,China,Asia,25.8226,98.8577,203977 +Luzhou,China,Asia,28.871,105.442,4218427 +Luzilândia,Brazil,South America,-3.4578,-42.37,25375 +Luziânia,Brazil,South America,-16.2528,-47.95,174531 +Luís Alves,Brazil,South America,-26.7208,-48.9328,11684 +Luís Antônio,Brazil,South America,-21.555,-47.7044,13378 +Luís Correia,Brazil,South America,-2.8789,-41.6669,30641 +Luís Gomes,Brazil,South America,-6.4139,-38.3889,9070 +Lučani,Serbia,Europe,43.8667,20.1333,20897 +Luče,Slovenia,Europe,46.3565,14.7436,402 +Lučenec,Slovakia,Europe,48.3314,19.6708,25902 +Lu’an,China,Asia,31.736,116.52,4393699 +Lviv,Ukraine,Europe,49.8425,24.0322,717273 +Lwengo,Uganda,Africa,-0.3911,31.4606,15527 +Lyakhavichy,Belarus,Europe,53.0333,26.2667,10605 +Lyantonde,Uganda,Africa,-0.4069,31.1575,16300 +Lycksele,Sweden,Europe,64.6,18.6667,8513 +Lyelchytsy,Belarus,Europe,51.7894,28.3214,12334 +Lynchburg,United States,North America,37.4003,-79.1909,123662 +Lyngdal,Norway,Europe,58.1333,7.0833,10751 +Lynn,United States,North America,42.4781,-70.9664,100905 +Lyon,France,Europe,45.76,4.84,520774 +Lysva,Russia,Asia,58.1003,57.8042,53057 +Lytkarino,Russia,Asia,55.5833,37.9,66526 +Lyuban,Belarus,Europe,52.7819,28.0525,11339 +Lyubertsy,Russia,Asia,55.6783,37.8944,236339 +Lyudinovo,Russia,Asia,53.8667,34.4333,41829 +Lào Cai,Vietnam,Asia,22.4806,103.975,130671 +Lábrea,Brazil,South America,-7.2589,-64.7978,45448 +Lárisa,Greece,Europe,39.6417,22.4167,112777 +Léo,Burkina Faso,Africa,11.1,-2.1,33717 +Lérida,Colombia,South America,4.9,-74.9167,17197 +Lévis,Canada,North America,46.8,-71.1833,149683 +Líbano,Colombia,South America,4.9167,-75.1667,39665 +Lívingston,Guatemala,North America,15.83,-88.75,80249 +Lörrach,Germany,Europe,47.6167,7.6667,50670 +Lørenskog,Norway,Europe,59.8989,10.9642,46797 +Lübben (Spreewald),Germany,Europe,51.95,13.9,13967 +Lübeck,Germany,Europe,53.8697,10.6864,219044 +Lüchow,Germany,Europe,52.9667,11.15,9494 +Lüdenscheid,Germany,Europe,51.2167,7.6333,71463 +Lüleburgaz,Turkey,Europe,41.4056,27.3569,125404 +Lüneburg,Germany,Europe,53.2525,10.4144,77511 +Lāmerd,Iran,Asia,27.3336,53.1886,29380 +Lębork,Poland,Europe,54.55,17.75,34500 +Līkak,Iran,Asia,30.8928,50.0928,19857 +Līvāni,Latvia,Europe,56.3539,26.1761,6790 +Lənkəran,Azerbaijan,Europe,38.7536,48.8511,51300 +Lạng Sơn,Vietnam,Asia,21.8478,106.7578,200108 +L’Aquila,Italy,Europe,42.354,13.392,69558 +L’Haÿ-les-Roses,France,Europe,48.78,2.3374,31338 +Maardu,Estonia,Europe,59.4781,25.0161,16170 +Maarssen,Netherlands,Europe,52.1351,5.0413,39752 +Maaseik,Belgium,Europe,51.1019,5.7856,25201 +Maasin,Philippines,Asia,10.13,124.85,87446 +Maassluis,Netherlands,Europe,51.9189,4.2567,33567 +Maastricht,Netherlands,Europe,50.8667,5.6833,120227 +Maba,Indonesia,Asia,0.7,128.3,12560 +Mabai,China,Asia,23.0128,104.4508,99930 +Mabaruma,Guyana,South America,8.2,-59.7833,1254 +Macajuba,Brazil,South America,-12.1358,-40.36,10454 +Macaparana,Brazil,South America,-7.555,-35.4528,23879 +Macapá,Brazil,South America,0.033,-51.05,512902 +Macas,Ecuador,South America,-2.3667,-78.1333,22398 +Macatuba,Brazil,South America,-22.5022,-48.7114,17013 +Macau,Brazil,South America,-5.115,-36.6339,27369 +Macaé,Brazil,South America,-22.3708,-41.7869,246391 +Macaíba,Brazil,South America,-5.8578,-35.3539,54883 +Macaúbas,Brazil,South America,-13.0189,-42.6989,41859 +Macedo de Cavaleiros,Portugal,Europe,41.5333,-6.95,15776 +Maceió,Brazil,South America,-9.6658,-35.735,957916 +Macenta,Guinea,Africa,8.5504,-9.48,43102 +Macerata,Italy,Europe,43.3003,13.4533,40496 +Machachi,Ecuador,South America,-0.51,-78.57,24188 +Machado,Brazil,South America,-21.675,-45.92,37684 +Machados,Brazil,South America,-7.6858,-35.515,11333 +Machagai,Argentina,South America,-26.9167,-60.0333,21997 +Machakos,Kenya,Africa,-1.5167,37.2667,114109 +Machala,Ecuador,South America,-3.2667,-79.9667,288072 +Machinga,Malawi,Africa,-14.9667,35.5167,1418 +Machiques,Venezuela,South America,10.0667,-72.5667,63000 +Macon,United States,North America,32.8065,-83.6974,156543 +Macuspana,Mexico,North America,17.7667,-92.6,158601 +Madalena,Brazil,South America,-4.8569,-39.5769,14864 +Madang,Papua New Guinea,Oceania,-5.2248,145.7853,29339 +Madaoua,Niger,Africa,14.0762,5.9586,24804 +Made,Netherlands,Europe,51.6764,4.7925,12470 +Madera,Mexico,North America,29.19,-108.1414,15447 +Madingou,Congo (Brazzaville),Africa,-4.1642,13.5517,36183 +Madison,United States,North America,43.0822,-89.393,460255 +Madona,Latvia,Europe,56.8542,26.2206,6561 +Madre de Deus,Brazil,South America,-12.7408,-38.6208,18504 +Madrid,Spain,Europe,40.4169,-3.7033,6211000 +Madruga,Cuba,North America,22.9114,-81.8561,26412 +Madurai,India,Asia,9.9252,78.1198,1561129 +Mae Hong Son,Thailand,Asia,19.3011,97.97,7066 +Mae Rim,Thailand,Asia,18.9106,98.948,9567 +Mae Sot,Thailand,Asia,16.7131,98.5747,52350 +Mae Wang,Thailand,Asia,18.6567,98.6816,10879 +Maebashi,Japan,Asia,36.3895,139.0634,331849 +Maevatanana,Madagascar,Africa,-16.947,46.8292,25928 +Mafeteng,Lesotho,Africa,-29.8167,27.25,57059 +Mafra,Portugal,Europe,38.9333,-9.3333,76685 +Magadan,Russia,Asia,59.5667,150.8,89193 +Magalang,Philippines,Asia,15.22,120.67,124188 +Magalhães de Almeida,Brazil,South America,-3.3958,-42.2039,13807 +Magangué,Colombia,South America,9.25,-74.7667,123906 +Magas,Russia,Asia,43.1667,44.8167,10333 +Magdagachi,Russia,Asia,53.45,125.8,11739 +Magdalena,Mexico,North America,20.9167,-103.95,21781 +Magdalena Milpas Altas,Guatemala,North America,14.5453,-90.6753,12766 +Magdalena de Kino,Mexico,North America,30.6167,-111.05,30429 +Magdeburg,Germany,Europe,52.1317,11.6392,240114 +Magitang,China,Asia,35.9506,101.9686,19226 +Maglaj,Bosnia and Herzegovina,Europe,44.5478,18.1,34980 +Magnitogorsk,Russia,Asia,53.3833,59.0333,408715 +Magong,Taiwan,Asia,23.5667,119.5833,63745 +Magway,Burma,Asia,20.15,94.95,289247 +Magé,Brazil,South America,-22.6528,-43.0408,228127 +Maha Sarakham,Thailand,Asia,16.1772,103.3008,40154 +Mahajanga,Madagascar,Africa,-15.67,46.345,246022 +Mahates,Colombia,South America,10.2333,-75.1833,26075 +Mahdia,Tunisia,Africa,35.5,11.0667,76513 +Mahdīshahr,Iran,Asia,35.7144,53.3569,24485 +Mahibadhoo,Maldives,Asia,3.7575,72.9686,3000 +Mahikeng,South Africa,Africa,-25.8656,25.6436,291527 +Mahilyow,Belarus,Europe,53.9167,30.35,353110 +Mahīn,Syria,Asia,34.2417,37.0584,11064 +Maia,Portugal,Europe,41.2333,-8.6167,134977 +Maicao,Colombia,South America,11.3778,-72.2389,123757 +Maiduguri,Nigeria,Africa,11.8372,13.1542,1197497 +Maintirano,Madagascar,Africa,-18.0666,44.0167,25788 +Mainz,Germany,Europe,49.9994,8.2736,222889 +Maipú,Argentina,South America,-32.9667,-68.75,106662 +Maiquetía,Venezuela,South America,10.5958,-66.9772,87909 +Maiquinique,Brazil,South America,-15.6208,-40.2658,8731 +Mairi,Brazil,South America,-11.7108,-40.1489,17674 +Mairinque,Brazil,South America,-23.5464,-47.1836,46015 +Mairiporã,Brazil,South America,-23.3189,-46.5869,93853 +Mai’Adua,Nigeria,Africa,13.1906,8.2117,201178 +Majagua,Cuba,North America,21.9244,-78.9906,26830 +Majagual,Colombia,South America,8.5,-74.6667,34010 +Majalengka,Indonesia,Asia,-6.8353,108.2278,73948 +Majdanpek,Serbia,Europe,44.4167,21.9333,18686 +Majene,Indonesia,Asia,-3.5403,118.9707,272377 +Major Isidoro,Brazil,South America,-9.5319,-36.985,17700 +Majuro,Marshall Islands,Oceania,7.0833,171.3833,30000 +Majšperk,Slovenia,Europe,46.3517,15.7336,606 +Makale,Indonesia,Asia,-3.1,119.85,9960 +Makamba,Burundi,Africa,-4.1333,29.8,9396 +Makariv,Ukraine,Europe,50.4597,29.8147,9390 +Makarov,Russia,Asia,48.6333,142.8,5691 +Makarska,Croatia,Europe,43.2931,17.02,13301 +Makassar,Indonesia,Asia,-5.1331,119.4136,1338663 +Makati City,Philippines,Asia,14.5567,121.0214,629616 +Makedonski Brod,North Macedonia,Europe,41.5133,21.2153,3643 +Makeni,Sierra Leone,Africa,8.8817,-12.0442,12304 +Makhachkala,Russia,Asia,42.9825,47.505,662660 +Makhambet,Kazakhstan,Asia,47.6667,51.5833,8905 +Makhmūr,Iraq,Asia,35.7833,43.5833,23828 +Makiivka,Ukraine,Europe,48.0556,37.9611,338968 +Makokou,Gabon,Africa,0.5667,12.8667,20653 +Makole,Slovenia,Europe,46.3172,15.6672,221 +Makurdi,Nigeria,Africa,7.7333,8.5,170925 +Makó,Hungary,Europe,46.22,20.4789,21759 +Maków Mazowiecki,Poland,Europe,52.8667,21.1,9173 +Makīnsk,Kazakhstan,Asia,52.6292,70.4169,16818 +Mala Vyska,Ukraine,Europe,48.65,31.6333,13132 +Malabo,Equatorial Guinea,Africa,3.7456,8.7744,297000 +Malabon,Philippines,Asia,14.66,120.96,380522 +Malacacheta,Brazil,South America,-17.8419,-42.0769,17516 +Malacatancito,Guatemala,North America,15.2167,-91.5167,22859 +Malacatán,Guatemala,North America,14.9,-92.0497,109318 +Malacky,Slovakia,Europe,48.4381,17.0236,18804 +Maladzyechna,Belarus,Europe,54.3208,26.8572,89068 +Malakal,South Sudan,Africa,9.5369,31.656,147450 +Malambo,Colombia,South America,10.85,-74.75,139566 +Malanje,Angola,Africa,-9.5333,16.35,604215 +Malargüe,Argentina,South America,-35.475,-69.5833,27660 +Malaryta,Belarus,Europe,51.7833,24.0833,12735 +Malatya,Turkey,Europe,38.3486,38.3194,485484 +Malaya Vishera,Russia,Asia,58.85,32.2333,9996 +Malaybalay,Philippines,Asia,8.1564,125.1333,190712 +Malazgirt,Turkey,Europe,39.1478,42.5442,18873 +Malbork,Poland,Europe,54.0333,19.0333,37635 +Malden,Netherlands,Europe,51.7808,5.8544,10930 +Maldonado,Uruguay,South America,-34.9,-54.95,135014 +Male,Maldives,Asia,4.1753,73.5089,133019 +Malekān,Iran,Asia,37.1428,46.1053,27431 +Malgobek,Russia,Asia,43.5,44.75,37999 +Malhada,Brazil,South America,-14.3358,-43.7739,15398 +Malhada de Pedras,Brazil,South America,-14.3878,-41.8789,8670 +Malhador,Brazil,South America,-10.6578,-37.3047,12653 +Mali,China,Asia,23.1262,104.7409,57320 +Mali Iđoš,Serbia,Europe,45.7069,19.6644,5464 +Maliana,Timor-Leste,Asia,-8.9917,125.2197,15800 +Malinalco,Mexico,North America,18.9483,-99.4947,22970 +Malishevë,Kosovo,Europe,42.4822,20.7458,3395 +Malita,Philippines,Asia,6.4108,125.6144,118197 +Malkara,Turkey,Europe,40.8933,26.9042,50988 +Mallet,Brazil,South America,-25.8778,-50.8208,13428 +Malmö,Sweden,Europe,55.5833,13.0333,360249 +Malo Crniće,Serbia,Europe,44.5667,21.2833,11422 +Malolos,Philippines,Asia,14.8436,120.8114,261189 +Maloyaroslavets,Russia,Asia,55.0167,36.4667,41511 +Malumfashi,Nigeria,Africa,11.8,7.6167,182920 +Malārd,Iran,Asia,35.6806,50.9783,281027 +Malāyer,Iran,Asia,34.3,48.8178,170237 +Mamak,Turkey,Europe,39.9422,32.9231,687535 +Mamanguape,Brazil,South America,-6.8389,-35.1258,44599 +Mambajao,Philippines,Asia,9.25,124.72,41094 +Mamborê,Brazil,South America,-24.3189,-52.53,13452 +Mamburao,Philippines,Asia,13.2233,120.596,47705 +Mamou,Guinea,Africa,10.3833,-12.0833,376269 +Mamoudzou,Mayotte,Africa,-12.7806,45.2278,71437 +Mamuju,Indonesia,Asia,-2.6833,118.9,278764 +Mamushë,Kosovo,Europe,42.3308,20.7269,5507 +Man,Côte d’Ivoire,Africa,7.4004,-7.55,241969 +Manacapuru,Brazil,South America,-3.2997,-60.6206,98502 +Manadhoo,Maldives,Asia,5.7628,73.4106,2041 +Manado,Indonesia,Asia,1.4931,124.8413,461636 +Manafwa,Uganda,Africa,0.9564,34.2844,16000 +Managua,Nicaragua,North America,12.1364,-86.2514,1051236 +Manakara,Madagascar,Africa,-22.1455,48.008,44237 +Manama,Bahrain,Asia,26.2233,50.5875,727000 +Manaquiri,Brazil,South America,-3.4281,-60.4594,17107 +Manari,Brazil,South America,-8.9639,-37.6278,23763 +Manatuto,Timor-Leste,Asia,-8.5114,126.0131,9022 +Manatí,Cuba,North America,21.3144,-76.9378,28773 +Manaure,Colombia,South America,11.7792,-72.4494,67584 +Manaus,Brazil,South America,-3.1189,-60.0217,175343 +Manavgat,Turkey,Europe,36.7833,31.4333,252941 +Manaíra,Brazil,South America,-7.7061,-38.1544,10972 +Manbij,Syria,Asia,36.5266,37.9563,78255 +Manchester,United States,North America,42.9848,-71.4447,162716 +Mandaguari,Brazil,South America,-23.5225,-51.6788,36716 +Mandaguaçu,Brazil,South America,-23.3469,-52.095,31457 +Mandal,Norway,Europe,58.0333,7.45,11053 +Mandalay,Burma,Asia,21.9831,96.0844,1319452 +Mandalgovĭ,Mongolia,Asia,45.7667,106.2708,12339 +Mandaluyong City,Philippines,Asia,14.58,121.03,425758 +Mandaue City,Philippines,Asia,10.33,123.93,364116 +Mandera,Kenya,Africa,3.9167,41.8333,30433 +Mandeville,United States,North America,30.3751,-90.0904,115292 +Mandi Bahauddin,Pakistan,Asia,32.5797,73.4814,157352 +Mandirituba,Brazil,South America,-25.7789,-49.3258,17540 +Manduri,Brazil,South America,-23.0033,-49.3219,9910 +Manga,Burkina Faso,Africa,11.6667,-1.0667,28612 +Mangaratiba,Brazil,South America,-22.96,-44.0408,41220 +Manggar,Indonesia,Asia,-2.89,108.27,39135 +Mangghystaū,Kazakhstan,Asia,43.6956,51.3261,21004 +Mangochi,Malawi,Africa,-14.4722,35.2639,51429 +Mangqu,China,Asia,35.5833,100.75,15609 +Mangualde,Portugal,Europe,40.6058,-7.7658,18303 +Mangueirinha,Brazil,South America,-25.9408,-52.1758,16603 +Manhattan,United States,North America,40.7834,-73.9662,1694263 +Manhuaçu,Brazil,South America,-20.2581,-42.0336,91169 +Manhumirim,Brazil,South America,-20.3578,-41.9578,20025 +Manicaragua,Cuba,North America,22.15,-79.9764,61990 +Manicoré,Brazil,South America,-5.8089,-61.3,53914 +Manila,Philippines,Asia,14.5958,120.9772,24922000 +Manisa,Turkey,Europe,38.6144,27.4292,385452 +Manizales,Colombia,South America,5.0675,-75.51,454494 +Mankono,Côte d’Ivoire,Africa,8.0586,-6.1897,74165 +Manlio Fabio Altamirano,Mexico,North America,19.1003,-96.3411,23918 +Mannheim,Germany,Europe,49.4878,8.4661,316877 +Manoel Vitorino,Brazil,South America,-14.145,-40.2428,13860 +Manokwari,Indonesia,Asia,-0.8667,134.0833,107325 +Manouba,Tunisia,Africa,36.8078,10.1011,36194 +Mansa,Zambia,Africa,-11.2,28.8833,129185 +Mansa Konko,"Gambia, The",Africa,13.4667,-15.55,18672 +Mansehra,Pakistan,Asia,34.3339,73.2014,69085 +Mansidão,Brazil,South America,-10.7158,-44.0339,13919 +Manta,Ecuador,South America,-0.95,-80.7162,258697 +Mantena,Brazil,South America,-18.7819,-40.98,26535 +Mantenópolis,Brazil,South America,-18.8628,-41.1228,12201 +Mantes-la-Jolie,France,Europe,48.9908,1.7172,44246 +Mantova,Italy,Europe,45.1564,10.7911,48653 +Manturovo,Russia,Asia,58.3333,44.7667,13043 +Manuel Ribas,Brazil,South America,-24.5158,-51.6678,14240 +Manuel Urbano,Brazil,South America,-8.8389,-69.26,11996 +Manyas,Turkey,Europe,40.0464,27.97,18066 +Manzanares,Colombia,South America,5.2519,-75.1569,22623 +Manzanillo,Mexico,North America,19.0522,-104.3158,191031 +Manzini,Eswatini,Africa,-26.495,31.388,110508 +Maní,Colombia,South America,4.8167,-72.2833,13291 +Manūjān,Iran,Asia,27.4031,57.4917,15634 +Mao,Dominican Republic,North America,19.5667,-71.0833,76863 +Maoming,China,Asia,21.6627,110.9255,6174050 +Mapastepec,Mexico,North America,15.4411,-92.8916,19271 +Maputo,Mozambique,Africa,-25.9153,32.5764,1133200 +Maqat,Kazakhstan,Asia,47.65,53.3167,14756 +Mar de Espanha,Brazil,South America,-21.8669,-43.01,12721 +Mar del Plata,Argentina,South America,-38.0,-57.55,682605 +Mara Rosa,Brazil,South America,-14.0169,-49.1778,10700 +Marabá,Brazil,South America,-5.3689,-49.1178,168020 +Maracaibo,Venezuela,South America,10.6333,-71.6333,2658355 +Maracaju,Brazil,South America,-21.6139,-55.1678,45047 +Maracanaú,Brazil,South America,-3.8769,-38.6258,234509 +Maracanã,Brazil,South America,-0.765,-47.45,25971 +Maracay,Venezuela,South America,10.2469,-67.5961,1723236 +Maracaçumé,Brazil,South America,-2.0428,-45.9589,21149 +Maracaí,Brazil,South America,-22.6106,-50.6672,13913 +Maracás,Brazil,South America,-13.4408,-40.4308,27620 +Maradi,Niger,Africa,13.501,7.1036,267249 +Maragogi,Brazil,South America,-9.0122,-35.2225,32174 +Maragogipe,Brazil,South America,-12.7778,-38.9189,35859 +Maraial,Brazil,South America,-8.8028,-35.8289,9359 +Maralal,Kenya,Africa,1.1,36.7,31350 +Marand,Iran,Asia,38.4308,45.7742,130825 +Maranguape,Brazil,South America,-3.89,-38.6858,105093 +Marapanim,Brazil,South America,-0.7139,-47.6939,24718 +Marataízes,Brazil,South America,-21.0428,-40.8239,41929 +Marathónas,Greece,Europe,38.15,23.95,31331 +Marau,Brazil,South America,-28.4489,-52.2,28361 +Maravatío de Ocampo,Mexico,North America,19.8976,-100.45,80258 +Maravilha,Brazil,South America,-26.77,-53.2167,28251 +Marawi City,Philippines,Asia,8.0031,124.285,207010 +Maraú,Brazil,South America,-14.1039,-39.0149,20617 +Marburg,Germany,Europe,50.81,8.7708,78203 +Marcali,Hungary,Europe,46.5831,17.4064,10502 +Marcação,Brazil,South America,-6.77,-35.015,8999 +Marche-en-Famenne,Belgium,Europe,50.2266,5.3437,17455 +Marco,Brazil,South America,-3.1239,-40.1469,25799 +Marco de Canavezes,Portugal,Europe,41.1833,-8.15,53450 +Marcolândia,Brazil,South America,-7.4428,-40.6608,8533 +Marcos Juárez,Argentina,South America,-32.7,-62.1,27004 +Marcos Paz,Argentina,South America,-34.7667,-58.8333,39151 +Mardan,Pakistan,Asia,34.2012,72.0258,368302 +Mardin,Turkey,Europe,37.3131,40.735,129864 +Marechal Cândido Rondon,Brazil,South America,-24.5561,-54.0567,53495 +Marechal Deodoro,Brazil,South America,-9.7103,-35.895,52380 +Marechal Floriano,Brazil,South America,-20.4128,-40.6828,12188 +Marechal Taumaturgo,Brazil,South America,-8.9411,-72.7917,19299 +Margarita,Colombia,South America,9.0833,-74.2,9876 +Marhamat,Uzbekistan,Asia,40.5,72.3333,13600 +Marhanets,Ukraine,Europe,47.6447,34.6042,44980 +Mari,Brazil,South America,-7.06,-35.3189,21512 +Maria da Fé,Brazil,South America,-22.3078,-45.375,14247 +Marialva,Brazil,South America,-23.485,-51.7919,41851 +Mariana,Brazil,South America,-20.3783,-43.4165,61288 +Mariano Escobedo,Mexico,North America,18.9167,-97.1333,37285 +Marib,Yemen,Asia,15.4606,45.3261,16794 +Maribojoc,Philippines,Asia,9.75,123.85,22178 +Maribondo,Brazil,South America,-9.5769,-36.305,13679 +Maribor,Slovenia,Europe,46.55,15.6333,96211 +Maricá,Brazil,South America,-22.9189,-42.8189,76737 +Mariehamn,Finland,Europe,60.1,19.9333,11812 +Mariel,Cuba,North America,22.9936,-82.7539,42500 +Mariental,Namibia,Africa,-24.6333,17.9667,12478 +Mariestad,Sweden,Europe,58.705,13.828,16611 +Marigot,Saint Martin,North America,18.0706,-63.0847,5700 +Marijampolė,Lithuania,Europe,54.5567,23.3544,36807 +Marikina City,Philippines,Asia,14.65,121.1,456059 +Mariluz,Brazil,South America,-24.0019,-53.1458,9847 +Marilândia,Brazil,South America,-19.4128,-40.5417,11107 +Marilândia do Sul,Brazil,South America,-23.745,-51.3078,8677 +Maringá,Brazil,South America,-23.425,-51.9389,409657 +Marinha Grande,Portugal,Europe,39.7478,-8.9322,39032 +Marinilla,Colombia,South America,6.1738,-75.3347,64645 +Mario Campos,Brazil,South America,-20.0558,-44.1878,10535 +Mariupol,Ukraine,Europe,47.0958,37.5494,120000 +Marka,Somalia,Africa,1.7156,44.7703,230100 +Markham,Canada,North America,43.8767,-79.2633,338503 +Markovci,Slovenia,Europe,46.3833,15.95,495 +Marktoberdorf,Germany,Europe,47.7667,10.6167,19033 +Marmande,France,Europe,44.5,0.17,17361 +Marmara Ereğlisi,Turkey,Europe,40.9697,27.9553,29549 +Marmaris,Turkey,Europe,36.85,28.2667,97818 +Marmeleiro,Brazil,South America,-26.1489,-53.0258,15901 +Marneuli,Georgia,Europe,41.4872,44.7981,24928 +Marondera,Zimbabwe,Africa,-18.1897,31.5467,66203 +Maroua,Cameroon,Africa,10.5971,14.3157,201371 +Maroúsi,Greece,Europe,38.05,23.8,71830 +Marquetalia,Colombia,South America,5.3333,-75.0,12146 +Marrakech,Morocco,Africa,31.63,-8.0089,966987 +Marsa,Malta,Europe,35.8833,14.4947,4401 +Marsabit,Kenya,Africa,2.3333,37.9833,17127 +Marsaskala,Malta,Europe,35.8625,14.5675,15579 +Marsaxlokk,Malta,Europe,35.8417,14.5447,3534 +Marseille,France,Europe,43.2964,5.37,877215 +Marsella,Colombia,South America,4.9167,-75.75,24131 +Martapura,Indonesia,Asia,-3.45,114.8333,198239 +Martin,Slovakia,Europe,49.0636,18.9214,51139 +Martinho Campos,Brazil,South America,-19.3319,-45.2369,14003 +Martinópole,Brazil,South America,-3.2258,-40.6969,10846 +Martinópolis,Brazil,South America,-22.1461,-51.1709,24881 +Martí,Cuba,North America,22.9522,-80.9167,21582 +Martínez de la Torre,Mexico,North America,20.0667,-97.05,110415 +Maruim,Brazil,South America,-10.7378,-37.0819,15719 +Marvdasht,Iran,Asia,29.8786,52.8067,148858 +Mary,Turkmenistan,Asia,37.6069,61.8344,167027 +Marysville,United States,North America,48.0809,-122.1561,168664 +María la Baja,Colombia,South America,10.0,-75.3333,49462 +Marília,Brazil,South America,-22.2139,-49.9458,240590 +Marāgheh,Iran,Asia,37.3894,46.2378,272400 +Marāveh Tappeh,Iran,Asia,37.9031,55.9567,8671 +Marīvān,Iran,Asia,35.5217,46.1806,136654 +Mar’’ina Horka,Belarus,Europe,53.5072,28.1522,20096 +Masagua,Guatemala,North America,14.2,-90.85,48552 +Masaka,Uganda,Africa,-0.3411,31.7361,285509 +Masallı,Azerbaijan,Europe,39.0324,48.6722,14746 +Masamba,Indonesia,Asia,-2.5531,120.3275,39266 +Masatepe,Nicaragua,North America,11.9167,-86.15,31583 +Masaya,Nicaragua,North America,11.9667,-86.1,139582 +Masbate,Philippines,Asia,12.27,123.58,908920 +Mascara,Algeria,Africa,35.4,0.1333,108587 +Mascota,Mexico,North America,20.5235,-104.7886,9272 +Mascote,Brazil,South America,-15.5628,-39.3028,13544 +Maseru,Lesotho,Africa,-29.31,27.48,343541 +Mashhad,Iran,Asia,36.3264,59.5433,3700000 +Masindi,Uganda,Africa,1.6836,31.7222,110500 +Masjed Soleymān,Iran,Asia,31.96,49.2881,100497 +Maskanah,Syria,Asia,35.9658,38.0416,15477 +Masku,Finland,Europe,60.5667,22.1,9619 +Massa,Italy,Europe,44.0333,10.1333,66160 +Massakory,Chad,Africa,13.0,15.7333,16237 +Massapê,Brazil,South America,-3.5228,-40.3428,37697 +Massaranduba,Brazil,South America,-26.6108,-49.0078,17162 +Massawa,Eritrea,Africa,15.6097,39.45,39758 +Massenya,Chad,Africa,11.4,16.1667,3680 +Massy,Kyrgyzstan,Asia,41.064,72.6375,19774 +Masty,Belarus,Europe,53.417,24.55,14447 +Masunga,Botswana,Africa,-20.6245,27.4488,5696 +Masvingo,Zimbabwe,Africa,-20.0744,30.8328,90286 +Mata Grande,Brazil,South America,-9.1178,-37.7339,21844 +Mata Roma,Brazil,South America,-3.625,-43.1108,17090 +Mata de São João,Brazil,South America,-12.53,-38.2989,42566 +Mata-Utu,Wallis and Futuna,Oceania,-13.2825,-176.1736,1029 +Matadi,Congo (Kinshasa),Africa,-5.8167,13.4833,448000 +Matagalpa,Nicaragua,North America,12.9167,-85.9167,133416 +Matam,Senegal,Africa,15.6167,-13.3333,17324 +Matamoros,Mexico,North America,25.533,-103.25,118337 +Matanzas,Cuba,North America,23.0494,-81.5736,151624 +Mataquescuintla,Guatemala,North America,14.5336,-90.1839,47280 +Mataram,Indonesia,Asia,-8.5833,116.1167,408900 +Mateare,Nicaragua,North America,12.2333,-86.4333,61234 +Matehuala,Mexico,North America,23.6528,-100.6444,102199 +Matelândia,Brazil,South America,-25.2408,-53.9958,18450 +Matera,Italy,Europe,40.6667,16.6,59685 +Mateus Leme,Brazil,South America,-19.9858,-44.4278,37841 +Mati,Philippines,Asia,6.9483,126.2272,147547 +Matias Barbosa,Brazil,South America,-21.8689,-43.3189,14121 +Matias Cardoso,Brazil,South America,-14.855,-43.9219,8895 +Matias Olímpio,Brazil,South America,-3.7158,-42.5556,10958 +Matiguás,Nicaragua,North America,12.8333,-85.4667,41127 +Matina,Brazil,South America,-13.9089,-42.8489,10330 +Matinha,Brazil,South America,-3.1006,-45.0336,23482 +Matinhos,Brazil,South America,-25.8178,-48.5428,39259 +Matipó,Brazil,South America,-20.2839,-42.3408,18552 +Mato Verde,Brazil,South America,-15.3969,-42.8658,12038 +Matosinhos,Portugal,Europe,41.1867,-8.6844,172557 +Matozinhos,Brazil,South America,-19.5578,-44.0814,38151 +Matriz de Camarajibe,Brazil,South America,-9.1519,-35.5328,23857 +Matsue,Japan,Asia,35.4681,133.0486,201802 +Matsuyama,Japan,Asia,33.8333,132.7667,506743 +Matulji,Croatia,Europe,45.3667,14.3167,10773 +Maturín,Venezuela,South America,9.75,-63.183,647459 +Matão,Brazil,South America,-21.6033,-48.3658,83626 +Matões,Brazil,South America,-5.5189,-43.1989,32174 +Mauldin,United States,North America,34.7821,-82.3032,179196 +Maumere,Indonesia,Asia,-8.6222,122.2122,77909 +Maun,Botswana,Africa,-19.9833,23.4167,57067 +Mauren,Liechtenstein,Europe,47.2167,9.5333,4404 +Maurilândia,Brazil,South America,-17.9708,-50.3389,10304 +Mauriti,Brazil,South America,-7.3889,-38.7739,45561 +Mauá,Brazil,South America,-23.6678,-46.4608,418261 +Maués,Brazil,South America,-3.3836,-57.7186,61204 +Mawlamyine,Burma,Asia,16.4847,97.6258,253734 +Maxaranguape,Brazil,South America,-5.5158,-35.2619,10255 +Maxcanú,Mexico,North America,20.5833,-89.9833,23991 +May Pen,Jamaica,North America,17.965,-77.245,61548 +Mayarí,Cuba,North America,20.6592,-75.6781,102354 +Mayenne,France,Europe,48.312,-0.6183,12854 +Maykop,Russia,Asia,44.6,40.0833,139084 +Mayuge,Uganda,Africa,0.4578,33.48,17151 +Mazagão,Brazil,South America,-0.115,-51.2889,11986 +Mazamitla,Mexico,North America,19.9153,-103.0208,14043 +Mazapil,Mexico,North America,24.6383,-101.5553,17813 +Mazatenango,Guatemala,North America,14.5333,-91.5,82969 +Mazatlán,Mexico,North America,23.2167,-106.4167,501441 +Mazatlán Villa de Flores,Mexico,North America,18.0167,-96.9167,13097 +Mazatán,Mexico,North America,29.0167,-110.1333,1058 +Mazhang,China,Asia,21.2757,110.3221,107819 +Mazyr,Belarus,Europe,52.05,29.25,105152 +Mazār-e Sharīf,Afghanistan,Asia,36.7,67.1167,469247 +Mazıdağı,Turkey,Europe,37.4792,40.4864,36442 +Maçka,Turkey,Europe,40.8119,39.6131,24709 +Maīdān Shahr,Afghanistan,Asia,34.3972,68.8697,35008 +Maīmanah,Afghanistan,Asia,35.9333,64.75,149040 +Maşyāf,Syria,Asia,35.0653,36.3406,37109 +Maţrūḩ,Egypt,Africa,31.3333,27.2167,108774 +Mažeikiai,Lithuania,Europe,56.3167,22.3333,33249 +Maḑāyā,Syria,Asia,33.6903,36.1035,9371 +Maḩallāt,Iran,Asia,33.9056,50.4572,43245 +Maḩmūd-e Rāqī,Afghanistan,Asia,35.0206,69.3306,50490 +Ma‘arrat an Nu‘mān,Syria,Asia,35.6483,36.6769,90000 +Ma‘arratmişrīn,Syria,Asia,36.0115,36.6718,37490 +Ma‘bar,Yemen,Asia,14.794,44.2935,24707 +Ma‘ān,Jordan,Asia,30.1962,35.7341,50350 +Ma’anshan,China,Asia,31.669,118.507,2159930 +Ma’erkang,China,Asia,31.9057,102.2065,58390 +Mbabane,Eswatini,Africa,-26.3167,31.1333,60691 +Mbale,Uganda,Africa,1.0806,34.175,96189 +Mbandaka,Congo (Kinshasa),Africa,0.0478,18.2558,1187837 +Mbanza Kongo,Angola,Africa,-6.2678,14.2481,180329 +Mbarara,Uganda,Africa,-0.6133,30.6583,195013 +Mbaïki,Central African Republic,Africa,3.8833,18.0,22166 +Mbeya,Tanzania,Africa,-8.9,33.45,541603 +Mbuji-Mayi,Congo (Kinshasa),Africa,-6.15,23.6,2892000 +Mburucuyá,Argentina,South America,-28.05,-58.2333,9012 +McAllen,United States,North America,26.2252,-98.2467,810034 +McKinney,United States,North America,33.2016,-96.667,202314 +Mchinji,Malawi,Africa,-13.8167,32.9,28011 +Mealhada,Portugal,Europe,40.3781,-8.4514,20428 +Meaux,France,Europe,48.9603,2.8883,56659 +Mecatlán,Mexico,North America,20.2167,-97.0167,12332 +Mecayapan,Mexico,North America,18.2167,-94.8333,17312 +Mecca,Saudi Arabia,Asia,21.4225,39.8261,2427924 +Mechelen,Belgium,Europe,51.0281,4.4803,86921 +Medan,Indonesia,Asia,3.5894,98.6739,3632000 +Medeiros Neto,Brazil,South America,-17.3739,-40.2208,22194 +Medellín,Colombia,South America,6.2308,-75.5906,2529403 +Medenine,Tunisia,Africa,33.3547,10.5053,79406 +Medford,United States,North America,42.3372,-122.854,164628 +Media Luna,Cuba,North America,20.1444,-77.4361,31589 +Medianeira,Brazil,South America,-25.295,-54.0939,54369 +Medina,Saudi Arabia,Asia,24.47,39.61,1411599 +Mednogorsk,Russia,Asia,51.4222,57.5953,23013 +Medveđa,Serbia,Europe,42.8333,21.5833,6360 +Medvode,Slovenia,Europe,46.1382,14.4147,5380 +Meerssen,Netherlands,Europe,50.8858,5.7519,18661 +Megion,Russia,Asia,61.0331,76.1097,52887 +Mehrān,Iran,Asia,33.1208,46.1642,17435 +Mehtar Lām,Afghanistan,Asia,34.6683,70.2089,144162 +Meicheng,China,Asia,30.631,116.581,441224 +Meihekou,China,Asia,42.5407,125.7107,509336 +Meiningen,Germany,Europe,50.55,10.4167,24867 +Meishan,China,Asia,34.167,112.844,936000 +Meizhou,China,Asia,24.289,116.122,3873239 +Meißen,Germany,Europe,51.1667,13.4833,29051 +Mekele,Ethiopia,Africa,13.4969,39.4769,323700 +Melaka,Malaysia,Asia,2.2064,102.2465,455300 +Melbourne,Australia,Oceania,-37.8142,144.9631,5031195 +Melchor Ocampo,Mexico,North America,19.7083,-99.1444,37706 +Melena del Sur,Cuba,North America,22.7814,-82.1486,20646 +Melenki,Russia,Asia,55.3333,41.65,13407 +Meleuz,Russia,Asia,52.95,55.9333,56505 +Melgar,Colombia,South America,4.2039,-74.6428,37224 +Melgaço,Brazil,South America,-1.8039,-50.7119,21064 +Melikgazi,Turkey,Europe,38.7167,35.5,594344 +Melipilla,Chile,South America,-33.6253,-71.2711,148899 +Melitopol,Ukraine,Europe,46.8489,35.3675,148851 +Mellieħa,Malta,Europe,35.9564,14.3664,10087 +Melo,Uruguay,South America,-32.3667,-54.1833,51830 +Melonguane,Indonesia,Asia,4.0045,126.6827,15670 +Melun,France,Europe,48.5406,2.66,43685 +Melíssia,Greece,Europe,38.05,23.8333,23618 +Memmingen,Germany,Europe,47.9878,10.1811,46178 +Memphis,United States,North America,35.1087,-89.9663,1033394 +Mende,France,Europe,44.5194,3.5014,12322 +Mendefera,Eritrea,Africa,14.8833,38.8167,28492 +Menderes,Turkey,Europe,38.254,27.134,106173 +Mendes,Brazil,South America,-22.5269,-43.7328,17502 +Mendi,Papua New Guinea,Oceania,-6.1478,143.6572,56055 +Mendoza,Argentina,South America,-32.8897,-68.8444,115041 +Menemen,Turkey,Europe,38.6,27.0667,200904 +Mengdong,China,Asia,23.1475,99.2492,35741 +Mengeš,Slovenia,Europe,46.1626,14.568,6119 +Menggala,Indonesia,Asia,-4.475,105.2414,51574 +Mengla,China,Asia,21.4686,101.5507,77425 +Menglang,China,Asia,22.5586,99.9336,87703 +Menglie,China,Asia,22.5833,101.8661,32032 +Mengmeng,China,Asia,23.465,99.8021,51908 +Menifee,United States,North America,33.6909,-117.1849,107020 +Menongue,Angola,Africa,-14.6556,17.6842,320914 +Meoqui,Mexico,North America,28.2722,-105.4819,22574 +Meppel,Netherlands,Europe,52.6958,6.1944,34386 +Meppen,Germany,Europe,52.6936,7.2928,36137 +Meram,Turkey,Europe,37.8364,32.4383,348325 +Merauke,Indonesia,Asia,-8.4932,140.4018,34412 +Mercaderes,Colombia,South America,1.8,-77.1667,14824 +Merced,United States,North America,37.3057,-120.4779,163257 +Mercedes,Argentina,South America,-34.65,-59.4333,63284 +Mercês,Brazil,South America,-21.1939,-43.3408,10373 +Meridian,United States,North America,43.6116,-116.3977,124865 +Meriç,Turkey,Europe,41.1833,26.4167,12841 +Merlo,Argentina,South America,-34.6653,-58.7275,244168 +Merošina,Serbia,Europe,43.2833,21.7167,13968 +Merseburg,Germany,Europe,51.3544,11.9928,34721 +Mersin,Turkey,Europe,36.8,34.6333,1040507 +Meru,Kenya,Africa,0.05,37.65,42677 +Meruoca,Brazil,South America,-3.5419,-40.455,15157 +Merzifon,Turkey,Europe,40.875,35.4633,61376 +Merzig,Germany,Europe,49.45,6.6167,30070 +Mesa,United States,North America,33.4015,-111.7178,507478 +Meschede,Germany,Europe,51.35,8.2833,29988 +Mesetas,Colombia,South America,3.3781,-74.0447,9751 +Meshgīn Shahr,Iran,Asia,38.3906,47.6742,74109 +Mesolóngi,Greece,Europe,38.3692,21.4278,12785 +Mesquite,United States,North America,32.7602,-96.5865,148848 +Messias,Brazil,South America,-9.3828,-35.8419,15405 +Messina,Italy,Europe,38.1936,15.5542,218786 +Messíni,Greece,Europe,37.05,22.0,8937 +Mesudiye,Turkey,Europe,40.4625,37.7725,13221 +Metairie,United States,North America,29.9977,-90.178,138995 +Metamórfosi,Greece,Europe,38.05,23.75,30174 +Metepec,Mexico,North America,19.2511,-99.6047,28205 +Metković,Croatia,Europe,43.05,17.65,15235 +Metlika,Slovenia,Europe,45.6517,15.32,3229 +Mettmann,Germany,Europe,51.25,6.9667,39197 +Metz,France,Europe,49.1203,6.1778,121695 +Meulaboh,Indonesia,Asia,4.1333,96.1167,64646 +Mexicali,Mexico,North America,32.6633,-115.4678,1102342 +Mexicaltzingo,Mexico,North America,19.2092,-99.5858,10161 +Mexico City,Mexico,North America,19.4333,-99.1333,21804000 +Mexquitic de Carmona,Mexico,North America,22.2667,-101.1167,58469 +Meybod,Iran,Asia,32.2456,54.0153,80712 +Mezen,Russia,Asia,65.8439,44.2464,2832 +Mezitli,Turkey,Europe,36.75,34.5333,222436 +Mezőkövesd,Hungary,Europe,47.8067,20.5647,15541 +Mezőtúr,Hungary,Europe,47.0042,20.6181,15469 +Mežica,Slovenia,Europe,46.5206,14.8536,3176 +Miacatlán,Mexico,North America,18.7722,-99.3547,26713 +Miahuatlán,Mexico,North America,18.5667,-97.4333,25228 +Miahuatlán de Porfirio Díaz,Mexico,North America,16.3167,-96.5833,35517 +Miami,United States,North America,25.784,-80.2101,6113982 +Miami Gardens,United States,North America,25.9433,-80.2426,111264 +Mianwali,Pakistan,Asia,32.5853,71.5436,95007 +Mianyang,China,Asia,31.468,104.679,4613862 +Miaoli,Taiwan,Asia,24.57,120.82,86327 +Miarinarivo,Madagascar,Africa,-18.9608,46.9036,13109 +Miass,Russia,Asia,55.0,60.1,147287 +Michalovce,Slovakia,Europe,48.7575,21.9183,36704 +Michurinsk,Russia,Asia,52.8833,40.4833,90451 +Middelburg,Netherlands,Europe,51.4997,3.6136,49956 +Middelfart,Denmark,Europe,55.4992,9.7445,16546 +Middelharnis,Netherlands,Europe,51.7535,4.1647,18050 +Midland,United States,North America,32.0243,-102.1147,143714 +Midyat,Turkey,Europe,37.4167,41.3697,120069 +Miechów,Poland,Europe,50.3578,20.0325,11215 +Mielec,Poland,Europe,50.2833,21.4333,58213 +Miercurea-Ciuc,Romania,Europe,46.3594,25.8017,42029 +Miesbach,Germany,Europe,47.7833,11.8333,12109 +Miguel Alves,Brazil,South America,-4.1658,-42.895,32150 +Miguel Calmon,Brazil,South America,-11.4289,-40.595,24661 +Miguel Pereira,Brazil,South America,-22.4539,-43.4689,23902 +Miguelópolis,Brazil,South America,-20.1794,-48.0319,21728 +Mikhaylov,Russia,Asia,54.2333,39.0333,10085 +Mikhaylovka,Russia,Asia,50.0667,43.25,56031 +Mikkeli,Finland,Europe,61.689,27.272,51919 +Miklavž na Dravskem Polju,Slovenia,Europe,46.5058,15.6972,3808 +Mikołów,Poland,Europe,50.1667,18.9,41266 +Mila,Algeria,Africa,36.4503,6.2644,69052 +Milagres,Brazil,South America,-7.3128,-38.9458,25900 +Milagro,Ecuador,South America,-2.1347,-79.5872,159970 +Milan,Italy,Europe,45.4669,9.19,1354196 +Milas,Turkey,Europe,37.3167,27.7833,147416 +Milhã,Brazil,South America,-5.675,-39.1939,14123 +Milicz,Poland,Europe,51.5333,17.2833,11025 +Milići,Bosnia and Herzegovina,Europe,44.1703,19.0919,11441 +Millau,France,Europe,44.0986,3.0783,21859 +Millerovo,Russia,Asia,48.9167,40.3833,34841 +Miltenberg,Germany,Europe,49.7039,9.2644,9622 +Milton,Canada,North America,43.5083,-79.8833,132979 +Miluo Chengguanzhen,China,Asia,28.761,113.16,560725 +Milwaukee,United States,North America,43.0642,-87.9675,1290221 +Mimoso do Sul,Brazil,South America,-21.0639,-41.3658,24475 +Minas,Uruguay,South America,-34.3667,-55.2333,38446 +Minas Novas,Brazil,South America,-17.2189,-42.59,24405 +Minas de Matahambre,Cuba,North America,22.5822,-83.9492,30946 +Minatitlán,Mexico,North America,17.9833,-94.55,356020 +Minaçu,Brazil,South America,-13.5328,-48.22,27075 +Mindelheim,Germany,Europe,48.0333,10.4667,16226 +Mindelo,Cabo Verde,Africa,16.886,-24.988,70468 +Minden,Germany,Europe,52.2883,8.9167,83100 +Mineiros,Brazil,South America,-17.5689,-52.5508,39024 +Mineiros do Tietê,Brazil,South America,-22.4089,-48.4508,11230 +Mineral de Angangueo,Mexico,North America,19.6167,-100.2833,10892 +Mineral del Monte,Mexico,North America,20.1333,-98.6667,14640 +Mineral’nyye Vody,Russia,Asia,44.2167,43.1333,69082 +Mingxing,China,Asia,37.4264,112.5442,82981 +Mingəçevir,Azerbaijan,Europe,40.77,47.0489,106100 +Minignan,Côte d’Ivoire,Africa,9.9974,-7.8359,14521 +Minna,Nigeria,Africa,9.6139,6.5569,322163 +Minneapolis,United States,North America,44.9635,-93.2678,2906807 +Minsk,Belarus,Europe,53.9006,27.5586,1992862 +Minusinsk,Russia,Asia,53.7,91.6833,68603 +Minzhu,China,Asia,43.7238,127.3448,328925 +Mionica,Serbia,Europe,44.25,20.0833,14263 +Mira,Portugal,Europe,40.4285,-8.7363,12465 +Mirabela,Brazil,South America,-16.2628,-44.1639,13651 +Miracatu,Brazil,South America,-24.2808,-47.46,18553 +Miracema,Brazil,South America,-21.4119,-42.1969,26881 +Miracema do Tocantins,Brazil,South America,-9.5669,-48.3919,18566 +Mirador,Brazil,South America,-6.3708,-44.3628,21030 +Miradouro,Brazil,South America,-20.8908,-42.3428,8968 +Miragoâne,Haiti,North America,18.4411,-73.0883,56864 +Miramar,United States,North America,25.9773,-80.3351,135986 +Miranda,Colombia,South America,3.25,-76.25,42639 +Miranda do Corvo,Portugal,Europe,40.1,-8.3333,13098 +Mirandela,Portugal,Europe,41.4833,-7.1833,23850 +Mirandiba,Brazil,South America,-8.1189,-38.7278,14166 +Mirandópolis,Brazil,South America,-21.1336,-51.1017,29043 +Mirangaba,Brazil,South America,-10.9539,-40.5758,15734 +Miranorte,Brazil,South America,-9.5289,-48.59,12701 +Mirante,Brazil,South America,-14.2419,-40.7728,10187 +Mirante da Serra,Brazil,South America,-11.0297,-62.675,9235 +Mirante do Paranapanema,Brazil,South America,-22.2919,-51.9064,18338 +Mirassol,Brazil,South America,-20.8189,-49.5208,63337 +Mirassol d’Oeste,Brazil,South America,-15.675,-58.0958,26785 +Miraí,Brazil,South America,-21.1953,-42.6142,15111 +Miraíma,Brazil,South America,-3.5689,-39.97,14196 +Miren,Slovenia,Europe,45.8962,13.6072,1506 +Mirinzal,Brazil,South America,-2.065,-44.7839,13978 +Mirna,Slovenia,Europe,45.9476,15.0664,1371 +Mirna Peč,Slovenia,Europe,45.858,15.0876,1015 +Mirnyy,Russia,Asia,62.55,113.9667,34013 +Mirpur Khas,Pakistan,Asia,25.525,69.0158,236961 +Misantla,Mexico,North America,19.9333,-96.85,64249 +Mishan,China,Asia,45.5298,131.8466,339103 +Miskolc,Hungary,Europe,48.1,20.7833,143502 +Mislinja,Slovenia,Europe,46.4431,15.1931,1862 +Mission Viejo,United States,North America,33.6096,-117.6551,634674 +Mississauga,Canada,North America,43.6,-79.65,717961 +Missão Velha,Brazil,South America,-7.25,-39.1428,36822 +Missões,Brazil,South America,-14.8839,-44.0908,13024 +Mistelbach,Austria,Europe,48.5667,16.5667,12036 +Mistrató,Colombia,South America,5.3,-75.8833,17527 +Mito,Japan,Asia,36.3658,140.4712,270445 +Mitoma,Uganda,Africa,-0.615,30.045,6000 +Mitrovicë,Kosovo,Europe,42.8833,20.8667,85360 +Mityana,Uganda,Africa,0.4006,32.0422,105200 +Mitú,Colombia,South America,1.1983,-70.1733,28382 +Mixco,Guatemala,North America,14.6331,-90.6064,527828 +Mixtla de Altamirano,Mexico,North America,18.6,-97.0,11646 +Miyazaki,Japan,Asia,31.9078,131.4203,397476 +Mizhhiria,Ukraine,Europe,48.5286,23.5019,9432 +Międzychód,Poland,Europe,52.6,15.8833,10288 +Międzyrzecz,Poland,Europe,52.4483,15.5883,17400 +Mińsk Mazowiecki,Poland,Europe,52.1833,21.5667,40467 +Mişrātah,Libya,Africa,32.3775,15.092,259056 +Mjölby,Sweden,Europe,58.3321,15.1312,14282 +Mkokotoni,Tanzania,Africa,-5.88,39.2731,2572 +Mladenovac,Serbia,Europe,44.4418,20.697,48683 +Mo i Rana,Norway,Europe,66.3167,14.1667,18899 +Moa,Cuba,North America,20.6556,-74.9417,73053 +Mobaye,Central African Republic,Africa,4.3254,21.1778,7176 +Mobile,United States,North America,30.6782,-88.1162,324067 +Mobārakeh,Iran,Asia,32.3494,51.5,62454 +Moca,Dominican Republic,North America,19.3833,-70.5167,94981 +Mocajuba,Brazil,South America,-2.5839,-49.5069,27198 +Mocha,Yemen,Asia,13.3203,43.25,16794 +Mochudi,Botswana,Africa,-24.4167,26.15,44339 +Mocoa,Colombia,South America,1.15,-76.6475,56398 +Mococa,Brazil,South America,-21.4678,-47.0047,68980 +Modena,Italy,Europe,44.6458,10.9257,184153 +Modesto,United States,North America,37.6375,-121.0028,355686 +Mogadishu,Somalia,Africa,2.0392,45.3419,2120000 +Mogeiro,Brazil,South America,-7.2989,-35.4789,13899 +Mogi Guaçu,Brazil,South America,-22.3719,-46.9419,124228 +Mogi Mirim,Brazil,South America,-22.4319,-46.9578,81467 +Mogi das Cruzes,Brazil,South America,-23.5228,-46.1931,450785 +Mogila,North Macedonia,Europe,41.1083,21.3786,1262 +Mogilno,Poland,Europe,52.65,17.95,11453 +Mogocha,Russia,Asia,53.7333,119.7667,12953 +Mogotes,Colombia,South America,6.4833,-72.9667,10860 +Mohale’s Hoek,Lesotho,Africa,-30.15,27.4667,24992 +Mohyliv-Podilskyi,Ukraine,Europe,48.45,27.7833,29925 +Mohács,Hungary,Europe,45.9958,18.6797,17200 +Moimenta da Beira,Portugal,Europe,40.9819,-7.6158,10212 +Moita,Portugal,Europe,38.65,-8.9833,66029 +Moita Bonita,Brazil,South America,-10.5778,-37.3428,11050 +Mojkovac,Montenegro,Europe,42.96,19.58,3590 +Moju,Brazil,South America,-1.8839,-48.7689,78629 +Mokhotlong,Lesotho,Africa,-29.2885,29.0656,8809 +Mokpo,"Korea, South",Asia,34.7589,126.38,214534 +Mokronog,Slovenia,Europe,45.9426,15.149,700 +Molde,Norway,Europe,62.7375,7.1631,21103 +Molepolole,Botswana,Africa,-24.4066,25.4951,67598 +Mollās̄ānī,Iran,Asia,31.585,48.885,17337 +Moloacán,Mexico,North America,17.9833,-94.35,17504 +Molsheim,France,Europe,48.54,7.49,9328 +Molėtai,Lithuania,Europe,55.2333,25.4167,5783 +Mombasa,Kenya,Africa,-4.05,39.6667,1200000 +Mombaça,Brazil,South America,-5.7428,-39.6278,37735 +Momil,Colombia,South America,9.2333,-75.6833,16264 +Momostenango,Guatemala,North America,15.0,2.0,139552 +Monaco,Monaco,Europe,43.7333,7.4167,36371 +Monaghan,Ireland,Europe,54.2478,-6.9708,7894 +Monastir,Tunisia,Africa,35.7694,10.8194,107127 +Monclova,Mexico,North America,26.9103,-101.4222,237169 +Moncton,Canada,North America,46.1328,-64.7714,119785 +Mondaí,Brazil,South America,-27.1028,-53.4019,10066 +Mongaguá,Brazil,South America,-24.0869,-46.6289,61951 +Mongar,Bhutan,Asia,27.275,91.24,3502 +Mongo,Chad,Africa,12.1837,18.7,27763 +Mongomo,Equatorial Guinea,Africa,1.6287,11.3168,7251 +Mongu,Zambia,Africa,-15.2775,23.1319,179585 +Moniquirá,Colombia,South America,5.9167,-73.5,20848 +Monjas,Guatemala,North America,14.5,-89.8667,31330 +Monkayo,Philippines,Asia,7.8239,126.0536,93937 +Monnickendam,Netherlands,Europe,52.4667,5.0333,9440 +Monor,Hungary,Europe,47.3475,19.4489,19743 +Monroe,United States,North America,32.5185,-92.0774,117507 +Monrovia,Liberia,Africa,6.3133,-10.8014,1021762 +Mons,Belgium,Europe,50.4547,3.9525,95299 +Monsenhor Gil,Brazil,South America,-5.5642,-42.6078,10564 +Monsenhor Tabosa,Brazil,South America,-4.7889,-40.0628,17149 +Mont-de-Marsan,France,Europe,43.89,-0.5,31455 +Montabaur,Germany,Europe,50.4375,7.8258,14677 +Montalbán,Venezuela,South America,10.2522,-68.3019,24908 +Montalegre,Portugal,Europe,41.8239,-7.79,10537 +Montalvânia,Brazil,South America,-14.4228,-44.3658,14060 +Montana,Bulgaria,Europe,43.4086,23.2257,40044 +Montanha,Brazil,South America,-18.1269,-40.3628,17263 +Montanhas,Brazil,South America,-6.4858,-35.2878,11444 +Montargis,France,Europe,47.9978,2.7333,14819 +Montauban,France,Europe,44.0181,1.3558,62487 +Montbrison,France,Europe,45.6083,4.0658,16098 +Montbéliard,France,Europe,47.51,6.8,25516 +Monte Alegre,Brazil,South America,-2.0078,-54.0689,55462 +Monte Alegre de Minas,Brazil,South America,-18.8708,-48.8808,20170 +Monte Alegre de Sergipe,Brazil,South America,-10.0269,-37.5619,14336 +Monte Alegre do Piauí,Brazil,South America,-9.7539,-45.3039,10660 +Monte Alegre do Sul,Brazil,South America,-22.68,-46.68,8627 +Monte Alto,Brazil,South America,-21.2611,-48.4964,49456 +Monte Aprazível,Brazil,South America,-20.7728,-49.7139,22280 +Monte Azul,Brazil,South America,-15.1553,-42.8589,20328 +Monte Azul Paulista,Brazil,South America,-20.9072,-48.6414,19304 +Monte Belo,Brazil,South America,-21.3258,-46.3678,13046 +Monte Carmelo,Brazil,South America,-18.725,-47.4989,43899 +Monte Caseros,Argentina,South America,-30.25,-57.65,23470 +Monte Cristi,Dominican Republic,North America,19.8667,-71.65,24674 +Monte Escobedo,Mexico,North America,22.3,-103.5667,8683 +Monte Mor,Brazil,South America,-22.9467,-47.315,55409 +Monte Plata,Dominican Republic,North America,18.81,-69.79,37614 +Monte Quemado,Argentina,South America,-25.8036,-62.8303,12543 +Monte Santo,Brazil,South America,-10.4378,-39.3328,47780 +Monte Santo de Minas,Brazil,South America,-21.19,-46.98,20890 +Monte Sião,Brazil,South America,-22.4328,-46.5728,24089 +Montecristo,Colombia,South America,8.2994,-74.4756,21229 +Montegiardino,San Marino,Europe,43.9089,12.4844,967 +Montego Bay,Jamaica,North America,18.4667,-77.9167,97890 +Monteiro,Brazil,South America,-7.8894,-37.12,33433 +Montelíbano,Colombia,South America,7.975,-75.4208,90450 +Montemor-o-Novo,Portugal,Europe,38.65,-8.2167,17437 +Montemor-o-Velho,Portugal,Europe,40.1725,-8.6847,26171 +Montemorelos,Mexico,North America,25.1872,-99.8267,45108 +Montenegro,Brazil,South America,-29.6889,-51.4608,54692 +Monteros,Argentina,South America,-27.1667,-65.5,23274 +Monterrey,Mexico,North America,25.6844,-100.3181,5324281 +Montería,Colombia,South America,8.75,-75.8833,400000 +Montes Altos,Brazil,South America,-5.8333,-47.0667,9111 +Montes Claros,Brazil,South America,-16.7322,-43.8636,306947 +Montevideo,Uruguay,South America,-34.9056,-56.1842,1719453 +Montfoort,Netherlands,Europe,52.05,4.95,13996 +Montgomery,United States,North America,32.3482,-86.2668,251545 +Montijo,Portugal,Europe,38.7049,-8.9757,51222 +Montluçon,France,Europe,46.3408,2.6033,33317 +Montpelier,United States,North America,44.2659,-72.5717,8038 +Montpellier,France,Europe,43.6119,3.8772,307101 +Montréal,Canada,North America,45.5089,-73.5617,3675219 +Monywa,Burma,Asia,22.1083,95.1417,207489 +Monza,Italy,Europe,45.5836,9.2736,121799 +Monção,Brazil,South America,-3.4919,-45.2508,27751 +Mopti,Mali,Africa,14.49,-4.18,108456 +Moquegua,Peru,South America,-17.2,-70.9333,69882 +Mora,Sweden,Europe,61.0096,14.5635,12830 +Morada Nova,Brazil,South America,-5.1067,-38.3725,61738 +Morales,Guatemala,North America,15.4725,-88.8414,107400 +Morant Bay,Jamaica,North America,17.8819,-76.4074,11121 +Moravske-Toplice,Slovenia,Europe,46.6875,16.2256,752 +Moravče,Slovenia,Europe,46.1356,14.7439,957 +Morazán,Guatemala,North America,14.9328,-90.1431,12672 +Moreira Sales,Brazil,South America,-24.0619,-53.0069,11175 +Morelia,Mexico,North America,19.7683,-101.1894,1002461 +Moreno,Brazil,South America,-8.1186,-35.0922,49205 +Moreno Valley,United States,North America,33.9244,-117.2045,210378 +Morioka,Japan,Asia,39.7021,141.1545,290553 +Morlaix,France,Europe,48.5783,-3.8267,15220 +Morogoro,Tanzania,Africa,-6.8242,37.6633,471409 +Moroleón,Mexico,North America,20.1333,-101.2,41909 +Morondava,Madagascar,Africa,-20.2833,44.2833,53510 +Moroni,Comoros,Africa,-11.699,43.256,17267 +Moroto,Uganda,Africa,2.53,34.67,16300 +Morozovsk,Russia,Asia,48.3667,41.8333,24258 +Morretes,Brazil,South America,-25.4769,-48.8339,18309 +Morrinhos,Brazil,South America,-17.7319,-49.1008,51351 +Morro Agudo,Brazil,South America,-20.7314,-48.0578,31620 +Morro da Fumaça,Brazil,South America,-28.6508,-49.21,18537 +Morro do Chapéu,Brazil,South America,-11.55,-41.157,33594 +Morroa,Colombia,South America,9.3333,-75.3167,14429 +Morros,Brazil,South America,-2.8639,-44.0389,18554 +Morshansk,Russia,Asia,53.45,41.8167,39023 +Mortugaba,Brazil,South America,-15.0228,-42.3678,11143 +Mortágua,Portugal,Europe,40.3947,-8.2328,9607 +Morungaba,Brazil,South America,-22.88,-46.7917,12934 +Morón,Argentina,South America,-34.65,-58.6167,99066 +Mosbach,Germany,Europe,49.3522,9.1467,23647 +Moscháto,Greece,Europe,37.95,23.6667,25322 +Moscow,Russia,Asia,55.7506,37.6175,17332000 +Moshi,Tanzania,Africa,-3.3349,37.3404,201150 +Mositai,China,Asia,45.5333,119.6528,102214 +Mosjøen,Norway,Europe,65.8333,13.2,9843 +Moskva,Tajikistan,Asia,37.6567,69.6292,23300 +Mosonmagyaróvár,Hungary,Europe,47.8737,17.2687,34165 +Mosquera,Colombia,South America,4.7078,-74.2328,128012 +Moss,Norway,Europe,59.4339,10.6575,6201 +Mossoró,Brazil,South America,-5.1878,-37.3439,264577 +Mosta,Malta,Europe,35.9097,14.4261,20241 +Mostaganem,Algeria,Africa,35.9333,0.0833,145696 +Mostar,Bosnia and Herzegovina,Europe,43.3436,17.8075,105448 +Mostardas,Brazil,South America,-31.1069,-50.9208,12090 +Mosul,Iraq,Asia,36.3667,43.1167,1792000 +Motala,Sweden,Europe,58.55,15.05,29823 +Motatán,Venezuela,South America,9.3931,-70.5933,20162 +Motozintla,Mexico,North America,15.3632,-92.2477,27815 +Mouila,Gabon,Africa,-1.8667,11.055,36061 +Moulins,France,Europe,46.5653,3.3333,19344 +Moundou,Chad,Africa,8.5667,16.0833,137929 +Mount Hagen,Papua New Guinea,Oceania,-5.8667,144.2167,29765 +Moura,Portugal,Europe,38.1397,-7.4505,15167 +Mouscron,Belgium,Europe,50.7333,3.2167,58234 +Moussoro,Chad,Africa,13.6431,16.4919,75210 +Moyo,Uganda,Africa,3.6504,31.72,22434 +Moyobamba,Peru,South America,-6.0333,-76.9667,56452 +Moyogalpa,Nicaragua,North America,11.5333,-85.7,9729 +Moyuta,Guatemala,North America,14.0333,-90.0833,11229 +Mozarlândia,Brazil,South America,-14.745,-50.5708,14750 +Mozdok,Russia,Asia,43.7333,44.7,35662 +Mozhga,Russia,Asia,56.45,52.2167,43557 +Mozirje,Slovenia,Europe,46.3381,14.9572,2052 +Moçâmedes,Angola,Africa,-15.1953,12.1508,255000 +Moñitos,Colombia,South America,9.2503,-76.1325,29117 +Mońki,Poland,Europe,53.4,22.8,9675 +Moḩammadābād,Iran,Asia,28.6517,59.0158,20720 +Mpanda,Tanzania,Africa,-6.35,31.0667,204338 +Mpigi,Uganda,Africa,0.23,32.33,49500 +Mrakovo,Russia,Asia,52.7139,56.6269,8690 +Mrągowo,Poland,Europe,53.8667,21.3,21325 +Mstsislaw,Belarus,Europe,54.0167,31.7167,10019 +Mtsensk,Russia,Asia,53.2833,36.5667,36070 +Mtskheta,Georgia,Europe,41.8431,44.7194,7606 +Mtwara,Tanzania,Africa,-10.2736,40.1828,140793 +Muang Sing,Laos,Asia,21.2,101.15,23500 +Muaná,Brazil,South America,-1.5283,-49.2167,40906 +Muara Teweh,Indonesia,Asia,-0.9535,114.8981,46652 +Mubende,Uganda,Africa,0.5575,31.395,103473 +Mubi,Nigeria,Africa,10.2667,13.2667,128900 +Mucambo,Brazil,South America,-3.9089,-40.7469,13666 +Mucugê,Brazil,South America,-13.005,-41.3708,12137 +Mucur,Turkey,Europe,39.0636,34.3783,13350 +Mucuri,Brazil,South America,-18.0858,-39.5508,28062 +Mudanjiang,China,Asia,44.5514,129.6329,2290208 +Mudanya,Turkey,Europe,40.3764,28.8833,108011 +Muelle de los Bueyes,Nicaragua,North America,12.0667,-84.5333,22082 +Muhos,Finland,Europe,64.8,26.0,8827 +Muisne,Ecuador,South America,0.6108,-80.0186,5574 +Mujuí dos Campos,Brazil,South America,-2.6847,-54.6403,23501 +Mukacheve,Ukraine,Europe,48.45,22.75,85569 +Mukdahan,Thailand,Asia,16.5431,104.7228,180600 +Mukono,Uganda,Africa,0.3533,32.7553,161996 +Mulanje,Malawi,Africa,-16.0333,35.5,20870 +Mulhouse,France,Europe,47.75,7.34,104924 +Mullingar,Ireland,Europe,53.5333,-7.35,22667 +Multan,Pakistan,Asia,30.1978,71.4697,1871843 +Mulukukú,Nicaragua,North America,13.1503,-84.97,49339 +Mulungu,Brazil,South America,-4.3058,-38.9958,10569 +Mulungu do Morro,Brazil,South America,-11.9658,-41.6389,13152 +Mumbai,India,Asia,19.0761,72.8775,24973000 +Muna,Mexico,North America,20.48,-89.72,11469 +Mundo Novo,Brazil,South America,-11.8589,-40.4719,17305 +Munduri,Brazil,South America,-8.1628,-40.6158,12106 +Munich,Germany,Europe,48.1375,11.575,2606021 +Muniz Freire,Brazil,South America,-20.4642,-41.4131,17319 +Muntinlupa City,Philippines,Asia,14.38,121.05,543445 +Munxar,Malta,Europe,36.0303,14.2353,1454 +Muping,China,Asia,30.3692,102.8139,11789 +Muqui,Brazil,South America,-20.9519,-41.3458,13670 +Muquém de São Francisco,Brazil,South America,-12.065,-43.5489,10443 +Muradiye,Turkey,Europe,38.9947,43.7681,45718 +Muramvya,Burundi,Africa,-3.25,29.6,5458 +Muratlı,Turkey,Europe,41.1742,27.5086,30067 +Murcia,Spain,Europe,37.9861,-1.1303,672773 +Muret,France,Europe,43.4611,1.3267,25653 +Murfreesboro,United States,North America,35.849,-86.4121,187601 +Muriaé,Brazil,South America,-21.1308,-42.3658,104108 +Muritiba,Brazil,South America,-12.6258,-38.99,28707 +Murmansk,Russia,Asia,68.9706,33.075,267422 +Murom,Russia,Asia,55.5667,42.0333,104806 +Murra,Nicaragua,North America,13.7667,-86.0167,14487 +Murrieta,United States,North America,33.5719,-117.1909,111621 +Murska Sobota,Slovenia,Europe,46.6586,16.1619,11190 +Murtosa,Portugal,Europe,40.7369,-8.6386,10585 +Murzuq,Libya,Africa,25.9,13.9,43732 +Muscat,Oman,Asia,23.6139,58.5922,1421409 +Mushin,Nigeria,Africa,6.5333,3.35,935400 +Muskegon,United States,North America,43.2281,-86.2562,162458 +Musoma,Tanzania,Africa,-1.5,33.8,164172 +Mustafakemalpaşa,Turkey,Europe,40.0353,28.4117,102284 +Mut,Turkey,Europe,36.6458,33.4375,62874 +Muta,Slovenia,Europe,46.6167,15.1667,2291 +Mutare,Zimbabwe,Africa,-18.9667,32.6333,224802 +Mutatá,Colombia,South America,7.2442,-76.4358,22505 +Muthallath al Azraq,Jordan,Asia,31.8342,36.815,14800 +Mutis,Colombia,South America,6.2167,-77.4,9400 +Mutsamudu,Comoros,Africa,-12.1675,44.3939,30000 +Mutum,Brazil,South America,-19.8,-41.4378,27635 +Mutuípe,Brazil,South America,-13.2289,-39.505,20037 +Muurame,Finland,Europe,62.1333,25.675,10551 +Muy Muy,Nicaragua,North America,12.7667,-85.6333,14721 +Muyinga,Burundi,Africa,-2.85,30.3333,9609 +Muzaffargarh,Pakistan,Asia,30.0694,71.1942,163268 +Muzambinho,Brazil,South America,-21.3758,-46.5258,21891 +Muzo,Colombia,South America,5.5313,-74.1073,8548 +Muğla,Turkey,Europe,37.2167,28.3667,82717 +Muş,Turkey,Europe,38.7333,41.4911,120699 +Muḩradah,Syria,Asia,35.2479,36.5725,17578 +Mwanza,Tanzania,Africa,-2.5167,32.9,1104521 +Mwatate,Kenya,Africa,-3.505,38.3772,5551 +Myitkyina,Burma,Asia,25.3833,97.4,243031 +Myjava,Slovakia,Europe,48.7492,17.5644,10985 +Mykhailivka,Ukraine,Europe,47.2694,35.2222,11530 +Mykolaiv,Ukraine,Europe,46.975,31.995,470011 +Mymensingh,Bangladesh,Asia,24.7504,90.38,576927 +Myrhorod,Ukraine,Europe,49.9667,33.6,37886 +Myronivka,Ukraine,Europe,49.65,30.9833,11103 +Myrtle Beach,United States,North America,33.7094,-78.8844,325829 +Myszków,Poland,Europe,50.5706,19.3144,30415 +Mysłowice,Poland,Europe,50.2333,19.1333,72553 +Mytilíni,Greece,Europe,39.1,26.55,25729 +Mytishchi,Russia,Asia,55.9167,37.7667,275313 +Myślenice,Poland,Europe,49.8347,19.9389,18051 +Myślibórz,Poland,Europe,52.9333,14.8667,10649 +Mzimba,Malawi,Africa,-11.9,33.6,26096 +Málaga,Spain,Europe,36.7194,-4.42,967250 +Mándra,Greece,Europe,38.0667,23.5,12365 +Mátészalka,Hungary,Europe,47.9431,22.3167,15157 +Mâcon,France,Europe,46.3063,4.8313,34759 +Mâncio Lima,Brazil,South America,-7.6142,-72.8958,19311 +Mäntsälä,Finland,Europe,60.6361,25.3194,20957 +Märsta,Sweden,Europe,59.6167,17.85,27034 +Médéa,Algeria,Africa,36.2675,2.75,138355 +Mégara,Greece,Europe,37.9964,23.3444,13863 +Ménaka,Mali,Africa,15.9167,2.4,20702 +Mérida,Mexico,North America,20.9667,-89.6167,892363 +Mêdog,China,Asia,29.4833,95.5,10963 +Móng Cái,Vietnam,Asia,21.5333,107.9667,108553 +Mór,Hungary,Europe,47.3717,18.2086,13509 +Mödling,Austria,Europe,48.0833,16.2667,20555 +Mölndal,Sweden,Europe,57.6542,12.0139,37233 +Mölnlycke,Sweden,Europe,57.65,12.1333,15608 +Mönchengladbach,Germany,Europe,51.2,6.4333,268943 +Mörön,Mongolia,Asia,49.6375,100.1614,134530 +Mühldorf,Germany,Europe,48.2456,12.5228,21860 +Mühlhausen,Germany,Europe,51.2167,10.45,36641 +Mülheim,Germany,Europe,51.4275,6.8825,173255 +Münster,Germany,Europe,51.9625,7.6256,322904 +Mýrina,Greece,Europe,39.8833,25.0667,8518 +Mādabā,Jordan,Asia,31.7167,35.8,84600 +Māmā Khēl,Afghanistan,Asia,34.25,70.01,9536 +Māri‘,Syria,Asia,36.4826,37.1971,16904 +Mārupe,Latvia,Europe,56.9069,24.0575,16544 +Mīnūdasht,Iran,Asia,37.2306,55.3736,30085 +Mīrjāveh,Iran,Asia,29.0147,61.4503,9359 +Mīāndasht,Iran,Asia,33.0758,50.1631,9933 +Mīāneh,Iran,Asia,37.4233,47.7136,98973 +Mława,Poland,Europe,53.1167,20.3667,30919 +Mūḩ Ḩasan,Syria,Asia,35.2335,40.3166,9501 +Mŭ’minobod,Tajikistan,Asia,38.1083,70.0292,14100 +Mộc Châu,Vietnam,Asia,20.9221,104.7521,148259 +Mỹ Tho,Vietnam,Asia,10.35,106.35,270700 +M’Sila,Algeria,Africa,35.7019,4.5472,132975 +Na Klang,Thailand,Asia,17.3072,102.1889,20609 +Na Wa,Thailand,Asia,17.4897,104.1006,12160 +Na Yung,Thailand,Asia,17.8975,102.1498,8564 +Naaldwijk,Netherlands,Europe,51.9936,4.2133,18858 +Naama,Algeria,Africa,33.2622,-0.3144,16251 +Naantali,Finland,Europe,60.4667,22.0333,19999 +Naas,Ireland,Europe,53.2158,-6.6669,21393 +Nabatîyé,Lebanon,Asia,33.3833,35.45,80000 +Nabeul,Tunisia,Africa,36.4542,10.7347,84291 +Nabire,Indonesia,Asia,-3.3622,135.5028,99848 +Nabunturan,Philippines,Asia,7.6008,125.9656,84340 +Nacajuca,Mexico,North America,18.1692,-93.0197,10956 +Nacaome,Honduras,North America,13.5325,-87.4881,57345 +Nacozari de García,Mexico,North America,30.3833,-109.6833,12987 +Nada,China,Asia,19.5209,109.5808,954259 +Nadur,Malta,Europe,36.0381,14.295,4509 +Nadvirna,Ukraine,Europe,48.6333,24.5833,22504 +Naftalan,Azerbaijan,Europe,40.5058,46.8192,10100 +Naga City,Philippines,Asia,13.6244,123.1864,342769 +Nagano,Japan,Asia,36.6486,138.1947,367184 +Nagarote,Nicaragua,North America,12.2667,-86.5667,32303 +Nagasaki,Japan,Asia,32.7447,129.8736,404656 +Nagoya,Japan,Asia,35.1833,136.9,9197000 +Nagqu,China,Asia,31.476,92.051,414554 +Nagua,Dominican Republic,North America,19.38,-69.85,76933 +Nagyatád,Hungary,Europe,46.2294,17.3575,9333 +Nagykanizsa,Hungary,Europe,46.4558,16.9975,42502 +Nagykálló,Hungary,Europe,47.8831,21.85,9033 +Nagykáta,Hungary,Europe,47.4178,19.7414,11855 +Nagykőrös,Hungary,Europe,47.0331,19.7839,23016 +Naha,Japan,Asia,26.2122,127.6792,316048 +Nahualá,Guatemala,North America,14.85,-91.3167,87319 +Nahuatzén,Mexico,North America,19.6536,-101.9158,27174 +Nahāvand,Iran,Asia,34.1928,48.3731,76162 +Naifaru,Maldives,Asia,5.4444,73.3657,4103 +Nairobi,Kenya,Africa,-1.2864,36.8172,5545000 +Najafābād,Iran,Asia,32.6331,51.365,235281 +Najasa,Cuba,North America,21.0836,-77.7472,15579 +Najrān,Saudi Arabia,Asia,17.4917,44.1322,72038 +Naju,"Korea, South",Asia,35.0333,126.7167,92582 +Nakasongola,Uganda,Africa,1.315,32.465,11700 +Nakhodka,Russia,Asia,42.8167,132.8667,136096 +Nakhon Nayok,Thailand,Asia,14.2069,101.2142,15585 +Nakhon Pathom,Thailand,Asia,13.8206,100.0625,120657 +Nakhon Phanom,Thailand,Asia,17.4069,104.7808,27591 +Nakhon Ratchasima,Thailand,Asia,14.9806,102.1,466098 +Nakhon Sawan,Thailand,Asia,15.7133,100.1353,82305 +Nakhon Si Thammarat,Thailand,Asia,8.4364,99.9631,102152 +Nakhon Thai,Thailand,Asia,17.1006,100.8374,9278 +Naklo,Slovenia,Europe,46.2749,14.3176,1776 +Nakuru,Kenya,Africa,-0.2833,36.0667,307990 +Nakło nad Notecią,Poland,Europe,53.1403,17.5928,17620 +Nalchik,Russia,Asia,43.4833,43.6167,265162 +Nalerigu,Ghana,Africa,10.5273,-0.3698,14927 +Nallıhan,Turkey,Europe,40.1836,31.3506,26553 +Nam Som,Thailand,Asia,17.7704,102.1895,8742 +Nam Định,Vietnam,Asia,20.42,106.1683,448225 +Namangan,Uzbekistan,Asia,41.0011,71.6683,1010000 +Namlea,Indonesia,Asia,-3.25,127.0833,37869 +Nampa,United States,North America,43.5844,-116.5638,201129 +Nampula,Mozambique,Africa,-15.1167,39.2667,743000 +Namsan,"Korea, North",Asia,42.2275,129.2541,80146 +Namsos,Norway,Europe,64.467,11.494,8471 +Namtsy,Russia,Asia,62.7333,129.6667,10364 +Namur,Belgium,Europe,50.4667,4.8667,114007 +Namysłów,Poland,Europe,51.0728,17.7069,16942 +Namīn,Iran,Asia,38.4264,48.4822,13659 +Nan,Thailand,Asia,18.7833,100.7833,20012 +Nanaimo,Canada,North America,49.1642,-123.9364,106079 +Nanchang,China,Asia,28.683,115.858,3576547 +Nanchital de Lázaro Cárdenas del Río,Mexico,North America,18.0667,-94.4167,30039 +Nanchong,China,Asia,30.8372,106.1106,5607565 +Nancy,France,Europe,48.6936,6.1846,104387 +Nandaime,Nicaragua,North America,11.75,-86.05,34288 +Nandasmo,Nicaragua,North America,11.9333,-86.1167,10732 +Nang Rong,Thailand,Asia,14.6283,102.7933,21257 +Nangan,Taiwan,Asia,26.15,119.9333,7815 +Nangandao,China,Asia,35.3036,113.9268,5708191 +Nangong,China,Asia,37.359,115.409,396718 +Nanjian,China,Asia,25.0531,100.5231,49854 +Nanjing,China,Asia,32.0608,118.7789,8422000 +Nannestad,Norway,Europe,60.2456,10.9528,15530 +Nanning,China,Asia,22.8167,108.3275,3837978 +Nanping,China,Asia,26.6418,118.1774,2680645 +Nansan,China,Asia,23.7784,98.8253,55436 +Nanterre,France,Europe,48.8988,2.1969,98119 +Nantes,France,Europe,47.2181,-1.5528,325070 +Nantong,China,Asia,31.981,120.894,2261382 +Nantou,Taiwan,Asia,23.9167,120.6833,97504 +Nanuque,Brazil,South America,-17.8392,-40.3539,40665 +Naogaon,Bangladesh,Asia,24.8118,88.947,150025 +Naolinco de Victoria,Mexico,North America,19.6367,-96.8525,22835 +Napak,Uganda,Africa,2.1156,34.2267,5278 +Naperville,United States,North America,41.748,-88.1659,149424 +Napier,New Zealand,Oceania,-39.4903,176.9178,62241 +Naples,Italy,Europe,40.8358,14.2486,913462 +Naqadeh,Iran,Asia,36.9547,45.3881,72975 +Nara,Japan,Asia,34.6844,135.805,352377 +Naranjal,Ecuador,South America,-2.6736,-79.6183,39323 +Naranjito,Ecuador,South America,-2.1667,-79.4653,34664 +Naranjos,Mexico,North America,21.3509,-97.6866,20073 +Narathiwat,Thailand,Asia,6.4167,101.8167,40521 +Narbonne,France,Europe,43.1836,3.0042,56692 +Narimanov,Russia,Asia,46.6833,47.85,11104 +Nariño,Colombia,South America,5.6092,-75.1764,8603 +Narman,Turkey,Europe,40.3472,41.8683,12292 +Naro-Fominsk,Russia,Asia,55.3833,36.7667,74493 +Narok,Kenya,Africa,-1.0833,35.8667,65430 +Nartkala,Russia,Asia,43.55,43.85,30832 +Narva,Estonia,Europe,59.3758,28.1961,53626 +Narvik,Norway,Europe,68.4383,17.4278,14051 +Naryn,Kyrgyzstan,Asia,41.4333,76.0,34822 +Nar’yan-Mar,Russia,Asia,67.6333,53.05,24775 +Nashua,United States,North America,42.7491,-71.491,247828 +Nashville,United States,North America,36.1715,-86.7842,1178679 +Nassau,"Bahamas, The",North America,25.0442,-77.3503,274400 +Natagaima,Colombia,South America,3.5833,-75.0833,22320 +Natal,Brazil,South America,-5.795,-35.2089,751300 +Natitingou,Benin,Africa,10.3,1.3667,104010 +Natividade,Brazil,South America,-11.71,-47.7228,8754 +Natividade do Carangola,Brazil,South America,-21.0419,-41.9728,15074 +Natuba,Brazil,South America,-7.6408,-35.55,8945 +Naucalpan de Juárez,Mexico,North America,19.4753,-99.2378,910407 +Naujoji Akmenė,Lithuania,Europe,56.3167,22.8833,8170 +Naumburg,Germany,Europe,51.15,11.8167,32336 +Naushahro Firoz,Pakistan,Asia,26.85,68.1333,38181 +Nautla,Mexico,North America,20.2167,-96.7833,2890 +Nava,Mexico,North America,28.4214,-100.7675,26963 +Naval,Philippines,Asia,11.58,124.45,58187 +Navapolatsk,Belarus,Europe,55.5333,28.65,95717 +Navarro,Argentina,South America,-35.0167,-59.2667,11562 +Navashino,Russia,Asia,55.55,42.2,14664 +Navegantes,Brazil,South America,-26.8989,-48.6539,86401 +Naviraí,Brazil,South America,-23.065,-54.1908,50457 +Navoiy,Uzbekistan,Asia,40.0844,65.3792,144158 +Navojoa,Mexico,North America,27.0813,-109.4461,120926 +Navolato,Mexico,North America,24.7656,-107.7019,28676 +Navotas,Philippines,Asia,14.6667,120.9417,247543 +Nawabshah,Pakistan,Asia,26.2442,68.41,263102 +Nawá,Syria,Asia,32.8865,36.039,63676 +Naxxar,Malta,Europe,35.915,14.4447,14890 +Naxçıvan,Azerbaijan,Europe,39.2089,45.4122,95100 +Nay Pyi Taw,Burma,Asia,19.7475,96.115,1160242 +Nazareth,Israel,Asia,32.7019,35.3033,83400 +Nazarje,Slovenia,Europe,46.3202,14.9531,880 +Nazarovo,Russia,Asia,56.0,90.3833,45333 +Nazaré,Brazil,South America,-13.035,-39.0139,26365 +Nazaré Paulista,Brazil,South America,-23.1808,-46.395,18217 +Nazaré da Mata,Brazil,South America,-7.7419,-35.2278,30648 +Nazilli,Turkey,Europe,37.9125,28.3206,162737 +Nazran,Russia,Asia,43.2167,44.7667,126292 +Nazária,Brazil,South America,-5.3608,-42.8083,10262 +Naz̧arābād,Iran,Asia,35.9561,50.6044,119512 +Našice,Croatia,Europe,45.4833,18.1,14291 +Naţanz,Iran,Asia,33.5072,51.9136,14122 +Ndalatando,Angola,Africa,-9.3,14.9167,161584 +Ndola,Zambia,Africa,-12.9689,28.6325,627503 +Ndélé,Central African Republic,Africa,8.4092,20.6531,13704 +Nebbi,Uganda,Africa,2.4792,31.09,41400 +Nechí,Colombia,South America,8.1,-74.7833,25790 +Necochea,Argentina,South America,-38.55,-58.7333,90000 +Necoclí,Colombia,South America,8.4167,-76.7833,69090 +Nedelišće,Croatia,Europe,46.38,16.39,11017 +Nederweert,Netherlands,Europe,51.2858,5.7486,17171 +Neerijnen,Netherlands,Europe,51.8333,5.2833,12397 +Neftekamsk,Russia,Asia,56.1028,54.2867,131942 +Neftekumsk,Russia,Asia,44.7833,44.7667,23075 +Nefteyugansk,Russia,Asia,61.0833,72.7,126690 +Neftçala,Azerbaijan,Europe,39.3791,49.2486,14241 +Negotin,Serbia,Europe,44.2167,22.5167,35000 +Negotino,North Macedonia,Europe,41.4839,22.0892,12488 +Nehbandān,Iran,Asia,31.5411,60.0397,18304 +Nehe,China,Asia,48.4833,124.8833,436906 +Neiba,Dominican Republic,North America,18.49,-71.42,36511 +Neijiang,China,Asia,29.5802,105.058,3140678 +Neira,Colombia,South America,5.1664,-75.5189,31399 +Neiva,Colombia,South America,2.9345,-75.2809,380019 +Nekā,Iran,Asia,36.65,53.2972,60991 +Nelas,Portugal,Europe,40.5167,-7.85,14037 +Nelidovo,Russia,Asia,56.2167,32.8,18603 +Nelson,New Zealand,Oceania,-41.2931,173.2381,46437 +Neman,Russia,Asia,55.0333,22.0333,9216 +Nemocón,Colombia,South America,5.05,-73.8833,13488 +Nemyriv,Ukraine,Europe,48.9667,28.85,11421 +Nenagh,Ireland,Europe,52.8619,-8.1967,9895 +Nenjiang,China,Asia,49.11,125.13,500000 +Nentón,Guatemala,North America,15.8,-91.75,51349 +Nepomuceno,Brazil,South America,-21.2358,-45.2358,25018 +Neqāb,Iran,Asia,36.7108,57.41,14723 +Nerchinsk,Russia,Asia,51.9944,116.5556,14919 +Neryungri,Russia,Asia,56.6833,124.6667,53526 +Nerópolis,Brazil,South America,-16.4058,-49.2189,31932 +Nesebar,Bulgaria,Europe,42.6595,27.7247,14596 +Nesoddtangen,Norway,Europe,59.8086,10.6556,20322 +Neu-Ulm,Germany,Europe,48.3833,10.0,61780 +Neubrandenburg,Germany,Europe,53.5569,13.2611,64390 +Neuburg,Germany,Europe,48.7333,11.1833,30881 +Neuchâtel,Switzerland,Europe,47.0,6.9333,33475 +Neumarkt,Germany,Europe,49.2833,11.4667,41255 +Neumünster,Germany,Europe,54.0714,9.99,80185 +Neunkirchen,Germany,Europe,49.35,7.1667,47097 +Neuquén,Argentina,South America,-38.9525,-68.0642,288896 +Neuruppin,Germany,Europe,52.9331,12.8,31803 +Neuss,Germany,Europe,51.2,6.7,155163 +Neustadt,Germany,Europe,49.35,8.15,53920 +Neuwied,Germany,Europe,50.4286,7.4614,66243 +Nevel,Russia,Asia,56.0333,29.9167,13980 +Nevelsk,Russia,Asia,46.6833,141.8667,10568 +Nevers,France,Europe,46.9933,3.1572,33172 +Neves,Sao Tome and Principe,Africa,0.3592,6.5517,10068 +Neves Paulista,Brazil,South America,-20.8458,-49.63,8772 +Nevinnomyssk,Russia,Asia,44.6333,41.9333,114556 +Nevyansk,Russia,Asia,57.4833,60.2,22030 +Nevşehir,Turkey,Europe,38.6264,34.7139,123882 +New Amsterdam,Guyana,South America,6.25,-57.5167,17329 +New Bedford,United States,North America,41.6697,-70.9428,152104 +New Delhi,India,Asia,28.6139,77.2089,249998 +New Haven,United States,North America,41.3113,-72.9246,565056 +New Mirpur,Pakistan,Asia,33.15,73.7333,124352 +New Orleans,United States,North America,30.0687,-89.9288,918752 +New York,United States,North America,40.6943,-73.9249,18832416 +Newark,United States,North America,40.7245,-74.1725,307188 +Newport News,United States,North America,37.1051,-76.5185,184774 +Neyrīz,Iran,Asia,29.1928,54.3197,49850 +Neyshābūr,Iran,Asia,36.22,58.82,221700 +Nezahualcóyotl,Mexico,North America,19.4081,-99.0186,1077208 +Neópolis,Brazil,South America,-10.32,-36.5794,16426 +Ngaoundéré,Cameroon,Africa,7.3167,13.5833,189800 +Ngerulmud,Palau,Oceania,7.5006,134.6242,271 +Ngora,Uganda,Africa,1.4575,33.7764,17700 +Ngozi,Burundi,Africa,-2.9083,29.8269,39884 +Nguigmi,Niger,Africa,14.2532,13.1108,17897 +Nguru,Nigeria,Africa,12.8792,10.4525,270632 +Ngã Bảy,Vietnam,Asia,9.8164,105.8197,101192 +Nha Trang,Vietnam,Asia,12.245,109.1917,579000 +Nhamundá,Brazil,South America,-2.1861,-56.7131,21443 +Nhandeara,Brazil,South America,-20.6939,-50.0378,9852 +Niamey,Niger,Africa,13.515,2.1175,1496260 +Nice,France,Europe,43.7034,7.2663,353701 +Nicolás Romero,Mexico,North America,19.5833,-99.3667,366602 +Nicosia,Cyprus,Europe,35.1725,33.365,330000 +Nida,Lithuania,Europe,55.3033,21.0056,2385 +Nidzica,Poland,Europe,53.3583,20.425,13052 +Nieuw Amsterdam,Suriname,South America,5.8833,-55.0833,5650 +Nieuw Nickerie,Suriname,South America,5.9333,-56.9833,12818 +Nieuwegein,Netherlands,Europe,52.0314,5.0919,63866 +Niigata,Japan,Asia,37.9161,139.0364,790646 +Nijkerk,Netherlands,Europe,52.2214,5.4842,43600 +Nijmegen,Netherlands,Europe,51.8425,5.8528,177659 +Nijverdal,Netherlands,Europe,52.3631,6.4617,25000 +Nikel,Russia,Asia,69.4139,30.2319,9519 +Nikolayevsk,Russia,Asia,50.0333,45.45,13460 +Nikolayevsk-na-Amure,Russia,Asia,53.1333,140.7333,150 +Nikopol,Ukraine,Europe,47.5667,34.4,105160 +Niksar,Turkey,Europe,40.5917,36.9517,37017 +Nikšić,Montenegro,Europe,42.7778,18.95,32046 +Nilka,China,Asia,43.7826,82.5089,31907 +Nilo Peçanha,Brazil,South America,-13.5989,-39.1069,11213 +Nilüfer,Turkey,Europe,40.2833,28.95,536365 +Nindirí,Nicaragua,North America,12.0047,-86.1194,38355 +Ningbo,China,Asia,29.8603,121.6245,4087523 +Ningde,China,Asia,26.6662,119.5477,2910000 +Ning’an,China,Asia,44.341,129.483,322127 +Ninh Bình,Vietnam,Asia,20.25,105.8333,1106913 +Ninh Hòa,Vietnam,Asia,12.4917,109.1258,76368 +Ninheira,Brazil,South America,-15.3208,-41.7539,10588 +Nioaque,Brazil,South America,-21.135,-55.83,13220 +Niono,Mali,Africa,14.25,-6.0,91554 +Nioro,Mali,Africa,15.1833,-9.55,60112 +Niort,France,Europe,46.3258,-0.4606,60074 +Niquelândia,Brazil,South America,-14.4739,-48.46,34964 +Niquero,Cuba,North America,20.0472,-77.5781,41252 +Niquinohomo,Nicaragua,North America,11.9,-86.1,14847 +Nirgua,Venezuela,South America,10.0833,-68.6667,58932 +Nisko,Poland,Europe,50.52,22.1394,14874 +Nisporeni,Moldova,Europe,47.0833,28.1833,7986 +Niterói,Brazil,South America,-22.8833,-43.1036,496696 +Nitra,Slovakia,Europe,48.3147,18.0875,77610 +Nittedal,Norway,Europe,60.0731,10.8722,25440 +Nivala,Finland,Europe,63.925,24.975,10454 +Nivelles,Belgium,Europe,50.5833,4.3167,28521 +Nizhneudinsk,Russia,Asia,54.9333,99.0,2400 +Nizhnevartovsk,Russia,Asia,60.95,76.6,283256 +Nizhniy Novgorod,Russia,Asia,56.3269,44.0075,1204985 +Nizhniy Tagil,Russia,Asia,57.9167,59.9667,330507 +Nizhnyaya Salda,Russia,Asia,58.0667,60.7167,16233 +Nizhnyaya Tura,Russia,Asia,58.6208,59.8478,17985 +Nizhyn,Ukraine,Europe,51.0381,31.8861,65830 +Nizip,Turkey,Europe,37.01,37.7972,148386 +Nizwá,Oman,Asia,22.9333,57.5333,83544 +Niğde,Turkey,Europe,37.9667,34.6792,170511 +Niš,Serbia,Europe,43.3192,21.8961,183164 +Niška Banja,Serbia,Europe,43.2943,22.0099,14098 +Njombe,Tanzania,Africa,-9.3333,34.7667,40607 +Nkhata Bay,Malawi,Africa,-11.6333,34.3,14274 +Nkhotakota,Malawi,Africa,-12.9163,34.3,28350 +Nkurenkuru,Namibia,Africa,-17.6167,18.6,10261 +Nnewi,Nigeria,Africa,6.0167,6.9167,391227 +Nobres,Brazil,South America,-14.72,-56.3278,15492 +Nobsa,Colombia,South America,5.7667,-72.95,16271 +Nochistlán de Mejía,Mexico,North America,21.3642,-102.8464,27932 +Nogales,Mexico,North America,18.8167,-97.1667,21113 +Nogent-le-Rotrou,France,Europe,48.3225,0.8225,9305 +Nogent-sur-Marne,France,Europe,48.8375,2.4833,32998 +Noginsk,Russia,Asia,55.85,38.4333,102392 +Nogoyá,Argentina,South America,-32.4,-59.8,22824 +Nokia,Finland,Europe,61.4767,23.5053,35730 +Nola,Central African Republic,Africa,3.5333,16.0667,29181 +Non Sung,Thailand,Asia,15.1801,102.2569,9730 +Nong Bua,Thailand,Asia,15.8646,100.5858,11951 +Nong Bua Lamphu,Thailand,Asia,17.2042,102.4444,21072 +Nong Khae,Thailand,Asia,14.3406,100.8673,12336 +Nong Khai,Thailand,Asia,17.8681,102.7444,46180 +Nong Ki,Thailand,Asia,14.6868,102.538,10089 +Nong Kung Si,Thailand,Asia,16.6559,103.2972,9213 +Nong Wua So,Thailand,Asia,17.1626,102.5727,9816 +Nong’an,China,Asia,44.4347,125.175,224387 +Nonoai,Brazil,South America,-27.3619,-52.7708,13719 +Nonthaburi,Thailand,Asia,13.8667,100.5167,3418 +Noordwijk,Netherlands,Europe,52.2333,4.45,44062 +Noordwijkerhout,Netherlands,Europe,52.2667,4.5,16605 +Norak,Tajikistan,Asia,38.3833,69.3167,61500 +Nordestina,Brazil,South America,-10.8228,-39.4278,11800 +Nordhausen,Germany,Europe,51.505,10.7911,41233 +Nordhorn,Germany,Europe,52.4319,7.0678,55619 +Norfolk,United States,North America,36.8945,-76.259,235037 +Noria de Ángeles,Mexico,North America,22.4433,-101.9097,16284 +Norilsk,Russia,Asia,69.3333,88.2167,176735 +Norman,United States,North America,35.2335,-97.3471,128714 +Norosí,Colombia,South America,8.5261,-74.0378,9850 +Norrköping,Sweden,Europe,58.5919,16.1856,137326 +Norrtälje,Sweden,Europe,59.7667,18.7,17275 +North Charleston,United States,North America,32.9067,-80.0722,117460 +North Hempstead,United States,North America,40.7912,-73.6688,236600 +North Las Vegas,United States,North America,36.2883,-115.0888,270773 +Northeim,Germany,Europe,51.7067,10.0011,29337 +Norwalk,United States,North America,33.9069,-118.0829,100713 +Norwich,United States,North America,41.5495,-72.0882,163299 +Nossa Senhora Aparecida,Brazil,South America,-10.4428,-37.4889,9232 +Nossa Senhora da Glória,Brazil,South America,-10.2178,-37.42,26910 +Nossa Senhora das Dores,Brazil,South America,-10.4919,-37.1928,24996 +Nossa Senhora do Livramento,Brazil,South America,-15.775,-56.3458,12940 +Nossa Senhora do Socorro,Brazil,South America,-10.855,-37.1258,192330 +Nossa Senhora dos Milagres,Brazil,South America,-12.87,-39.8589,11071 +Nossa Senhora dos Remédios,Brazil,South America,-3.9789,-42.6208,8525 +Notodden,Norway,Europe,59.5594,9.2585,9072 +Nouadhibou,Mauritania,Africa,20.9333,-17.0333,118000 +Nouakchott,Mauritania,Africa,18.0858,-15.9785,1077169 +Nouméa,New Caledonia,Oceania,-22.2625,166.4443,182341 +Nouna,Burkina Faso,Africa,12.7333,-3.8667,32428 +Nova Bassano,Brazil,South America,-28.7239,-51.705,9649 +Nova Brasilândia d’Oeste,Brazil,South America,-11.7197,-62.3158,15679 +Nova Canaã,Brazil,South America,-14.7939,-40.1419,13715 +Nova Crixás,Brazil,South America,-14.0989,-50.3269,12815 +Nova Crnja,Serbia,Europe,45.6667,20.6,8147 +Nova Cruz,Brazil,South America,-6.4778,-35.4339,34269 +Nova Era,Brazil,South America,-19.7606,-43.0289,17438 +Nova Esperança,Brazil,South America,-23.1839,-52.205,26585 +Nova Europa,Brazil,South America,-21.7783,-48.5608,10439 +Nova Floresta,Brazil,South America,-6.455,-36.2028,9724 +Nova Friburgo,Brazil,South America,-22.2819,-42.5308,173418 +Nova Gorica,Slovenia,Europe,45.9558,13.6433,13031 +Nova Gradiška,Croatia,Europe,45.25,17.3833,11690 +Nova Granada,Brazil,South America,-20.5339,-49.3139,19419 +Nova Ipixuna,Brazil,South America,-4.9208,-49.0769,13955 +Nova Laranjeiras,Brazil,South America,-25.3069,-52.5408,12074 +Nova Lima,Brazil,South America,-19.9858,-43.8469,111697 +Nova Londrina,Brazil,South America,-22.7658,-52.985,12923 +Nova Odesa,Ukraine,Europe,47.3097,31.7792,11510 +Nova Odessa,Brazil,South America,-22.7797,-47.2962,62019 +Nova Olinda,Brazil,South America,-7.0919,-39.6808,15399 +Nova Olinda do Norte,Brazil,South America,-3.8878,-59.0939,27062 +Nova Olímpia,Brazil,South America,-14.7969,-57.2878,16352 +Nova Petrópolis,Brazil,South America,-29.3758,-51.1119,16891 +Nova Ponte,Brazil,South America,-19.1983,-47.7272,14598 +Nova Prata,Brazil,South America,-28.7839,-51.61,25692 +Nova Resende,Brazil,South America,-21.1258,-46.42,16387 +Nova Russas,Brazil,South America,-4.7,-40.5667,30699 +Nova Serrana,Brazil,South America,-19.8758,-44.9839,105552 +Nova Soure,Brazil,South America,-11.2328,-38.4828,24236 +Nova Timboteua,Brazil,South America,-1.2058,-47.3858,12806 +Nova Trento,Brazil,South America,-27.2867,-48.9303,13727 +Nova Varoš,Serbia,Europe,43.4667,19.8203,13507 +Nova Vas,Slovenia,Europe,45.7726,14.5064,293 +Nova Veneza,Brazil,South America,-28.6369,-49.4978,13664 +Nova Venécia,Brazil,South America,-18.7108,-40.4008,43015 +Nova Viçosa,Brazil,South America,-17.8919,-39.3719,39509 +Nova Xavantina,Brazil,South America,-14.6728,-52.3528,24345 +Novaci,North Macedonia,Europe,41.0419,21.4561,1283 +Novara,Italy,Europe,45.45,8.62,101257 +Novaya Lyalya,Russia,Asia,59.0667,60.6,10530 +Novhorod-Siverskyi,Ukraine,Europe,51.9972,33.2667,12375 +Novi Bečej,Serbia,Europe,45.6,20.1167,13133 +Novi Grad,Bosnia and Herzegovina,Europe,45.0481,16.3769,10120 +Novi Kneževac,Serbia,Europe,46.05,20.1,5688 +Novi Marof,Croatia,Europe,46.1667,16.3333,11795 +Novi Pazar,Serbia,Europe,43.15,20.5167,66527 +Novi Sad,Serbia,Europe,45.2542,19.8425,260438 +Novi Travnik,Bosnia and Herzegovina,Europe,44.1748,17.6634,25107 +Novo Airão,Brazil,South America,-2.6208,-60.9439,15761 +Novo Aripuanã,Brazil,South America,-5.1214,-60.3806,17119 +Novo Cruzeiro,Brazil,South America,-17.4678,-41.875,26975 +Novo Hamburgo,Brazil,South America,-29.6778,-51.1308,85364 +Novo Horizonte,Brazil,South America,-21.4678,-49.2208,38324 +Novo Lino,Brazil,South America,-8.915,-35.6467,12764 +Novo Mesto,Slovenia,Europe,45.8,15.1667,24183 +Novo Oriente,Brazil,South America,-5.5339,-40.7739,27545 +Novo Selo,North Macedonia,Europe,41.4128,22.88,1246 +Novoanninskiy,Russia,Asia,50.5333,42.6833,15351 +Novoazovsk,Ukraine,Europe,47.1117,38.0825,11051 +Novobërdë,Kosovo,Europe,42.6178,21.4197,7121 +Novocherkassk,Russia,Asia,47.4222,40.0939,160529 +Novodvinsk,Russia,Asia,64.4167,40.8333,32826 +Novohrad-Volynskyi,Ukraine,Europe,50.5833,27.6333,55086 +Novomoskovsk,Russia,Asia,54.0833,38.2167,117175 +Novomyrhorod,Ukraine,Europe,48.7833,31.65,10715 +Novorossiysk,Russia,Asia,44.7167,37.7833,261937 +Novosibirsk,Russia,Asia,55.05,82.95,1633851 +Novotroitsk,Russia,Asia,51.2039,58.3114,74469 +Novotroitske,Ukraine,Europe,46.35,34.3333,10535 +Novoukrainka,Ukraine,Europe,48.3231,31.5242,16080 +Novouralsk,Russia,Asia,57.25,60.0833,77894 +Novozybkov,Russia,Asia,52.5333,31.9333,38680 +Novoīshīmskīy,Kazakhstan,Asia,53.1981,66.7694,11583 +Novska,Croatia,Europe,45.3333,16.9833,11137 +Novyi Buh,Ukraine,Europe,47.6833,32.5,15000 +Novyy Oskol,Russia,Asia,50.7583,37.8736,18359 +Nové Mesto nad Váhom,Slovakia,Europe,48.7547,17.8308,19644 +Nové Zámky,Slovakia,Europe,47.9831,18.1728,37791 +Nowa Sól,Poland,Europe,51.8,15.7167,36843 +Nowe Miasto Lubawskie,Poland,Europe,53.4256,19.5878,10492 +Nowshahr,Iran,Asia,36.6475,51.4931,49403 +Nowshera,Pakistan,Asia,34.0153,71.9747,118594 +Nowy Dwór Gdański,Poland,Europe,54.2167,19.1167,9618 +Nowy Dwór Mazowiecki,Poland,Europe,52.4333,20.7167,28651 +Nowy Sącz,Poland,Europe,49.6239,20.6972,81281 +Nowy Targ,Poland,Europe,49.4667,20.0167,33273 +Nowy Tomyśl,Poland,Europe,52.3167,16.1333,14116 +Nsanje,Malawi,Africa,-16.9167,35.2667,26844 +Nsukka,Nigeria,Africa,6.8567,7.3958,111017 +Ntcheu,Malawi,Africa,-14.8167,34.6333,21241 +Ntchisi,Malawi,Africa,-13.3753,34.0036,9357 +Ntungamo,Uganda,Africa,-0.8819,30.2653,16400 +Nubl,Syria,Asia,36.3787,36.9929,21039 +Nuenen,Netherlands,Europe,51.4667,5.55,20580 +Nueva Concepción,Guatemala,North America,14.1997,-91.2997,82216 +Nueva Gerona,Cuba,North America,21.8847,-82.8011,45577 +Nueva Granada,Colombia,South America,9.8031,-74.3903,21612 +Nueva Guinea,Nicaragua,North America,11.6833,-84.45,66936 +Nueva Loja,Ecuador,South America,0.0847,-76.8828,55627 +Nueva Ocotepeque,Honduras,North America,14.4355,-89.1845,8780 +Nueva Paz,Cuba,North America,22.7667,-81.75,24277 +Nueva Santa Rosa,Guatemala,North America,14.3811,-90.2761,39286 +Nueve de Julio,Argentina,South America,-35.45,-60.8833,36494 +Nuevo Arraiján,Panama,North America,8.92,-79.72,26298 +Nuevo Casas Grandes,Mexico,North America,30.4167,-107.9167,55553 +Nuevo Ideal,Mexico,North America,24.8875,-105.0728,12850 +Nuevo Laredo,Mexico,North America,27.4861,-99.5069,384033 +Nuevo Progreso,Guatemala,North America,14.8,-91.9167,31968 +Nuevo San Carlos,Guatemala,North America,14.6,-91.7,41956 +Nuevo San Juan Parangaricutiro,Mexico,North America,19.4,-102.1333,16745 +Nukus,Uzbekistan,Asia,42.4667,59.6,332500 +Nuku‘alofa,Tonga,Oceania,-21.1333,-175.2,27600 +Numan,Nigeria,Africa,9.4669,12.0328,141200 +Nunchía,Colombia,South America,5.6333,-72.2,8928 +Nunspeet,Netherlands,Europe,52.3667,5.7833,28021 +Nuoro,Italy,Europe,40.3201,9.3281,33850 +Nuquí,Colombia,South America,5.7167,-77.2667,8958 +Nurdağı,Turkey,Europe,37.1789,36.7397,41322 +Nuremberg,Germany,Europe,49.4539,11.0775,526091 +Nurhak,Turkey,Europe,37.9658,37.4272,12257 +Nurmes,Finland,Europe,63.545,29.1333,9130 +Nurmijärvi,Finland,Europe,60.4667,24.8083,44833 +Nurobod Shahri,Uzbekistan,Asia,39.6086,66.2867,9600 +Nusaybin,Turkey,Europe,37.075,41.2153,115586 +Nuth,Netherlands,Europe,50.9167,5.8833,15423 +Nuuk,Greenland,North America,64.1767,-51.7361,18326 +Nwoya,Uganda,Africa,2.635,32.0,4295 +Nyagan,Russia,Asia,62.1333,65.3833,63034 +Nyala,Sudan,Africa,12.05,24.8833,492984 +Nyandoma,Russia,Asia,61.6667,40.2167,18146 +Nyanza,Rwanda,Africa,-2.35,29.7333,25417 +Nyasvizh,Belarus,Europe,53.2167,26.6667,15968 +Nyazepetrovsk,Russia,Asia,56.05,59.6,10198 +Nyborg,Denmark,Europe,55.3058,10.8034,17900 +Nybro,Sweden,Europe,56.75,15.9,13583 +Nyeri,Kenya,Africa,-0.4167,36.95,98908 +Nyköping,Sweden,Europe,58.7582,17.0185,39770 +Nykøbing Falster,Denmark,Europe,54.7655,11.8751,20105 +Nykøbing Mors,Denmark,Europe,56.7917,8.8499,9068 +Nynäshamn,Sweden,Europe,58.9044,17.9464,13510 +Nysa,Poland,Europe,50.4714,17.3339,42151 +Nytva,Russia,Asia,57.95,55.3333,16463 +Nyurba,Russia,Asia,63.2833,118.3333,10055 +Nyzhnohirskyi,Ukraine,Europe,45.4436,34.7406,8741 +Nyírbátor,Hungary,Europe,47.8353,22.13,11237 +Nyíregyháza,Hungary,Europe,47.9558,21.7168,115359 +Náfpaktos,Greece,Europe,38.3939,21.8306,11191 +Náfplio,Greece,Europe,37.5658,22.8,14532 +Náousa,Greece,Europe,40.6333,22.0667,17830 +Não-Me-Toque,Brazil,South America,-28.4589,-52.8208,17898 +Närpes,Finland,Europe,62.4667,21.3333,9621 +Nässjö,Sweden,Europe,57.65,14.6833,16678 +Næstved,Denmark,Europe,55.2249,11.7842,44996 +Néa Filadélfeia,Greece,Europe,38.0367,23.7367,25198 +Néa Ionía,Greece,Europe,38.0333,23.75,64611 +Néa Smýrni,Greece,Europe,37.95,23.7167,72853 +Néma,Mauritania,Africa,16.6171,-7.25,21979 +Níkaia,Greece,Europe,37.9667,23.6333,88077 +Nísia Floresta,Brazil,South America,-6.0908,-35.2089,31942 +Nîmes,France,Europe,43.8383,4.3597,150444 +Nālūt,Libya,Africa,31.8685,10.9812,66609 +Nīkshahr,Iran,Asia,26.2253,60.2167,17732 +N’Djamena,Chad,Africa,12.1053,15.0447,807000 +N’Zérékoré,Guinea,Africa,7.7522,-8.8217,315000 +Oakland,United States,North America,37.7904,-122.2166,438072 +Oakville,Canada,North America,43.45,-79.6833,213759 +Oaxaca,Mexico,North America,17.0606,-96.7253,300050 +Obando,Colombia,South America,4.5833,-75.9167,14980 +Oberhausen,Germany,Europe,51.4967,6.8706,211099 +Oberá,Argentina,South America,-27.4833,-55.1333,66112 +Obiaruku,Nigeria,Africa,5.85,6.15,26277 +Obikiik,Tajikistan,Asia,38.1586,68.6722,12300 +Obiliq,Kosovo,Europe,42.6869,21.0703,6864 +Obleševo,North Macedonia,Europe,41.8833,22.3339,887 +Obluchye,Russia,Asia,49.0167,131.05,550 +Obninsk,Russia,Asia,55.0931,36.6106,132477 +Obo,Central African Republic,Africa,5.4,26.5,7187 +Obock,Djibouti,Africa,11.9667,43.2833,21200 +Oborniki,Poland,Europe,52.65,16.8167,17629 +Oboyan,Russia,Asia,51.2,36.2833,11671 +Obukhiv,Ukraine,Europe,50.13,30.6567,33287 +Ocala,United States,North America,29.178,-82.1511,197770 +Ocara,Brazil,South America,-4.4908,-38.5969,24493 +Ocaña,Colombia,South America,8.2333,-73.35,116232 +Oceanside,United States,North America,33.2247,-117.3083,172542 +Ochakiv,Ukraine,Europe,46.6186,31.5392,13663 +Ochër,Russia,Asia,57.8833,54.7333,14294 +Ocniţa,Moldova,Europe,48.3853,27.4392,3133 +Ocosingo,Mexico,North America,16.9072,-92.0961,47688 +Ocotal,Nicaragua,North America,13.6333,-86.4833,48765 +Ocotlán,Mexico,North America,20.3467,-102.7744,106050 +Ocotlán de Morelos,Mexico,North America,16.7914,-96.675,21676 +Ocoyoacac,Mexico,North America,19.2739,-99.4606,66190 +Ocozocoautla de Espinosa,Mexico,North America,16.75,-93.3667,43247 +Ocuilan de Arteaga,Mexico,North America,19.0,-99.4,34485 +Ocumare del Tuy,Venezuela,South America,10.1136,-66.7814,166112 +Ocós,Guatemala,North America,14.5094,-92.1933,12776 +Odder,Denmark,Europe,55.9772,10.1683,12914 +Odemira,Portugal,Europe,37.5833,-8.6333,26066 +Odense,Denmark,Europe,55.4004,10.3833,182387 +Odesa,Ukraine,Europe,46.492,30.7081,1010537 +Odessa,United States,North America,31.8801,-102.3448,162648 +Odienné,Côte d’Ivoire,Africa,9.5,-7.5667,86279 +Odintsovo,Russia,Asia,55.6667,37.2667,111612 +Odivelas,Portugal,Europe,38.8,-9.1833,148034 +Odranci,Slovenia,Europe,46.5833,16.2833,1632 +Odžaci,Serbia,Europe,45.5167,19.2667,7556 +Odžak,Bosnia and Herzegovina,Europe,45.0246,18.3211,18821 +Oegstgeest,Netherlands,Europe,52.1833,4.4667,25064 +Oeiras,Portugal,Europe,38.697,-9.3017,171658 +Oeiras do Pará,Brazil,South America,-2.0028,-49.8539,33844 +Of,Turkey,Europe,40.945,40.2644,43591 +Offa,Nigeria,Africa,8.147,4.72,166112 +Offenburg,Germany,Europe,48.4667,7.9333,62195 +Ogbomoso,Nigeria,Africa,8.1333,4.25,1032000 +Ogden,United States,North America,41.2279,-111.9682,623688 +Ogre,Latvia,Europe,56.8169,24.6047,22767 +Ogulin,Croatia,Europe,45.2669,15.2248,12246 +Ohrid,North Macedonia,Europe,41.1169,20.8019,38818 +Oiapoque,Brazil,South America,3.8428,-51.835,27482 +Oiba,Colombia,South America,6.2667,-73.3,12063 +Oirschot,Netherlands,Europe,51.5047,5.3128,18842 +Oisterwijk,Netherlands,Europe,51.5833,5.2,32373 +Ojinaga,Mexico,North America,29.5644,-104.4164,22744 +Ojuelos de Jalisco,Mexico,North America,21.8667,-101.5833,11881 +Okara,Pakistan,Asia,30.8092,73.4536,232386 +Okayama,Japan,Asia,34.65,133.9167,720043 +Okene,Nigeria,Africa,7.5619,6.2411,320260 +Okhotsk,Russia,Asia,59.383,143.217,5570 +Okhtyrka,Ukraine,Europe,50.303,34.892,46660 +Okigwe,Nigeria,Africa,5.483,7.55,132237 +Oklahoma City,United States,North America,35.4676,-97.5136,1000207 +Oktyabr’sk,Kazakhstan,Asia,49.4556,57.4161,29169 +Oktyabr’skiy,Russia,Asia,54.4667,53.4667,116427 +Olaine,Latvia,Europe,56.7844,23.9369,9908 +Olathe,United States,North America,38.8833,-94.82,143720 +Olavarría,Argentina,South America,-36.9,-60.3333,89721 +Olbia,Italy,Europe,40.9167,9.5,61048 +Oldebroek,Netherlands,Europe,52.4667,5.9167,23760 +Oldenburg,Germany,Europe,53.1439,8.2139,174629 +Oldenzaal,Netherlands,Europe,52.3125,6.9292,31701 +Olecko,Poland,Europe,54.0333,22.5,16076 +Oleksandriia,Ukraine,Europe,48.6667,33.1167,76097 +Olenëk,Russia,Asia,68.5,112.4667,2438 +Oleshky,Ukraine,Europe,46.6167,32.7167,24124 +Olesno,Poland,Europe,50.875,18.4167,9122 +Olevsk,Ukraine,Europe,51.2167,27.65,10032 +Oleśnica,Poland,Europe,51.2,17.3833,35856 +Olho d’Água das Cunhãs,Brazil,South America,-4.1389,-45.12,17919 +Olho d’Água das Flores,Brazil,South America,-9.5358,-37.2939,20695 +Olhão,Portugal,Europe,37.0278,-7.8389,45396 +Olinda,Brazil,South America,-8.0089,-34.855,349976 +Olindina,Brazil,South America,-11.3669,-38.3328,22633 +Olintepeque,Guatemala,North America,14.8833,-91.5167,39383 +Oliva,Argentina,South America,-32.0333,-63.5667,11672 +Oliveira,Brazil,South America,-20.6958,-44.8269,39262 +Oliveira de Azemeis,Portugal,Europe,40.84,-8.4764,20760 +Oliveira de Frades,Portugal,Europe,40.7314,-8.1767,10261 +Oliveira do Bairro,Portugal,Europe,40.5167,-8.5,23028 +Oliveira do Hospital,Portugal,Europe,40.3667,-7.8667,20855 +Oliveira dos Brejinhos,Brazil,South America,-12.3169,-42.8958,20715 +Olivença,Brazil,South America,-9.5186,-37.1906,10812 +Olkusz,Poland,Europe,50.2833,19.5667,33462 +Olomouc,Czechia,Europe,49.5939,17.2508,102293 +Olongapo,Philippines,Asia,14.83,120.28,260317 +Olopa,Guatemala,North America,14.6833,-89.35,30534 +Oloron-Sainte-Marie,France,Europe,43.1947,-0.6056,10658 +Olovo,Bosnia and Herzegovina,Europe,44.1277,18.5807,10175 +Olpe,Germany,Europe,51.0167,7.8333,24961 +Olsztyn,Poland,Europe,53.7778,20.4792,170225 +Oltiariq,Uzbekistan,Asia,40.3917,71.4767,25543 +Oltu,Turkey,Europe,40.5456,41.9958,30075 +Olympia,United States,North America,47.0417,-122.8959,215677 +Olëkminsk,Russia,Asia,60.3667,120.4167,9348 +Olímpia,Brazil,South America,-20.7369,-48.915,46013 +Omaha,United States,North America,41.2627,-96.0529,826161 +Ometepec,Mexico,North America,16.6833,-98.4167,30271 +Omiš,Croatia,Europe,43.4458,16.6883,14139 +Ommen,Netherlands,Europe,52.5167,6.4167,18295 +Omsk,Russia,Asia,54.9833,73.3667,1104485 +Omuthiya,Namibia,Africa,-18.3606,16.5812,7560 +Omutinskiy,Russia,Asia,56.4781,67.6678,9101 +Omutninsk,Russia,Asia,58.6667,52.1833,6400 +Omīdīyeh,Iran,Asia,30.7583,49.705,67427 +Ondjiva,Angola,Africa,-17.0667,15.7333,121537 +Ondo,Nigeria,Africa,7.0833,4.8333,358430 +Ondokuzmayıs,Turkey,Europe,41.4944,36.0789,26989 +Onega,Russia,Asia,63.9167,38.0833,16449 +Onitsha,Nigeria,Africa,6.1667,6.7833,1483000 +Onoto,Venezuela,South America,9.5958,-65.1897,12358 +Ontario,United States,North America,34.0393,-117.6064,178347 +Onverwacht,Suriname,South America,5.5931,-55.1939,2223 +Oosterhout,Netherlands,Europe,51.6431,4.8569,56206 +Oosterwolde,Netherlands,Europe,52.9903,6.2914,9510 +Oostzaan,Netherlands,Europe,52.4333,4.8833,9689 +Opatija,Croatia,Europe,45.3347,14.3069,10619 +Oplotnica,Slovenia,Europe,46.386,15.4458,1420 +Opmeer,Netherlands,Europe,52.7,4.95,12009 +Opochka,Russia,Asia,56.7,28.6667,9928 +Opoczno,Poland,Europe,51.3772,20.2869,19246 +Opole,Poland,Europe,50.6667,17.9333,127387 +Oporapa,Colombia,South America,2.05,-75.9667,11111 +Opovo,Serbia,Europe,45.0514,20.4247,4546 +Opuwo,Namibia,Africa,-18.0556,13.8406,12335 +Oqtosh Shahri,Uzbekistan,Asia,39.9269,65.9256,41600 +Oradea,Romania,Europe,47.0722,21.9211,183105 +Oraiókastro,Greece,Europe,40.7333,22.9167,40004 +Oral,Kazakhstan,Asia,51.2225,51.3725,234184 +Oran,Algeria,Africa,35.6969,-0.6331,803329 +Orange,United States,North America,33.8038,-117.8218,138723 +Orange Walk,Belize,North America,18.075,-88.5583,13750 +Oranienburg,Germany,Europe,52.7544,13.2369,48492 +Oranjestad,Aruba,South America,12.5186,-70.0358,28658 +Oratorio,Guatemala,North America,14.2281,-90.1758,27115 +Orašje,Bosnia and Herzegovina,Europe,45.0336,18.6933,19861 +Ordino,Andorra,Europe,42.555,1.5331,4858 +Ordu,Turkey,Europe,40.9833,37.8833,229214 +Ordubad,Azerbaijan,Europe,38.9047,46.0231,10372 +Orekhovo-Zuyevo,Russia,Asia,55.8,38.9667,104728 +Orenburg,Russia,Asia,51.7833,55.1,536862 +Orestiáda,Greece,Europe,41.5,26.5333,31686 +Orhaneli,Turkey,Europe,39.9028,28.9875,18543 +Orhangazi,Turkey,Europe,40.4889,29.3083,81110 +Orhei,Moldova,Europe,47.3831,28.8231,21065 +Orimattila,Finland,Europe,60.8042,25.7333,15669 +Oristano,Italy,Europe,39.9058,8.5916,30363 +Orito,Colombia,South America,0.6661,-76.8708,57774 +Orivesi,Finland,Europe,61.675,24.3583,8842 +Oriximiná,Brazil,South America,-1.7658,-55.8658,68294 +Orizaba,Mexico,North America,18.85,-97.1,123182 +Orizona,Brazil,South America,-17.0314,-48.2958,15725 +Orlando,United States,North America,28.4773,-81.337,1913597 +Orleães,Brazil,South America,-28.3589,-49.2908,23661 +Orlu,Nigeria,Africa,5.7837,7.0333,9351 +Orlândia,Brazil,South America,-20.7203,-47.8867,42678 +Orléans,France,Europe,47.9025,1.909,116344 +Ormoc,Philippines,Asia,11.0106,124.6075,230998 +Ormož,Slovenia,Europe,46.4071,16.1505,1962 +Orobó,Brazil,South America,-7.745,-35.6019,21808 +Orocué,Colombia,South America,4.7942,-71.34,8460 +Orocó,Brazil,South America,-8.62,-39.6019,13613 +Orodara,Burkina Faso,Africa,10.98,-4.91,33422 +Oroqen Zizhiqi,China,Asia,50.5667,123.7167,40128 +Oroquieta,Philippines,Asia,8.48,123.8,72301 +Orosháza,Hungary,Europe,46.5678,20.6428,25206 +Oroszlány,Hungary,Europe,47.4892,18.3164,17873 +Orsha,Belarus,Europe,54.5092,30.4258,102759 +Orsk,Russia,Asia,51.2,58.5667,187517 +Ortaca,Turkey,Europe,36.8389,28.7644,54478 +Ortaklar,Turkey,Europe,37.8833,27.5,13242 +Ortaköy,Turkey,Europe,38.7361,34.0403,20511 +Ortega,Colombia,South America,3.9167,-75.25,32164 +Ortigueira,Brazil,South America,-24.2078,-50.9489,24192 +Oruro,Bolivia,South America,-17.9799,-67.13,246501 +Orël,Russia,Asia,52.9686,36.0694,303696 +Orós,Brazil,South America,-6.2439,-38.9139,19675 +Orūmīyeh,Iran,Asia,37.5439,45.0647,736224 +Os,Norway,Europe,60.1835,5.4666,19742 +Osa,Russia,Asia,57.3,55.4667,19369 +Osakarovka,Kazakhstan,Asia,50.5619,72.5681,8046 +Osasco,Brazil,South America,-23.5328,-46.7919,652593 +Osečina,Serbia,Europe,44.3667,19.6,12571 +Osh,Kyrgyzstan,Asia,40.5333,72.7833,353080 +Oshakati,Namibia,Africa,-17.7833,15.6995,39679 +Oshawa,Canada,North America,43.9,-78.85,335949 +Oshnavīyeh,Iran,Asia,37.0364,45.0956,32723 +Osijek,Croatia,Europe,45.5603,18.6703,96313 +Osilnica,Slovenia,Europe,45.5289,14.6983,76 +Osisioma,Nigeria,Africa,5.1497,7.3303,219632 +Oskarshamn,Sweden,Europe,57.2667,16.4333,17258 +Oskū,Iran,Asia,37.9161,46.1244,18459 +Oslo,Norway,Europe,59.9133,10.7389,709037 +Osmancık,Turkey,Europe,40.9667,34.8,30537 +Osmaneli,Turkey,Europe,40.35,30.0167,16192 +Osmangazi,Turkey,Europe,40.1983,29.06,891250 +Osmaniye,Turkey,Europe,37.075,36.25,252186 +Osnabrück,Germany,Europe,52.2833,8.05,166960 +Osogbo,Nigeria,Africa,7.7667,4.5667,731000 +Osorno,Chile,South America,-40.5725,-73.1353,249728 +Ospina,Colombia,South America,1.0581,-77.5664,8834 +Ospino,Venezuela,South America,9.3,-69.45,39215 +Oss,Netherlands,Europe,51.7667,5.5167,92526 +Ostashkov,Russia,Asia,57.15,33.1,16674 +Ostend,Belgium,Europe,51.2258,2.9194,71557 +Osterholz-Scharmbeck,Germany,Europe,53.2167,8.8,30717 +Osterode,Germany,Europe,51.7286,10.2522,21382 +Ostrava,Czechia,Europe,49.8356,18.2925,284765 +Ostrov,Russia,Asia,57.35,28.35,20923 +Ostrowiec Świętokrzyski,Poland,Europe,50.9333,21.4,64796 +Ostrołęka,Poland,Europe,53.0667,21.5667,48712 +Ostrzeszów,Poland,Europe,51.4167,17.9333,14036 +Ostróda,Poland,Europe,53.7,19.9667,17795 +Ostrów Mazowiecka,Poland,Europe,52.8,21.9,22013 +Ostrów Wielkopolski,Poland,Europe,51.6494,17.8164,70725 +Ostuncalco,Guatemala,North America,14.8667,-91.6167,61517 +Osvaldo Cruz,Brazil,South America,-21.7967,-50.8786,33000 +Osório,Brazil,South America,-29.8869,-50.27,36131 +Otacílio Costa,Brazil,South America,-27.4828,-50.1219,17312 +Otavalo,Ecuador,South America,0.2303,-78.2625,41718 +Oteapan,Mexico,North America,18.0,-94.6667,16222 +Otjiwarongo,Namibia,Africa,-20.4642,16.6528,19614 +Ottawa,Canada,North America,45.4247,-75.695,1068821 +Otukpo,Nigeria,Africa,7.1904,8.13,68220 +Otumba,Mexico,North America,19.6989,-98.7572,9242 +Otwock,Poland,Europe,52.1167,21.2667,44524 +Ouagadougou,Burkina Faso,Africa,12.3686,-1.5275,3063271 +Ouahigouya,Burkina Faso,Africa,13.5833,-2.4167,124580 +Ouargaye,Burkina Faso,Africa,11.5,0.05,13431 +Ouargla,Algeria,Africa,31.95,5.3167,191136 +Oud-Beijerland,Netherlands,Europe,51.82,4.42,24301 +Oudenaarde,Belgium,Europe,50.85,3.6,31866 +Oudenbosch,Netherlands,Europe,51.5892,4.5239,12780 +Oudewater,Netherlands,Europe,52.025,4.8681,10201 +Ouidah,Benin,Africa,6.3667,2.0833,91688 +Oujda-Angad,Morocco,Africa,34.6867,-1.9114,614000 +Ouled Djellal,Algeria,Africa,34.4167,5.0667,63237 +Oulu,Finland,Europe,65.0142,25.4719,214814 +Oum el Bouaghi,Algeria,Africa,35.8775,7.1136,80359 +Oumé,Côte d’Ivoire,Africa,6.3833,-5.4167,127153 +Ourense,Spain,Europe,42.3364,-7.8633,104891 +Ouricuri,Brazil,South America,-7.8828,-40.0819,65245 +Ourilândia do Norte,Brazil,South America,-6.755,-51.0839,32467 +Ourinhos,Brazil,South America,-22.9789,-49.8708,103970 +Ouro Branco,Brazil,South America,-20.5208,-43.6919,38724 +Ouro Fino,Brazil,South America,-22.2828,-46.3689,32094 +Ouro Preto d’Oeste,Brazil,South America,-10.7481,-62.2158,35044 +Ouro Prêto,Brazil,South America,-20.3853,-43.5036,74558 +Ouro Verde,Brazil,South America,-21.4894,-51.7003,8620 +Ouroeste,Brazil,South America,-20.0008,-50.3719,10294 +Ourém,Portugal,Europe,39.65,-8.5833,45932 +Outapi,Namibia,Africa,-17.5044,14.9847,4606 +Ouésso,Congo (Brazzaville),Africa,1.6106,16.0514,28179 +Ovalle,Chile,South America,-30.6,-71.2,124401 +Ovar,Portugal,Europe,40.8597,-8.6253,55398 +Ovejas,Colombia,South America,9.5,-75.1667,20846 +Overland Park,United States,North America,38.887,-94.687,197199 +Ovidiopol,Ukraine,Europe,46.2447,30.4447,11407 +Oviedo,Spain,Europe,43.36,-5.845,220543 +Owando,Congo (Brazzaville),Africa,-0.4833,15.8975,24736 +Owerri,Nigeria,Africa,5.485,7.035,908109 +Owo,Nigeria,Africa,7.1961,5.5864,222262 +Oxchuc,Mexico,North America,16.7833,-92.2833,54932 +Oxelösund,Sweden,Europe,58.6667,17.1,11488 +Oxnard,United States,North America,34.1964,-119.1815,373423 +Oyam,Uganda,Africa,2.235,32.385,14500 +Oyem,Gabon,Africa,1.6,11.5736,60685 +Oyo,Nigeria,Africa,7.8417,3.9306,386723 +Oyster Bay,United States,North America,40.7846,-73.514,299519 +Ozumba,Mexico,North America,19.0392,-98.7936,29114 +Ozurgeti,Georgia,Europe,41.923,42.005,844 +Ozërsk,Russia,Asia,55.75,60.7167,76434 +Oğuz,Azerbaijan,Europe,41.0708,47.4583,7002 +Oğuzeli,Turkey,Europe,36.965,37.5092,33397 +Oława,Poland,Europe,50.9333,17.3,33268 +Oświęcim,Poland,Europe,50.0381,19.2214,36956 +Ożarów Mazowiecki,Poland,Europe,52.2167,20.8,14375 +Pa Mok,Thailand,Asia,14.4899,100.4485,8758 +Pa Sang,Thailand,Asia,18.5262,98.9394,8645 +Pabellón de Arteaga,Mexico,North America,22.15,-102.2667,47646 +Pabianice,Poland,Europe,51.65,19.3833,62954 +Pacaembu,Brazil,South America,-21.5622,-51.2606,13961 +Pacajus,Brazil,South America,-4.1725,-38.4606,73188 +Pacatu,Brazil,South America,-11.9578,-38.975,20952 +Pacatuba,Brazil,South America,-3.9839,-38.62,51696 +Pachalum,Guatemala,North America,14.9269,-90.665,9897 +Pacho,Colombia,South America,5.1306,-74.1583,27977 +Pachuca,Mexico,North America,20.1,-98.75,297848 +Pacora,Panama,North America,9.08,-79.28,52494 +Pacoti,Brazil,South America,-4.225,-38.9228,11186 +Padang,Indonesia,Asia,-0.95,100.3531,12000 +Paderborn,Germany,Europe,51.7181,8.7542,155749 +Padilla,Colombia,South America,3.2333,-76.3167,9937 +Padova,Italy,Europe,45.4064,11.8778,206496 +Padre Bernardo,Brazil,South America,-15.16,-48.2839,34967 +Padre Burgos,Philippines,Asia,13.9226,121.8116,23488 +Padre Paraíso,Brazil,South America,-17.0719,-41.5239,17334 +Pagadian,Philippines,Asia,7.8272,123.4364,210452 +Pageralam,Indonesia,Asia,-4.0167,103.25,136244 +Pago Pago,American Samoa,Oceania,-14.274,-170.7046,12576 +Pagqên,China,Asia,33.9686,99.901,9530 +Pagėgiai,Lithuania,Europe,55.1333,21.9167,57 +Pahuatlán de Valle,Mexico,North America,20.2756,-98.1503,20619 +Paianía,Greece,Europe,37.95,23.85,28036 +Paide,Estonia,Europe,58.8833,25.5572,7793 +Pailitas,Colombia,South America,8.9667,-73.6333,22083 +Paimio,Finland,Europe,60.45,22.7,11236 +Painan,Indonesia,Asia,-1.3511,100.5756,14425 +Paipa,Colombia,South America,5.8333,-73.15,31868 +Paispamba,Colombia,South America,2.25,-76.5833,11958 +Paiçandu,Brazil,South America,-23.4575,-52.0486,41773 +Pajacuarán,Mexico,North America,20.1178,-102.5667,10014 +Pajapan,Mexico,North America,18.2667,-94.6833,17480 +Pajapita,Guatemala,North America,14.7167,-92.0333,23171 +Paju,"Korea, South",Asia,37.8667,126.8,415345 +Pak Chong,Thailand,Asia,14.708,101.4161,35736 +Pak Kret,Thailand,Asia,13.9125,100.4978,190272 +Pak Phanang,Thailand,Asia,8.3511,100.202,20425 +Pak Thong Chai,Thailand,Asia,14.7167,102.0167,14272 +Pakpattan,Pakistan,Asia,30.3442,73.3839,139525 +Pakruojis,Lithuania,Europe,55.9833,23.85,4490 +Paks,Hungary,Europe,46.6219,18.8558,17917 +Pakwach,Uganda,Africa,2.4619,31.4983,28700 +Pakxan,Laos,Asia,18.3964,103.6558,45000 +Pakxé,Laos,Asia,15.1167,105.7833,77900 +Palacagüina,Nicaragua,North America,13.45,-86.4,12825 +Palaiseau,France,Europe,48.7145,2.2457,36067 +Palaió Fáliro,Greece,Europe,37.9333,23.7,64863 +Palana,Russia,Asia,59.084,159.95,3671 +Palanga,Lithuania,Europe,55.9167,21.0667,18132 +Palangkaraya,Indonesia,Asia,-2.21,113.92,249434 +Palayan City,Philippines,Asia,15.5422,121.0844,45383 +Pale,Bosnia and Herzegovina,Europe,43.8167,18.5667,22282 +Palembang,Indonesia,Asia,-2.9861,104.7556,1535952 +Palencia,Spain,Europe,42.0167,-4.5333,76738 +Palenque,Mexico,North America,17.4333,-91.8,51797 +Palermo,Colombia,South America,2.8917,-75.4375,800000 +Palestina,Colombia,South America,5.0833,-75.6667,13560 +Palestina de los Altos,Guatemala,North America,14.9333,-91.7,20719 +Palhano,Brazil,South America,-4.745,-37.9589,9346 +Palhoça,Brazil,South America,-27.6444,-48.6678,102742 +Palikir,"Micronesia, Federated States of",Oceania,6.9172,158.1589,6227 +Pallasovka,Russia,Asia,50.05,46.8833,14966 +Pallisa,Uganda,Africa,1.1675,33.71,42300 +Palm Bay,United States,North America,27.9631,-80.6593,547659 +Palma,Spain,Europe,39.5667,2.65,550000 +Palma Soriano,Cuba,North America,20.2139,-75.9919,119740 +Palmar de Varela,Colombia,South America,10.75,-74.75,27728 +Palmares,Brazil,South America,-8.6828,-35.5919,54584 +Palmares Paulista,Brazil,South America,-21.0828,-48.8008,9650 +Palmares do Sul,Brazil,South America,-30.2578,-50.51,12844 +Palmas,Brazil,South America,-10.1886,-48.3336,302692 +Palmas de Monte Alto,Brazil,South America,-14.2672,-43.1619,21796 +Palmdale,United States,North America,34.5944,-118.1057,165487 +Palmeira,Brazil,South America,-25.4289,-50.0058,33855 +Palmeira das Missões,Brazil,South America,-27.8994,-53.3136,33131 +Palmeira dos Índios,Brazil,South America,-9.4069,-36.6278,71574 +Palmeira d’Oeste,Brazil,South America,-20.4158,-50.7619,8903 +Palmeirais,Brazil,South America,-5.9778,-43.0633,14587 +Palmeiras,Brazil,South America,-12.5289,-41.5589,10339 +Palmeiras de Goiás,Brazil,South America,-16.805,-49.9258,31858 +Palmela,Portugal,Europe,38.5675,-8.8991,62831 +Palmerston North,New Zealand,Oceania,-40.3549,175.6095,76236 +Palmira,Colombia,South America,3.5833,-76.25,358895 +Palmital,Brazil,South America,-22.7889,-50.2175,22094 +Palmito,Colombia,South America,9.3333,-75.55,13682 +Palmitos,Brazil,South America,-27.0678,-53.1608,15626 +Palmácia,Brazil,South America,-4.15,-38.8458,10242 +Palo Negro,Venezuela,South America,10.1739,-67.5419,153706 +Palocabildo,Colombia,South America,5.1333,-75.0333,9120 +Palotina,Brazil,South America,-24.2839,-53.84,35011 +Palpalá,Argentina,South America,-24.25,-65.1167,50183 +Palu,Indonesia,Asia,-0.895,119.8594,359350 +Palé,Equatorial Guinea,Africa,-1.4069,5.6322,5008 +Palín,Guatemala,North America,14.4039,-90.6986,72203 +Pama,Burkina Faso,Africa,11.25,0.7,13579 +Pamekasan,Indonesia,Asia,-7.1574,113.4718,91160 +Pamiers,France,Europe,43.1175,1.6117,16512 +Pampa del Infierno,Argentina,South America,-26.5167,-61.1667,8176 +Pampanito,Venezuela,South America,9.4114,-70.4958,31836 +Pampas,Peru,South America,-12.3989,-74.8667,11566 +Pampatar,Venezuela,South America,10.9994,-63.7944,35400 +Pamplona,Spain,Europe,42.8167,-1.65,207777 +Pampán,Venezuela,South America,9.4511,-70.4758,47549 +Pamukova,Turkey,Europe,40.5167,30.1667,30482 +Panagyurishte,Bulgaria,Europe,42.5031,24.1839,29992 +Panajachel,Guatemala,North America,14.7406,-91.1594,16101 +Panaji,India,Asia,15.4989,73.8278,40017 +Panama City,Panama,North America,8.9711,-79.5347,880691 +Panambi,Brazil,South America,-28.2928,-53.5019,43515 +Panay,Philippines,Asia,11.5553,122.7931,48890 +Pancas,Brazil,South America,-19.225,-40.8508,21548 +Panchkula,India,Asia,30.74,76.8,211355 +Panelas,Brazil,South America,-8.6639,-36.0058,22991 +Panevėžys,Lithuania,Europe,55.725,24.3639,87913 +Pangkalan Bun,Indonesia,Asia,-2.6833,111.6167,105514 +Pangkalpinang,Indonesia,Asia,-2.1333,106.1167,218569 +Pangururan,Indonesia,Asia,2.6075,98.6959,34209 +Panj,Tajikistan,Asia,37.2353,69.0972,12500 +Panjakent,Tajikistan,Asia,39.5,67.6167,303000 +Panjgur,Pakistan,Asia,26.9683,64.1014,157693 +Panjāb,Afghanistan,Asia,34.3833,67.0167,9900 +Panorama,Brazil,South America,-21.3564,-51.86,15458 +Panshi,China,Asia,42.95,126.0667,370238 +Pantelhó,Mexico,North America,17.0,-92.4833,8629 +Pantepec,Mexico,North America,17.1833,-93.05,12266 +Panzos,Guatemala,North America,15.4,-89.6667,81063 +Panórama,Greece,Europe,40.5833,23.0333,17679 +Pančevo,Serbia,Europe,44.8739,20.6519,73401 +Paola,Malta,Europe,35.8728,14.5075,8706 +Papagaios,Brazil,South America,-19.4489,-44.7478,13920 +Papanduva,Brazil,South America,-26.37,-50.1439,19150 +Papantla de Olarte,Mexico,North America,20.4478,-97.32,161097 +Papeete,French Polynesia,Oceania,-17.5334,-149.5667,131695 +Papendrecht,Netherlands,Europe,51.8333,4.6833,32171 +Paphos,Cyprus,Europe,34.7667,32.4167,70635 +Paracambi,Brazil,South America,-22.6083,-43.7083,52683 +Paracatu,Brazil,South America,-17.2217,-46.875,75216 +Paracuru,Brazil,South America,-3.41,-39.0308,27541 +Paradise,United States,North America,36.0872,-115.1355,186178 +Paragominas,Brazil,South America,-2.995,-47.3528,76450 +Paraguarí,Paraguay,South America,-25.62,-57.16,22154 +Paraguaçu,Brazil,South America,-21.5331,-45.7664,21605 +Paraguaçu Paulista,Brazil,South America,-22.4197,-50.5797,45945 +Paraibano,Brazil,South America,-6.4328,-43.9839,18274 +Paraibuna,Brazil,South America,-23.3861,-45.6622,18263 +Paraipaba,Brazil,South America,-3.4389,-39.1478,32216 +Paraisópolis,Brazil,South America,-22.5539,-45.78,20445 +Parakou,Benin,Africa,9.35,2.6167,206667 +Paramaribo,Suriname,South America,5.8522,-55.2039,240924 +Parambu,Brazil,South America,-6.2108,-40.6939,31445 +Paramirim,Brazil,South America,-13.4425,-42.2389,21695 +Paramoti,Brazil,South America,-4.0969,-39.2389,10384 +Paranacity,Brazil,South America,-22.93,-52.1508,9557 +Paranaguá,Brazil,South America,-25.5208,-48.5094,127339 +Paranapanema,Brazil,South America,-23.3886,-48.7228,19357 +Paranatama,Brazil,South America,-8.9208,-36.6581,12199 +Paranavaí,Brazil,South America,-23.0728,-52.465,75750 +Paranaíba,Brazil,South America,-19.6769,-51.1908,40957 +Paranhos,Brazil,South America,-23.8928,-55.4308,12921 +Paraná,Argentina,South America,-31.7331,-60.5297,268889 +Paranã,Brazil,South America,-12.615,-47.8828,10542 +Paraopeba,Brazil,South America,-19.2739,-44.4039,24107 +Parapuã,Brazil,South America,-21.7681,-50.7717,11098 +Paratinga,Brazil,South America,-12.6908,-43.1839,29252 +Paraty,Brazil,South America,-23.2194,-44.7147,29544 +Paraíba do Sul,Brazil,South America,-22.1619,-43.2928,42063 +Paraíso,Mexico,North America,18.3961,-93.2128,96741 +Paraíso do Norte,Brazil,South America,-23.2808,-52.6019,13245 +Paraíso do Tocantins,Brazil,South America,-10.1758,-48.8669,52360 +Paraćin,Serbia,Europe,43.8667,21.4167,54267 +Parchim,Germany,Europe,53.4167,11.8333,18270 +Parczew,Poland,Europe,51.6333,22.8667,10519 +Pardubice,Czechia,Europe,50.0386,15.7792,92362 +Paredes,Portugal,Europe,41.2,-8.3333,86854 +Paredes de Coura,Portugal,Europe,41.9111,-8.5597,9198 +Parelhas,Brazil,South America,-6.6878,-36.6578,21499 +Pargas,Finland,Europe,60.3,22.3,12266 +Pariaman,Indonesia,Asia,-0.6264,100.1178,85485 +Paricônia,Brazil,South America,-9.2539,-38.0058,10573 +Parigi,Indonesia,Asia,-7.6914,108.5372,44857 +Parintins,Brazil,South America,-2.6278,-56.7358,96372 +Paripiranga,Brazil,South America,-10.6875,-37.8617,29058 +Paripueira,Brazil,South America,-9.465,-35.5519,13835 +Pariquera-Açu,Brazil,South America,-24.715,-47.8811,19391 +Paris,France,Europe,48.8567,2.3522,11060000 +Parma,Italy,Europe,44.8015,10.328,196764 +Parnaguá,Brazil,South America,-10.2269,-44.6389,10103 +Parnamirim,Brazil,South America,-5.9158,-35.2628,252716 +Parnarama,Brazil,South America,-5.6819,-43.0928,31250 +Parnaíba,Brazil,South America,-2.905,-41.7769,162159 +Paro,Bhutan,Asia,27.4333,89.4167,2362 +Parobé,Brazil,South America,-29.6286,-50.8347,58858 +Parral,Mexico,North America,26.9333,-105.6667,104836 +Parramos,Guatemala,North America,14.6078,-90.8031,20632 +Parras de la Fuente,Mexico,North America,25.4403,-102.1792,33817 +Partesh,Kosovo,Europe,42.4019,21.4336,1738 +Parthenay,France,Europe,46.6462,-0.2478,10121 +Partizansk,Russia,Asia,43.1167,133.1167,33165 +Partizánske,Slovakia,Europe,48.6333,18.3667,21439 +Pará de Minas,Brazil,South America,-19.86,-44.6078,97139 +Pasaco,Guatemala,North America,13.9833,-90.2,11229 +Pasadena,United States,North America,29.6575,-95.1498,149345 +Pasay City,Philippines,Asia,14.5439,120.995,440656 +Pasca,Colombia,South America,4.3075,-74.3008,12175 +Pasig City,Philippines,Asia,14.5605,121.0765,803159 +Pasinler,Turkey,Europe,39.9797,41.6756,27055 +Paso de Ovejas,Mexico,North America,19.285,-96.44,33392 +Paso de los Libres,Argentina,South America,-29.7167,-57.0833,43251 +Paso del Macho,Mexico,North America,18.9667,-96.7167,31890 +Passa Quatro,Brazil,South America,-22.39,-44.9669,16393 +Passa e Fica,Brazil,South America,-6.4358,-35.6428,10896 +Passagem Franca,Brazil,South America,-6.1797,-43.7836,19137 +Passau,Germany,Europe,48.5744,13.4647,54401 +Passira,Brazil,South America,-7.995,-35.5808,28340 +Passo Fundo,Brazil,South America,-28.2628,-52.4069,168458 +Passo de Camarajibe,Brazil,South America,-9.2378,-35.4928,13804 +Passo do Sertão,Brazil,South America,-29.2228,-49.81,8668 +Passos,Brazil,South America,-20.7189,-46.61,111939 +Pastavy,Belarus,Europe,55.1167,26.8333,18618 +Pasto,Colombia,South America,1.2078,-77.2772,410835 +Pastores,Guatemala,North America,14.6,-90.75,19661 +Pastos Bons,Brazil,South America,-6.6019,-44.0769,18802 +Pasvalys,Lithuania,Europe,56.0611,24.4,6411 +Paterson,United States,North America,40.9147,-74.1624,157660 +Pathein,Burma,Asia,16.7842,94.7333,287071 +Pathum Thani,Thailand,Asia,14.05,100.4833,18320 +Pati,Indonesia,Asia,-6.7415,111.0347,122785 +Pati do Alferes,Brazil,South America,-22.4289,-43.4189,29619 +Patna,India,Asia,25.594,85.1376,1684222 +Patnos,Turkey,Europe,39.2358,42.8686,61837 +Pato Branco,Brazil,South America,-26.2289,-52.6708,91836 +Patos,Brazil,South America,-7.0244,-37.28,103165 +Patos de Minas,Brazil,South America,-18.5871,-46.5149,159235 +Patrocínio,Brazil,South America,-18.9439,-46.9928,89826 +Patrocínio Paulista,Brazil,South America,-20.6394,-47.2817,14807 +Pattani,Thailand,Asia,6.8664,101.2508,44353 +Patu,Brazil,South America,-6.11,-37.6369,11007 +Patulul,Guatemala,North America,14.4167,-91.1667,41543 +Patzicía,Guatemala,North America,14.6317,-90.9269,40848 +Patzún,Guatemala,North America,14.6833,-91.0167,71790 +Pau,France,Europe,43.3,-0.37,78620 +Pau Brasil,Brazil,South America,-15.4639,-39.6508,9370 +Pau dos Ferros,Brazil,South America,-6.1108,-38.2089,30479 +Pau d’Alho,Brazil,South America,-7.8969,-35.18,45138 +Pauini,Brazil,South America,-7.7136,-66.9764,19522 +Paula Cândido,Brazil,South America,-20.8739,-42.98,8659 +Paulino Neves,Brazil,South America,-2.7189,-42.5328,17056 +Paulista,Brazil,South America,-7.9408,-34.8728,342167 +Paulistana,Brazil,South America,-8.1439,-41.15,21055 +Paulo Afonso,Brazil,South America,-9.4078,-38.2219,112870 +Paulínia,Brazil,South America,-22.7611,-47.1542,97702 +Pavia,Italy,Europe,45.1853,9.155,70636 +Pavlodar,Kazakhstan,Asia,52.3,76.95,343091 +Pavlohrad,Ukraine,Europe,48.5167,35.8667,101430 +Pavlovo,Russia,Asia,55.9653,43.0711,57116 +Pavlovsk,Russia,Asia,50.4578,40.1081,22384 +Pavlovskiy Posad,Russia,Asia,55.7667,38.6667,65297 +Paxtakor Shahri,Uzbekistan,Asia,40.3153,67.9544,23900 +Paxtaobod,Uzbekistan,Asia,40.9294,72.4969,34400 +Payariq Shahri,Uzbekistan,Asia,39.9892,66.8458,12345 +Paysandú,Uruguay,South America,-32.3214,-58.0756,76412 +Payshamba Shahri,Uzbekistan,Asia,40.0078,66.2372,11606 +Paz de Ariporo,Colombia,South America,5.8833,-71.9,26237 +Pazarcık,Turkey,Europe,37.4894,37.2967,70173 +Pazardzhik,Bulgaria,Europe,42.1935,24.3328,73931 +Pazin,Croatia,Europe,45.2392,13.9386,8279 +Paço do Lumiar,Brazil,South America,-2.5319,-44.1078,145643 +Paços de Ferreira,Portugal,Europe,41.2833,-8.3833,56340 +Peabiru,Brazil,South America,-23.9128,-52.3428,13346 +Pearland,United States,North America,29.5581,-95.3218,125983 +Pechory,Russia,Asia,57.8167,27.6167,9808 +Pedernales,Ecuador,South America,0.07,-80.055,27068 +Pederneiras,Brazil,South America,-22.3517,-48.775,44910 +Pedersöre,Finland,Europe,63.6642,22.7006,11225 +Pedra,Brazil,South America,-8.5006,-36.9456,22795 +Pedra Azul,Brazil,South America,-16.005,-41.2969,24410 +Pedra Badejo,Cabo Verde,Africa,15.1375,-23.5333,9530 +Pedra Branca,Brazil,South America,-5.4539,-39.7169,40187 +Pedra Preta,Brazil,South America,-16.6228,-54.4739,18066 +Pedralva,Brazil,South America,-22.2428,-45.4658,10760 +Pedras de Fogo,Brazil,South America,-7.4019,-35.1158,29662 +Pedras de Maria da Cruz,Brazil,South America,-15.6069,-44.3908,8871 +Pedregulho,Brazil,South America,-20.2569,-47.4767,16517 +Pedreira,Brazil,South America,-22.7419,-46.9014,48463 +Pedro Afonso,Brazil,South America,-8.9678,-48.175,14055 +Pedro Betancourt,Cuba,North America,22.7303,-81.2908,29565 +Pedro II,Brazil,South America,-4.4338,-41.4534,37894 +Pedro Juan Caballero,Paraguay,South America,-22.5446,-55.76,115583 +Pedro Velho,Brazil,South America,-6.4389,-35.2208,13824 +Pedro de Toledo,Brazil,South America,-24.275,-47.2328,11281 +Pehuajó,Argentina,South America,-35.8,-61.9,31553 +Pehčevo,North Macedonia,Europe,41.7592,22.8906,1687 +Peine,Germany,Europe,52.3203,10.2336,51521 +Peixe,Brazil,South America,-12.025,-48.5389,9317 +Pejë,Kosovo,Europe,42.66,20.2922,96450 +Pekanbaru,Indonesia,Asia,0.5092,101.4453,983356 +Pelabuhanratu,Indonesia,Asia,-6.9878,106.5439,120488 +Pelaya,Colombia,South America,8.6833,-73.6667,17910 +Pelileo,Ecuador,South America,-1.3306,-78.5428,11403 +Pelotas,Brazil,South America,-31.7719,-52.3428,323158 +Pemba,Mozambique,Africa,-12.9667,40.5167,141316 +Pembroke,Malta,Europe,35.9264,14.4808,3645 +Pembroke Pines,United States,North America,26.0128,-80.3382,170557 +Pena Forte,Brazil,South America,-7.8289,-39.0769,8972 +Penacova,Portugal,Europe,40.2667,-8.2667,15251 +Penafiel,Portugal,Europe,41.2,-8.2833,72265 +Penalva,Brazil,South America,-3.2942,-45.1736,38731 +Pendik,Turkey,Europe,40.8747,29.235,750435 +Pendências,Brazil,South America,-5.26,-36.7219,12278 +Penedo,Brazil,South America,-10.29,-36.5858,63846 +Penha,Brazil,South America,-26.7689,-48.6458,33663 +Peniche,Portugal,Europe,39.3633,-9.3792,26431 +Penonomé,Panama,North America,8.5187,-80.3553,21748 +Pensacola,United States,North America,30.4413,-87.1911,401535 +Pensilvania,Colombia,South America,5.5,-75.0833,26342 +Pentecoste,Brazil,South America,-3.7928,-39.27,37813 +Penza,Russia,Asia,53.2,45.0,488299 +Penápolis,Brazil,South America,-21.42,-50.0778,61679 +Peoria,United States,North America,40.752,-89.6154,262461 +Peraía,Greece,Europe,40.5,22.925,16995 +Perdigão,Brazil,South America,-19.9528,-45.0839,12268 +Perdizes,Brazil,South America,-19.3528,-47.2928,17151 +Perdões,Brazil,South America,-21.0908,-45.0908,21384 +Pereira,Colombia,South America,4.8143,-75.6946,590554 +Pereira Barreto,Brazil,South America,-20.6383,-51.1092,25677 +Pereiras,Brazil,South America,-23.0761,-47.9758,8773 +Pereiro,Brazil,South America,-6.045,-38.4608,15274 +Pereslavl’-Zalesskiy,Russia,Asia,56.7333,38.85,38649 +Perevalsk,Ukraine,Europe,48.4333,38.8167,24817 +Perez,Philippines,Asia,14.18,121.93,12767 +Perg,Austria,Europe,48.2503,14.6336,9459 +Pergamino,Argentina,South America,-33.8833,-60.5667,104985 +Peri-Mirim,Brazil,South America,-2.5778,-44.8539,11108 +Perico,Cuba,North America,22.7753,-81.0153,29746 +Peristéri,Greece,Europe,38.0167,23.6833,133630 +Perito Moreno,Argentina,South America,-46.5886,-70.9242,4617 +Peritoró,Brazil,South America,-4.3828,-44.3328,20479 +Perleberg,Germany,Europe,53.0667,11.8667,12026 +Perm,Russia,Asia,58.0,56.3167,1026908 +Pernik,Bulgaria,Europe,42.6,23.033,97181 +Perote,Mexico,North America,19.5619,-97.2419,77432 +Perpignan,France,Europe,42.6986,2.8956,120996 +Perth,Australia,Oceania,-31.9558,115.8597,2141834 +Perugia,Italy,Europe,43.1121,12.3888,161748 +Peruíbe,Brazil,South America,-24.32,-46.9983,69001 +Pervomaisk,Ukraine,Europe,48.0439,30.85,62426 +Pervomaysk,Russia,Asia,54.8667,43.8,13828 +Pervouralsk,Russia,Asia,56.9167,59.9333,111751 +Perşembe,Turkey,Europe,41.0656,37.7714,30101 +Pesaro,Italy,Europe,43.9102,12.9133,95376 +Pesca,Colombia,South America,5.5833,-73.05,9322 +Pescara,Italy,Europe,42.4643,14.2142,118657 +Pescaria Brava,Brazil,South America,-28.3833,-48.8833,10190 +Peschanokopskoye,Russia,Asia,46.1958,41.0778,10593 +Peshawar,Pakistan,Asia,34.0144,71.5675,1970042 +Peshkopi,Albania,Europe,41.6833,20.4281,13251 +Peshtera,Bulgaria,Europe,42.033,24.3,23972 +Pesnica,Slovenia,Europe,46.6069,15.6767,843 +Peso da Régua,Portugal,Europe,41.1632,-7.789,17131 +Pesqueira,Brazil,South America,-8.3578,-36.6964,67735 +Pesquería,Mexico,North America,25.785,-100.0511,147624 +Pestovo,Russia,Asia,58.6,35.8167,14032 +Petapa,Guatemala,North America,14.5,-90.5667,129124 +Petare,Venezuela,South America,10.4833,-66.8167,369000 +Petatlán,Mexico,North America,17.5383,-101.2739,44485 +Petnjica,Montenegro,Europe,42.9089,19.9644,5482 +Petrinja,Croatia,Europe,45.4375,16.29,19950 +Petrolina,Brazil,South America,-9.3928,-40.5078,218538 +Petropavl,Kazakhstan,Asia,54.8833,69.1667,201446 +Petropavlovsk-Kamchatskiy,Russia,Asia,53.0167,158.65,162992 +Petrovac na Mlavi,Serbia,Europe,44.3783,21.4194,31259 +Petrovaradin,Serbia,Europe,45.25,19.8667,33865 +Petrovec,North Macedonia,Europe,41.9389,21.615,121 +Petrovsk-Zabaykal’skiy,Russia,Asia,51.2667,108.8333,16213 +Petrozavodsk,Russia,Asia,61.7833,34.3333,279190 +Petroúpoli,Greece,Europe,38.0333,23.6833,60146 +Petrópolis,Brazil,South America,-22.505,-43.1789,278881 +Petushki,Russia,Asia,55.9333,39.4667,13317 +Pevek,Russia,Asia,69.7,170.2833,4083 +Peyziwat,China,Asia,39.4955,76.7428,33971 +Pezinok,Slovakia,Europe,48.2919,17.2661,24900 +Peçanha,Brazil,South America,-18.5489,-42.5569,17446 +Peñamiller,Mexico,North America,21.0519,-99.815,17007 +Pećinci,Serbia,Europe,44.9089,19.9664,19720 +Pfaffenhofen,Germany,Europe,48.5333,11.5167,27143 +Pfarrkirchen,Germany,Europe,48.4167,12.9167,13694 +Pforzheim,Germany,Europe,48.895,8.705,128992 +Pfäffikon,Switzerland,Europe,47.3667,8.7822,11931 +Phai Sali,Thailand,Asia,15.6,100.6494,10450 +Phak Hai,Thailand,Asia,14.4574,100.3699,9905 +Phalombe,Malawi,Africa,-15.8,35.65,6242 +Phan Rang-Tháp Chàm,Vietnam,Asia,11.5667,108.9833,207998 +Phan Thiết,Vietnam,Asia,10.9333,108.1,205333 +Phanat Nikhom,Thailand,Asia,13.4458,101.1844,11032 +Phangnga,Thailand,Asia,8.4644,98.5317,9559 +Phatthalung,Thailand,Asia,7.5,100.0,38576 +Phayao,Thailand,Asia,19.1653,99.9036,19118 +Phetchabun,Thailand,Asia,16.4169,101.1533,23823 +Phetchaburi,Thailand,Asia,13.1119,99.9458,26181 +Phibun Mangsahan,Thailand,Asia,15.2447,105.2291,10842 +Phichit,Thailand,Asia,16.4431,100.3467,20408 +Philadelphia,United States,North America,40.0077,-75.1339,5696588 +Philippeville,Belgium,Europe,50.1964,4.5436,9228 +Philipsburg,Sint Maarten,North America,18.0237,-63.0458,1894 +Phimai,Thailand,Asia,15.2232,102.4947,8696 +Phitsanulok,Thailand,Asia,16.8158,100.2636,281929 +Phnom Penh,Cambodia,Asia,11.5696,104.921,2129371 +Phoenix,United States,North America,33.5722,-112.0892,4065338 +Phon,Thailand,Asia,15.816,102.5998,11593 +Phon Charoen,Thailand,Asia,18.0258,103.7094,10254 +Photharam,Thailand,Asia,13.6923,99.8497,9575 +Phra Nakhon Si Ayutthaya,Thailand,Asia,14.3478,100.5606,50830 +Phra Phutthabat,Thailand,Asia,14.7253,100.7954,34322 +Phra Pradaeng,Thailand,Asia,13.6592,100.5331,10190 +Phra Samut Chedi,Thailand,Asia,13.6022,100.5721,13082 +Phrae,Thailand,Asia,18.1453,100.1419,15316 +Phrai Bueng,Thailand,Asia,14.7483,104.3626,10864 +Phuket,Thailand,Asia,7.8881,98.3975,79308 +Phôngsali,Laos,Asia,21.6833,102.1,15083 +Phônsavan,Laos,Asia,19.46,103.18,37507 +Phú Quốc,Vietnam,Asia,10.2289,103.9669,70000 +Phú Thọ,Vietnam,Asia,21.4003,105.2225,91650 +Phúc Yên,Vietnam,Asia,21.2333,105.7,83352 +Phủ Lý,Vietnam,Asia,20.5411,105.9139,136654 +Piacenza,Italy,Europe,45.05,9.7,102465 +Piamonte,Colombia,South America,1.1194,-76.3269,9259 +Piancó,Brazil,South America,-7.1978,-37.9289,16441 +Piaseczno,Poland,Europe,52.0667,21.0167,51945 +Piatra Neamţ,Romania,Europe,46.9275,26.3708,79679 +Piatykhatky,Ukraine,Europe,48.4136,33.7028,18140 +Piatã,Brazil,South America,-13.1519,-41.7728,20086 +Piaçabuçu,Brazil,South America,-10.4086,-36.4339,15908 +Pichilemu,Chile,South America,-34.3919,-72.0139,12776 +Pichucalco,Mexico,North America,17.5,-93.1167,15244 +Picos,Brazil,South America,-7.0769,-41.4669,83090 +Picuí,Brazil,South America,-6.555,-36.3489,18333 +Pie de Pató,Colombia,South America,5.5333,-77.0,40733 +Piedade,Brazil,South America,-23.7119,-47.4278,54717 +Piedecuesta,Colombia,South America,7.0833,-73.0,150483 +Piedras Negras,Mexico,North America,28.7,-100.5231,173959 +Piekary Śląskie,Poland,Europe,50.3833,18.95,51876 +Pieksämäki,Finland,Europe,62.3003,27.1642,17050 +Piendamó,Colombia,South America,2.6408,-76.5284,36225 +Pierre,United States,North America,44.3748,-100.3205,14008 +Pietermaritzburg,South Africa,Africa,-29.6167,30.3833,839327 +Pietà,Malta,Europe,35.8931,14.4936,4020 +Piešťany,Slovakia,Europe,48.5842,17.8336,26668 +Pigüé,Argentina,South America,-37.6167,-62.4167,13822 +Pihuamo,Mexico,North America,18.95,-103.167,11386 +Pijijiapan,Mexico,North America,15.6833,-93.2167,18219 +Pijiño del Carmen,Colombia,South America,9.3333,-74.45,15759 +Pijnacker,Netherlands,Europe,52.0183,4.4361,27130 +Pilar,Argentina,South America,-34.45,-58.9167,81120 +Pilar do Sul,Brazil,South America,-23.8128,-47.7158,23948 +Pilisvörösvár,Hungary,Europe,47.6211,18.9108,14468 +Pilón,Cuba,North America,19.9053,-77.3208,28752 +Pimenta,Brazil,South America,-20.4811,-45.8006,8563 +Pimenta Bueno,Brazil,South America,-11.6725,-61.1936,31752 +Pimenteiras,Brazil,South America,-6.245,-41.4189,11341 +Pinal de Amoles,Mexico,North America,21.1342,-99.4586,25325 +Pinamar,Argentina,South America,-37.1,-56.85,45000 +Pinar del Río,Cuba,North America,22.4122,-83.6719,188614 +Pindamonhangaba,Brazil,South America,-22.9239,-45.4617,160614 +Pindaré-Mirim,Brazil,South America,-3.6078,-45.3428,27517 +Pindaí,Brazil,South America,-14.4928,-42.6869,14731 +Pindobaçu,Brazil,South America,-10.7428,-40.3628,19083 +Pindorama,Brazil,South America,-21.1858,-48.9069,14542 +Pindoretama,Brazil,South America,-4.0278,-38.3058,23391 +Pingliang,China,Asia,35.5424,106.6649,1848607 +Pingquan,China,Asia,41.0184,118.702,399378 +Pingshan,China,Asia,38.2506,114.1969,120631 +Pingtung,Taiwan,Asia,22.6761,120.4942,193563 +Pingxiang,China,Asia,27.659,113.887,1804805 +Pinhal,Brazil,South America,-22.1908,-46.7408,39816 +Pinhalzinho,Brazil,South America,-26.8478,-52.9919,21972 +Pinheiral,Brazil,South America,-22.5128,-44.0008,24298 +Pinheiro,Brazil,South America,-2.5208,-45.0828,68030 +Pinheiro Machado,Brazil,South America,-31.5778,-53.3808,11214 +Pinhel,Portugal,Europe,40.7833,-7.0667,9627 +Pinhão,Brazil,South America,-25.6958,-51.66,29886 +Pinillos,Colombia,South America,8.9167,-74.4667,24923 +Pinneberg,Germany,Europe,53.6333,9.8,44756 +Pinotepa,Mexico,North America,16.3401,-98.053,53148 +Pinsk,Belarus,Europe,52.1153,26.1031,124295 +Pintadas,Brazil,South America,-11.8128,-39.9089,10325 +Pio IX,Brazil,South America,-6.8375,-40.5792,18459 +Pio XII,Brazil,South America,-3.8939,-45.17,21886 +Pionerskiy,Russia,Asia,54.9517,20.2333,12928 +Piotrków Trybunalski,Poland,Europe,51.4,19.6833,68978 +Piquet Carneiro,Brazil,South America,-5.8039,-39.4178,16616 +Piquete,Brazil,South America,-22.6136,-45.1761,13575 +Piracaia,Brazil,South America,-23.0539,-46.3581,26688 +Piracanjuba,Brazil,South America,-17.3028,-49.0178,24883 +Piracicaba,Brazil,South America,-22.7252,-47.6493,423323 +Piracuruca,Brazil,South America,-3.9278,-41.7089,28846 +Piraeus,Greece,Europe,37.943,23.6469,448051 +Piraju,Brazil,South America,-23.1936,-49.3839,29664 +Pirajuí,Brazil,South America,-21.9989,-49.4569,22431 +Piran,Slovenia,Europe,45.5283,13.5683,3787 +Piranga,Brazil,South America,-20.685,-43.3,17018 +Pirangi,Brazil,South America,-21.0914,-48.6578,11471 +Piranguinho,Brazil,South America,-22.4008,-45.5319,9120 +Piranhas,Brazil,South America,-9.6239,-37.7569,25183 +Pirané,Argentina,South America,-25.7328,-59.11,64556 +Pirapemas,Brazil,South America,-3.7269,-44.2228,17714 +Pirapetinga,Brazil,South America,-21.6558,-42.3458,11077 +Pirapora,Brazil,South America,-17.345,-44.9419,56640 +Pirapora do Bom Jesus,Brazil,South America,-23.3972,-47.0028,17646 +Pirapòzinho,Brazil,South America,-22.2753,-51.5,27754 +Piraquara,Brazil,South America,-25.4419,-49.0628,118730 +Piratini,Brazil,South America,-31.4478,-53.1039,17504 +Piratininga,Brazil,South America,-22.4128,-49.135,10584 +Piraçununga,Brazil,South America,-21.9961,-47.4258,74587 +Piraí,Brazil,South America,-22.6287,-43.8982,27474 +Piraí do Norte,Brazil,South America,-13.7619,-39.3789,10974 +Piraí do Sul,Brazil,South America,-24.5258,-49.9489,21647 +Piraúba,Brazil,South America,-21.2758,-43.0258,11610 +Pirenópolis,Brazil,South America,-15.8526,-48.9589,26690 +Pires Ferreira,Brazil,South America,-4.2469,-40.645,10606 +Pires do Rio,Brazil,South America,-17.3008,-48.28,32373 +Piripiri,Brazil,South America,-4.2728,-41.7769,65538 +Piripá,Brazil,South America,-14.94,-41.72,9152 +Piritiba,Brazil,South America,-11.73,-40.555,17566 +Pirkkala,Finland,Europe,61.4667,23.65,20763 +Pirmasens,Germany,Europe,49.2,7.6,40941 +Pirna,Germany,Europe,50.9622,13.9403,39303 +Pirot,Serbia,Europe,43.1519,22.585,49601 +Pirpirituba,Brazil,South America,-6.78,-35.4989,9340 +Piru,Indonesia,Asia,-3.0811,128.1411,16368 +Pisa,Italy,Europe,43.7167,10.4,88737 +Pishin,Pakistan,Asia,30.5848,66.9948,107646 +Pistoia,Italy,Europe,43.9333,10.9167,89309 +Pisz,Poland,Europe,53.6167,21.8,18245 +Pita,Guinea,Africa,11.08,-12.401,20052 +Pital,Colombia,South America,2.987,-75.826,12246 +Pitalito,Colombia,South America,1.8989,-76.0419,133205 +Pitanga,Brazil,South America,-24.7569,-51.7608,33567 +Pitangueiras,Brazil,South America,-21.0094,-48.2217,38211 +Pitangui,Brazil,South America,-19.6828,-44.89,26685 +Piteå,Sweden,Europe,65.3333,21.5,23350 +Piteşti,Romania,Europe,44.8606,24.8678,141275 +Pithiviers,France,Europe,48.1725,2.2525,8981 +Pitimbu,Brazil,South America,-7.4706,-34.8086,19275 +Pittsburgh,United States,North America,40.4397,-79.9763,1712828 +Piura,Peru,South America,-5.2,-80.6333,473025 +Piuí,Brazil,South America,-20.465,-45.9578,36062 +Pivijay,Colombia,South America,10.4667,-74.6167,33225 +Pivka,Slovenia,Europe,45.6833,14.2,2059 +Pizarro,Colombia,South America,4.95,-77.3667,18561 +Piçarras,Brazil,South America,-26.75,-48.6667,27127 +Piên,Brazil,South America,-26.0978,-49.4289,13655 +Piñas,Ecuador,South America,-3.6806,-79.6806,18482 +Piúma,Brazil,South America,-20.835,-40.7289,22300 +Piła,Poland,Europe,53.15,16.7333,71735 +Pińczów,Poland,Europe,50.5333,20.5333,10078 +Placetas,Cuba,North America,22.3158,-79.6556,71208 +Planadas,Colombia,South America,3.1964,-75.6444,30117 +Planaltina,Brazil,South America,-15.4528,-47.6139,105031 +Planalto,Brazil,South America,-27.3289,-53.0589,10406 +Plandište,Serbia,Europe,45.2269,21.1217,3832 +Planeta Rica,Colombia,South America,8.4089,-75.5819,69285 +Planken,Liechtenstein,Europe,47.1833,9.5333,478 +Plano,United States,North America,33.0502,-96.7486,287339 +Planura,Brazil,South America,-20.1378,-48.7019,11145 +Plasnica,North Macedonia,Europe,41.4667,21.1167,1584 +Plato,Colombia,South America,9.7919,-74.7872,61856 +Platón Sánchez,Mexico,North America,21.2833,-98.3667,10009 +Plauen,Germany,Europe,50.4833,12.1167,65218 +Plav,Montenegro,Europe,42.6,19.94,13805 +Plavsk,Russia,Asia,53.7167,37.2833,16893 +Playa Grande,Guatemala,North America,15.9833,-90.7667,12710 +Playa Vicente,Mexico,North America,17.8333,-95.8167,39205 +Playa del Carmen,Mexico,North America,20.6281,-87.0755,304942 +Playas de Rosarito,Mexico,North America,32.3422,-117.0561,100660 +Pleiku,Vietnam,Asia,13.9833,108.0,146073 +Pleszew,Poland,Europe,51.8833,17.7833,16641 +Pleternica,Croatia,Europe,45.2864,17.8019,9138 +Pleven,Bulgaria,Europe,43.4167,24.6167,119596 +Pljevlja,Montenegro,Europe,43.3567,19.3583,19136 +Ploieşti,Romania,Europe,44.9411,26.0225,180539 +Plovdiv,Bulgaria,Europe,42.1434,24.751,346893 +Plungė,Lithuania,Europe,55.9167,21.85,17385 +Plužine,Montenegro,Europe,43.15,18.83,1102 +Plzeň,Czechia,Europe,49.7475,13.3775,185599 +Plácido de Castro,Brazil,South America,-10.2758,-67.15,16560 +Plön,Germany,Europe,54.1622,10.4214,8919 +Poblacion,Philippines,Asia,6.8,124.63,79739 +Pocheon,"Korea, South",Asia,37.8947,127.2002,163388 +Pochuta,Guatemala,North America,14.545,-91.0889,112139 +Pochëp,Russia,Asia,52.9333,33.45,14991 +Pocinhos,Brazil,South America,-7.0769,-36.0608,17469 +Poconé,Brazil,South America,-16.2569,-56.6228,31217 +Podgorica,Montenegro,Europe,42.4414,19.2628,172139 +Podilsk,Ukraine,Europe,47.7419,29.535,39220 +Podlehnik,Slovenia,Europe,46.3353,15.88,378 +Podolsk,Russia,Asia,55.4311,37.5456,312911 +Podporozhye,Russia,Asia,60.9,34.2,15512 +Podstrana,Croatia,Europe,43.48,16.58,10403 +Podujevë,Kosovo,Europe,42.9111,21.1899,88499 +Podvelka,Slovenia,Europe,46.5864,15.3286,299 +Podčetrtek,Slovenia,Europe,46.1569,15.5986,578 +Pohang,"Korea, South",Asia,36.0322,129.365,503780 +Pohrebyshche,Ukraine,Europe,49.4833,29.2667,9209 +Point Fortin,Trinidad and Tobago,North America,10.1667,-61.6667,29579 +Pointe-Noire,Congo (Brazzaville),Africa,-4.7975,11.8503,1420612 +Poitiers,France,Europe,46.58,0.34,89472 +Pojuca,Brazil,South America,-12.426,-38.3267,26203 +Pokhara,Nepal,Asia,28.2097,83.9853,353841 +Pokrovka,Kyrgyzstan,Asia,42.75,71.6,8522 +Pokrovsk,Russia,Asia,61.4833,129.15,11991 +Pokrovskoye,Russia,Asia,47.4164,38.8989,11842 +Polatlı,Turkey,Europe,39.5842,32.1472,128378 +Polatsk,Belarus,Europe,55.4833,28.8,79579 +Poldasht,Iran,Asia,39.3447,45.0681,11472 +Poldokhtar,Iran,Asia,33.1489,47.7164,26352 +Polevskoy,Russia,Asia,56.45,60.1833,54364 +Police,Poland,Europe,53.5333,14.5667,30623 +Poljčane,Slovenia,Europe,46.3119,15.5792,1118 +Polkowice,Poland,Europe,51.5,16.0667,21786 +Polohy,Ukraine,Europe,47.4833,36.25,18111 +Polokwane,South Africa,Africa,-23.9,29.45,130028 +Polonuevo,Colombia,South America,10.7833,-74.8667,19454 +Polotitlán de la Ilustración,Mexico,North America,20.2253,-99.8154,13851 +Poltava,Ukraine,Europe,49.5894,34.5514,279593 +Polyarnyy,Russia,Asia,69.2,33.4667,12154 +Polzela,Slovenia,Europe,46.2809,15.074,2351 +Polýgyros,Greece,Europe,40.3667,23.4333,21351 +Polýkastro,Greece,Europe,41.0,22.5667,11338 +Pombal,Portugal,Europe,39.9161,-8.6281,55217 +Pombas,Cabo Verde,Africa,17.149,-25.015,1184 +Pombos,Brazil,South America,-8.1492,-35.4011,27552 +Pomerode,Brazil,South America,-26.7408,-49.1769,22127 +Pomona,United States,North America,34.0585,-117.7626,148391 +Pomorie,Bulgaria,Europe,42.5569,27.6405,14856 +Pompano Beach,United States,North America,26.2428,-80.1312,112212 +Pompeu,Brazil,South America,-19.2239,-44.935,31047 +Pompéia,Brazil,South America,-22.1089,-50.1719,20196 +Ponedera,Colombia,South America,10.65,-74.75,18430 +Pong Nam Ron,Thailand,Asia,12.9057,102.2622,9210 +Ponta Delgada,Portugal,Europe,37.74,-25.67,68809 +Ponta Grossa,Brazil,South America,-25.095,-50.1619,273616 +Ponta Porã,Brazil,South America,-22.5361,-55.7256,93937 +Ponta de Pedras,Brazil,South America,-1.39,-48.8708,24984 +Ponta do Sol,Cabo Verde,Africa,17.202,-25.093,4738 +Pontal,Brazil,South America,-21.0225,-48.0372,50852 +Pontal do Paraná,Brazil,South America,-25.5768,-48.3581,30425 +Pontalina,Brazil,South America,-17.5258,-49.4489,18309 +Pontarlier,France,Europe,46.9067,6.3556,17928 +Ponte Nova,Brazil,South America,-20.4164,-42.9086,57390 +Ponte Serrada,Brazil,South America,-26.8719,-52.0158,10649 +Ponte da Barca,Portugal,Europe,41.8078,-8.4194,12061 +Ponte de Lima,Portugal,Europe,41.7667,-8.5833,43498 +Ponte de Sôr,Portugal,Europe,39.2478,-8.0097,16722 +Pontes e Lacerda,Brazil,South America,-15.2258,-59.335,52018 +Pontevedra,Spain,Europe,42.4333,-8.6333,83077 +Pontianak,Indonesia,Asia,-0.0206,109.3414,680880 +Pontivy,France,Europe,48.0692,-2.9614,14547 +Ponto Novo,Brazil,South America,-10.8628,-40.1336,14819 +Popayán,Colombia,South America,2.4542,-76.6092,300837 +Popondetta,Papua New Guinea,Oceania,-8.7656,148.2347,65000 +Popovača,Croatia,Europe,45.5697,16.625,10255 +Poprad,Slovakia,Europe,49.05,20.3,49855 +Poptún,Guatemala,North America,16.3222,-89.4222,69437 +Poranga,Brazil,South America,-4.745,-40.9258,12065 +Porangaba,Brazil,South America,-23.1758,-48.125,10067 +Porangatu,Brazil,South America,-13.4408,-49.1486,45633 +Porciúncula,Brazil,South America,-20.9628,-42.0408,17288 +Pordenone,Italy,Europe,45.9626,12.6563,51725 +Porecatu,Brazil,South America,-22.7558,-51.3789,11624 +Poreč,Croatia,Europe,45.2272,13.5952,16607 +Pori,Finland,Europe,61.4833,21.8,84026 +Porkeri,Faroe Islands,Europe,61.4814,-6.7458,297 +Porlamar,Venezuela,South America,10.95,-63.85,144830 +Porsgrunn,Norway,Europe,59.1419,9.6568,37056 +Port Antonio,Jamaica,North America,18.1757,-76.4503,14816 +Port Arthur,United States,North America,29.8554,-93.9264,114860 +Port Blair,India,Asia,11.6683,92.7378,140572 +Port Harcourt,Nigeria,Africa,4.8242,7.0336,1865000 +Port Laoise,Ireland,Europe,53.0309,-7.3008,23494 +Port Loko,Sierra Leone,Africa,8.7667,-12.7833,44900 +Port Louis,Mauritius,Africa,-20.1644,57.5042,149194 +Port Maria,Jamaica,North America,18.3702,-76.8903,7906 +Port Moresby,Papua New Guinea,Oceania,-9.4789,147.1494,317374 +Port Said,Egypt,Africa,31.2625,32.3061,524433 +Port St. Lucie,United States,North America,27.2796,-80.3884,473733 +Port Sudan,Sudan,Africa,19.6158,37.2164,579942 +Port of Spain,Trinidad and Tobago,North America,10.6667,-61.5167,84126 +Port-Gentil,Gabon,Africa,-0.7167,8.7833,136462 +Port-Vila,Vanuatu,Oceania,-17.7333,168.3167,49034 +Port-au-Prince,Haiti,North America,18.5425,-72.3386,987310 +Port-de-Paix,Haiti,North America,19.95,-72.8333,121220 +Portalegre,Portugal,Europe,39.3167,-7.4167,24930 +Porteiras,Brazil,South America,-7.535,-39.1178,17050 +Porteirinha,Brazil,South America,-15.7428,-43.0278,37438 +Portel,Brazil,South America,-1.9358,-50.8208,38043 +Portimão,Portugal,Europe,37.1333,-8.5333,59845 +Portland,United States,North America,45.5371,-122.65,2084045 +Porto,Portugal,Europe,41.1495,-8.6108,231800 +Porto Alegre,Brazil,South America,-30.0328,-51.23,1332845 +Porto Alegre do Norte,Brazil,South America,-10.8778,-51.6328,8623 +Porto Belo,Brazil,South America,-27.1578,-48.5528,27688 +Porto Calvo,Brazil,South America,-9.0562,-35.3985,27288 +Porto Feliz,Brazil,South America,-23.215,-47.5239,56497 +Porto Franco,Brazil,South America,-6.3378,-47.3989,23903 +Porto Inglês,Cabo Verde,Africa,15.1375,-23.2083,3056 +Porto Nacional,Brazil,South America,-10.7078,-48.4169,64418 +Porto Novo,Cabo Verde,Africa,17.019,-25.064,18028 +Porto Real,Brazil,South America,-22.42,-44.29,20373 +Porto Real do Colégio,Brazil,South America,-10.1858,-36.84,18355 +Porto Valter,Brazil,South America,-8.2686,-72.7439,12241 +Porto Velho,Brazil,South America,-8.7619,-63.9039,334661 +Porto da Folha,Brazil,South America,-9.9169,-37.2778,26576 +Porto de Mós,Portugal,Europe,39.6,-8.8167,24342 +Porto de Pedras,Brazil,South America,-9.1578,-35.295,9295 +Porto-Novo,Benin,Africa,6.4972,2.605,133168 +Portoviejo,Ecuador,South America,-1.0561,-80.4553,244129 +Portão,Brazil,South America,-29.7019,-51.2419,24657 +Porvoo,Finland,Europe,60.3944,25.6639,51464 +Posadas,Argentina,South America,-27.3667,-55.9,275988 +Poso,Indonesia,Asia,-1.4,120.75,47477 +Posoltega,Nicaragua,North America,12.55,-86.9833,16771 +Posse,Brazil,South America,-14.0928,-46.3689,34914 +Postojna,Slovenia,Europe,45.7759,14.2137,9605 +Posušje,Bosnia and Herzegovina,Europe,43.4722,17.3267,20477 +Potengi,Brazil,South America,-7.0908,-40.0269,8833 +Potenza,Italy,Europe,40.6333,15.8,64406 +Poti,Georgia,Europe,42.15,41.6667,41500 +Potiraguá,Brazil,South America,-15.595,-39.8769,10274 +Potirendaba,Brazil,South America,-21.0428,-49.3769,18496 +Potiskum,Nigeria,Africa,11.7104,11.08,86002 +Potosí,Bolivia,South America,-19.5892,-65.7533,267907 +Potsdam,Germany,Europe,52.4006,13.0592,187119 +Poté,Brazil,South America,-17.8069,-41.7858,13666 +Poughkeepsie,United States,North America,41.695,-73.9211,307738 +Pouso Alegre,Brazil,South America,-22.2281,-45.9336,152549 +Pouso Redondo,Brazil,South America,-27.2578,-49.9339,12203 +Považská Bystrica,Slovakia,Europe,49.1167,18.45,37706 +Poxoréo,Brazil,South America,-15.8369,-54.3889,23283 +Poytug‘,Uzbekistan,Asia,40.9,72.25,26000 +Poza Rica de Hidalgo,Mexico,North America,20.5333,-97.45,189457 +Pozantı,Turkey,Europe,37.4278,34.8711,19852 +Poznań,Poland,Europe,52.4083,16.9336,546859 +Pozo Almonte,Chile,South America,-20.2667,-69.8,9277 +Poá,Brazil,South America,-23.5286,-46.345,113793 +Poço Branco,Brazil,South America,-5.6228,-35.6628,12390 +Poço Fundo,Brazil,South America,-21.7808,-45.965,15148 +Poço Redondo,Brazil,South America,-9.8058,-37.685,33439 +Poço Verde,Brazil,South America,-10.7078,-38.1828,21794 +Poço das Trincheiras,Brazil,South America,-9.3125,-37.2856,14493 +Poços de Caldas,Brazil,South America,-21.7878,-46.5608,163742 +Poção,Brazil,South America,-8.1858,-36.705,10500 +Poção de Pedras,Brazil,South America,-4.75,-44.9333,17161 +Poções,Brazil,South America,-14.5297,-40.3653,46879 +Požarevac,Serbia,Europe,44.62,21.1897,68648 +Požega,Croatia,Europe,45.3314,17.6744,22294 +Prabumulih,Indonesia,Asia,-3.4328,104.2356,188082 +Prachin Buri,Thailand,Asia,14.0567,101.3739,25157 +Prachuap Khiri Khan,Thailand,Asia,11.8167,99.8,26926 +Pradera,Colombia,South America,3.4167,-76.1667,57990 +Prado,Brazil,South America,-17.3408,-39.2208,35003 +Prados,Brazil,South America,-21.0578,-44.08,9048 +Pradópolis,Brazil,South America,-21.3594,-48.0656,19814 +Prague,Czechia,Europe,50.0875,14.4214,1384732 +Praia,Cabo Verde,Africa,14.9177,-23.5092,61644 +Praia Grande,Brazil,South America,-24.0058,-46.4028,349935 +Prainha,Brazil,South America,-1.8,-53.48,27301 +Prakhon Chai,Thailand,Asia,14.6107,103.0792,10404 +Pran Buri,Thailand,Asia,12.3849,99.9016,16851 +Prata,Brazil,South America,-19.3069,-48.9239,28342 +Prato,Italy,Europe,43.8808,11.0966,195736 +Praya,Indonesia,Asia,-8.7223,116.2923,35183 +Prebold,Slovenia,Europe,46.2369,15.0919,1605 +Preddvor,Slovenia,Europe,46.3052,14.4203,835 +Preiļi,Latvia,Europe,56.2942,26.7247,5841 +Prenzlau,Germany,Europe,53.3167,13.8667,19022 +Presidencia Roque Sáenz Peña,Argentina,South America,-26.7833,-60.45,101086 +Presidencia de la Plaza,Argentina,South America,-26.9833,-59.8333,12231 +President Quirino,Philippines,Asia,6.7,124.73,42244 +Presidente Bernardes,Brazil,South America,-22.0061,-51.5531,13568 +Presidente Dutra,Brazil,South America,-5.29,-44.49,45155 +Presidente Epitácio,Brazil,South America,-21.7633,-52.1156,43535 +Presidente Figueiredo,Brazil,South America,-2.0172,-60.025,30668 +Presidente Getúlio,Brazil,South America,-27.0508,-49.6228,20010 +Presidente Jânio Quadros,Brazil,South America,-14.6889,-41.6778,12621 +Presidente Kennedy,Brazil,South America,-21.0989,-41.0469,13696 +Presidente Médici,Brazil,South America,-11.1758,-61.9008,19327 +Presidente Olegário,Brazil,South America,-18.4178,-46.4178,18765 +Presidente Prudente,Brazil,South America,-22.1258,-51.3889,225668 +Presidente Vargas,Brazil,South America,-3.4069,-44.0239,10483 +Presidente Venceslau,Brazil,South America,-21.8761,-51.8439,39407 +Pretoria,South Africa,Africa,-25.7461,28.1881,2818100 +Prevalje,Slovenia,Europe,46.5438,14.9119,4620 +Prey Veng,Cambodia,Asia,11.484,105.324,36254 +Prešov,Slovakia,Europe,49.0,21.2333,84824 +Prienai,Lithuania,Europe,54.6333,23.95,8894 +Prievidza,Slovakia,Europe,48.7714,18.6242,45017 +Prijedor,Bosnia and Herzegovina,Europe,44.9808,16.7133,89397 +Prijepolje,Serbia,Europe,43.5439,19.6514,37059 +Prilep,North Macedonia,Europe,41.3464,21.5542,63308 +Primavera,Brazil,South America,-8.3378,-35.355,13838 +Primeira Cruz,Brazil,South America,-2.51,-43.4378,13614 +Primeiro de Maio,Brazil,South America,-22.8508,-51.0278,10082 +Primero de Enero,Cuba,North America,21.9453,-78.4189,29117 +Primorsko-Akhtarsk,Russia,Asia,46.05,38.1667,32257 +Princes Town,Trinidad and Tobago,North America,10.2667,-61.3833,28335 +Princesa Isabel,Brazil,South America,-7.7369,-37.9928,21114 +Prinzapolka,Nicaragua,North America,13.4,-83.5667,46730 +Priozërsk,Russia,Asia,61.05,30.1333,18235 +Pristina,Kosovo,Europe,42.6666,21.1724,198897 +Privas,France,Europe,44.7358,4.597,8552 +Privolzhsk,Russia,Asia,57.3825,41.2861,14332 +Prizren,Kosovo,Europe,42.2139,20.7397,220128 +Probištip,North Macedonia,Europe,41.9936,22.1767,10826 +Progreso,Mexico,North America,21.28,-89.67,37369 +Progress,Russia,Asia,49.7504,129.6167,146 +Prokhladnyy,Russia,Asia,43.75,44.0333,60023 +Prokuplje,Serbia,Europe,43.2339,21.5861,38054 +Proletarsk,Russia,Asia,46.7,41.7333,18983 +Promissão,Brazil,South America,-21.5369,-49.8583,40828 +Propriá,Brazil,South America,-10.2111,-36.8403,26618 +Prosperidad,Philippines,Asia,8.61,125.92,88321 +Providence,United States,North America,41.823,-71.4187,1270529 +Provins,France,Europe,48.5604,3.299,11824 +Provo,United States,North America,40.2457,-111.6457,639820 +Prudente de Morais,Brazil,South America,-19.4819,-44.155,11466 +Prudentópolis,Brazil,South America,-25.2128,-50.9778,49393 +Prudnik,Poland,Europe,50.3228,17.5767,19747 +Pruszcz Gdański,Poland,Europe,54.2667,18.6333,31582 +Pruszków,Poland,Europe,52.1667,20.8,65283 +Pruzhany,Belarus,Europe,52.5567,24.4644,18994 +Pryluky,Ukraine,Europe,50.5892,32.3856,51637 +Prymorsk,Ukraine,Europe,46.7333,36.35,11157 +Przasnysz,Poland,Europe,53.0167,20.8833,16662 +Przemyśl,Poland,Europe,49.7833,22.7667,58408 +Przeworsk,Poland,Europe,50.0667,22.5,14881 +Préveza,Greece,Europe,38.9575,20.7517,14421 +Pskov,Russia,Asia,57.8167,28.3333,189315 +Psychikó,Greece,Europe,38.0167,23.7667,9595 +Pszczyna,Poland,Europe,49.9833,18.95,25565 +Ptolemaḯda,Greece,Europe,40.5167,21.6833,32127 +Ptuj,Slovenia,Europe,46.4186,15.8714,17880 +Pua,Thailand,Asia,19.175,100.9162,10831 +Puan,Argentina,South America,-37.55,-62.7667,16381 +Pucallpa,Peru,South America,-8.3833,-74.55,326040 +Puck,Poland,Europe,54.7,18.4,10868 +Puconci,Slovenia,Europe,46.7067,16.1564,633 +Pudong,China,Asia,31.2347,121.5064,5187200 +Puducherry,India,Asia,11.9167,79.8167,244377 +Puebla,Mexico,North America,19.0333,-98.1833,1576259 +Pueblo,United States,North America,38.2701,-104.6114,120020 +Pueblo Bello,Colombia,South America,10.4167,-73.5833,27007 +Pueblo Nuevo,Colombia,South America,8.5008,-75.5072,41684 +Pueblo Nuevo Viñas,Guatemala,North America,14.2333,-90.4667,29418 +Pueblo Rico,Colombia,South America,5.25,-76.1667,12423 +Pueblorrico,Colombia,South America,5.8,-75.85,8664 +Puebloviejo,Colombia,South America,10.9942,-74.2833,33030 +Puente Alto,Chile,South America,-33.6167,-70.5667,573935 +Puente Nacional,Mexico,North America,19.3302,-96.4837,22454 +Puerto Armuelles,Panama,North America,8.2833,-82.8667,19763 +Puerto Asís,Colombia,South America,0.5167,-76.5,63067 +Puerto Ayacucho,Venezuela,South America,5.6631,-67.6264,80000 +Puerto Aysén,Chile,South America,-45.4,-72.6833,17441 +Puerto Baquerizo Moreno,Ecuador,South America,-0.9025,-89.6092,7290 +Puerto Barrios,Guatemala,North America,15.7133,-88.5899,110846 +Puerto Berrío,Colombia,South America,6.49,-74.4025,39314 +Puerto Boyacá,Colombia,South America,6.0,-74.4167,56930 +Puerto Cabello,Venezuela,South America,10.4667,-68.0167,209080 +Puerto Caicedo,Colombia,South America,0.685,-76.6044,11122 +Puerto Caimito,Panama,North America,8.8739,-79.7157,16951 +Puerto Carreño,Colombia,South America,6.1903,-67.4836,16763 +Puerto Colombia,Colombia,South America,11.0167,-74.8833,42803 +Puerto Concordia,Colombia,South America,2.6833,-72.7667,23131 +Puerto Cumarebo,Venezuela,South America,11.4861,-69.3503,33166 +Puerto Deseado,Argentina,South America,-47.75,-65.9167,14183 +Puerto Escondido,Colombia,South America,8.95,-76.25,32745 +Puerto Francisco de Orellana,Ecuador,South America,-0.4625,-76.9842,51281 +Puerto Gaitán,Colombia,South America,4.3142,-72.0825,41513 +Puerto Guzmán,Colombia,South America,0.9667,-76.5833,24355 +Puerto La Cruz,Venezuela,South America,10.2,-64.6333,454312 +Puerto Leguízamo,Colombia,South America,-0.1939,-74.7806,28468 +Puerto Lempira,Honduras,North America,15.2664,-83.7725,47528 +Puerto Libertador,Colombia,South America,7.9067,-75.6733,53990 +Puerto Limón,Costa Rica,North America,10.0022,-83.084,637 +Puerto Lleras,Colombia,South America,3.2694,-73.375,8982 +Puerto López,Colombia,South America,4.0872,-72.9606,35097 +Puerto Madryn,Argentina,South America,-42.7667,-65.05,115353 +Puerto Maldonado,Peru,South America,-12.6,-69.1833,85024 +Puerto Montt,Chile,South America,-41.4667,-72.9333,169736 +Puerto Nare,Colombia,South America,6.1917,-74.5867,12161 +Puerto Nariño,Colombia,South America,-3.7733,-70.3819,9744 +Puerto Natales,Chile,South America,-51.7333,-72.5167,21477 +Puerto Padre,Cuba,North America,21.195,-76.6017,90218 +Puerto Peñasco,Mexico,North America,31.3167,-113.5369,62301 +Puerto Pilón,Panama,North America,9.3632,-79.7933,16517 +Puerto Plata,Dominican Republic,North America,19.8,-70.6833,318419 +Puerto Princesa,Philippines,Asia,9.75,118.75,307079 +Puerto Píritu,Venezuela,South America,10.0667,-65.05,11000 +Puerto Rico,Colombia,South America,1.9142,-75.145,33765 +Puerto Salgar,Colombia,South America,5.5,-74.5833,15019 +Puerto Santander,Colombia,South America,8.3636,-72.4075,10249 +Puerto Tejada,Colombia,South America,3.25,-76.4167,46166 +Puerto Tirol,Argentina,South America,-27.3667,-59.0667,9763 +Puerto Triunfo,Colombia,South America,5.8667,-74.65,21745 +Puerto Vallarta,Mexico,North America,20.6458,-105.2222,224166 +Puerto Wilches,Colombia,South America,7.35,-73.9,31509 +Puerto Williams,Chile,South America,-54.9333,-67.6167,1646 +Pul-e Khumrī,Afghanistan,Asia,35.95,68.7,113500 +Pul-e ‘Alam,Afghanistan,Asia,33.9808,69.035,22914 +Pula,Croatia,Europe,44.8703,13.8456,52220 +Punakha,Bhutan,Asia,27.5833,89.8583,6262 +Puno,Peru,South America,-15.8433,-70.0236,128637 +Punta Alta,Argentina,South America,-38.88,-62.075,58315 +Punta Arenas,Chile,South America,-53.1667,-70.9333,123403 +Punta Gorda,Belize,North America,16.1,-88.8,5026 +Puntarenas,Costa Rica,North America,9.9667,-84.8333,41528 +Punto Fijo,Venezuela,South America,11.7167,-70.1833,131784 +Pupiales,Colombia,South America,0.8667,-77.65,16431 +Purificación,Colombia,South America,3.8586,-74.9306,29539 +Purmerend,Netherlands,Europe,52.5,4.95,81515 +Pursat,Cambodia,Asia,12.5337,103.9167,58255 +Puruk Cahu,Indonesia,Asia,-0.6384,114.5677,21781 +Purulhá,Guatemala,North America,15.2667,-90.2,65340 +Purwakarta,Indonesia,Asia,-6.5533,107.4472,179233 +Purwokerto,Indonesia,Asia,-7.4278,109.2417,233865 +Purísima de Bustos,Mexico,North America,21.0333,-101.8667,68795 +Purísima de la Concepción,Colombia,South America,9.2333,-75.7333,14705 +Pushkino,Russia,Asia,56.0167,37.8333,111580 +Pustomyty,Ukraine,Europe,49.7139,23.9108,9372 +Putian,China,Asia,25.4526,119.0078,2900000 +Putla Villa de Guerrero,Mexico,North America,17.0167,-97.9167,32640 +Putrajaya,Malaysia,Asia,2.914,101.7019,67964 +Putten,Netherlands,Europe,52.2592,5.6069,24365 +Putussibau,Indonesia,Asia,0.8575,112.925,12459 +Puxi,China,Asia,35.2125,114.735,789300 +Puxinanã,Brazil,South America,-7.1608,-35.9608,14277 +Puyang,China,Asia,35.7627,115.0292,3598740 +Puyang Chengguanzhen,China,Asia,35.7004,114.98,666322 +Puyo,Ecuador,South America,-1.4861,-78.0028,33325 +Puławy,Poland,Europe,51.4167,21.9667,45267 +Pułtusk,Poland,Europe,52.7,21.0833,19327 +Pyatigorsk,Russia,Asia,44.05,43.0667,143428 +Pyetrykaw,Belarus,Europe,52.1333,28.5,10401 +Pyongyang,"Korea, North",Asia,39.0167,125.7475,2863000 +Pyrzyce,Poland,Europe,53.1333,14.8833,12041 +Pácora,Colombia,South America,5.5258,-75.4589,13214 +Pánuco,Mexico,North America,22.05,-98.1833,33122 +Pápa,Hungary,Europe,47.3237,17.468,28549 +Pátra,Greece,Europe,38.25,21.7333,173600 +Pátzcuaro,Mexico,North America,19.5164,-101.6097,98382 +Pântano Grande,Brazil,South America,-30.1908,-52.3739,10212 +Pão de Açúcar,Brazil,South America,-9.7478,-37.4369,23823 +Pärnu,Estonia,Europe,58.3833,24.5,40228 +Pé de Serra,Brazil,South America,-11.8339,-39.6128,13243 +Pécs,Hungary,Europe,46.0708,18.2331,140330 +Péfki,Greece,Europe,38.0667,23.8,21293 +Pénjamo,Mexico,North America,20.4311,-101.7222,149936 +Pérama,Greece,Europe,37.9667,23.5667,25628 +Périgueux,France,Europe,45.1929,0.7217,29876 +Píritu,Venezuela,South America,10.0395,-65.0304,23248 +Póvoa de Lanhoso,Portugal,Europe,41.5667,-8.2667,21886 +Póvoa de Varzim,Portugal,Europe,41.3828,-8.7628,64320 +Pô,Burkina Faso,Africa,11.1667,-1.15,28079 +Pôrto Acre,Brazil,South America,-9.5878,-67.5328,16693 +Pôrto Esperidião,Brazil,South America,-15.8528,-58.46,10204 +Pôrto Ferreira,Brazil,South America,-21.8539,-47.4789,52649 +Pôrto Firme,Brazil,South America,-20.6728,-43.0839,10569 +Pôrto Grande,Brazil,South America,0.7128,-51.4128,17848 +Pôrto Murtinho,Brazil,South America,-21.6989,-57.8828,12859 +Pôrto Seguro,Brazil,South America,-16.45,-39.065,95721 +Pôrto União,Brazil,South America,-26.2378,-51.0778,32970 +Pôrto Xavier,Brazil,South America,-27.9058,-55.1378,9938 +Pôrto de Moz,Brazil,South America,-1.7478,-52.2378,40597 +Põlva,Estonia,Europe,58.0603,27.0694,5535 +Púchov,Slovakia,Europe,49.12,18.3306,17068 +Püspökladány,Hungary,Europe,47.3197,21.1139,13121 +Pütürge,Turkey,Europe,38.1967,38.8697,12492 +Pýrgos,Greece,Europe,37.6667,21.4333,45365 +Pākdasht,Iran,Asia,35.4828,51.6814,236319 +Pārūn,Afghanistan,Asia,35.4167,70.9169,1647 +Pāveh,Iran,Asia,35.0433,46.355,25771 +Pīrānshahr,Iran,Asia,36.6956,45.1456,91515 +Pınarbaşı,Turkey,Europe,38.722,36.391,21240 +Płock,Poland,Europe,52.55,19.7,112483 +Płońsk,Poland,Europe,52.6231,20.3706,21800 +P’yŏngsŏng-si,"Korea, North",Asia,39.2605,125.8618,236609 +Qabqa,China,Asia,36.2667,100.6167,52500 +Qadsayyā,Syria,Asia,33.5483,36.2156,33571 +Qagan Us,China,Asia,36.3972,98.0122,20570 +Qajortêbu,China,Asia,30.9347,88.7047,17000 +Qala,Malta,Europe,36.0353,14.3103,2284 +Qalādizay,Iraq,Asia,36.1811,45.1286,36792 +Qalāt,Afghanistan,Asia,32.1061,66.9069,49158 +Qal‘ah-ye Now,Afghanistan,Asia,34.9867,63.1292,64125 +Qal‘at al Maḑīq,Syria,Asia,35.4147,36.3908,12925 +Qal‘eh Ganj,Iran,Asia,27.525,57.88,13169 +Qamdo,China,Asia,31.143,97.17,798067 +Qanliko‘l,Uzbekistan,Asia,42.8333,59.0,10303 +Qapqal,China,Asia,43.834,81.1581,22111 +Qapshaghay,Kazakhstan,Asia,43.8844,77.0687,42167 +Qaqortoq,Greenland,North America,60.7222,-46.0403,3050 +Qarabalyq,Kazakhstan,Asia,53.7506,62.0502,9838 +Qarabulaq,Kazakhstan,Asia,44.9089,78.4922,16037 +Qaraghandy,Kazakhstan,Asia,49.8028,73.1056,515632 +Qarah Ẕīā’ od Dīn,Iran,Asia,38.89,45.0283,26767 +Qarataū,Kazakhstan,Asia,43.1667,70.45,30204 +Qarazhal,Kazakhstan,Asia,48.0253,70.7999,22170 +Qarchak,Iran,Asia,35.4267,51.5842,231075 +Qarqan,China,Asia,38.1338,85.5333,65572 +Qarqaraly,Kazakhstan,Asia,49.4167,75.4167,9212 +Qarqīn,Afghanistan,Asia,37.4128,66.0472,16500 +Qarshi,Uzbekistan,Asia,38.8667,65.8,278300 +Qashyr,Kazakhstan,Asia,53.0804,76.09,8931 +Qaskeleng,Kazakhstan,Asia,43.2,76.62,58418 +Qax,Azerbaijan,Europe,41.4194,46.9181,11415 +Qazaly,Kazakhstan,Asia,45.7667,62.1,7686 +Qazax,Azerbaijan,Europe,41.0933,45.3661,35102 +Qazvīn,Iran,Asia,36.2886,50.0069,402748 +Qazyqurt,Kazakhstan,Asia,41.7598,69.388,14563 +Qaşr-e Qand,Iran,Asia,26.2356,60.7408,11605 +Qaşr-e Shīrīn,Iran,Asia,34.5117,45.5792,18473 +Qaţanā,Syria,Asia,33.4376,36.0789,22831 +Qeshm,Iran,Asia,26.9494,56.2681,40678 +Qianzhou,China,Asia,28.276,109.712,301000 +Qian’an,China,Asia,39.999,118.701,728160 +Qiaotou,China,Asia,36.935,101.6736,114712 +Qincheng,China,Asia,34.5815,105.7248,2984659 +Qingan,China,Asia,46.8719,127.5118,53206 +Qingdao,China,Asia,36.0669,120.3827,5818255 +Qinggang,China,Asia,46.69,126.1,64182 +Qingnian,China,Asia,36.8494,115.7061,760781 +Qingping,China,Asia,34.539,113.391,826031 +Qingquan,China,Asia,38.7931,101.0887,69902 +Qingyang,China,Asia,24.7816,118.5517,2061600 +Qingyuan,China,Asia,23.682,113.056,3874000 +Qinhe,China,Asia,36.5014,112.3247,50143 +Qinhuangdao,China,Asia,39.8882,119.5202,3107400 +Qinzhou,China,Asia,21.981,108.654,3302238 +Qinā,Egypt,Africa,26.1667,32.7167,201191 +Qionghu,China,Asia,28.945,112.598,567159 +Qiqihar,China,Asia,47.3549,123.9182,4067489 +Qitai,China,Asia,41.5494,113.5339,18831 +Qobustan,Azerbaijan,Europe,40.5336,48.9282,3945 +Qom,Iran,Asia,34.64,50.8764,1201158 +Qorako‘l Shahri,Uzbekistan,Asia,39.5028,63.8556,22300 +Qorao‘zak,Uzbekistan,Asia,43.0275,60.0125,12300 +Qorashina,Uzbekistan,Asia,38.3394,66.5619,11900 +Qorday,Kazakhstan,Asia,43.0369,74.7114,38958 +Qormi,Malta,Europe,35.8794,14.4722,93897 +Qorovul,Uzbekistan,Asia,41.5538,60.5823,8520 +Qorveh,Iran,Asia,35.1678,47.8008,78276 +Qoryooley,Somalia,Africa,1.7878,44.53,62700 +Qostanay,Kazakhstan,Asia,53.2,63.62,217135 +Qo‘ng‘irot Shahri,Uzbekistan,Asia,43.0521,58.846,37100 +Qo‘rg‘ontepa,Uzbekistan,Asia,40.7336,72.7583,30800 +Qo’shko’pir,Uzbekistan,Asia,41.5333,60.35,18700 +Qrendi,Malta,Europe,35.8342,14.4589,2752 +Quanzhou,China,Asia,24.8744,118.6757,6480000 +Quaraí,Brazil,South America,-30.3878,-56.4508,23500 +Quatipuru,Brazil,South America,-0.9008,-47.0058,11524 +Quatis,Brazil,South America,-22.4069,-44.2578,13682 +Quatro Barras,Brazil,South America,-25.3658,-49.0769,24191 +Quatá,Brazil,South America,-22.2475,-50.6983,13702 +Quba,Azerbaijan,Europe,41.3611,48.5134,38100 +Qubadlı,Azerbaijan,Europe,39.3439,46.5797,672 +Quebec City,Canada,North America,46.8139,-71.2081,733156 +Quebrangulo,Brazil,South America,-9.3189,-36.4711,11248 +Queens,United States,North America,40.7498,-73.7976,2405464 +Queimada Nova,Brazil,South America,-8.5789,-41.4189,8738 +Queimadas,Brazil,South America,-7.3578,-35.8978,47658 +Quelimane,Mozambique,Africa,-17.8764,36.8872,192876 +Queluz,Brazil,South America,-22.5372,-44.7742,12600 +Quemado de Güines,Cuba,North America,22.79,-80.2561,20533 +Querétaro,Mexico,North America,20.5875,-100.3928,794789 +Querência do Norte,Brazil,South America,-23.0839,-53.4839,10685 +Quesada,Guatemala,North America,14.2667,-90.0333,23822 +Quetta,Pakistan,Asia,30.1833,67.0,1001205 +Quetzaltenango,Guatemala,North America,14.8333,-91.5167,207620 +Quevedo,Ecuador,South America,-1.0333,-79.45,177792 +Quezalguaque,Nicaragua,North America,12.5061,-86.9033,8591 +Quezaltepeque,Guatemala,North America,14.6333,-89.45,30136 +Quezon City,Philippines,Asia,14.65,121.0475,2960048 +Quibdó,Colombia,South America,5.6922,-76.6581,130825 +Quijingue,Brazil,South America,-10.7525,-39.2092,27626 +Quilalí,Nicaragua,North America,13.5667,-86.0333,26461 +Quillota,Chile,South America,-32.8667,-71.25,73261 +Quilmes,Argentina,South America,-34.7167,-58.2667,262379 +Quilombo,Brazil,South America,-26.7258,-52.7208,9773 +Quilpué,Chile,South America,-33.05,-71.45,147991 +Quimbaya,Colombia,South America,4.6333,-75.75,35276 +Quimilí,Argentina,South America,-27.6333,-62.4167,10959 +Quimper,France,Europe,47.9967,-4.0964,64530 +Quinchía,Colombia,South America,5.3379,-75.7296,34005 +Quincy,United States,North America,42.2506,-71.0187,101361 +Quinhámel,Guinea-Bissau,Africa,11.8833,-15.85,42659 +Quipapá,Brazil,South America,-8.8278,-36.0119,17928 +Quirinópolis,Brazil,South America,-18.4483,-50.4517,50701 +Quiroga,Mexico,North America,19.6667,-101.5333,27862 +Quissamã,Brazil,South America,-22.1069,-41.4719,22393 +Quitandinha,Brazil,South America,-25.8719,-49.4978,18398 +Quitilipi,Argentina,South America,-26.8667,-60.2167,32083 +Quito,Ecuador,South America,-0.22,-78.5125,1763275 +Quivicán,Cuba,North America,22.8247,-82.3558,29253 +Quixabeira,Brazil,South America,-11.4119,-40.1278,9461 +Quixadá,Brazil,South America,-4.9708,-39.015,69654 +Quixelô,Brazil,South America,-6.2539,-39.2019,15910 +Quixeramobim,Brazil,South America,-5.1989,-39.2928,82177 +Quixeré,Brazil,South America,-5.0739,-37.9889,20874 +Qulan,Kazakhstan,Asia,42.91,72.715,13128 +Qulsary,Kazakhstan,Asia,46.9833,54.0167,37103 +Qusar,Azerbaijan,Europe,41.4219,48.4214,16500 +Qusmuryn,Kazakhstan,Asia,52.458,64.6,8049 +Quthing,Lesotho,Africa,-30.4001,27.7002,15000 +Quva,Uzbekistan,Asia,40.5247,72.07,46400 +Quvasoy,Uzbekistan,Asia,40.3,71.9667,96900 +Quy Nhơn,Vietnam,Asia,13.7667,109.2333,311000 +Quyang,China,Asia,26.5755,109.7008,118100 +Quíbor,Venezuela,South America,9.9311,-69.6247,70536 +Quảng Ngãi,Vietnam,Asia,15.1167,108.8,260252 +Quảng Yên,Vietnam,Asia,20.917,106.833,180028 +Qyzylorda,Kazakhstan,Asia,44.85,65.5167,242462 +Qā’en,Iran,Asia,33.7333,59.1811,42323 +Qıvraq,Azerbaijan,Europe,39.3994,45.1151,4444 +Qūchān,Iran,Asia,37.106,58.5095,145531 +Raahe,Finland,Europe,64.6833,24.4667,23797 +Raalte,Netherlands,Europe,52.3828,6.2828,37911 +Raamsdonksveer,Netherlands,Europe,51.6993,4.8792,12470 +Raba,Indonesia,Asia,-8.4614,118.7469,106101 +Rabat,Morocco,Africa,34.0209,-6.8416,572717 +Rabinal,Guatemala,North America,15.068,-90.489,45000 +Racibórz,Poland,Europe,50.0833,18.2333,51257 +Racine,United States,North America,42.7274,-87.8135,135512 +Radenci,Slovenia,Europe,46.6472,16.0442,2171 +Radeče,Slovenia,Europe,46.0658,15.1728,1993 +Radlje ob Dravi,Slovenia,Europe,46.6152,15.2264,2856 +Radom,Poland,Europe,51.4036,21.1567,201601 +Radomsko,Poland,Europe,51.0667,19.45,44441 +Radomyshl,Ukraine,Europe,50.4947,29.2333,13685 +Radoviš,North Macedonia,Europe,41.6381,22.4644,14460 +Radovljica,Slovenia,Europe,46.3425,14.1721,5981 +Raduzhnyy,Russia,Asia,62.1333,77.4667,43577 +Radviliškis,Lithuania,Europe,55.8,23.55,15112 +Radzyń Podlaski,Poland,Europe,51.7828,22.6242,14806 +Radā‘,Yemen,Asia,14.4151,44.8371,45233 +Rafael Delgado,Mexico,North America,18.8167,-97.0667,23112 +Rafaela,Argentina,South America,-31.2667,-61.4833,92945 +Rafard,Brazil,South America,-23.0117,-47.5269,9004 +Rafaḩ,Egypt,Africa,31.282,34.2387,80823 +Rafsanjān,Iran,Asia,30.3983,55.9919,161909 +Rafína,Greece,Europe,38.0167,24.0,14620 +Ragusa,Italy,Europe,36.925,14.7306,73159 +Raha,Indonesia,Asia,-4.8311,122.7239,69980 +Rahachow,Belarus,Europe,53.1,30.05,31821 +Rahovec,Kosovo,Europe,42.3994,20.6547,56208 +Raipur,India,Asia,21.2444,81.6306,1010087 +Raisio,Finland,Europe,60.4858,22.1692,25331 +Rajin,"Korea, North",Asia,42.3444,130.3844,196954 +Rakai,Uganda,Africa,-0.71,31.405,8500 +Rakhiv,Ukraine,Europe,48.05,24.2,15536 +Rakitovo,Bulgaria,Europe,41.9898,24.0866,16444 +Rakvere,Estonia,Europe,59.35,26.35,15516 +Raleigh,United States,North America,35.8324,-78.6429,1150010 +Ramallo,Argentina,South America,-33.3167,-60.2,11428 +Ramapo,United States,North America,41.1404,-74.1072,150225 +Rambouillet,France,Europe,48.6444,1.8308,27145 +Ramiriquí,Colombia,South America,5.4,-73.3333,10015 +Ramla,Israel,Asia,31.9275,34.8625,75500 +Ramos Arizpe,Mexico,North America,25.55,-100.9667,66554 +Ramotswa,Botswana,Africa,-24.8667,25.8167,27760 +Ranai,Indonesia,Asia,3.9333,108.3833,8735 +Rancagua,Chile,South America,-34.1667,-70.75,231370 +Rancharia,Brazil,South America,-22.2289,-50.8928,28588 +Rancho Cucamonga,United States,North America,34.1247,-117.5667,174693 +Rancho Grande,Nicaragua,North America,13.2536,-85.5528,26223 +Ranchos,Argentina,South America,-35.5333,-58.2833,10319 +Ranchuelo,Cuba,North America,22.3358,-80.1131,50708 +Randaberg,Norway,Europe,58.9994,5.6186,11671 +Randers,Denmark,Europe,56.4607,10.0364,64057 +Rangkasbitung,Indonesia,Asia,-6.3667,106.25,144733 +Rangoon,Burma,Asia,16.795,96.16,6874000 +Rangpur,Bangladesh,Asia,25.75,89.2444,294265 +Ranillug,Kosovo,Europe,42.4922,21.5989,5800 +Rankovce,North Macedonia,Europe,42.1719,22.1167,744 +Ranong,Thailand,Asia,9.9619,98.6389,16163 +Ransiki,Indonesia,Asia,-1.5,134.1667,16245 +Rantau Prapat,Indonesia,Asia,2.3333,100.05,175960 +Rantepao,Indonesia,Asia,-2.969,119.9,43123 +Rapla,Estonia,Europe,58.9944,24.8011,5132 +Raposos,Brazil,South America,-19.9669,-43.8039,16279 +Rappang,Indonesia,Asia,-3.8447,119.8205,27116 +Rapperswil-Jona,Switzerland,Europe,47.2286,8.8317,26995 +Raseborg,Finland,Europe,59.975,23.4333,27209 +Raseiniai,Lithuania,Europe,55.3797,23.1239,9686 +Rasht,Iran,Asia,37.2744,49.5889,679995 +Rasskazovo,Russia,Asia,52.6667,41.8833,47644 +Rastatt,Germany,Europe,48.85,8.2,51800 +Ratchaburi,Thailand,Asia,13.5356,99.8133,36169 +Rathenow,Germany,Europe,52.6,12.3333,24918 +Ratnapura,Sri Lanka,Asia,6.693,80.386,165998 +Rattaphum,Thailand,Asia,7.1348,100.2565,17257 +Ratzeburg,Germany,Europe,53.7,10.75,14552 +Rauch,Argentina,South America,-36.7667,-59.1,14434 +Raul Soares,Brazil,South America,-20.1019,-42.4528,23423 +Rauma,Finland,Europe,61.1333,21.5,38832 +Ravenna,Italy,Europe,44.4161,12.2017,155751 +Ravensburg,Germany,Europe,47.7831,9.6114,51788 +Ravne na Koroškem,Slovenia,Europe,46.5437,14.9642,7363 +Ravānsar,Iran,Asia,34.7156,46.6519,24527 +Rawa Mazowiecka,Poland,Europe,51.7658,20.2533,16429 +Rawalpindi,Pakistan,Asia,33.6,73.0333,2098231 +Rawicz,Poland,Europe,51.6092,16.8575,20305 +Rawson,Argentina,South America,-43.3,-65.1,24616 +Rawānduz,Iraq,Asia,36.6121,44.5237,95089 +Raxruhá,Guatemala,North America,15.8666,-90.0424,40094 +Raychikhinsk,Russia,Asia,49.7833,129.4167,15860 +Raydah,Yemen,Asia,15.8233,44.0386,13971 +Rayevskiy,Russia,Asia,54.0664,54.9358,12767 +Rayong,Thailand,Asia,12.6742,101.2789,64256 +Rayón,Mexico,North America,19.1481,-99.58,8590 +Razan,Iran,Asia,35.3925,49.0331,14275 +Razgrad,Bulgaria,Europe,43.5409,26.5288,32391 +Razkrižje,Slovenia,Europe,46.5217,16.2811,141 +Rača,Serbia,Europe,44.2333,20.9833,11475 +Rače,Slovenia,Europe,46.4519,15.6814,2693 +Raška,Serbia,Europe,43.2856,20.6092,24680 +Ražanj,Serbia,Europe,43.6667,21.55,9150 +Ra’s al Khaymah,United Arab Emirates,Asia,25.7667,55.95,115949 +Ra’s al ‘Ayn,Syria,Asia,36.8503,40.0706,29347 +Reading,United States,North America,40.34,-75.9267,280030 +Realeza,Brazil,South America,-25.7669,-53.5269,19247 +Rebouças,Brazil,South America,-25.6208,-50.6928,14514 +Rechytsa,Belarus,Europe,54.3667,30.4,65213 +Recife,Brazil,South America,-8.0539,-34.8808,1488920 +Recklinghausen,Germany,Europe,51.585,7.1619,111693 +Reconquista,Argentina,South America,-29.2333,-59.9333,73293 +Recreio,Brazil,South America,-21.525,-42.4692,10520 +Red Deer,Canada,North America,52.2681,-113.8111,100844 +Redding,United States,North America,40.5698,-122.365,120849 +Redentora,Brazil,South America,-27.6639,-53.6378,9738 +Redenção,Brazil,South America,-8.0289,-50.0308,85597 +Redon,France,Europe,47.6522,-2.0836,9336 +Reforma,Mexico,North America,17.8658,-93.1472,29018 +Regen,Germany,Europe,48.9667,13.1333,11009 +Regeneração,Brazil,South America,-6.2378,-42.6878,17133 +Regensburg,Germany,Europe,49.0167,12.0833,159465 +Regente Feijó,Brazil,South America,-22.2208,-51.3028,20145 +Reggio Emilia,Italy,Europe,44.7,10.6333,169545 +Reggio di Calabria,Italy,Europe,38.1144,15.65,170951 +Regidor,Colombia,South America,8.6667,-73.8333,10489 +Regina,Canada,North America,50.4547,-104.6067,226404 +Registro,Brazil,South America,-24.4878,-47.8439,53752 +Reguengos de Monsaraz,Portugal,Europe,38.4167,-7.5333,10828 +Reims,France,Europe,49.2628,4.0347,178478 +Rekovac,Serbia,Europe,43.8667,21.1333,10971 +Relizane,Algeria,Africa,35.7333,0.55,130094 +Remanso,Brazil,South America,-9.6219,-42.0808,40586 +Remedios,Cuba,North America,22.4922,-79.5458,45621 +Remich,Luxembourg,Europe,49.5444,6.3667,4015 +Remscheid,Germany,Europe,51.1833,7.2,112970 +Remígio,Brazil,South America,-6.966,-35.7951,19798 +Rendsburg,Germany,Europe,54.3,9.6667,30545 +Renens,Switzerland,Europe,46.5333,6.5833,20927 +Reni,Ukraine,Europe,45.4575,28.2711,17736 +Rennes,France,Europe,48.1147,-1.6794,227830 +Reno,United States,North America,39.5497,-119.8483,454814 +Renqiu,China,Asia,38.686,116.084,816401 +Renton,United States,North America,47.4784,-122.1918,105279 +Repelón,Colombia,South America,10.55,-75.1333,22196 +Reriutaba,Brazil,South America,-4.1419,-40.5819,18606 +Resende,Brazil,South America,-22.4689,-44.4469,125214 +Resende Costa,Brazil,South America,-20.9219,-44.2378,11230 +Reserva,Brazil,South America,-24.65,-50.8508,24573 +Reshetylivka,Ukraine,Europe,49.5667,34.0667,9021 +Resistencia,Argentina,South America,-27.4514,-58.9867,298611 +Resplendor,Brazil,South America,-19.3258,-41.255,17226 +Restinga Sêca,Brazil,South America,-29.8128,-53.375,14939 +Restrepo,Colombia,South America,4.25,-73.5667,17610 +Retalhuleu,Guatemala,North America,14.5333,-91.6833,109935 +Retiro,Colombia,South America,6.0572,-75.5025,16974 +Retirolândia,Brazil,South America,-11.495,-39.4258,13651 +Reutlingen,Germany,Europe,48.4833,9.2167,118528 +Reutov,Russia,Asia,55.7622,37.8567,87300 +Revda,Russia,Asia,56.8053,59.9283,59521 +Revúca,Slovakia,Europe,48.6833,20.1167,11484 +Reyhanlı,Turkey,Europe,36.2681,36.5675,108092 +Reykjavík,Iceland,Europe,64.1458,-21.9425,139875 +Reynosa,Mexico,North America,26.0922,-98.2778,589466 +Rezh,Russia,Asia,57.3667,61.3667,36226 +Rezina,Moldova,Europe,47.7492,28.9622,11032 +Rečica,Slovenia,Europe,46.3247,14.9228,516 +Reşadiye,Turkey,Europe,40.3914,37.3383,9795 +Reşiţa,Romania,Europe,45.3008,21.8892,83324 +Reẕvānshahr,Iran,Asia,37.5481,49.1356,12355 +Rheinfelden,Switzerland,Europe,47.5539,7.7958,13528 +Rhenen,Netherlands,Europe,51.9597,5.5689,20203 +Riacho das Almas,Brazil,South America,-8.1339,-35.6892,18142 +Riacho de Santana,Brazil,South America,-13.6089,-42.9389,30711 +Riacho dos Machados,Brazil,South America,-16.0058,-43.0489,8756 +Riachão das Neves,Brazil,South America,-11.7458,-44.91,21642 +Riachão do Dantas,Brazil,South America,-11.0689,-37.725,18313 +Riachão do Jacuípe,Brazil,South America,-11.81,-39.3819,33386 +Rialma,Brazil,South America,-15.315,-49.5844,10940 +Rialto,United States,North America,34.1175,-117.3892,103866 +Ribas do Rio Pardo,Brazil,South America,-20.4428,-53.7589,23150 +Ribeira Grande,Cabo Verde,Africa,17.183,-25.065,2950 +Ribeira do Pombal,Brazil,South America,-10.8339,-38.5358,54010 +Ribeiro do Amparo,Brazil,South America,-11.0469,-38.4328,13841 +Ribeirão,Brazil,South America,-8.5139,-35.3778,33507 +Ribeirão Bonito,Brazil,South America,-22.0669,-48.1758,10989 +Ribeirão Branco,Brazil,South America,-24.2208,-48.7658,18627 +Ribeirão Claro,Brazil,South America,-23.1939,-49.7578,12364 +Ribeirão Pires,Brazil,South America,-23.7108,-46.4128,115559 +Ribeirão Prêto,Brazil,South America,-21.1776,-47.8101,504923 +Ribeirão das Neves,Brazil,South America,-19.7669,-44.0869,329794 +Ribeirão do Largo,Brazil,South America,-15.4589,-40.7389,9740 +Ribeirão do Pinhal,Brazil,South America,-23.4167,-50.35,13060 +Ribeirópolis,Brazil,South America,-10.5389,-37.4361,17033 +Ribnica,Slovenia,Europe,45.74,14.7308,3612 +Ricaurte,Colombia,South America,1.2108,-77.9933,18067 +Richardson,United States,North America,32.9716,-96.7093,118269 +Richmond,United States,North America,37.5295,-77.4756,1081248 +Richmond Hill,Canada,North America,43.8667,-79.4333,202022 +Ridder,Kazakhstan,Asia,50.35,83.5167,49695 +Ridderkerk,Netherlands,Europe,51.8667,4.6,46671 +Ried im Innkreis,Austria,Europe,48.21,13.4894,11903 +Rietavas,Lithuania,Europe,55.7167,21.9333,3253 +Rieti,Italy,Europe,42.4044,12.8567,45276 +Riga,Latvia,Europe,56.9475,24.1069,605273 +Riihimäki,Finland,Europe,60.7333,24.7667,28483 +Rijeka,Croatia,Europe,45.3333,14.4333,107964 +Rijen,Netherlands,Europe,51.592,4.9168,16800 +Rijswijk,Netherlands,Europe,52.0456,4.33,55220 +Rimavská Sobota,Slovakia,Europe,48.3814,20.02,22048 +Rimini,Italy,Europe,44.0594,12.5683,149211 +Rinconada,Argentina,South America,-22.4333,-66.1667,364 +Rincão,Brazil,South America,-21.5869,-48.0708,9098 +Rincón de Romos,Mexico,North America,22.2333,-102.3167,94978 +Ringkøbing,Denmark,Europe,56.0864,8.2487,9975 +Ringsted,Denmark,Europe,55.4411,11.8094,23498 +Rinópolis,Brazil,South America,-21.7258,-50.7219,9259 +Rio Acima,Brazil,South America,-20.0878,-43.7889,10261 +Rio Azul,Brazil,South America,-25.7328,-50.7958,14025 +Rio Bananal,Brazil,South America,-19.265,-40.3333,17530 +Rio Bonito,Brazil,South America,-22.7078,-42.6258,56276 +Rio Branco,Brazil,South America,-9.9781,-67.8117,364756 +Rio Branco do Sul,Brazil,South America,-25.19,-49.3139,37558 +Rio Brilhante,Brazil,South America,-21.8019,-54.5458,37601 +Rio Casca,Brazil,South America,-20.2258,-42.6508,12789 +Rio Claro,Brazil,South America,-22.4108,-47.5608,201418 +Rio Formoso,Brazil,South America,-8.6639,-35.1589,20009 +Rio Grande,Brazil,South America,-32.035,-52.0989,191900 +Rio Grande da Serra,Brazil,South America,-23.7439,-46.3978,44170 +Rio Largo,Brazil,South America,-9.4778,-35.8528,93927 +Rio Maior,Portugal,Europe,39.3333,-8.9333,21192 +Rio Maria,Brazil,South America,-7.3108,-50.0478,18384 +Rio Negrinho,Brazil,South America,-26.2539,-49.5178,37707 +Rio Negro,Brazil,South America,-26.1058,-49.7978,31324 +Rio Paranaíba,Brazil,South America,-19.1939,-46.2469,14532 +Rio Pardo,Brazil,South America,-29.99,-52.3778,34654 +Rio Pardo de Minas,Brazil,South America,-15.61,-42.54,28271 +Rio Piracicaba,Brazil,South America,-19.9289,-43.1739,14631 +Rio Pomba,Brazil,South America,-21.275,-43.1789,17443 +Rio Preto da Eva,Brazil,South America,-2.6989,-59.7,24936 +Rio Rancho,United States,North America,35.2873,-106.6982,106533 +Rio Real,Brazil,South America,-11.4833,-37.9344,35362 +Rio Tinto,Brazil,South America,-6.8028,-35.0808,24581 +Rio Verde,Brazil,South America,-17.7978,-50.9278,225696 +Rio Verde de Mato Grosso,Brazil,South America,-18.9178,-54.8439,19818 +Rio Vermelho,Brazil,South America,-18.2939,-43.0089,12635 +Rio das Flores,Brazil,South America,-22.1678,-43.5858,8954 +Rio das Ostras,Brazil,South America,-22.5269,-41.945,156491 +Rio das Pedras,Brazil,South America,-22.8428,-47.6058,31328 +Rio de Contas,Brazil,South America,-13.5789,-41.8108,13184 +Rio de Janeiro,Brazil,South America,-22.9111,-43.2056,12592000 +Rio do Antônio,Brazil,South America,-14.4108,-42.0758,13146 +Rio do Fogo,Brazil,South America,-5.2728,-35.3828,10351 +Rio do Pires,Brazil,South America,-13.1278,-42.2919,10497 +Rio do Sul,Brazil,South America,-27.2139,-49.6428,72587 +Rio dos Cedros,Brazil,South America,-26.7378,-49.2739,10865 +Riobamba,Ecuador,South America,-1.6731,-78.6483,177213 +Rioblanco,Colombia,South America,3.5,-75.8333,24139 +Riofrío,Colombia,South America,4.1561,-76.2878,14716 +Riolândia,Brazil,South America,-19.99,-49.6808,10309 +Riom,France,Europe,45.8944,3.1133,18680 +Rionegro,Colombia,South America,6.1535,-75.3743,135465 +Riosucio,Colombia,South America,5.4208,-75.7025,62296 +Rioverde,Mexico,North America,21.93,-99.98,69613 +Risaralda,Colombia,South America,5.1667,-75.75,9254 +Rishton,Uzbekistan,Asia,40.3567,71.2847,34800 +Rivadavia,Argentina,South America,-31.5303,-68.5947,82582 +Rivas,Nicaragua,North America,11.4393,-85.827,41080 +Rivera,Uruguay,South America,-30.9025,-55.5506,64465 +Riverside,United States,North America,33.9381,-117.3949,2288508 +Riverview,United States,North America,27.8227,-82.3023,107776 +Rivne,Ukraine,Europe,50.6192,26.2519,243873 +Riyadh,Saudi Arabia,Asia,24.65,46.71,7237000 +Rize,Turkey,Europe,41.0247,40.5222,119828 +Rizhao,China,Asia,35.417,119.527,2801013 +Road Town,"Virgin Islands, British",North America,18.4167,-64.6167,12603 +Roanne,France,Europe,46.0367,4.0689,35364 +Roanoke,United States,North America,37.2785,-79.9581,214019 +Roatán,Honduras,North America,16.33,-86.519,5070 +Robertsport,Liberia,Africa,6.75,-11.3667,3933 +Robāţ Karīm,Iran,Asia,35.4819,51.0808,105393 +Roca Sales,Brazil,South America,-29.2839,-51.8678,10418 +Rocafuerte,Ecuador,South America,-0.9236,-80.4495,10574 +Rocha,Uruguay,South America,-34.4833,-54.35,25422 +Rochefort,France,Europe,45.9421,-0.9588,23188 +Rochester,United States,North America,43.168,-77.6162,694707 +Rock Hill,United States,North America,34.9415,-81.0241,244403 +Rockford,United States,North America,42.2596,-89.064,274256 +Rodas,Cuba,North America,22.3428,-80.5553,32719 +Rodeio,Brazil,South America,-26.9228,-49.3658,12757 +Rodeiro,Brazil,South America,-21.2,-42.865,8664 +Rodelas,Brazil,South America,-8.8508,-38.7558,10308 +Rodeo,Argentina,South America,-30.2164,-69.1394,2625 +Rodez,France,Europe,44.3506,2.575,24136 +Roelofarendsveen,Netherlands,Europe,52.2,4.6333,10070 +Roermond,Netherlands,Europe,51.1933,5.9872,57308 +Roeselare,Belgium,Europe,50.9333,3.1167,64495 +Rogatec,Slovenia,Europe,46.2294,15.7003,1540 +Rogatica,Bosnia and Herzegovina,Europe,43.7989,19.0036,10723 +Rogaška Slatina,Slovenia,Europe,46.2375,15.6397,5082 +Rogašovci,Slovenia,Europe,46.8,16.0333,2946 +Roghun,Tajikistan,Asia,38.6978,69.7522,44100 +Roi Et,Thailand,Asia,16.0531,103.6511,34229 +Rokiškis,Lithuania,Europe,55.9667,25.5833,11606 +Rokytne,Ukraine,Europe,49.6667,30.4833,10938 +Rolante,Brazil,South America,-29.6508,-50.5758,21253 +Roldanillo,Colombia,South America,4.4167,-76.15,33697 +Rolim de Moura,Brazil,South America,-11.7254,-61.7778,56406 +Rolândia,Brazil,South America,-23.3097,-51.3692,67383 +Romblon,Philippines,Asia,12.55,122.28,40554 +Rome,Italy,Europe,41.8931,12.4828,2748109 +Romita,Mexico,North America,20.8708,-101.5169,65766 +Romitan Shahri,Uzbekistan,Asia,39.9333,64.3833,14300 +Romny,Ukraine,Europe,50.75,33.4667,37765 +Roncador,Brazil,South America,-24.6028,-52.275,11251 +Ronda Alta,Brazil,South America,-27.7669,-52.8019,9777 +Rondon,Brazil,South America,-23.4108,-52.7608,9097 +Rondon do Pará,Brazil,South America,-4.7758,-48.0669,53143 +Rondonópolis,Brazil,South America,-16.4708,-54.6358,150227 +Rong Kwang,Thailand,Asia,18.339,100.3174,12553 +Rongcheng,China,Asia,29.8402,112.9048,1120822 +Rongjiawan,China,Asia,29.15,113.1024,826000 +Rongwo,China,Asia,35.5161,102.0183,92601 +Ronneby,Sweden,Europe,56.2,15.3,12029 +Roosendaal,Netherlands,Europe,51.5314,4.4556,77200 +Ropaži,Latvia,Europe,56.9718,24.6318,1681 +Ropczyce,Poland,Europe,50.0861,21.6333,15815 +Roque Pérez,Argentina,South America,-35.4167,-59.3167,10902 +Rorschach,Switzerland,Europe,47.4786,9.4936,9439 +Ros Comáin,Ireland,Europe,53.6333,-8.1833,6555 +Rosa Zarate,Ecuador,South America,0.33,-79.48,31120 +Rosario,Argentina,South America,-32.9575,-60.6394,1276000 +Rosario de Lerma,Argentina,South America,-24.9833,-65.5833,21592 +Rosario de la Frontera,Argentina,South America,-25.8,-64.9667,26174 +Rosario del Tala,Argentina,South America,-32.3,-59.1333,12801 +Rosas,Colombia,South America,2.2667,-76.75,13302 +Roseau,Dominica,North America,15.3014,-61.3883,14725 +Roseira,Brazil,South America,-22.8978,-45.305,10832 +Roseller Lim,Philippines,Asia,7.6583,122.4639,43575 +Rosenheim,Germany,Europe,47.85,12.1333,65192 +Roseville,United States,North America,38.7703,-121.3196,152438 +Rosita,Nicaragua,North America,13.8833,-84.4,22723 +Roskilde,Denmark,Europe,55.65,12.0833,52580 +Roslavl,Russia,Asia,53.95,32.8833,43592 +Rosoman,North Macedonia,Europe,41.5161,21.9497,2553 +Rosso,Mauritania,Africa,16.5128,-15.805,51026 +Rossosh,Russia,Asia,50.2,39.55,60879 +Rostov,Russia,Asia,47.2225,39.71,1135968 +Rostuša,North Macedonia,Europe,41.61,20.6,872 +Rosário,Brazil,South America,-2.9339,-44.235,38475 +Rosário Oeste,Brazil,South America,-14.8358,-56.4278,15453 +Rosário do Catete,Brazil,South America,-10.6958,-37.0308,9295 +Rosário do Sul,Brazil,South America,-30.2578,-54.9139,36630 +Rotterdam,Netherlands,Europe,51.92,4.48,664311 +Rottweil,Germany,Europe,48.1681,8.6247,25548 +Rouen,France,Europe,49.4428,1.0886,116331 +Round Lake Beach,United States,North America,42.3791,-88.0811,252224 +Round Rock,United States,North America,30.527,-97.6642,124288 +Roura,French Guiana,South America,4.73,-52.33,2229 +Rovaniemi,Finland,Europe,66.5,25.7333,65329 +Rovigo,Italy,Europe,45.0809,11.794,49985 +Rovinj,Croatia,Europe,45.0833,13.6333,12968 +Rovira,Colombia,South America,4.25,-75.3333,20255 +Roxas City,Philippines,Asia,11.5894,122.7511,179292 +Rozdilna,Ukraine,Europe,46.8486,30.0792,17441 +Rožaje,Montenegro,Europe,42.84,20.16,13608 +Rožňava,Slovakia,Europe,48.6586,20.5314,17569 +Rubiataba,Brazil,South America,-15.1639,-49.8028,19788 +Rubim,Brazil,South America,-16.375,-40.5378,10298 +Rubio,Venezuela,South America,7.7,-72.35,87249 +Rubirizi,Uganda,Africa,-0.2661,30.1069,8204 +Rucheng,China,Asia,32.246,120.591,1241700 +Rucphen,Netherlands,Europe,51.5325,4.5583,23080 +Ruda Śląska,Poland,Europe,50.2628,18.8536,133793 +Rudnyy,Kazakhstan,Asia,52.9667,63.1333,128200 +Ruggell,Liechtenstein,Europe,47.2433,9.53,2295 +Rukungiri,Uganda,Africa,-0.79,29.925,37200 +Rulin,China,Asia,26.3585,110.3117,75900 +Ruma,Serbia,Europe,45.0031,19.8289,54339 +Rumbek,South Sudan,Africa,6.8,29.6833,32100 +Rumonge,Burundi,Africa,-3.9736,29.4386,35931 +Rumphi,Malawi,Africa,-11.0172,33.8539,22358 +Rumuruti,Kenya,Africa,0.2725,36.5381,31649 +Rundu,Namibia,Africa,-17.9167,19.7667,118632 +Ruoqiang,China,Asia,39.0181,88.1681,9872 +Ruse,Bulgaria,Europe,43.8445,25.9539,140537 +Rushan,China,Asia,36.87,121.505,464078 +Russas,Brazil,South America,-4.94,-37.9758,72928 +Rustavi,Georgia,Europe,41.5436,45.0117,132333 +Rutana,Burundi,Africa,-3.9236,30.0061,5865 +Ruteng,Indonesia,Asia,-8.6127,120.463,65694 +Ruy Barbosa,Brazil,South America,-12.2839,-40.4939,28282 +Ruyigi,Burundi,Africa,-3.4667,30.25,7139 +Ruza,Russia,Asia,55.7,36.2,15269 +Ruzayevka,Russia,Asia,54.0667,44.9333,9700 +Ruše,Slovenia,Europe,46.5383,15.5153,4206 +Ružomberok,Slovakia,Europe,49.0786,19.3083,27407 +Rwamagana,Rwanda,Africa,-1.9525,30.4378,18009 +Ryazan,Russia,Asia,54.63,39.7425,520509 +Ryazhsk,Russia,Asia,53.7,40.05,20197 +Rybinsk,Russia,Asia,58.05,38.8333,171810 +Rybnik,Poland,Europe,50.0833,18.55,133772 +Rybnoye,Russia,Asia,54.7333,39.5167,21200 +Rygge,Norway,Europe,59.3747,10.7147,16145 +Ryki,Poland,Europe,51.6333,21.9333,9229 +Rylsk,Russia,Asia,51.5667,34.6667,14843 +Rypin,Poland,Europe,53.0667,19.45,15832 +Rzeszów,Poland,Europe,50.0333,22.0,195871 +Rzhev,Russia,Asia,56.25,34.3167,55757 +Ráckeve,Hungary,Europe,47.1608,18.9456,11080 +Ráquira,Colombia,South America,5.5333,-73.6333,13588 +Râmnicu Vâlcea,Romania,Europe,45.1047,24.3756,93151 +Réo,Burkina Faso,Africa,12.3167,-2.4667,33893 +Réthymno,Greece,Europe,35.3689,24.4739,24893 +Río Blanco,Nicaragua,North America,12.9333,-85.2167,30785 +Río Bravo,Guatemala,North America,14.4011,-91.3195,31303 +Río Caribe,Venezuela,South America,10.7008,-63.1086,13667 +Río Cauto,Cuba,North America,20.5636,-76.9172,45545 +Río Cuarto,Argentina,South America,-33.1333,-64.35,200000 +Río Gallegos,Argentina,South America,-51.6233,-69.2161,95796 +Río Grande,Argentina,South America,-53.7833,-67.7,98017 +Río Hondo,Guatemala,North America,15.0464,-89.5847,23384 +Río de Oro,Colombia,South America,8.0,-73.5,14041 +Ríohacha,Colombia,South America,11.5442,-72.9069,167865 +Rîşcani,Moldova,Europe,47.9572,27.5539,9259 +Ródos,Greece,Europe,36.4412,28.2225,56969 +Rønne,Denmark,Europe,55.1009,14.7066,13798 +Rørvik,Norway,Europe,64.868,11.2053,3226 +Røyken,Norway,Europe,59.7336,10.4289,22635 +Rābor,Iran,Asia,29.2903,56.9133,13263 +Rājshāhi,Bangladesh,Asia,24.3667,88.6,448087 +Rāmhormoz,Iran,Asia,31.2753,49.6044,74285 +Rāmsar,Iran,Asia,36.9225,50.6431,35997 +Rāmshīr,Iran,Asia,30.8936,49.4086,25009 +Rānchi,India,Asia,23.36,85.33,1073440 +Rāniyah,Iraq,Asia,36.255,44.8822,257577 +Rāsak,Iran,Asia,26.2356,61.4014,10115 +Rāvar,Iran,Asia,31.2633,56.8072,22729 +Rāwah,Iraq,Asia,34.4792,41.9083,19629 +Rēzekne,Latvia,Europe,56.5067,27.3308,26131 +Rūdsar,Iran,Asia,37.1444,50.2936,37998 +Rạch Giá,Vietnam,Asia,10.0167,105.0833,250660 +Sa Kaeo,Thailand,Asia,13.8206,102.0589,16591 +Sa Pa,Vietnam,Asia,22.3356,103.8419,9412 +Sa Đéc,Vietnam,Asia,10.3,105.7667,214610 +Saalfeld,Germany,Europe,50.65,11.3667,29121 +Saanich,Canada,North America,48.484,-123.381,117735 +Saarbrücken,Germany,Europe,49.2333,7.0,183509 +Saarijärvi,Finland,Europe,62.705,25.2583,8847 +Saarlouis,Germany,Europe,49.3167,6.75,34893 +Saatlı,Azerbaijan,Europe,39.9311,48.3697,17900 +Sabana de Torres,Colombia,South America,7.4,-73.5,22512 +Sabanagrande,Colombia,South America,10.8,-74.75,33437 +Sabanalarga,Colombia,South America,6.85,-75.8167,100049 +Sabaneta,Colombia,South America,6.15,-75.6,82375 +Sabanitas,Panama,North America,9.3452,-79.8054,19052 +Sabará,Brazil,South America,-19.8858,-43.8069,115352 +Sabhā,Libya,Africa,27.0389,14.4264,116016 +Sabinas,Mexico,North America,27.9167,-101.3,64811 +Sabinov,Slovakia,Europe,49.1,21.1,12349 +Sabinópolis,Brazil,South America,-18.6658,-43.0839,14240 +Sabirabad,Azerbaijan,Europe,40.0053,48.4719,30866 +Saboeiro,Brazil,South America,-6.5419,-39.9069,13854 +Saboyá,Colombia,South America,5.7,-73.7667,12372 +Sabugal,Portugal,Europe,40.3519,-7.0894,12544 +Sabzevār,Iran,Asia,36.2125,57.6764,243700 +Sabáudia,Brazil,South America,-23.3178,-51.5528,8822 +Sacapulas,Guatemala,North America,15.2892,-91.0892,55398 +Sacramento,United States,North America,38.5677,-121.4685,1962998 +Sadao,Thailand,Asia,6.6388,100.4234,21086 +Safi,Malta,Europe,35.8333,14.485,2126 +Safonovo,Russia,Asia,55.1167,33.25,37055 +Safotu,Samoa,Oceania,-13.4528,-172.4036,1270 +Safranbolu,Turkey,Europe,41.2494,32.6833,52999 +Saga,Japan,Asia,33.2636,130.3008,232359 +Sagarejo,Georgia,Europe,41.7369,45.3258,10871 +Saginaw,United States,North America,43.4199,-83.9501,117412 +Sagua de Tánamo,Cuba,North America,20.5858,-75.2417,44872 +Sagua la Grande,Cuba,North America,22.8086,-80.0711,53077 +Saguenay,Canada,North America,48.4167,-71.0667,144723 +Sahagún,Colombia,South America,8.95,-75.45,90487 +Sahiwal,Pakistan,Asia,30.6611,73.1083,247706 +Sahuayo de Morelos,Mexico,North America,20.05,-102.7333,78477 +Sai Buri,Thailand,Asia,6.7013,101.6167,14137 +Sai Mai,Thailand,Asia,13.9207,100.6455,23316 +Sai Ngam,Thailand,Asia,16.4669,99.8902,8918 +Saidu Sharif,Pakistan,Asia,34.75,72.3572,65000 +Saimbeyli,Turkey,Europe,37.9833,36.0833,13621 +Saint Ann’s Bay,Jamaica,North America,18.436,-77.202,13671 +Saint George’s,Grenada,North America,12.05,-61.75,33734 +Saint Helier,Jersey,Europe,49.1858,-2.11,33522 +Saint John’s,Antigua and Barbuda,North America,17.1167,-61.85,22219 +Saint Paul’s Bay,Malta,Europe,35.9483,14.4017,32042 +Saint Peter Port,Guernsey,Europe,49.4555,-2.5368,18958 +Saint Petersburg,Russia,Asia,59.9375,30.3086,5597763 +Saint-Amand-Montrond,France,Europe,46.7,2.51,9690 +Saint-Brieuc,France,Europe,48.5136,-2.7653,44607 +Saint-Claude,France,Europe,46.3878,5.8639,8556 +Saint-Denis,Reunion,Africa,-20.8789,55.4481,319141 +Saint-Dizier,France,Europe,48.6383,4.9497,22811 +Saint-Dié-des-Vosges,France,Europe,48.28,6.95,19324 +Saint-Gaudens,France,Europe,43.1089,0.7242,11869 +Saint-Georges,French Guiana,South America,3.9105,-51.81,2742 +Saint-Germain-en-Laye,France,Europe,48.8989,2.0938,40806 +Saint-Julien-en-Genevois,France,Europe,46.1436,6.0811,15925 +Saint-Jérôme,Canada,North America,45.7833,-74.0,100859 +Saint-Laurent-du-Maroni,French Guiana,South America,5.4976,-54.0325,24287 +Saint-Louis,Senegal,Africa,16.0333,-16.5,154555 +Saint-Lô,France,Europe,49.12,-1.09,19352 +Saint-Malo,France,Europe,48.6494,-2.0261,47255 +Saint-Nazaire,France,Europe,47.2736,-2.2139,73111 +Saint-Omer,France,Europe,50.7461,2.2617,14358 +Saint-Pierre,Saint Pierre and Miquelon,North America,46.7817,-56.1736,5223 +Saint-Quentin,France,Europe,49.8486,3.2864,52995 +Saint-Étienne,France,Europe,45.4347,4.3903,172569 +Saintes,France,Europe,45.7464,-0.6333,25312 +Sairé,Brazil,South America,-8.3278,-35.7058,10887 +Saitama,Japan,Asia,35.8614,139.6456,1325843 +Sakarya,Turkey,Europe,40.7833,30.4,281489 +Saki,Nigeria,Africa,8.6667,3.4,388225 +Sakon Nakhon,Thailand,Asia,17.1564,104.1456,76000 +Saky,Ukraine,Europe,45.1336,33.5772,25146 +Sakété,Benin,Africa,6.7364,2.6581,114207 +Sakākā,Saudi Arabia,Asia,29.9697,40.2,241669 +Sal Rei,Cabo Verde,Africa,16.176,-22.9171,6357 +Sala,Sweden,Europe,59.9167,16.6,12289 +Saladas,Argentina,South America,-28.25,-58.6167,18349 +Saladoblanco,Colombia,South America,2.0167,-76.05,10076 +Salamanca,Mexico,North America,20.5703,-101.1972,327169 +Salamina,Colombia,South America,5.4083,-75.49,18704 +Salamá,Guatemala,North America,15.1,-90.2667,71478 +Salamína,Greece,Europe,37.9667,23.5,30235 +Salanso,Burkina Faso,Africa,12.1833,-4.0833,24783 +Salaspils,Latvia,Europe,56.8614,24.35,17826 +Salavan,Laos,Asia,15.7167,106.4167,100900 +Salavat,Russia,Asia,53.3667,55.9333,146633 +Salaya,Thailand,Asia,13.802,100.3211,11180 +Salcajá,Guatemala,North America,14.8833,-91.45,21577 +Salcedo,Dominican Republic,North America,19.378,-70.418,39557 +Saldaña,Colombia,South America,3.9347,-75.0203,14329 +Saldus,Latvia,Europe,56.6667,22.4936,9553 +Saleaula,Samoa,Oceania,-13.4501,-172.3335,600 +Salekhard,Russia,Asia,66.5333,66.6,51188 +Salem,United States,North America,44.9233,-123.0244,272331 +Salerno,Italy,Europe,40.6806,14.7594,127186 +Sales Oliveira,Brazil,South America,-20.7719,-47.8378,11411 +Salesópolis,Brazil,South America,-23.5319,-45.8458,15202 +Salgado,Brazil,South America,-11.0319,-37.475,20279 +Salgado de São Félix,Brazil,South America,-7.3569,-35.4406,12131 +Salgar,Colombia,South America,5.9617,-75.9753,17804 +Salgueiro,Brazil,South America,-8.0742,-39.1192,61249 +Salgótarján,Hungary,Europe,48.0853,19.7867,30910 +Salihli,Turkey,Europe,38.4811,28.1392,165182 +Salihorsk,Belarus,Europe,52.8,27.5333,97818 +Salima,Malawi,Africa,-13.7829,34.4333,36789 +Salinas,United States,North America,36.6883,-121.6317,172995 +Salinas Victoria,Mexico,North America,25.9667,-100.3,86766 +Salinas da Margarida,Brazil,South America,-12.8708,-38.7639,14987 +Salinas de Hidalgo,Mexico,North America,22.628,-101.7156,16821 +Salinópolis,Brazil,South America,-0.6136,-47.3561,40922 +Salitre,Brazil,South America,-7.2839,-40.4606,16633 +Salmās,Iran,Asia,38.2028,44.7669,127864 +Salo,Finland,Europe,60.3861,23.125,51056 +Saloá,Brazil,South America,-8.9758,-36.6878,13836 +Salqīn,Syria,Asia,36.1387,36.4524,23700 +Salsk,Russia,Asia,46.4833,41.5333,57937 +Salt Lake City,United States,North America,40.7776,-111.9311,1169033 +Salta,Argentina,South America,-24.7833,-65.4167,535303 +Saltillo,Mexico,North America,25.4231,-100.9919,864431 +Salto,Brazil,South America,-23.2008,-47.2869,134319 +Salto Grande,Brazil,South America,-22.8928,-49.9858,9050 +Salto de Pirapora,Brazil,South America,-23.6489,-47.5728,43748 +Salto del Guairá,Paraguay,South America,-24.0625,-54.3069,37600 +Salvador,Brazil,South America,-12.9831,-38.4928,2417678 +Salvaterra,Brazil,South America,-0.7528,-48.5169,24129 +Salvaterra de Magos,Portugal,Europe,39.0253,-8.7933,22159 +Salvatierra,Mexico,North America,20.2156,-100.8961,37203 +Salyan,Azerbaijan,Europe,39.5961,48.9847,30396 +Salzburg,Austria,Europe,47.8,13.045,155021 +Salzgitter,Germany,Europe,52.15,10.3333,105039 +Salzwedel,Germany,Europe,52.85,11.15,23394 +Sam Ko,Thailand,Asia,14.6074,100.2186,10696 +Sam Phran,Thailand,Asia,13.727,100.2153,17611 +Samacá,Colombia,South America,5.5,-73.4833,20767 +Samamea,Samoa,Oceania,-13.9333,-171.5333,45 +Samandağ,Turkey,Europe,36.085,35.9806,165494 +Samaniego,Colombia,South America,1.35,-77.6,49178 +Samaná,Dominican Republic,North America,19.2053,-69.3364,58156 +Samara,Russia,Asia,53.2028,50.1408,1156659 +Samarinda,Indonesia,Asia,-0.5,117.1378,831460 +Samarkand,Uzbekistan,Asia,39.6547,66.9758,513572 +Samayac,Guatemala,North America,14.5833,-91.4667,26665 +Sambava,Madagascar,Africa,-14.2662,50.1666,84039 +Sambir,Ukraine,Europe,49.5167,23.2028,34152 +Samborondón,Ecuador,South America,-2.1833,-79.8833,72425 +Samdrup Jongkhar,Bhutan,Asia,26.8,91.505,9325 +Same,Timor-Leste,Asia,-9.0042,125.6486,2100 +Samobor,Croatia,Europe,45.8011,15.711,37435 +Sampit,Indonesia,Asia,-2.5333,112.95,166773 +Sampués,Colombia,South America,9.1833,-75.3833,37925 +Samraong,Cambodia,Asia,14.25,103.5833,70944 +Samsun,Turkey,Europe,41.2903,36.3336,1356079 +Samtredia,Georgia,Europe,42.1625,42.3417,22201 +Samtse,Bhutan,Asia,27.0333,89.0833,4981 +Samut Prakan,Thailand,Asia,13.5897,100.6386,31887 +Samut Sakhon,Thailand,Asia,13.5486,100.2775,60103 +Samut Songkhram,Thailand,Asia,13.4097,100.0017,25623 +Samux,Azerbaijan,Europe,40.7642,46.4083,10228 +San,Mali,Africa,13.3004,-4.9,41386 +San Agustín,Colombia,South America,1.9,-76.2833,34120 +San Agustín Acasaguastlán,Guatemala,North America,14.95,-89.9667,55508 +San Alberto,Colombia,South America,7.7525,-73.3892,26551 +San Andrés,Colombia,South America,12.5847,-81.7006,71946 +San Andrés Itzapa,Guatemala,North America,14.6167,-90.85,35579 +San Andrés Sajcabajá,Guatemala,North America,15.1756,-90.9417,27962 +San Andrés Timilpan,Mexico,North America,19.8667,-99.7333,14335 +San Andrés Tuxtla,Mexico,North America,18.4483,-95.2122,162428 +San Andrés Villa Seca,Guatemala,North America,14.5667,-91.5833,56060 +San Andrés Xecul,Guatemala,North America,14.9,-91.4833,29849 +San Andrés de Giles,Argentina,South America,-34.4333,-59.4333,26510 +San Andrés de Sotavento,Colombia,South America,9.15,-75.5,47282 +San Antero,Colombia,South America,9.3833,-75.75,33662 +San Antonio,United States,North America,29.4632,-98.5238,2069843 +San Antonio Aguas Calientes,Guatemala,North America,14.5333,-90.7667,12716 +San Antonio Huista,Guatemala,North America,15.65,-91.7667,19311 +San Antonio Ilotenango,Guatemala,North America,15.0544,-91.2294,30864 +San Antonio La Paz,Guatemala,North America,14.75,-90.2833,22493 +San Antonio Oeste,Argentina,South America,-40.7333,-64.95,16265 +San Antonio Palopó,Guatemala,North America,14.7,-91.1167,15752 +San Antonio Sacatepéquez,Guatemala,North America,14.9667,-91.7333,20994 +San Antonio Suchitepéquez,Guatemala,North America,14.5333,-91.4167,65501 +San Antonio de Areco,Argentina,South America,-34.25,-59.4667,26897 +San Antonio de Ibarra,Ecuador,South America,0.3627,-78.1307,17500 +San Antonio de Los Altos,Venezuela,South America,10.3744,-66.9669,74422 +San Antonio de los Baños,Cuba,North America,22.8889,-82.4989,33811 +San Antonio de los Cobres,Argentina,South America,-24.2178,-66.32,5482 +San Antonio del Golfo,Venezuela,South America,10.4333,-63.7833,18144 +San Antonio del Sur,Cuba,North America,20.0569,-74.8078,25752 +San Antonio del Tequendama,Colombia,South America,4.6328,-74.3522,13084 +San Antonio del Táchira,Venezuela,South America,7.8167,-72.45,61630 +San Antonio la Isla,Mexico,North America,19.1677,-99.5542,110560 +San Bartolo,Guatemala,North America,15.0844,-91.4558,12459 +San Bartolo Tutotepec,Mexico,North America,20.4,-98.2,17837 +San Bartolo Yautepec,Mexico,North America,16.42,-95.91,12101 +San Bartolomé Jocotenango,Guatemala,North America,15.1928,-91.0775,15777 +San Bartolomé Milpas Altas,Guatemala,North America,14.6072,-90.6781,8642 +San Benito,Guatemala,North America,16.9167,-89.9,54189 +San Benito Abad,Colombia,South America,8.9333,-75.0333,26595 +San Bernardino,United States,North America,34.1416,-117.2943,221774 +San Bernardo,Chile,South America,-33.5833,-70.7,301313 +San Bernardo del Viento,Colombia,South America,9.35,-75.95,34782 +San Blas,Mexico,North America,21.5397,-105.2856,10187 +San Blas Atempa,Mexico,North America,16.3167,-95.2167,18406 +San Buenaventura,United States,North America,34.2741,-119.2314,109900 +San Calixto,Colombia,South America,8.4,-73.2167,9961 +San Carlos,Venezuela,South America,9.65,-68.5833,83957 +San Carlos Alzatate,Guatemala,North America,14.5,-90.0667,31938 +San Carlos Sija,Guatemala,North America,14.9833,-91.55,36001 +San Carlos Yautepec,Mexico,North America,16.5,-96.1,9857 +San Carlos de Bariloche,Argentina,South America,-41.15,-71.3,112887 +San Carlos de Guaroa,Colombia,South America,3.7111,-73.2425,11512 +San Carlos del Zulia,Venezuela,South America,9.0,-71.9333,56500 +San Cristóbal,Venezuela,South America,7.7667,-72.2333,282830 +San Cristóbal Cucho,Guatemala,North America,14.9,-91.7833,17987 +San Cristóbal Totonicapán,Guatemala,North America,14.9167,-91.4333,42937 +San Cristóbal Verapaz,Guatemala,North America,15.365,-90.4792,80613 +San Diego,United States,North America,32.8313,-117.1222,3057778 +San Dionisio,Nicaragua,North America,12.75,-85.85,16273 +San Estanislao,Colombia,South America,10.4,-75.15,16518 +San Felipe,Venezuela,South America,10.3406,-68.7369,220786 +San Felipe del Progreso,Mexico,North America,19.7125,-99.9511,100201 +San Fernando,Philippines,Asia,15.03,120.68,354666 +San Francisco,United States,North America,37.7558,-122.4449,3364862 +San Francisco El Alto,Guatemala,North America,14.95,-91.45,65521 +San Francisco Libre,Nicaragua,North America,12.5,-86.3,9416 +San Francisco Zapotitlán,Guatemala,North America,14.5833,-91.5167,24763 +San Francisco de Macorís,Dominican Republic,North America,19.3,-70.25,188118 +San Francisco de los Romo,Mexico,North America,22.0833,-102.2667,61992 +San Francisco del Rincón,Mexico,North America,21.0228,-101.86,71139 +San Francisco la Unión,Guatemala,North America,14.9167,-91.5333,9665 +San Gabriel,Mexico,North America,19.7,-103.7333,16548 +San Gabriel Chilac,Mexico,North America,18.3167,-97.35,13687 +San Gaspar Ixchil,Guatemala,North America,15.3833,-91.7167,10581 +San Germán,Cuba,North America,20.6011,-76.1325,38097 +San Gil,Colombia,South America,6.5592,-73.135,51874 +San Gregorio Atzompa,Mexico,North America,19.0224,-98.3445,9671 +San Ignacio,Peru,South America,-5.1456,-79.0017,127523 +San Ignacio Cerro Gordo,Mexico,North America,20.7333,-102.5167,18952 +San Isidro,Argentina,South America,-34.4739,-58.525,43595 +San Isidro de Lules,Argentina,South America,-26.9333,-65.35,21088 +San Jacinto,Colombia,South America,9.8311,-75.1219,21644 +San Jacinto de Buena Fe,Ecuador,South America,-0.8986,-79.4889,46779 +San Jacinto del Cauca,Colombia,South America,8.25,-74.7167,13426 +San Javier,Argentina,South America,-30.5833,-59.95,13604 +San Jerónimo,Guatemala,North America,15.0611,-90.2403,28321 +San Jorge,Guatemala,North America,14.9333,-89.6,13525 +San Jose,United States,North America,37.3012,-121.848,1771563 +San José,Costa Rica,North America,9.9325,-84.08,1543000 +San José Acatempa,Guatemala,North America,14.2667,-90.1333,13938 +San José El Ídolo,Guatemala,North America,14.45,-91.4167,12017 +San José La Arada,Guatemala,North America,14.7167,-89.5833,9249 +San José Ojetenam,Guatemala,North America,15.2167,-91.9667,22768 +San José Pinula,Guatemala,North America,14.55,-90.4167,89639 +San José Poaquil,Guatemala,North America,14.8167,-90.9,33932 +San José Tenango,Mexico,North America,18.15,-96.7333,18316 +San José Villa de Allende,Mexico,North America,19.3747,-100.1475,52641 +San José de Aerocuar,Venezuela,South America,10.6027,-63.3273,11162 +San José de Bocay,Nicaragua,North America,13.5333,-85.5333,42029 +San José de Feliciano,Argentina,South America,-30.3833,-58.75,10282 +San José de Gracia,Mexico,North America,22.15,-102.4167,8896 +San José de Guaribe,Venezuela,South America,9.8602,-65.8131,11426 +San José de Jáchal,Argentina,South America,-30.2333,-68.75,21018 +San José de Ocoa,Dominican Republic,North America,18.55,-70.5,39451 +San José de las Lajas,Cuba,North America,22.9614,-82.1511,81261 +San José del Cabo,Mexico,North America,23.0614,-109.7081,136285 +San José del Fragua,Colombia,South America,1.3286,-75.9728,11364 +San José del Guaviare,Colombia,South America,2.5667,-72.6333,68878 +San José del Rincón Centro,Mexico,North America,19.6606,-100.1525,91345 +San Juan,Puerto Rico,North America,18.3985,-66.061,1809800 +San Juan Atitán,Guatemala,North America,15.4333,-91.6333,26031 +San Juan Bautista,Paraguay,South America,-26.68,-57.15,16593 +San Juan Cancuc,Mexico,North America,16.9333,-92.3833,37948 +San Juan Chamelco,Guatemala,North America,15.4257,-90.3263,61634 +San Juan Cotzal,Guatemala,North America,15.4353,-91.0356,36298 +San Juan Ermita,Guatemala,North America,14.7667,-89.4333,18751 +San Juan Evangelista,Mexico,North America,17.8833,-95.1333,33929 +San Juan Ixcoy,Guatemala,North America,15.6,-91.45,31081 +San Juan La Laguna,Guatemala,North America,14.7,-91.2833,13400 +San Juan Nepomuceno,Colombia,South America,9.95,-75.0833,34012 +San Juan Sacatepéquez,Guatemala,North America,14.7189,-90.6442,284798 +San Juan Tecuaco,Guatemala,North America,14.0836,-90.2664,12825 +San Juan de Arama,Colombia,South America,3.3736,-73.8767,8728 +San Juan de Betulia,Colombia,South America,9.2756,-75.2456,13437 +San Juan de Limay,Nicaragua,North America,13.1667,-86.6167,13455 +San Juan de Río Coco,Nicaragua,North America,13.55,-86.1667,21114 +San Juan de Urabá,Colombia,South America,8.7667,-76.5333,27149 +San Juan de los Lagos,Mexico,North America,21.2458,-102.3308,69725 +San Juan de los Morros,Venezuela,South America,9.901,-67.354,87739 +San Juan del Cesar,Colombia,South America,10.7667,-73.0,39472 +San Juan del Rio del Centauro del Norte,Mexico,North America,24.7667,-104.4667,11855 +San Juan del Río,Mexico,North America,20.3833,-99.9833,301237 +San Juan del Sur,Nicaragua,North America,11.25,-85.8667,14741 +San Juan y Martínez,Cuba,North America,22.2667,-83.8333,44344 +San Juanito de Escobedo,Mexico,North America,20.8,-104.0,9433 +San Julián,Mexico,North America,21.0167,-102.1667,15890 +San Justo,Argentina,South America,-34.6833,-58.55,136604 +San Kamphaeng,Thailand,Asia,18.7449,99.1195,17857 +San Lawrenz,Malta,Europe,36.055,14.2042,748 +San Lorenzo,Nicaragua,North America,12.3667,-85.6667,23666 +San Lucas,Nicaragua,North America,13.4167,-86.6,12975 +San Lucas Sacatepéquez,Guatemala,North America,14.6167,-90.65,28819 +San Lucas Tolimán,Guatemala,North America,14.6333,-91.1333,31805 +San Luis,Argentina,South America,-33.3,-66.3333,169947 +San Luis Acatlán,Mexico,North America,16.5333,-98.05,41884 +San Luis Jilotepeque,Guatemala,North America,14.65,-89.7333,26874 +San Luis Potosí,Mexico,North America,22.1511,-100.9761,772828 +San Luis Río Colorado,Mexico,North America,32.4767,-114.7625,176685 +San Luis de Sincé,Colombia,South America,9.25,-75.15,35022 +San Luis de la Paz,Mexico,North America,21.3,-100.5167,51894 +San Luis del Palmar,Argentina,South America,-27.5167,-58.5667,15347 +San Manuel Chaparrón,Guatemala,North America,14.5167,-89.7667,9126 +San Marcos,Colombia,South America,8.6611,-75.1347,59976 +San Marino,San Marino,Europe,43.9346,12.4473,4040 +San Martin Texmelucan de Labastida,Mexico,North America,19.2833,-98.4333,163922 +San Martín,Argentina,South America,-33.0806,-68.4706,79476 +San Martín Jilotepeque,Guatemala,North America,14.7833,-90.8,94901 +San Martín Sacatepéquez,Guatemala,North America,14.8246,-91.6425,33826 +San Martín Zapotitlán,Guatemala,North America,14.6,-91.6,13094 +San Martín de Loba,Colombia,South America,8.8333,-73.9167,14504 +San Martín de las Pirámides,Mexico,North America,19.7333,-98.8167,21511 +San Martín de los Andes,Argentina,South America,-40.1667,-71.35,36913 +San Mateo,United States,North America,37.5522,-122.3121,103555 +San Mateo Atenco,Mexico,North America,19.2667,-99.5333,66740 +San Mateo Ixtatán,Guatemala,North America,15.8333,-91.4833,60203 +San Miguel,El Salvador,North America,13.4814,-88.1775,290612 +San Miguel Acatán,Guatemala,North America,15.7,-91.6167,36245 +San Miguel Chicaj,Guatemala,North America,15.1,-90.4,36845 +San Miguel Dueñas,Guatemala,North America,14.5167,-90.8,15562 +San Miguel Ixtahuacán,Guatemala,North America,15.25,-91.75,52452 +San Miguel Panán,Guatemala,North America,14.5333,-91.3667,12475 +San Miguel Sigüilá,Guatemala,North America,14.9,-91.6167,9869 +San Miguel de Allende,Mexico,North America,20.9142,-100.7436,69811 +San Miguel de Salcedo,Ecuador,South America,-1.05,-78.5833,16751 +San Miguel de Tucumán,Argentina,South America,-26.8167,-65.2167,605767 +San Miguelito,Panama,North America,9.0503,-79.4707,315019 +San Nicolás,Cuba,North America,22.7819,-81.9069,21563 +San Nicolás de los Arroyos,Argentina,South America,-33.3333,-60.2167,160000 +San Nicolás de los Garza,Mexico,North America,25.75,-100.2833,443273 +San Nicolás de los Ranchos,Mexico,North America,19.0667,-98.4833,11734 +San Onofre,Colombia,South America,9.7333,-75.5333,51991 +San Pablo,Guatemala,North America,14.9333,-92.0,52217 +San Pablo Huixtepec,Mexico,North America,16.8167,-96.7833,9313 +San Pablo Jocopilas,Guatemala,North America,14.5833,-91.45,22490 +San Pablo Villa de Mitla,Mexico,North America,16.917,-96.4,7547 +San Pedro,Argentina,South America,-33.6792,-59.6669,75616 +San Pedro Ayampuc,Guatemala,North America,14.7833,-90.45,67946 +San Pedro Carchá,Guatemala,North America,15.4667,-90.3,255006 +San Pedro Garza García,Mexico,North America,25.6667,-100.3,122659 +San Pedro Huamelula,Mexico,North America,16.0167,-95.6667,10014 +San Pedro Jicayán,Mexico,North America,16.4167,-97.9833,12387 +San Pedro La Laguna,Guatemala,North America,14.694,-91.272,11828 +San Pedro Mixtepec,Mexico,North America,16.0,-97.1169,48336 +San Pedro Necta,Guatemala,North America,15.5,-91.7667,41562 +San Pedro Pinula,Guatemala,North America,14.6667,-89.85,78244 +San Pedro Pochutla,Mexico,North America,15.7476,-96.4666,13685 +San Pedro Sacatepéquez,Guatemala,North America,14.9667,-91.7667,85671 +San Pedro Sula,Honduras,North America,15.5062,-88.0249,719064 +San Pedro de Coche,Venezuela,South America,10.7772,-63.9311,8756 +San Pedro de Macorís,Dominican Republic,North America,18.4572,-69.3061,195307 +San Pedro de Urabá,Colombia,South America,8.2833,-76.3833,32328 +San Pedro de Ycuamandiyú,Paraguay,South America,-24.09,-57.08,35021 +San Pelayo,Colombia,South America,8.9667,-75.85,45396 +San Pédro Jocopilas,Guatemala,North America,15.0953,-91.1508,37990 +San Rafael,Argentina,South America,-34.6,-68.3333,215020 +San Rafael La Independencia,Guatemala,North America,15.7167,-91.5333,18733 +San Rafael Las Flores,Guatemala,North America,14.4814,-90.1733,13850 +San Rafael Petzal,Guatemala,North America,15.4167,-91.6667,13786 +San Rafael Pie de la Cuesta,Guatemala,North America,14.9333,-91.9167,18352 +San Rafael del Norte,Nicaragua,North America,13.2167,-86.1167,17789 +San Rafael del Sur,Nicaragua,North America,11.85,-86.45,42417 +San Raimundo,Guatemala,North America,14.7647,-90.5956,38891 +San Ramón,Nicaragua,North America,12.9167,-85.8333,30682 +San Ramón de la Nueva Orán,Argentina,South America,-23.1333,-64.3333,76070 +San Roque,Colombia,South America,6.4853,-75.0197,16789 +San Salvador,El Salvador,North America,13.6989,-89.1914,1538525 +San Salvador de Jujuy,Argentina,South America,-24.1833,-65.3,321789 +San Sebastián,Guatemala,North America,14.5667,-91.65,30724 +San Sebastián Coatán,Guatemala,North America,15.7333,-91.5667,25935 +San Sebastián Huehuetenango,Guatemala,North America,15.3833,-91.5833,43261 +San Sebastián de Buenavista,Colombia,South America,9.2403,-74.3556,18865 +San Sebastián de Mariquita,Colombia,South America,5.25,-74.9167,32642 +San Sebastián de Yalí,Nicaragua,North America,13.3,-86.1833,26979 +San Tan Valley,United States,North America,33.1786,-111.5629,105231 +San Vicente,El Salvador,North America,13.6453,-88.7842,53213 +San Vicente Pacaya,Guatemala,North America,14.4161,-90.6392,18417 +San Vicente de Chucurí,Colombia,South America,6.8833,-73.4167,28084 +San Vicente del Caguán,Colombia,South America,2.1167,-74.7667,72949 +San Zenón,Colombia,South America,9.245,-74.4992,9107 +San Ġiljan,Malta,Europe,35.9186,14.49,13792 +San Ġwann,Malta,Europe,35.9094,14.4786,14720 +San-Pédro,Côte d’Ivoire,Africa,4.7704,-6.64,390654 +Sanaa,Yemen,Asia,15.3483,44.2064,2545000 +Sanana,Indonesia,Asia,-2.0545,125.9754,32831 +Sanandaj,Iran,Asia,35.3114,46.9961,412767 +Sananduva,Brazil,South America,-27.9497,-51.8067,16328 +Sanarate,Guatemala,North America,14.795,-90.1922,42422 +Sanare,Venezuela,South America,9.7822,-69.7931,48764 +Sancti Spíritus,Cuba,North America,21.9339,-79.4439,138504 +Sandnes,Norway,Europe,58.85,5.7333,58694 +Sandomierz,Poland,Europe,50.6833,21.75,22189 +Sandoná,Colombia,South America,1.2833,-77.4667,25220 +Sandvika,Norway,Europe,59.89,10.5264,115543 +Sandviken,Sweden,Europe,60.6167,16.7667,22965 +Sandy Springs,United States,North America,33.9366,-84.3703,107198 +Sangar,Russia,Asia,63.9241,127.4739,4633 +Sangerhausen,Germany,Europe,51.4667,11.3,25300 +Sanghar,Pakistan,Asia,26.0442,68.9536,434087 +Sangolquí,Ecuador,South America,-0.3344,-78.4475,96647 +Sangre Grande,Trinidad and Tobago,North America,10.5667,-61.1333,18127 +Sangão,Brazil,South America,-28.6378,-49.1289,12882 +Sangīn,Afghanistan,Asia,32.0733,64.8339,20000 +Sanharó,Brazil,South America,-8.3608,-36.5658,18624 +Sanhe,China,Asia,40.0167,117.0833,760107 +Sanjiang,China,Asia,24.7265,112.2884,39398 +Sanjiaocheng,China,Asia,36.8908,100.9997,11196 +Sankt Gallen,Switzerland,Europe,47.4233,9.3772,78213 +Sankt Johann im Pongau,Austria,Europe,47.35,13.2,11235 +Sankt Pölten,Austria,Europe,48.2,15.6167,55514 +Sankt Veit an der Glan,Austria,Europe,46.7667,14.3667,12547 +Sankt Wendel,Germany,Europe,49.4667,7.1667,25503 +Sanmenxia,China,Asia,34.7732,111.2004,2034872 +Sanming,China,Asia,26.2634,117.6389,2486450 +Sannat,Malta,Europe,36.0244,14.2458,2117 +Sanniquellie,Liberia,Africa,7.3622,-8.7061,11415 +Sanok,Poland,Europe,49.55,22.2167,35546 +Sansare,Guatemala,North America,14.75,-90.1167,13674 +Sanski Most,Bosnia and Herzegovina,Europe,44.7667,16.6667,16913 +Sant Julià de Lòria,Andorra,Europe,42.465,1.4911,9915 +Santa Adélia,Brazil,South America,-21.2428,-48.8039,14018 +Santa Ana,United States,North America,33.7367,-117.8819,311639 +Santa Ana Chiautempan,Mexico,North America,19.3167,-98.1833,46776 +Santa Ana Huista,Guatemala,North America,15.6833,-91.8167,10687 +Santa Ana Jilotzingo,Mexico,North America,19.4,-99.2333,19013 +Santa Ana Maya,Mexico,North America,20.0,-101.0167,12466 +Santa Ana Nextlalpan,Mexico,North America,19.7167,-99.0667,34374 +Santa Apolonia,Guatemala,North America,14.7833,-90.9667,23751 +Santa Barbara,United States,North America,34.4285,-119.7202,194950 +Santa Branca,Brazil,South America,-23.3969,-45.8839,13975 +Santa Brígida,Brazil,South America,-9.7358,-38.1258,14965 +Santa Bárbara,Guatemala,North America,15.3167,-91.6167,44809 +Santa Bárbara de Pinto,Colombia,South America,9.4333,-74.7,12610 +Santa Bárbara d’Oeste,Brazil,South America,-22.7539,-47.4139,183347 +Santa Catalina,Colombia,South America,10.6039,-75.2878,14039 +Santa Catarina Ixtahuacán,Guatemala,North America,14.8,-91.3667,61274 +Santa Catarina Mita,Guatemala,North America,14.45,-89.75,31709 +Santa Catarina Pinula,Guatemala,North America,14.5667,-90.5,87362 +Santa Cecília,Brazil,South America,-26.9608,-50.4269,15546 +Santa Clara,Cuba,North America,22.4067,-79.9531,247436 +Santa Clara La Laguna,Guatemala,North America,14.7167,-91.3,10390 +Santa Clara del Cobre,Mexico,North America,19.405,-101.638,38502 +Santa Clarita,United States,North America,34.4175,-118.4964,265529 +Santa Comba Dão,Portugal,Europe,40.3981,-8.1303,11597 +Santa Cruz,Spain,Europe,28.4667,-16.25,538000 +Santa Cruz Atizapán,Mexico,North America,19.1756,-99.4886,12984 +Santa Cruz Balanyá,Guatemala,North America,14.6833,-90.9167,10981 +Santa Cruz Cabrália,Brazil,South America,-16.2778,-39.025,29185 +Santa Cruz Muluá,Guatemala,North America,14.5833,-91.6167,16138 +Santa Cruz Naranjo,Guatemala,North America,14.3858,-90.3708,17875 +Santa Cruz Verapaz,Guatemala,North America,15.3667,-90.4333,35878 +Santa Cruz da Baixa Verde,Brazil,South America,-7.8208,-38.1528,11567 +Santa Cruz das Palmeiras,Brazil,South America,-21.8269,-47.2489,28864 +Santa Cruz de Los Taques,Venezuela,South America,11.824,-70.2509,41579 +Santa Cruz del Norte,Cuba,North America,23.1537,-81.9261,34907 +Santa Cruz del Quiché,Guatemala,North America,15.03,-91.15,99479 +Santa Cruz del Sur,Cuba,North America,20.7118,-77.9986,39919 +Santa Cruz do Capibaribe,Brazil,South America,-7.9573,-36.2047,98254 +Santa Cruz do Monte Castelo,Brazil,South America,-22.9528,-53.2969,8613 +Santa Cruz do Rio Pardo,Brazil,South America,-22.8989,-49.6328,46442 +Santa Cruz do Sul,Brazil,South America,-29.7178,-52.4258,133230 +Santa Elena,Ecuador,South America,-2.2267,-80.8583,54565 +Santa Elena de Uairén,Venezuela,South America,4.0,36.0,29795 +Santa Eulalia,Guatemala,North America,15.7333,-91.4586,51910 +Santa Fe,Argentina,South America,-31.6333,-60.7,405683 +Santa Fe de Antioquia,Colombia,South America,6.55,-75.8167,22613 +Santa Fé,Brazil,South America,-23.0378,-51.805,11378 +Santa Fé do Sul,Brazil,South America,-20.2108,-50.9258,34794 +Santa Genoveva de Docordó,Colombia,South America,4.2586,-77.3675,11579 +Santa Gertrudes,Brazil,South America,-22.4569,-47.53,23611 +Santa Helena,Brazil,South America,-2.2308,-45.3,41561 +Santa Helena de Goiás,Brazil,South America,-17.8136,-50.5969,38808 +Santa Inês,Brazil,South America,-3.6669,-45.38,85014 +Santa Isabel,Brazil,South America,-23.3156,-46.2214,57966 +Santa Isabel Cholula,Mexico,North America,19.0,-98.3667,11498 +Santa Isabel do Ivaí,Brazil,South America,-23.0053,-53.1878,8912 +Santa Isabel do Pará,Brazil,South America,-1.2989,-48.1608,73019 +Santa Isabel do Rio Negro,Brazil,South America,-0.4139,-65.0192,25865 +Santa Juliana,Brazil,South America,-19.3089,-47.5242,15734 +Santa Leopoldina,Brazil,South America,-20.0987,-40.5269,12197 +Santa Lucia La Reforma,Guatemala,North America,15.1333,-91.2333,25668 +Santa Lucía,Venezuela,South America,10.2606,-66.6639,112357 +Santa Lucía Cotzumalguapa,Guatemala,North America,14.35,-91.0167,127683 +Santa Lucía Milpas Altas,Guatemala,North America,14.5833,-90.6833,17422 +Santa Lucía Utatlán,Guatemala,North America,14.7667,-91.2667,21790 +Santa Luzia,Brazil,South America,-19.7697,-43.8514,220444 +Santa Luzia do Itanhy,Brazil,South America,-11.3508,-37.4478,13616 +Santa Margarida,Brazil,South America,-20.3839,-42.2508,16395 +Santa Maria,Brazil,South America,-29.6839,-53.8069,243611 +Santa Maria Madalena,Brazil,South America,-21.955,-42.0078,10232 +Santa Maria da Boa Vista,Brazil,South America,-8.8089,-39.825,40578 +Santa Maria da Vitória,Brazil,South America,-13.3978,-44.1978,38604 +Santa Maria das Barreiras,Brazil,South America,-8.855,-49.7219,16548 +Santa Maria de Itabira,Brazil,South America,-19.4489,-43.1128,10346 +Santa Maria do Pará,Brazil,South America,-1.3519,-47.5758,24624 +Santa Maria do Suaçuí,Brazil,South America,-18.19,-42.4139,12788 +Santa Mariana,Brazil,South America,-23.1499,-50.52,11066 +Santa Marta,Colombia,South America,11.2419,-74.2053,515556 +Santa María,Argentina,South America,-26.6833,-66.0333,26929 +Santa María Chilchotla,Mexico,North America,18.2333,-96.8167,20328 +Santa María Chiquimula,Guatemala,North America,15.0292,-91.3294,58918 +Santa María Colotepec,Mexico,North America,15.8833,-96.9167,24076 +Santa María Huatulco,Mexico,North America,15.85,-96.3333,45680 +Santa María Ixhuatán,Guatemala,North America,14.1833,-90.2667,26721 +Santa María Jacatepec,Mexico,North America,17.85,-96.2,9474 +Santa María Jalapa del Marqués,Mexico,North America,16.4401,-95.4434,13148 +Santa María Tonameca,Mexico,North America,15.7458,-96.5472,25130 +Santa María Xadani,Mexico,North America,16.3667,-95.0167,8795 +Santa María de Ipire,Venezuela,South America,8.8156,-65.3225,13161 +Santa María de Jesús,Guatemala,North America,14.4933,-90.7094,26769 +Santa María del Tule,Mexico,North America,17.0465,-96.6363,8918 +Santa Quitéria,Brazil,South America,-4.3319,-40.1569,40183 +Santa Quitéria do Maranhão,Brazil,South America,-3.5158,-42.5469,23957 +Santa Rita,Brazil,South America,-7.1139,-34.9778,149910 +Santa Rita do Passa Quatro,Brazil,South America,-21.71,-47.4778,24833 +Santa Rita do Sapucaí,Brazil,South America,-22.2519,-45.7028,40635 +Santa Rosa,United States,North America,38.4458,-122.7067,299986 +Santa Rosa de Cabal,Colombia,South America,4.8667,-75.6167,73028 +Santa Rosa de Copán,Honduras,North America,14.7675,-88.7781,61083 +Santa Rosa de Lima,Guatemala,North America,14.3833,-90.3,21415 +Santa Rosa de Osos,Colombia,South America,6.65,-75.4667,37435 +Santa Rosa de Río Primero,Argentina,South America,-31.15,-63.3833,10873 +Santa Rosa de Viterbo,Brazil,South America,-21.4728,-47.3628,23411 +Santa Rosa del Peñón,Nicaragua,North America,12.8,-86.3667,9529 +Santa Rosa del Sur,Colombia,South America,7.9667,-74.05,42003 +Santa Rosalía,Mexico,North America,27.3389,-112.2669,14357 +Santa Sylvina,Argentina,South America,-27.7833,-61.15,9040 +Santa Tecla,El Salvador,North America,13.6731,-89.2406,139908 +Santa Teresa,Brazil,South America,-19.9358,-40.6,20622 +Santa Teresa del Tuy,Venezuela,South America,10.2333,-66.6639,260899 +Santa Teresinha,Brazil,South America,-12.7719,-39.5228,10441 +Santa Teresinha (2),Brazil,South America,-7.3778,-37.48,10244 +Santa Terezinha de Goiás,Brazil,South America,-14.4378,-49.7058,10645 +Santa Venera,Malta,Europe,35.8897,14.4778,6932 +Santa Vitória,Brazil,South America,-18.8389,-50.1208,20973 +Santaluz,Brazil,South America,-11.2558,-39.375,37834 +Santana,Brazil,South America,-0.035,-51.175,80439 +Santana de Parnaíba,Brazil,South America,-23.4439,-46.9178,154105 +Santana do Acaraú,Brazil,South America,-3.4608,-40.2119,30628 +Santana do Cariri,Brazil,South America,-7.1878,-39.7369,16954 +Santana do Ipanema,Brazil,South America,-9.3778,-37.245,46220 +Santana do Livramento,Brazil,South America,-30.8908,-55.5328,84421 +Santana do Manhuaçu,Brazil,South America,-20.1078,-41.925,8987 +Santana do Matos,Brazil,South America,-5.9578,-36.6558,12456 +Santana do Paraíso,Brazil,South America,-19.3639,-42.5689,44800 +Santander,Spain,Europe,43.4628,-3.805,174101 +Santander de Quilichao,Colombia,South America,3.0167,-76.4833,180000 +Santanópolis,Brazil,South America,-12.0169,-38.8669,8716 +Santarém,Portugal,Europe,39.2333,-8.6833,62200 +Santiago,Chile,South America,-33.4372,-70.6506,7171000 +Santiago Atitlán,Guatemala,North America,14.6333,-91.2333,44854 +Santiago Chimaltenango,Guatemala,North America,15.4814,-91.7261,10507 +Santiago Ixcuintla,Mexico,North America,21.811,-105.2079,18365 +Santiago Papasquiaro,Mexico,North America,25.0439,-105.4192,26121 +Santiago Sacatepéquez,Guatemala,North America,14.6531,-90.6525,36982 +Santiago Suchilquitongo,Mexico,North America,17.25,-96.8833,10397 +Santiago Tangamandapio,Mexico,North America,19.95,-102.4333,10463 +Santiago Tianguistenco,Mexico,North America,19.1797,-99.4706,64365 +Santiago Tulantepec,Mexico,North America,20.0397,-98.3575,16078 +Santiago Tuxtla,Mexico,North America,18.4653,-95.3025,57466 +Santiago de Compostela,Spain,Europe,42.8778,-8.5444,99536 +Santiago de Cuba,Cuba,North America,20.0217,-75.8294,444851 +Santiago de Tolú,Colombia,South America,9.5333,-75.5833,35670 +Santiago del Estero,Argentina,South America,-27.7833,-64.2667,911506 +Santiago do Cacém,Portugal,Europe,38.0167,-8.7,29749 +Santo Amaro,Brazil,South America,-12.5469,-38.7119,61702 +Santo Amaro da Imperatriz,Brazil,South America,-27.6878,-48.7789,27272 +Santo Anastácio,Brazil,South America,-21.9769,-51.6519,17963 +Santo André,Brazil,South America,-23.6533,-46.528,748919 +Santo António,Sao Tome and Principe,Africa,1.6367,7.4178,2620 +Santo Antônio,Brazil,South America,-29.8178,-50.52,37035 +Santo Antônio da Platina,Brazil,South America,-23.295,-50.0769,44369 +Santo Antônio de Jesus,Brazil,South America,-12.9692,-39.2611,103055 +Santo Antônio de Posse,Brazil,South America,-22.6058,-46.9189,23244 +Santo Antônio de Pádua,Brazil,South America,-21.5389,-42.18,41325 +Santo Antônio do Amparo,Brazil,South America,-20.9458,-44.9189,17285 +Santo Antônio do Descoberto,Brazil,South America,-15.94,-48.255,51897 +Santo Antônio do Içá,Brazil,South America,-3.1019,-67.94,28211 +Santo Antônio do Jacinto,Brazil,South America,-16.5339,-40.1758,10327 +Santo Antônio do Leverger,Brazil,South America,-15.8656,-56.0767,16999 +Santo Antônio do Monte,Brazil,South America,-20.0869,-45.2939,27295 +Santo Antônio do Sudoeste,Brazil,South America,-26.07,-53.7228,23673 +Santo Antônio do Tauá,Brazil,South America,-1.1519,-48.1294,31918 +Santo Antônio dos Lopes,Brazil,South America,-4.8689,-44.36,14304 +Santo Augusto,Brazil,South America,-27.8508,-53.7769,13902 +Santo Domingo,Dominican Republic,North America,18.4764,-69.8933,1128678 +Santo Domingo Este,Dominican Republic,North America,18.4855,-69.8734,948855 +Santo Domingo Suchitepéquez,Guatemala,North America,14.4667,-91.4833,48825 +Santo Domingo Tehuantepec,Mexico,North America,16.3244,-95.2389,64639 +Santo Domingo Xenacoj,Guatemala,North America,14.6822,-90.7067,14455 +Santo Domingo de los Colorados,Ecuador,South America,-0.2542,-79.1719,334826 +Santo Estêvão,Brazil,South America,-12.43,-39.2508,41145 +Santo Tirso,Portugal,Europe,41.3425,-8.4775,71530 +Santo Tomás,Colombia,South America,10.7667,-74.9167,32000 +Santo Tomás La Unión,Guatemala,North America,14.6333,-91.3833,12800 +Santo Tomás de los Plátanos,Mexico,North America,19.1817,-100.2589,9682 +Santo Tomé,Argentina,South America,-28.55,-56.05,23299 +Santo Ângelo,Brazil,South America,-28.2989,-54.2628,76917 +Santos,Brazil,South America,-23.9608,-46.3339,418608 +Santos Dumont,Brazil,South America,-21.4567,-43.5525,42406 +Santuario,Colombia,South America,6.1375,-75.2639,35422 +Sanya,China,Asia,18.2533,109.5036,1031396 +Sapanca,Turkey,Europe,40.6906,30.2703,46080 +Saparua,Indonesia,Asia,-3.5749,128.647,34460 +Sapeaçu,Brazil,South America,-12.7278,-39.1819,17963 +Sapele,Nigeria,Africa,5.9,5.6667,242652 +Sapiranga,Brazil,South America,-29.6378,-51.0069,75648 +Sapna,Bosnia and Herzegovina,Europe,44.4917,19.0028,12136 +Saponé,Burkina Faso,Africa,12.0528,-1.6036,21148 +Sapouy,Burkina Faso,Africa,11.5544,-1.7736,26345 +Sapporo,Japan,Asia,43.0619,141.3544,1959313 +Sapucaia,Brazil,South America,-21.995,-42.9139,17729 +Sapé,Brazil,South America,-7.095,-35.2328,51306 +Saqqez,Iran,Asia,36.2411,46.2706,165258 +Saquarema,Brazil,South America,-22.92,-42.51,89559 +Sar-e Pul,Afghanistan,Asia,36.2214,65.9278,51075 +Saraburi,Thailand,Asia,14.5286,100.9114,60809 +Sarajevo,Bosnia and Herzegovina,Europe,43.8564,18.4131,244000 +Sarakhs,Iran,Asia,36.5425,61.1589,42179 +Sarandi,Brazil,South America,-27.9439,-52.9228,22851 +Saransk,Russia,Asia,54.1833,45.1833,310898 +Sarapul,Russia,Asia,56.4667,53.8,89442 +Sarapuí,Brazil,South America,-23.6408,-47.825,10369 +Sarare,Venezuela,South America,9.7839,-69.1611,12535 +Saratov,Russia,Asia,51.53,46.035,838042 +Saravena,Colombia,South America,6.9556,-71.8722,55554 +Saray,Turkey,Europe,41.4411,27.9216,50766 +Sarayköy,Turkey,Europe,37.9264,28.9267,30834 +Sarayönü,Turkey,Europe,38.2661,32.4064,27771 +Sarbīsheh,Iran,Asia,32.5775,59.7961,8715 +Sarcelles,France,Europe,48.9956,2.3808,58576 +Sardasht,Iran,Asia,36.1553,45.48,46412 +Sardinata,Colombia,South America,8.0833,-72.8,22583 +Sargodha,Pakistan,Asia,32.0836,72.6711,659862 +Sarh,Chad,Africa,9.15,18.3833,103269 +Sariwŏn,"Korea, North",Asia,38.5064,125.7597,307764 +Sarkad,Hungary,Europe,46.74,21.3778,9093 +Sarlat-la-Canéda,France,Europe,44.8928,1.2153,8786 +Sarmiento,Argentina,South America,-45.6,-69.0833,10858 +Sarmīn,Syria,Asia,35.904,36.725,14530 +Sarnen,Switzerland,Europe,46.8969,8.2469,10368 +Sarny,Ukraine,Europe,51.3372,26.6058,28626 +Sarpol-e Z̄ahāb,Iran,Asia,34.4589,45.8614,34632 +Sarpsborg,Norway,Europe,59.2839,11.1096,54678 +Sarqan,Kazakhstan,Asia,45.41,79.9186,14305 +Sarrebourg,France,Europe,48.74,7.05,12274 +Sarreguemines,France,Europe,49.11,7.07,20324 +Saruhanlı,Turkey,Europe,38.7342,27.5658,56523 +Sarvestān,Iran,Asia,29.2739,53.2203,18187 +Sarykemer,Kazakhstan,Asia,43.0106,71.5172,24314 +Sarzedo,Brazil,South America,-20.035,-44.145,36844 +Sarāb,Iran,Asia,37.9422,47.5361,42057 +Sarābleh,Iran,Asia,33.7683,46.5619,12393 +Sarāqib,Syria,Asia,35.8645,36.8059,39901 +Sarāvān,Iran,Asia,27.3708,62.3325,60014 +Sarāyān,Iran,Asia,33.8636,58.5211,13795 +Sarıgöl,Turkey,Europe,38.2381,28.6961,35537 +Sarıkamış,Turkey,Europe,40.3381,42.5731,15260 +Sarıkaya,Turkey,Europe,39.4936,35.3769,18457 +Sarıoğlan,Turkey,Europe,39.0769,35.9667,13396 +Sarıyer,Turkey,Europe,41.1669,29.0572,350454 +Sarız,Turkey,Europe,38.4792,36.5006,9282 +Sarıçam,Turkey,Europe,37.0333,35.4167,221733 +Sasaima,Colombia,South America,4.965,-74.4347,9807 +Saskatoon,Canada,North America,52.1333,-106.6833,266141 +Saskylakh,Russia,Asia,71.9653,114.0922,2345 +Sason,Turkey,Europe,38.3803,41.3953,12696 +Sasovo,Russia,Asia,54.35,41.9167,21220 +Sassandra,Côte d’Ivoire,Africa,4.9504,-6.0833,87945 +Sassari,Italy,Europe,40.7267,8.5592,121021 +Sassenheim,Netherlands,Europe,52.2258,4.5225,14886 +Sastamala,Finland,Europe,61.3417,22.9083,23515 +Satka,Russia,Asia,55.05,59.0333,42062 +Sattahip,Thailand,Asia,12.6664,100.9007,23418 +Satu Mare,Romania,Europe,47.79,22.89,91520 +Satuba,Brazil,South America,-9.5628,-35.8239,24278 +Satuek,Thailand,Asia,15.297,103.2919,11444 +Satun,Thailand,Asia,6.6147,100.0681,21498 +Saubara,Brazil,South America,-12.7378,-38.7689,11438 +Sauce,Argentina,South America,-30.0667,-58.7667,9115 +Saucillo,Mexico,North America,28.0333,-105.2833,11004 +Saudade,Brazil,South America,-26.9239,-53.0028,10265 +Saulkrasti,Latvia,Europe,57.2636,24.4164,3149 +Saumlaki,Indonesia,Asia,-7.975,131.3075,149790 +Saumur,France,Europe,47.26,-0.0769,26074 +Saurimo,Angola,Africa,-9.65,20.4,393000 +Savalou,Benin,Africa,7.9333,1.9667,35433 +Savanna-la-Mar,Jamaica,North America,18.2167,-78.1333,22600 +Savannah,United States,North America,32.0286,-81.1821,316446 +Savannakhet,Laos,Asia,16.55,104.75,124000 +Savaştepe,Turkey,Europe,39.3839,27.6547,16765 +Saverne,France,Europe,48.74,7.36,11323 +Savona,Italy,Europe,44.3081,8.4811,58194 +Savonlinna,Finland,Europe,61.8681,28.8861,31842 +Savur,Turkey,Europe,37.5375,40.8892,24821 +Sawankhalok,Thailand,Asia,17.316,99.8319,17148 +Sawrān,Iraq,Asia,36.6532,44.5441,350000 +Sayaxché,Guatemala,North America,16.5167,-90.1833,99787 +Saymayl,Iraq,Asia,36.8582,42.8482,71557 +Saynshand,Mongolia,Asia,44.8917,110.1367,19891 +Sayula,Mexico,North America,19.8667,-103.6167,36778 +Sayula de Alemán,Mexico,North America,17.8833,-94.95,32721 +Sayyid Şādiq,Iraq,Asia,35.35,45.8667,61600 +Say’ūn,Yemen,Asia,15.943,48.7873,68747 +Saïda,Algeria,Africa,34.8333,0.15,128413 +Saúde,Brazil,South America,-10.9408,-40.4178,10478 +Saūmalköl,Kazakhstan,Asia,53.2914,68.1094,9288 +Saḩāb,Jordan,Asia,31.8667,36.0,169434 +Sa‘ādat Shahr,Iran,Asia,30.0772,53.1328,17131 +Scarborough,Trinidad and Tobago,North America,11.1833,-60.7375,17537 +Schaan,Liechtenstein,Europe,47.1667,9.5097,6039 +Schaffhausen,Switzerland,Europe,47.6965,8.6339,36587 +Schagen,Netherlands,Europe,52.7833,4.8,46553 +Schellenberg,Liechtenstein,Europe,47.2336,9.5481,1091 +Scherpenzeel,Netherlands,Europe,52.0792,5.4894,10128 +Schiedam,Netherlands,Europe,51.9167,4.4,79279 +Schinnen,Netherlands,Europe,50.9428,5.8894,12911 +Schleiz,Germany,Europe,50.5833,11.8167,8788 +Schleswig,Germany,Europe,54.5186,9.5653,25904 +Schoonhoven,Netherlands,Europe,51.9475,4.8486,11922 +Schroeder,Brazil,South America,-26.4128,-49.0728,20061 +Schwabach,Germany,Europe,49.3292,11.0208,41380 +Schwandorf,Germany,Europe,49.3283,12.11,30239 +Schwaz,Austria,Europe,47.35,11.7,13810 +Schwelm,Germany,Europe,51.2667,7.2667,28711 +Schwerin,Germany,Europe,53.6333,11.4167,98733 +Schwyz,Switzerland,Europe,47.0211,8.6536,15181 +Schwäbisch Hall,Germany,Europe,49.1122,9.7375,42743 +Scottsdale,United States,North America,33.6872,-111.8651,242169 +Scranton,United States,North America,41.4044,-75.6649,375462 +Seabra,Brazil,South America,-12.4189,-41.77,46160 +Seara,Brazil,South America,-27.1489,-52.3108,18620 +Seaside,United States,North America,36.6224,-121.8199,118398 +Seattle,United States,North America,47.6211,-122.3244,3555253 +Sebastião Laranjeiras,Brazil,South America,-14.5728,-42.94,9360 +Sebba,Burkina Faso,Africa,13.4333,0.5333,11298 +Seberi,Brazil,South America,-27.4778,-53.4028,11950 +Sedan,France,Europe,49.7034,4.9388,16727 +Seferhisar,Turkey,Europe,38.2,26.8333,60914 +Segezha,Russia,Asia,63.7333,34.3167,4845 +Segovia,Spain,Europe,40.9481,-4.1183,51525 +Seia,Portugal,Europe,40.4203,-7.7033,21755 +Seinäjoki,Finland,Europe,62.7917,22.8417,66271 +Seixal,Portugal,Europe,38.6428,-9.1061,184269 +Sejong,"Korea, South",Asia,36.487,127.2822,204088 +Selendi,Turkey,Europe,38.7444,28.8678,19203 +Selibe Phikwe,Botswana,Africa,-21.9667,27.9167,49724 +Selnica ob Dravi,Slovenia,Europe,46.55,15.495,1348 +Seltso,Russia,Asia,53.3678,34.0978,15906 +Selçuk,Turkey,Europe,37.95,27.3667,38151 +Selçuklu,Turkey,Europe,37.8814,32.4922,690667 +Semarang,Indonesia,Asia,-6.99,110.4225,1621384 +Sembabule,Uganda,Africa,-0.08,31.46,7800 +Semera,Ethiopia,Africa,11.7922,41.0086,50000 +Semey,Kazakhstan,Asia,50.4333,80.2667,350201 +Semikarakorsk,Russia,Asia,47.5167,40.8,21719 +Semič,Slovenia,Europe,45.6546,15.1783,2017 +Semnān,Iran,Asia,35.5811,53.3833,185129 +Semīrom,Iran,Asia,31.4094,51.5736,26260 +Sen Monorom,Cambodia,Asia,12.4558,107.1881,7500 +Sena Madureira,Brazil,South America,-9.0658,-68.6569,29420 +Senador Canedo,Brazil,South America,-16.7081,-49.0931,118451 +Senador Guiomard,Brazil,South America,-10.1497,-67.7374,19761 +Senador José Porfírio,Brazil,South America,-2.5908,-51.9539,22576 +Senador Pompeu,Brazil,South America,-5.5878,-39.3719,24266 +Senahú,Guatemala,North America,15.4164,-89.8203,98703 +Senaki,Georgia,Europe,42.2689,42.0678,21596 +Sendai,Japan,Asia,38.2682,140.8694,1061177 +Senec,Slovakia,Europe,48.2189,17.3997,20116 +Senftenberg,Germany,Europe,51.5167,14.0167,23282 +Senglea,Malta,Europe,35.8878,14.5169,2784 +Senguio,Mexico,North America,19.7328,-100.3522,19146 +Sengés,Brazil,South America,-24.1128,-49.4639,17270 +Senhor do Bonfim,Brazil,South America,-10.4614,-40.1894,79424 +Senhora dos Remédios,Brazil,South America,-21.0278,-43.5828,10384 +Senica,Slovakia,Europe,48.6806,17.3667,19430 +Senlis,France,Europe,49.2081,2.5875,15238 +Sens,France,Europe,48.1975,3.2877,27275 +Sensuntepeque,El Salvador,North America,13.8667,-88.6333,41216 +Senta,Serbia,Europe,45.9314,20.09,20302 +Sentani,Indonesia,Asia,-2.5636,140.5146,84648 +Seoul,"Korea, South",Asia,37.5667,126.9833,23016000 +Sepīdān,Iran,Asia,30.2594,51.9853,14633 +Serafimovich,Russia,Asia,49.5833,42.7333,8633 +Serafina Corêa,Brazil,South America,-28.7119,-51.935,16961 +Serang,Indonesia,Asia,-6.12,106.1503,613356 +Seremban,Malaysia,Asia,2.7105,101.94,419536 +Serere,Uganda,Africa,1.5,33.55,12700 +Sergiyev Posad,Russia,Asia,56.315,38.1367,98251 +Serik,Turkey,Europe,36.9167,31.1,139545 +Seringueiras,Brazil,South America,-11.7981,-63.0311,11171 +Serinhisar,Turkey,Europe,37.5806,29.2697,14355 +Serinyol,Turkey,Europe,36.3667,36.2,15804 +Seropédica,Brazil,South America,-22.7439,-43.7078,80596 +Serov,Russia,Asia,59.6,60.5667,93192 +Serowe,Botswana,Africa,-22.3833,26.7167,47447 +Serpa,Portugal,Europe,37.9333,-7.5833,15623 +Serpukhov,Russia,Asia,54.9167,37.4,133756 +Serra,Brazil,South America,-20.1289,-40.3078,321181 +Serra Azul,Brazil,South America,-21.3108,-47.5658,12746 +Serra Branca,Brazil,South America,-7.4828,-36.665,13614 +Serra Caiada,Brazil,South America,-6.1058,-35.7128,10125 +Serra Negra,Brazil,South America,-22.6119,-46.7008,29894 +Serra Preta,Brazil,South America,-12.16,-39.3319,17996 +Serra Talhada,Brazil,South America,-7.9858,-38.2958,92228 +Serra do Salitre,Brazil,South America,-19.1108,-46.69,11801 +Serrana,Brazil,South America,-21.2108,-47.5958,43909 +Serravalle,San Marino,Europe,43.9717,12.4817,10878 +Serrinha,Brazil,South America,-11.6642,-39.0075,83275 +Serrita,Brazil,South America,-7.9328,-39.2958,18207 +Serrolândia,Brazil,South America,-11.4158,-40.3019,13335 +Sertanópolis,Brazil,South America,-23.0589,-51.0358,15930 +Sertânia,Brazil,South America,-8.0706,-37.2658,32811 +Sertã,Portugal,Europe,39.8008,-8.1003,15880 +Sertãozinho,Brazil,South America,-21.1378,-47.99,126887 +Serui,Indonesia,Asia,-1.878,136.237,13568 +Sesimbra,Portugal,Europe,38.4437,-9.0996,49500 +Sesquilé,Colombia,South America,5.0453,-73.7972,13936 +Sete Barras,Brazil,South America,-24.3878,-47.9258,12730 +Sete Lagoas,Brazil,South America,-19.4658,-44.2469,241835 +Setubinha,Brazil,South America,-17.6,-42.1589,9917 +Setúbal,Portugal,Europe,38.5243,-8.8926,10000 +Sevastopol,Ukraine,Europe,44.605,33.5225,547820 +Sever do Vouga,Portugal,Europe,40.7167,-8.3667,12356 +Severo-Kuril’sk,Russia,Asia,50.6667,156.1167,2439 +Severodvinsk,Russia,Asia,64.5667,39.8667,155647 +Severomorsk,Russia,Asia,69.0667,33.4167,43394 +Severouralsk,Russia,Asia,60.15,59.9667,24029 +Seversk,Russia,Asia,56.6,84.85,105304 +Severínia,Brazil,South America,-20.8089,-48.8028,14576 +Sevilla,Spain,Europe,37.39,-5.99,1100000 +Sevnica,Slovenia,Europe,46.0092,15.3041,4533 +Seyah Cheshmeh,Iran,Asia,39.0619,44.3825,17804 +Seydişehir,Turkey,Europe,37.4183,31.8506,65465 +Seyhan,Turkey,Europe,36.9831,35.3328,795012 +Seyitgazi,Turkey,Europe,39.4456,30.6939,12587 +Sečanj,Serbia,Europe,45.3667,20.7725,10544 +Sežana,Slovenia,Europe,45.7034,13.8706,6037 +Sfax,Tunisia,Africa,34.74,10.76,341999 +Sfântu-Gheorghe,Romania,Europe,45.8636,25.7875,50080 +Shabestar,Iran,Asia,38.18,45.7031,22181 +Shacheng,China,Asia,40.4039,115.5189,114569 +Shadrinsk,Russia,Asia,56.1333,63.65,817 +Shagamu,Nigeria,Africa,6.8333,3.65,1132270 +Shah Alam,Malaysia,Asia,3.0667,101.55,584340 +Shahbā,Syria,Asia,32.8524,36.6285,16745 +Shahe,China,Asia,36.8549,114.5033,431746 +Shahr-e Bābak,Iran,Asia,30.1214,55.1228,43916 +Shahr-e Herāt,Iran,Asia,30.0547,54.3717,13032 +Shahr-e Kord,Iran,Asia,32.3322,50.8564,190441 +Shahr-e Qods,Iran,Asia,35.7103,51.1131,309605 +Shahreẕā,Iran,Asia,32.0122,51.8594,134952 +Shahrinav,Tajikistan,Asia,38.5667,68.3333,11123 +Shahriston,Tajikistan,Asia,39.7667,68.8167,22903 +Shahritus,Tajikistan,Asia,37.2667,68.1333,17200 +Shahrixon,Uzbekistan,Asia,40.7167,72.05,71400 +Shahrīār,Iran,Asia,35.6531,51.06,309607 +Shakhtarsk,Ukraine,Europe,48.0469,38.4686,48208 +Shakhty,Russia,Asia,47.7,40.2333,220802 +Shakhtīnsk,Kazakhstan,Asia,49.71,72.5872,37899 +Shakhunya,Russia,Asia,57.6667,46.6167,10200 +Shali,Russia,Asia,43.15,45.9,57060 +Shalqar,Kazakhstan,Asia,47.8333,59.6,27957 +Shanghai,China,Asia,31.2286,121.4747,24073000 +Shangpa,China,Asia,26.9195,98.9516,45658 +Shangrao,China,Asia,28.4551,117.9431,6435300 +Shangtianba,China,Asia,28.039,103.8584,15210 +Shangzhi,China,Asia,45.207,127.965,463358 +Shanhe,China,Asia,35.495,108.3519,40728 +Shanhu,China,Asia,29.6,120.8167,675226 +Shantou,China,Asia,23.354,116.682,4312192 +Shanwei,China,Asia,22.7872,115.3756,2672819 +Shaoguan,China,Asia,24.811,113.597,2855131 +Shaowu,China,Asia,27.3403,117.4925,273721 +Shaoxing,China,Asia,30.0511,120.5833,2521964 +Shaoyang,China,Asia,32.912,119.8526,1542600 +Shaqlāwah,Iraq,Asia,36.3964,44.3436,25000 +Shar,Kazakhstan,Asia,49.5858,81.0478,6412 +Sharbaqty,Kazakhstan,Asia,52.4999,78.1499,7915 +Sharjah,United Arab Emirates,Asia,25.3575,55.3908,1247749 +Sharya,Russia,Asia,58.3667,45.5,8000 +Sharypovo,Russia,Asia,55.5333,89.2,33961 +Shatura,Russia,Asia,55.5667,39.55,36714 +Shazhou,China,Asia,40.1376,94.6638,44870 +Shchelkovo,Russia,Asia,55.9167,38.0,135918 +Shchigry,Russia,Asia,51.8667,36.9333,14386 +Shchuchyn,Belarus,Europe,53.6167,24.7333,15371 +Shchëkino,Russia,Asia,54.0,37.5167,86088 +Shchūchīnsk,Kazakhstan,Asia,52.9333,70.2,45004 +Shebekino,Russia,Asia,50.4167,36.9,39680 +Shekhupura,Pakistan,Asia,31.7111,73.9878,473129 +Shelekhov,Russia,Asia,52.2167,104.1167,41998 +Shemonaīkha,Kazakhstan,Asia,50.6269,81.9109,19127 +Shengli,China,Asia,37.434,118.6746,2035338 +Shengping,China,Asia,28.4856,98.9114,10001 +Shenyang,China,Asia,41.8025,123.4281,7964000 +Shenze,China,Asia,38.1831,115.2037,48121 +Shenzhen,China,Asia,22.5415,114.0596,17619000 +Shenzhou,China,Asia,38.002,115.56,613000 +Shepetivka,Ukraine,Europe,50.1833,27.0667,40299 +Sherbrooke,Canada,North America,45.4,-71.9,172950 +Shetpe,Kazakhstan,Asia,44.1667,52.1167,13346 +Shibirghān,Afghanistan,Asia,36.665,65.752,175599 +Shibīn al Kawm,Egypt,Africa,30.55,31.0,267945 +Shihezi,China,Asia,44.3054,86.0804,717000 +Shijiazhuang,China,Asia,38.0425,114.51,4098243 +Shilka,Russia,Asia,51.85,116.0333,12663 +Shillong,India,Asia,25.5822,91.8944,143229 +Shilu,China,Asia,19.2792,109.0518,61258 +Shimanovsk,Russia,Asia,52.0,127.7,16178 +Shimla,India,Asia,31.1033,77.1722,206575 +Shinyanga,Tanzania,Africa,-3.6619,33.4231,139727 +Shishi,China,Asia,24.7167,118.7,685930 +Shiyan,China,Asia,32.629,110.7987,3209004 +Shiyeli,Kazakhstan,Asia,44.1789,66.7328,32480 +Shizuoka,Japan,Asia,34.9756,138.3828,685589 +Shklow,Belarus,Europe,54.2236,30.2864,14870 +Shkodër,Albania,Europe,42.0681,19.5119,76000 +Shofirkon Shahri,Uzbekistan,Asia,40.1167,64.5,14000 +Sholaqqorghan,Kazakhstan,Asia,43.765,69.1758,9213 +Shongzhy,Kazakhstan,Asia,43.5417,79.4703,17263 +Shostka,Ukraine,Europe,51.8667,33.4833,71966 +Shovot,Uzbekistan,Asia,41.65,60.3,19000 +Showţ,Iran,Asia,39.2197,44.7722,25381 +Shreveport,United States,North America,32.4653,-93.7955,278269 +Shtime,Kosovo,Europe,42.4331,21.0397,27324 +Shtërpcë,Kosovo,Europe,42.2394,21.0272,13858 +Shuangcheng,China,Asia,45.3503,126.28,130710 +Shuanghe,China,Asia,30.3746,106.7903,360000 +Shuangluan,China,Asia,40.9611,117.7962,18250 +Shuangqiao,China,Asia,35.067,112.602,727265 +Shuangyashan,China,Asia,46.6762,131.1416,1208803 +Shuiding,China,Asia,44.05,80.8667,27702 +Shulan,China,Asia,44.4059,126.9655,406744 +Shumen,Bulgaria,Europe,43.2746,26.9349,81207 +Shuozhou,China,Asia,39.3317,112.4329,1593444 +Shuya,Russia,Asia,56.85,41.3667,62400 +Shymkent,Kazakhstan,Asia,42.3167,69.5958,1184113 +Shyroke,Ukraine,Europe,47.6844,33.2728,9623 +Shādegān,Iran,Asia,30.6492,48.6653,41733 +Shāhrūd,Iran,Asia,36.4133,54.9781,165000 +Shāhīn Dezh,Iran,Asia,36.6806,46.5681,34204 +Shāhīn Shahr,Iran,Asia,32.8606,51.5531,173329 +Shāzand,Iran,Asia,33.9292,49.4103,21181 +Shīn,Syria,Asia,34.7786,36.4237,13020 +Shīrvān,Iran,Asia,37.4019,57.9281,82689 +Shīrāz,Iran,Asia,29.61,52.5425,1565572 +Shū,Kazakhstan,Asia,43.5983,73.7614,35000 +Shūsh,Iran,Asia,32.1947,48.2475,77148 +Shūshtar,Iran,Asia,32.045,48.8594,101878 +Shŭrobod,Tajikistan,Asia,37.8403,70.0419,10700 +Si Racha,Thailand,Asia,13.174,100.93,327172 +Si Sa Ket,Thailand,Asia,15.1069,104.3294,40117 +Si Satchanalai,Thailand,Asia,17.5203,99.764,15691 +Si Wilai,Thailand,Asia,18.1819,103.7479,11090 +Siachoque,Colombia,South America,5.5,-73.2333,8964 +Sialkot City,Pakistan,Asia,32.4925,74.5311,591668 +Sibanicú,Cuba,North America,21.2389,-77.5211,28930 +Sibaté,Colombia,South America,4.4908,-74.2594,38412 +Sibay,Russia,Asia,52.7,58.65,56514 +Sibilia,Guatemala,North America,15.0,-91.6167,9071 +Sibinal,Guatemala,North America,15.1342,-92.05,17069 +Sibiti,Congo (Brazzaville),Africa,-3.685,13.3511,32296 +Sibiu,Romania,Europe,45.7928,24.1519,134308 +Sibundoy,Colombia,South America,1.2033,-76.9192,14104 +Sibut,Central African Republic,Africa,5.7333,19.0833,22419 +Siderópolis,Brazil,South America,-28.5978,-49.4244,14092 +Sidi Bel Abbès,Algeria,Africa,35.1939,-0.6414,212935 +Sidi Bouzid,Tunisia,Africa,35.0381,9.4858,52339 +Sidon,Lebanon,Asia,33.5606,35.3758,163554 +Sidrolândia,Brazil,South America,-20.9319,-54.9608,47118 +Siedlce,Poland,Europe,52.165,22.2714,76357 +Siegburg,Germany,Europe,50.8014,7.2044,42025 +Siegen,Germany,Europe,50.8833,8.0167,102114 +Siem Reap,Cambodia,Asia,13.3622,103.8597,147866 +Siemianowice Śląskie,Poland,Europe,50.2758,18.9858,63657 +Siemiatycze,Poland,Europe,52.4272,22.8625,13782 +Siena,Italy,Europe,43.3183,11.3314,52812 +Sieradz,Poland,Europe,51.6,18.75,40014 +Sierpc,Poland,Europe,52.8833,19.6667,17308 +Sierra Colorada,Argentina,South America,-40.5833,-67.8,1542 +Sievierodonetsk,Ukraine,Europe,48.9481,38.4933,99067 +Sigmaringen,Germany,Europe,48.0869,9.2167,18127 +Siguiri,Guinea,Africa,11.4167,-9.1667,183875 +Sigulda,Latvia,Europe,57.1539,24.8544,14632 +Sihanoukville,Cambodia,Asia,10.6333,103.5,71082 +Siilinjärvi,Finland,Europe,63.075,27.66,21290 +Siirt,Turkey,Europe,37.925,41.9458,160340 +Sikasso,Mali,Africa,11.3167,-5.6667,213977 +Sikhio,Thailand,Asia,14.9,101.7167,18209 +Siklós,Hungary,Europe,45.8519,18.2986,8679 +Silao,Mexico,North America,20.9478,-101.4281,74242 +Siliana,Tunisia,Africa,36.0819,9.3747,35951 +Silifke,Turkey,Europe,36.3761,33.9322,132665 +Silistra,Bulgaria,Europe,44.1092,27.2654,33480 +Silivri,Turkey,Europe,41.0736,28.2478,217163 +Silkeborg,Denmark,Europe,56.1701,9.5652,50866 +Sillamäe,Estonia,Europe,59.3931,27.7742,12439 +Silopi,Turkey,Europe,37.2486,42.4703,108880 +Silva Jardim,Brazil,South America,-22.6508,-42.3919,21352 +Silvan,Turkey,Europe,38.1422,41.0014,86161 +Silvania,Colombia,South America,4.4033,-74.3881,22020 +Silves,Portugal,Europe,37.1833,-8.4333,37126 +Silvia,Colombia,South America,2.6167,-76.3833,32692 +Silvânia,Brazil,South America,-16.6589,-48.6078,22245 +Simav,Turkey,Europe,39.0833,28.9833,26872 +Simferopol,Ukraine,Europe,44.9519,34.1022,332317 +Simi Valley,United States,North America,34.2663,-118.749,126503 +Simijaca,Colombia,South America,5.5019,-73.8519,13077 +Simití,Colombia,South America,7.9667,-73.95,20271 +Simojovel de Allende,Mexico,North America,17.15,-92.7167,52935 +Simonésia,Brazil,South America,-20.1239,-42.0008,19750 +Simpelveld,Netherlands,Europe,50.8339,5.9814,10516 +Simplício Mendes,Brazil,South America,-7.8539,-41.91,13870 +Simão Dias,Brazil,South America,-10.7378,-37.8108,42578 +Simón Bolívar,Ecuador,South America,-2.0025,-79.4856,8696 +Simões,Brazil,South America,-7.5989,-40.8178,14350 +Simões Filho,Brazil,South America,-12.7839,-38.4039,94066 +Sincan,Turkey,Europe,39.9594,32.5767,572609 +Sincelejo,Colombia,South America,9.295,-75.3961,286716 +Sines,Portugal,Europe,37.9547,-8.8642,14238 +Sinfra,Côte d’Ivoire,Africa,6.621,-5.9114,137210 +Sing Buri,Thailand,Asia,14.8911,100.4031,17843 +Singa,Sudan,Africa,13.1483,33.9311,259000 +Singaparna,Indonesia,Asia,-7.3497,108.1106,73965 +Singapore,Singapore,Asia,1.3,103.8,5983000 +Singaraja,Indonesia,Asia,-8.1167,115.0833,153930 +Singhanakhon,Thailand,Asia,7.239,100.5527,42423 +Singida,Tanzania,Africa,-4.8167,34.75,232459 +Singuilucan,Mexico,North America,19.9675,-98.5178,13143 +Sinimbu,Brazil,South America,-29.5389,-52.5219,8578 +Sinincay,Ecuador,South America,-2.8333,-79.0,12650 +Sinj,Croatia,Europe,43.7017,16.6369,23452 +Sinjār,Iraq,Asia,36.3208,41.8764,22549 +Sinnamary,French Guiana,South America,5.38,-52.96,3180 +Sinop,Brazil,South America,-11.8639,-55.5039,74831 +Sint Anthonis,Netherlands,Europe,51.6258,5.8811,11606 +Sint-Michielsgestel,Netherlands,Europe,51.6433,5.3586,29498 +Sint-Niklaas,Belgium,Europe,51.1667,4.1333,80167 +Sint-Oedenrode,Netherlands,Europe,51.5636,5.4608,17893 +Sintra,Portugal,Europe,38.7974,-9.3904,385606 +Sinŭiju,"Korea, North",Asia,40.1,124.4,352000 +Sion,Switzerland,Europe,46.2304,7.3661,34708 +Sioux City,United States,North America,42.4959,-96.3901,112823 +Sioux Falls,United States,North America,43.5396,-96.7311,207795 +Sipacapa,Guatemala,North America,15.2122,-91.6347,27270 +Siparia,Trinidad and Tobago,North America,10.1333,-61.5,14535 +Sipilou,Côte d’Ivoire,Africa,7.8667,-8.1,22417 +Siping,China,Asia,43.1668,124.3506,1814733 +Sipoo,Finland,Europe,60.375,25.2667,22595 +Siqueira Campos,Brazil,South America,-23.6889,-49.8339,22811 +Siquijor,Philippines,Asia,9.18,123.58,28915 +Siquinalá,Guatemala,North America,14.3,-90.9667,26317 +Siracusa,Italy,Europe,37.0692,15.2875,116244 +Sirinhaém,Brazil,South America,-8.5908,-35.1158,37596 +Sironko,Uganda,Africa,1.2306,34.2481,18900 +Sisak,Croatia,Europe,45.4872,16.3761,40121 +Sisimiut,Greenland,North America,66.9389,-53.6722,5582 +Sisophon,Cambodia,Asia,13.5839,102.9736,99019 +Siteki,Eswatini,Africa,-26.455,31.952,6381 +Siteía,Greece,Europe,35.2,26.1,7453 +Sitionuevo,Colombia,South America,10.7833,-74.7167,31706 +Sittard,Netherlands,Europe,51.0,5.8667,37085 +Sittwe,Burma,Asia,20.167,92.785,100748 +Siuna,Nicaragua,North America,13.7333,-84.7667,64092 +Sivas,Turkey,Europe,39.75,37.0167,365274 +Siverek,Turkey,Europe,37.75,39.3167,267942 +Sivrihisar,Turkey,Europe,39.45,31.5378,20087 +Siyang,China,Asia,27.2116,108.7463,38261 +Siyəzən,Azerbaijan,Europe,41.0783,49.1061,18655 +Siófok,Hungary,Europe,46.9,18.05,24021 +Siġġiewi,Malta,Europe,35.8542,14.4383,8721 +Sjenica,Serbia,Europe,43.2667,20.0,14060 +Sjöbo,Sweden,Europe,55.6333,13.7,19497 +Skadovsk,Ukraine,Europe,46.1167,32.9167,16969 +Skalica,Slovakia,Europe,48.8422,17.2267,15471 +Skanderborg,Denmark,Europe,56.0313,9.9317,20079 +Skara,Sweden,Europe,58.3667,13.4167,18580 +Skarżysko-Kamienna,Poland,Europe,51.1167,20.9167,78636 +Skellefteå,Sweden,Europe,64.65,20.85,74402 +Skenderaj,Kosovo,Europe,42.7467,20.7886,50858 +Ski,Norway,Europe,59.7167,10.8333,29307 +Skien,Norway,Europe,59.2096,9.609,50607 +Skierniewice,Poland,Europe,51.9528,20.1417,46437 +Skikda,Algeria,Africa,36.8667,6.9,163318 +Skive,Denmark,Europe,56.5638,9.0402,20176 +Skoghall,Sweden,Europe,59.3333,13.4333,13265 +Skopin,Russia,Asia,53.8167,39.55,25708 +Skopje,North Macedonia,Europe,41.9961,21.4317,422540 +Skopun,Faroe Islands,Europe,61.9125,-6.8719,446 +Skovorodino,Russia,Asia,53.9833,123.9333,6687 +Skuodas,Lithuania,Europe,56.2667,21.5333,5346 +Skvyra,Ukraine,Europe,49.7167,29.6667,15165 +Skövde,Sweden,Europe,58.3995,13.8538,40422 +Skýdra,Greece,Europe,40.7667,22.15,18325 +Slagelse,Denmark,Europe,55.4027,11.3541,34648 +Slantsy,Russia,Asia,59.1167,28.0667,33514 +Slateng Dua,Indonesia,Asia,-8.1324,113.9836,8630 +Slatina,Romania,Europe,44.4297,24.3642,63487 +Slavonski Brod,Croatia,Europe,45.1553,18.0144,16681 +Slavyansk-na-Kubani,Russia,Asia,45.25,38.1167,52000 +Sliedrecht,Netherlands,Europe,51.8222,4.7744,25597 +Sliema,Malta,Europe,35.9122,14.5042,22591 +Sligo,Ireland,Europe,54.2667,-8.4833,19199 +Sliven,Bulgaria,Europe,42.6833,26.3333,115241 +Slobodskoy,Russia,Asia,58.7208,50.185,29148 +Slobozia,Romania,Europe,44.5639,27.3661,41550 +Slonim,Belarus,Europe,53.0833,25.3167,48907 +Slovenj Gradec,Slovenia,Europe,46.5094,15.0792,7249 +Slovenska Bistrica,Slovenia,Europe,46.3941,15.5707,8219 +Slovenske Konjice,Slovenia,Europe,46.3383,15.4233,5157 +Slutsk,Belarus,Europe,53.0333,27.5667,60056 +Slyudyanka,Russia,Asia,51.6567,103.7186,18058 +Smarhon,Belarus,Europe,54.4836,26.4,35422 +Smederevo,Serbia,Europe,44.6633,20.9289,97930 +Smederevska Palanka,Serbia,Europe,44.3655,20.9587,42192 +Smila,Ukraine,Europe,49.2336,31.8822,65675 +Smiltene,Latvia,Europe,57.4242,25.9064,5129 +Smithtown,United States,North America,40.8663,-73.2164,115988 +Smolensk,Russia,Asia,54.7828,32.0453,312896 +Smolyan,Bulgaria,Europe,41.583,24.7,34465 +Sneek,Netherlands,Europe,53.0325,5.66,33855 +Snezhinsk,Russia,Asia,56.0833,60.7333,50717 +Sniatyn,Ukraine,Europe,48.45,25.5667,9718 +Snihurivka,Ukraine,Europe,47.0736,32.8,12045 +Snina,Slovakia,Europe,48.9881,22.1567,18712 +Soacha,Colombia,South America,4.5872,-74.2214,522442 +Sobinka,Russia,Asia,56.0,40.0333,17444 +Sobrado,Brazil,South America,-9.455,-40.8228,25475 +Sobral,Brazil,South America,-3.6861,-40.3497,210711 +Sobral de Monte Agraço,Portugal,Europe,39.0167,-9.15,10156 +Sochaczew,Poland,Europe,52.2333,20.2333,34439 +Sochi,Russia,Asia,43.5853,39.7203,444989 +Socopó,Venezuela,South America,8.2306,-70.822,110512 +Socorro,Brazil,South America,-22.5908,-46.5289,40122 +Socotá,Colombia,South America,6.05,-72.6333,9812 +Sodankylä,Finland,Europe,67.4167,26.5833,8126 +Sodo,Ethiopia,Africa,6.855,37.7808,254295 +Sodražica,Slovenia,Europe,45.7616,14.6363,842 +Soe,Indonesia,Asia,-9.8667,124.2833,41640 +Soest,Germany,Europe,51.5711,8.1092,48250 +Sofia,Bulgaria,Europe,42.6979,23.3217,1404116 +Sofifi,Indonesia,Asia,0.7244,127.5806,2000 +Sogamoso,Colombia,South America,5.7167,-72.9208,116031 +Sogndal,Norway,Europe,61.2315,7.1027,12198 +Soignies,Belgium,Europe,50.5667,4.0667,27603 +Soissons,France,Europe,49.3817,3.3236,28667 +Sokcho,"Korea, South",Asia,38.2069,128.5919,79846 +Sokhumi,Georgia,Europe,43.0,41.0167,64441 +Sokobanja,Serbia,Europe,43.6394,21.8694,7982 +Sokodé,Togo,Africa,8.9833,1.1333,189000 +Sokoto,Nigeria,Africa,13.0833,5.25,427760 +Sokołów Podlaski,Poland,Europe,52.4067,22.2464,18526 +Sokuluk,Kyrgyzstan,Asia,42.854,74.3026,30540 +Sokyriany,Ukraine,Europe,48.45,27.3667,8547 +Sokółka,Poland,Europe,53.4,23.5,17616 +Solano,Colombia,South America,0.6983,-75.2539,25546 +Soledad,Colombia,South America,10.9167,-74.75,676014 +Soledad Atzompa,Mexico,North America,18.75,-97.15,23130 +Soledad de Doblado,Mexico,North America,19.05,-96.4167,27770 +Soledad de Graciano Sánchez,Mexico,North America,22.1833,-100.9333,332072 +Soledade,Brazil,South America,-28.8178,-52.51,29991 +Solhan,Turkey,Europe,38.9681,41.0536,20217 +Solikamsk,Russia,Asia,59.6433,56.75,88789 +Solin,Croatia,Europe,43.5394,16.4831,23926 +Solingen,Germany,Europe,51.1719,7.0847,161545 +Solita,Colombia,South America,0.8667,-75.65,9139 +Sollefteå,Sweden,Europe,63.1667,17.2667,8643 +Solnechnogorsk,Russia,Asia,56.1833,36.9833,47514 +Sololá,Guatemala,North America,14.7667,-91.1833,99934 +Soloma,Guatemala,North America,15.7167,-91.45,64772 +Solonópole,Brazil,South America,-5.7328,-39.0078,18179 +Solothurn,Switzerland,Europe,47.2081,7.5375,16777 +Solrød Strand,Denmark,Europe,55.5364,12.2252,17382 +Solwezi,Zambia,Africa,-12.1433,26.3858,90856 +Solânea,Brazil,South America,-6.7778,-35.6969,26774 +Solčava,Slovenia,Europe,46.4201,14.6918,208 +Sol’-Iletsk,Russia,Asia,51.1667,55.0,11100 +Soma,Turkey,Europe,39.1883,27.6089,111789 +Sombor,Serbia,Europe,45.7833,19.1167,51471 +Sombrerete,Mexico,North America,23.6333,-103.6397,21702 +Someren,Netherlands,Europe,51.3847,5.7119,19428 +Somolu,Nigeria,Africa,6.5408,3.3872,597400 +Somoniyon,Tajikistan,Asia,38.4422,68.7769,25200 +Somotillo,Nicaragua,North America,13.0333,-86.9167,29030 +Somoto,Nicaragua,North America,13.4833,-86.5833,33788 +Son en Breugel,Netherlands,Europe,51.5158,5.5022,17552 +Sondershausen,Germany,Europe,51.3667,10.8667,21183 +Sondrio,Italy,Europe,46.1697,9.87,21066 +Song Phi Nong,Thailand,Asia,14.2241,100.0209,12926 +Songea,Tanzania,Africa,-10.6833,35.65,286285 +Songjiang,China,Asia,31.0058,121.2333,1909713 +Songkhla,Thailand,Asia,7.2061,100.5967,61758 +Songyang,China,Asia,34.4553,113.0253,729332 +Sonneberg,Germany,Europe,50.35,11.1667,23435 +Sonoita,Mexico,North America,31.8614,-112.8544,11440 +Sonora,Brazil,South America,-17.5769,-54.7578,14516 +Sonqor,Iran,Asia,34.7828,47.5983,44256 +Sonsonate,El Salvador,North America,13.7167,-89.7167,71980 +Sonsón,Colombia,South America,5.7097,-75.3108,36445 +Sonthofen,Germany,Europe,47.5158,10.2811,22035 +Sopetrán,Colombia,South America,6.5017,-75.7433,14453 +Sopište,North Macedonia,Europe,41.95,21.4333,690 +Sopot,Poland,Europe,54.4333,18.55,35049 +Sopron,Hungary,Europe,47.6849,16.5831,98479 +Sopó,Colombia,South America,4.9167,-73.9333,29120 +Soran,Kazakhstan,Asia,49.7833,72.85,42058 +Soreang,Indonesia,Asia,-7.0372,107.5167,116780 +Sorgun,Turkey,Europe,39.8144,35.1903,55079 +Soria,Spain,Europe,41.7667,-2.4667,40750 +Sorkheh,Iran,Asia,35.4644,53.2086,9951 +Soroca,Moldova,Europe,48.1667,28.3,22196 +Sorocaba,Brazil,South America,-23.5019,-47.4578,723574 +Sorochinsk,Russia,Asia,52.4167,53.1,17900 +Sorong,Indonesia,Asia,-0.8667,131.25,295809 +Soroti,Uganda,Africa,1.715,33.6111,60900 +Sorriso,Brazil,South America,-12.545,-55.7108,110635 +Sorsogon,Philippines,Asia,12.9742,124.0058,182237 +Sortavala,Russia,Asia,61.7056,30.6958,14787 +Sorø,Denmark,Europe,55.433,11.5667,8271 +Sosnogorsk,Russia,Asia,63.6,53.9,32411 +Sosnovoborsk,Russia,Asia,56.1333,93.3667,12650 +Sosnowiec,Poland,Europe,50.2833,19.1333,193660 +Soteapan,Mexico,North America,18.2333,-94.8667,35155 +Sotkamo,Finland,Europe,64.1333,28.3833,10274 +Sotomayor,Colombia,South America,1.4933,-77.5214,20865 +Soubré,Côte d’Ivoire,Africa,5.7836,-6.5939,272773 +Souk Ahras,Algeria,Africa,36.2864,7.9511,155259 +Soure,Brazil,South America,-0.7169,-48.5228,24204 +Sousa,Brazil,South America,-6.7608,-38.2308,67259 +Sousse,Tunisia,Africa,35.8333,10.6333,314071 +South Bend,United States,North America,41.6767,-86.2696,275725 +South Fulton,United States,North America,33.6273,-84.58,109157 +South Lyon,United States,North America,42.4614,-83.6526,151231 +Souto Soares,Brazil,South America,-12.0889,-41.6378,14795 +Sovetsk,Russia,Asia,55.0808,21.8864,38614 +Sovetskaya Gavan’,Russia,Asia,48.9667,140.2833,169 +Sovetskiy,Russia,Asia,61.3614,63.5842,31138 +Sowa Town,Botswana,Africa,-20.5636,26.2244,2914 +Soyaló,Mexico,North America,16.9333,-92.9833,10890 +Soyaniquilpan,Mexico,North America,19.9892,-99.4361,13290 +Spanish Town,Jamaica,North America,17.9959,-76.9551,131056 +Sparks,United States,North America,39.5736,-119.7161,109106 +Spartanburg,United States,North America,34.9442,-81.9251,210388 +Spassk-Dal’niy,Russia,Asia,44.6,132.8167,35096 +Speyer,Germany,Europe,49.3194,8.4311,51203 +Spijkenisse,Netherlands,Europe,51.8333,4.3167,72740 +Spittal an der Drau,Austria,Europe,46.7833,13.4833,15413 +Spišská Nová Ves,Slovakia,Europe,48.95,20.5667,35431 +Split,Croatia,Europe,43.51,16.44,160577 +Spodnje Hoče,Slovenia,Europe,46.5,15.65,2109 +Spodnji Duplek,Slovenia,Europe,46.5031,15.7453,1689 +Spokane,United States,North America,47.6671,-117.433,458988 +Spokane Valley,United States,North America,47.6626,-117.2346,105460 +Spring Hill,United States,North America,28.4798,-82.53,188743 +Spring Valley,United States,North America,36.0952,-115.2636,219913 +Springfield,United States,North America,42.1155,-72.5395,438889 +Spárti,Greece,Europe,37.0739,22.4294,7900 +Spáta,Greece,Europe,37.9667,23.9167,12991 +Srbobran,Serbia,Europe,45.5333,19.7833,14357 +Srebrenica,Bosnia and Herzegovina,Europe,44.1,19.3,13409 +Srebrenik,Bosnia and Herzegovina,Europe,44.7082,18.4883,39678 +Središče ob Dravi,Slovenia,Europe,46.3942,16.2681,984 +Srednekolymsk,Russia,Asia,67.4667,153.7167,3118 +Sremska Mitrovica,Serbia,Europe,44.9661,19.6106,72580 +Sremski Karlovci,Serbia,Europe,45.2,19.9333,8750 +Sri Jayewardenepura Kotte,Sri Lanka,Asia,6.9,79.9164,115826 +Srīnagar,India,Asia,34.09,74.79,1180570 +St. Catharines,Canada,North America,43.1833,-79.2333,242460 +St. Cloud,United States,North America,45.534,-94.1718,116347 +St. George,United States,North America,37.0758,-113.5752,147347 +St. John's,Canada,North America,47.4817,-52.7971,185565 +St. Louis,United States,North America,38.6359,-90.2451,2127843 +St. Paul,United States,North America,44.9478,-93.1039,307762 +St. Petersburg,United States,North America,27.7931,-82.6652,260646 +Stade,Germany,Europe,53.6008,9.4764,48708 +Stadskanaal,Netherlands,Europe,52.9895,6.9504,31789 +Stadthagen,Germany,Europe,52.3247,9.2069,22924 +Staffanstorp,Sweden,Europe,55.6333,13.2167,31718 +Stalowa Wola,Poland,Europe,50.5667,22.05,57620 +Stamford,United States,North America,41.1039,-73.5585,135806 +Stange,Norway,Europe,60.7167,11.1833,21356 +Stanley,Falkland Islands (Islas Malvinas),South America,-51.7,-57.85,2213 +Stans,Switzerland,Europe,46.9594,8.3667,8393 +Staphorst,Netherlands,Europe,52.6333,6.2,17261 +Star Dojran,North Macedonia,Europe,41.1865,22.7203,105 +Stara Pazova,Serbia,Europe,44.9833,20.1667,71931 +Stara Zagora,Bulgaria,Europe,42.4333,25.65,142746 +Starachowice,Poland,Europe,51.05,21.0667,46270 +Staraya Russa,Russia,Asia,58.0,31.3333,27487 +Stargard Szczeciński,Poland,Europe,53.3333,15.0333,67348 +Stari Trg,Slovenia,Europe,45.7142,14.4695,838 +Starnberg,Germany,Europe,47.9972,11.3406,23940 +Staro Nagoričane,North Macedonia,Europe,42.2,21.83,493 +Starobilsk,Ukraine,Europe,49.2775,38.9242,15947 +Starodub,Russia,Asia,52.5833,32.7667,17687 +Starogard Gdański,Poland,Europe,53.9667,18.5333,46373 +Staryy Oskol,Russia,Asia,51.2967,37.835,217107 +Staryya Darohi,Belarus,Europe,53.0394,28.265,10898 +Stará Ľubovňa,Slovakia,Europe,49.3167,20.6833,15707 +Starše,Slovenia,Europe,46.4658,15.7672,757 +Staszów,Poland,Europe,50.5633,21.1717,14158 +Staten Island,United States,North America,40.5834,-74.1496,495747 +Stavanger,Norway,Europe,58.97,5.7314,146011 +Stavropol,Russia,Asia,45.05,41.9833,450680 +Stavroúpoli,Greece,Europe,40.6667,22.9333,45891 +Steenbergen,Netherlands,Europe,51.5842,4.3194,25054 +Stein,Netherlands,Europe,50.9679,5.7652,24875 +Steinfurt,Germany,Europe,52.1475,7.3442,35456 +Steinkjer,Norway,Europe,64.0147,11.4942,12985 +Stendal,Germany,Europe,52.6,11.85,38946 +Stenungsund,Sweden,Europe,58.0833,11.8167,9987 +Stepnogorsk,Kazakhstan,Asia,52.3497,71.89,46712 +Sterling Heights,United States,North America,42.5809,-83.0305,133473 +Sterlitamak,Russia,Asia,53.6333,55.95,277410 +Steyr,Austria,Europe,48.0333,14.4167,38034 +Stjørdal,Norway,Europe,63.4703,10.9122,24541 +Stockholm,Sweden,Europe,59.3275,18.0547,995574 +Stockton,United States,North America,37.9765,-121.3109,416005 +Stolac,Bosnia and Herzegovina,Europe,43.0839,17.9592,14889 +Stord,Norway,Europe,59.7738,5.4903,19098 +Storozhynets,Ukraine,Europe,48.1619,25.72,14077 +Stowbtsy,Belarus,Europe,53.4833,26.7333,17737 +Stralsund,Germany,Europe,54.3092,13.0819,59450 +Strasbourg,France,Europe,48.5833,7.7458,291709 +Stratford,New Zealand,Oceania,-39.341,174.284,5740 +Straubing,Germany,Europe,48.8833,12.5667,49775 +Straşeni,Moldova,Europe,47.1414,28.6103,18376 +Straža,Slovenia,Europe,45.7864,15.0745,1945 +Strezhevoy,Russia,Asia,60.7333,77.5833,39169 +Strijen,Netherlands,Europe,51.7422,4.5514,8793 +Stroitel,Russia,Asia,50.7833,36.4833,23780 +Stropkov,Slovakia,Europe,49.205,21.6514,10067 +Struer,Denmark,Europe,56.4862,8.6044,10112 +Struga,North Macedonia,Europe,41.1775,20.6789,15009 +Strumica,North Macedonia,Europe,41.4375,22.6431,33825 +Stryi,Ukraine,Europe,49.25,23.85,59425 +Strzelce Krajeńskie,Poland,Europe,52.8756,15.5319,9912 +Strzelce Opolskie,Poland,Europe,50.5,18.2833,17512 +Strzelin,Poland,Europe,50.7833,17.0667,12131 +Strängnäs,Sweden,Europe,59.3833,17.0333,38526 +Studeničani,North Macedonia,Europe,41.9158,21.5306,59 +Stung Treng,Cambodia,Asia,13.5259,105.9683,29665 +Stupino,Russia,Asia,54.8833,38.1,63506 +Stuttgart,Germany,Europe,48.7775,9.18,2787724 +Støvring,Denmark,Europe,56.8909,9.831,9190 +Su-ngai Kolok,Thailand,Asia,6.0333,101.9667,38612 +Suaita,Colombia,South America,6.1019,-73.4406,10006 +Suan,Colombia,South America,10.3333,-74.8833,9344 +Suaza,Colombia,South America,1.9833,-75.8167,21119 +Subachoque,Colombia,South America,4.9281,-74.1736,16117 +Subang,Indonesia,Asia,-6.5714,107.7614,141038 +Subotica,Serbia,Europe,46.1003,19.6656,29919 +Suceava,Romania,Europe,47.6514,26.2556,119694 +Sucha Beskidzka,Poland,Europe,49.7403,19.5886,8951 +Suchiapa,Mexico,North America,16.6181,-93.0942,19571 +Sucre,Bolivia,South America,-19.0475,-65.26,300000 +Sucupira do Norte,Brazil,South America,-6.4769,-44.1919,10238 +Sucúa,Ecuador,South America,-2.46,-78.17,10846 +Sudbury,Canada,North America,46.49,-81.01,166004 +Sudogda,Russia,Asia,55.95,40.8667,10408 +Suesca,Colombia,South America,5.1,-73.8,17318 +Suez,Egypt,Africa,29.9667,32.5333,516959 +Sugar Land,United States,North America,29.5935,-95.6358,109735 +Suharekë,Kosovo,Europe,42.3586,20.825,59722 +Suhl,Germany,Europe,50.6106,10.6931,36986 +Suifenhe,China,Asia,44.4124,131.1518,60000 +Suihua,China,Asia,46.654,126.969,3756167 +Suileng,China,Asia,47.2419,127.1161,57789 +Suining,China,Asia,30.533,105.593,2814196 +Sukhinichi,Russia,Asia,54.1,35.35,14407 +Sukhothai,Thailand,Asia,17.0167,99.7,35713 +Sukhoy Log,Russia,Asia,56.9167,62.0167,32461 +Sukkur,Pakistan,Asia,27.7061,68.8483,499900 +Sultanhanı,Turkey,Europe,38.25,33.55,10958 +Sultanhisar,Turkey,Europe,37.8897,28.1575,20230 +Sultepec,Mexico,North America,18.8667,-99.95,24986 +Suluova,Turkey,Europe,40.8333,35.65,39286 +Sulęcin,Poland,Europe,52.45,15.1167,9978 +Sumaré,Brazil,South America,-22.8219,-47.2669,265955 +Sumbawa Besar,Indonesia,Asia,-8.5,117.4167,63362 +Sumbawanga,Tanzania,Africa,-7.9667,31.6167,303986 +Sumbe,Angola,Africa,-11.2053,13.8417,279968 +Sumber,Indonesia,Asia,-6.7544,108.4858,102973 +Sumenep,Indonesia,Asia,-7.0049,113.8496,84656 +Sumidouro,Brazil,South America,-22.05,-42.675,15206 +Sumpango,Guatemala,North America,14.6478,-90.7347,46082 +Sumqayıt,Azerbaijan,Europe,40.5961,49.67,341200 +Sumy,Ukraine,Europe,50.9119,34.8028,256474 +Sumé,Brazil,South America,-7.6719,-36.88,17166 +Suncheon,"Korea, South",Asia,34.9506,127.4875,265390 +Sundsvall,Sweden,Europe,62.4001,17.3167,99825 +Sung Noen,Thailand,Asia,14.8992,101.8208,10061 +Sungailiat,Indonesia,Asia,-1.8561,106.1192,100750 +Sungurlu,Turkey,Europe,40.161,34.377,30275 +Sunne,Sweden,Europe,59.8333,13.15,13000 +Sunnyvale,United States,North America,37.3836,-122.0255,153455 +Sunrise Manor,United States,North America,36.1783,-115.0487,199099 +Suntar,Russia,Asia,62.1667,117.6333,10035 +Sunyani,Ghana,Africa,7.336,-2.336,208496 +Suohe,China,Asia,34.7833,113.35,730135 +Suonan,China,Asia,35.6633,103.39,23499 +Suong,Cambodia,Asia,11.9118,105.6582,35054 +Suphan Buri,Thailand,Asia,14.4675,100.1169,26656 +Supía,Colombia,South America,5.4667,-75.65,27489 +Sur,Turkey,Europe,37.9106,40.2358,100613 +Surabaya,Indonesia,Asia,-7.2458,112.7378,6556000 +Surat Thani,Thailand,Asia,9.1397,99.3306,132040 +Surazh,Russia,Asia,53.0167,32.3833,11176 +Surdulica,Serbia,Europe,42.695,22.1672,22190 +Surgut,Russia,Asia,61.25,73.4333,395900 +Surigao,Philippines,Asia,9.7897,125.4958,171107 +Surin,Thailand,Asia,14.0,53.0,39179 +Surovikino,Russia,Asia,48.6,42.85,18227 +Surprise,United States,North America,33.6815,-112.4515,149519 +Surrey,Canada,North America,49.19,-122.8489,568322 +Sursee,Switzerland,Europe,47.1742,8.1081,9900 +Surt,Libya,Africa,31.205,16.5886,76788 +Surubim,Brazil,South America,-7.8331,-35.7547,65647 +Suruç,Turkey,Europe,36.9764,38.4269,100961 +Surčin,Serbia,Europe,44.7944,20.2781,45452 +Susa,Colombia,South America,5.4528,-73.814,12302 +Susques,Argentina,South America,-23.4167,-66.4833,1093 +Susurluk,Turkey,Europe,39.9139,28.1603,37724 +Suva,Fiji,Oceania,-18.1333,178.4333,88271 +Suvorov,Russia,Asia,54.15,36.55,17598 +Suwannaphum,Thailand,Asia,15.6035,103.8021,9183 +Suwałki,Poland,Europe,54.0989,22.9286,68752 +Suwon,"Korea, South",Asia,37.2667,127.0167,1234300 +Suzak,Kyrgyzstan,Asia,40.8982,72.9048,30534 +Suzano,Brazil,South America,-23.5428,-46.3108,228690 +Suzdal,Russia,Asia,56.4167,40.45,9286 +Suzhou,China,Asia,31.3,120.6194,4330000 +Suárez,Colombia,South America,2.9589,-76.6953,19690 +Suşehri,Turkey,Europe,40.1658,38.0942,15960 +Svatove,Ukraine,Europe,49.415,38.155,16145 +Svay Rieng,Cambodia,Asia,11.0833,105.8,41424 +Svedala,Sweden,Europe,55.5,13.2333,10627 +Svendborg,Denmark,Europe,55.0704,10.6167,27594 +Sveta Ana,Slovenia,Europe,46.6492,15.8442,2285 +Sveta Nedjelja,Croatia,Europe,45.8,15.7833,18221 +Sveta Trojica v Slovenskih Goricah,Slovenia,Europe,46.5767,15.8769,754 +Sveti Ivan Zelina,Croatia,Europe,45.95,16.2333,14602 +Sveti Jurij,Slovenia,Europe,46.5695,16.0235,209 +Sveti Nikole,North Macedonia,Europe,41.865,21.9425,11728 +Svetlogorsk,Russia,Asia,54.95,20.15,16771 +Svetlyy,Russia,Asia,54.6667,20.1333,21054 +Svidník,Slovakia,Europe,49.3056,21.5678,10168 +Svilajnac,Serbia,Europe,44.2167,21.2,23391 +Svirsk,Russia,Asia,53.0833,103.3333,12750 +Svishtov,Bulgaria,Europe,43.6113,25.3569,22584 +Svitlovodsk,Ukraine,Europe,49.0503,33.2419,43130 +Svolvær,Norway,Europe,68.2333,14.5667,4686 +Svrljig,Serbia,Europe,43.4167,22.1167,14249 +Svyetlahorsk,Belarus,Europe,52.6333,29.7333,62602 +Swabi,Pakistan,Asia,34.1202,72.4702,115018 +Swakopmund,Namibia,Africa,-22.6667,14.5333,34300 +Swieqi,Malta,Europe,35.9208,14.48,10064 +Swords,Ireland,Europe,53.4597,-6.2181,36924 +Sydney,Australia,Oceania,-33.8667,151.2,4840600 +Sykiés,Greece,Europe,40.65,22.95,35545 +Syktyvkar,Russia,Asia,61.6667,50.8167,245083 +Sylhet,Bangladesh,Asia,24.9,91.8667,479837 +Synelnykove,Ukraine,Europe,48.3178,35.5119,29651 +Syracuse,United States,North America,43.0407,-76.1437,407625 +Sysert,Russia,Asia,56.5,60.8167,20436 +Szamotuły,Poland,Europe,52.6,16.5833,18398 +Szarvas,Hungary,Europe,46.85,20.6,14464 +Szczecin,Poland,Europe,53.4325,14.5481,396168 +Szczecinek,Poland,Europe,53.7167,16.6833,38446 +Szczytno,Poland,Europe,53.5628,20.9853,22389 +Szeged,Hungary,Europe,46.25,20.1667,157930 +Szekszárd,Hungary,Europe,46.3558,18.7039,29707 +Szentendre,Hungary,Europe,47.6733,19.0725,28444 +Szentes,Hungary,Europe,46.6519,20.2572,24905 +Szigetszentmiklós,Hungary,Europe,47.3453,19.0483,40069 +Szigetvár,Hungary,Europe,46.0475,17.7994,9496 +Szolnok,Hungary,Europe,47.1747,20.1764,66426 +Szombathely,Hungary,Europe,47.2351,16.6219,147920 +Sztum,Poland,Europe,53.9217,19.0336,9515 +Szydłowiec,Poland,Europe,51.2333,20.85,10959 +Székesfehérvár,Hungary,Europe,47.191,18.4108,96024 +Sào Amaro das Brotas,Brazil,South America,-10.7889,-37.0544,11410 +Sámos,Greece,Europe,37.7475,26.9825,12506 +Sárbogárd,Hungary,Europe,46.8878,18.6193,11518 +Sárospatak,Hungary,Europe,48.3189,21.5661,10583 +Sárvár,Hungary,Europe,47.25,16.9333,14200 +Sátiro Dias,Brazil,South America,-11.6,-38.6,16008 +Sátoraljaújhely,Hungary,Europe,48.3941,21.6561,12973 +Sátão,Portugal,Europe,40.7333,-7.7167,12444 +São Benedito,Brazil,South America,-4.0489,-40.865,47640 +São Benedito do Rio Preto,Brazil,South America,-3.3339,-43.5278,18364 +São Bento,Brazil,South America,-6.4858,-37.4508,32235 +São Bento do Sapucaí,Brazil,South America,-22.6889,-45.7308,10355 +São Bento do Sul,Brazil,South America,-26.25,-49.3789,65437 +São Bento do Una,Brazil,South America,-8.5228,-36.4439,45360 +São Bernardo,Brazil,South America,-3.3608,-42.4178,26943 +São Bernardo do Campo,Brazil,South America,-23.6939,-46.565,810729 +São Borja,Brazil,South America,-28.6606,-56.0044,60019 +São Brás de Alportel,Portugal,Europe,37.15,-7.8833,10662 +São Caetano de Odivelas,Brazil,South America,-0.75,-48.02,16666 +São Caetano do Sul,Brazil,South America,-23.6228,-46.5508,165655 +São Caitano,Brazil,South America,-8.3258,-36.1428,37126 +São Carlos,Brazil,South America,-22.0178,-47.8908,254484 +São Cristóvão,Brazil,South America,-11.015,-37.2058,95612 +São Desidério,Brazil,South America,-12.3633,-44.9733,34266 +São Domingos,Brazil,South America,-13.3983,-46.3183,13103 +São Domingos do Maranhão,Brazil,South America,-5.5758,-44.385,34034 +São Domingos do Prata,Brazil,South America,-19.865,-42.9678,17392 +São Felipe,Brazil,South America,-12.8469,-39.0889,20283 +São Fidélis,Brazil,South America,-21.6458,-41.7469,36789 +São Filipe,Cabo Verde,Africa,14.895,-24.498,22227 +São Francisco,Brazil,South America,-15.9489,-44.8639,52762 +São Francisco de Assis,Brazil,South America,-29.55,-55.1308,17618 +São Francisco de Paula,Brazil,South America,-29.4478,-50.5839,21893 +São Francisco do Conde,Brazil,South America,-12.6278,-38.68,38733 +São Francisco do Guaporé,Brazil,South America,-12.0522,-63.5675,16286 +São Francisco do Maranhão,Brazil,South America,-6.2508,-42.8569,12064 +São Francisco do Sul,Brazil,South America,-26.2433,-48.6381,53746 +São Félix,Brazil,South America,-12.605,-38.9719,11026 +São Félix do Araguaia,Brazil,South America,-11.6169,-50.6689,10625 +São Félix do Xingu,Brazil,South America,-6.645,-51.995,65418 +São Gabriel,Brazil,South America,-30.3358,-54.32,58487 +São Gabriel da Cachoeira,Brazil,South America,-0.13,-67.0889,51795 +São Geraldo,Brazil,South America,-20.9228,-42.8339,10282 +São Geraldo do Araguaia,Brazil,South America,-6.4008,-48.555,24255 +São Gonçalo,Brazil,South America,-22.8269,-43.0539,896744 +São Gonçalo do Amarante,Brazil,South America,-5.7928,-35.3289,115838 +São Gonçalo do Pará,Brazil,South America,-19.9828,-44.8589,11770 +São Gonçalo do Rio Abaixo,Brazil,South America,-19.8258,-43.3619,11850 +São Gonçalo do Sapucaí,Brazil,South America,-21.8919,-45.595,23959 +São Gonçalo dos Campos,Brazil,South America,-12.4328,-38.9669,39513 +São Gotardo,Brazil,South America,-19.3111,-46.0489,31819 +São Jerônimo,Brazil,South America,-29.9589,-51.7219,21028 +São Jerônimo da Serra,Brazil,South America,-23.7278,-50.7408,10830 +São Joaquim,Brazil,South America,-28.2939,-49.9319,25939 +São Joaquim da Barra,Brazil,South America,-20.5808,-47.855,48558 +São Joaquim de Bicas,Brazil,South America,-20.0489,-44.2739,34348 +São Joaquim do Monte,Brazil,South America,-8.4319,-35.8039,20037 +São Jorge d’Oeste,Brazil,South America,-25.7058,-52.9178,9378 +São José,Brazil,South America,-27.615,-48.6278,173559 +São José da Coroa Grande,Brazil,South America,-8.8978,-35.1478,18825 +São José da Laje,Brazil,South America,-9.01,-36.0578,20813 +São José da Tapera,Brazil,South America,-9.5578,-37.3808,30604 +São José de Mipibu,Brazil,South America,-6.075,-35.2378,47286 +São José de Piranhas,Brazil,South America,-7.1208,-38.5019,19067 +São José de Ribamar,Brazil,South America,-2.5619,-44.0539,244579 +São José do Belmonte,Brazil,South America,-7.8608,-38.76,34843 +São José do Calçado,Brazil,South America,-21.0253,-41.6544,10546 +São José do Campestre,Brazil,South America,-6.3158,-35.7139,11121 +São José do Cedro,Brazil,South America,-26.455,-53.4939,14167 +São José do Cerrito,Brazil,South America,-27.6628,-50.58,8708 +São José do Egito,Brazil,South America,-7.4733,-37.2744,31004 +São José do Jacuípe,Brazil,South America,-11.4119,-39.8669,10187 +São José do Norte,Brazil,South America,-32.015,-52.0419,25443 +São José do Rio Pardo,Brazil,South America,-21.5958,-46.8889,50077 +São José do Rio Preto,Brazil,South America,-22.1508,-42.9239,22080 +São José do Rio Prêto,Brazil,South America,-20.82,-49.3789,358523 +São José dos Campos,Brazil,South America,-23.1789,-45.8869,539313 +São José dos Pinhais,Brazil,South America,-25.535,-49.2058,204316 +São João,Brazil,South America,-8.8756,-36.3667,21312 +São João Batista,Brazil,South America,-27.2758,-48.8489,32687 +São João Evangelista,Brazil,South America,-18.5478,-42.7628,15315 +São João Nepomuceno,Brazil,South America,-21.54,-43.0108,25565 +São João da Barra,Brazil,South America,-21.64,-41.0508,36573 +São João da Boa Vista,Brazil,South America,-21.9689,-46.7978,77387 +São João da Madeira,Portugal,Europe,40.9,-8.5,21713 +São João da Ocalina,Brazil,South America,-23.98,-51.8178,10667 +São João da Ponte,Brazil,South America,-15.9289,-44.0078,23930 +São João del Rei,Brazil,South America,-21.1358,-44.2619,90225 +São João do Manhuaçu,Brazil,South America,-20.3939,-42.1508,11246 +São João do Paraíso,Brazil,South America,-15.3139,-42.0139,23910 +São João do Piauí,Brazil,South America,-8.3578,-42.2469,21421 +São João do Triunfo,Brazil,South America,-25.6828,-50.2969,13726 +São João dos Angolares,Sao Tome and Principe,Africa,0.1333,6.65,3605 +São João dos Patos,Brazil,South America,-6.495,-43.7019,25020 +São Leopoldo,Brazil,South America,-29.76,-51.1469,193547 +São Lourenço,Brazil,South America,-22.1158,-45.0539,44798 +São Lourenço da Mata,Brazil,South America,-8.0019,-35.0178,90402 +São Lourenço da Serra,Brazil,South America,-23.8528,-46.9428,12199 +São Lourenço do Sul,Brazil,South America,-31.365,-51.9778,41989 +São Lourenço d’Oeste,Brazil,South America,-26.3589,-52.8508,19647 +São Ludgero,Brazil,South America,-28.3258,-49.1767,13509 +São Luís,Brazil,South America,-2.53,-44.3028,1037775 +São Luís Gonzaga,Brazil,South America,-28.4078,-54.9608,34752 +São Luís Gonzaga do Maranhão,Brazil,South America,-4.38,-44.67,17818 +São Luís de Montes Belos,Brazil,South America,-16.525,-50.3719,33852 +São Luís do Curu,Brazil,South America,-3.67,-39.2428,10822 +São Luís do Paraitinga,Brazil,South America,-23.2219,-45.31,10337 +São Luís do Quitunde,Brazil,South America,-9.3178,-35.5608,30873 +São Manuel,Brazil,South America,-22.7308,-48.5708,37289 +São Marcos,Brazil,South America,-28.9708,-51.0678,21084 +São Mateus,Brazil,South America,-18.7201,-39.8589,132642 +São Mateus do Maranhão,Brazil,South America,-4.04,-44.47,38829 +São Mateus do Sul,Brazil,South America,-25.8739,-50.3828,36569 +São Miguel,Brazil,South America,-6.2119,-38.4969,23537 +São Miguel Arcanjo,Brazil,South America,-23.8778,-47.9969,32039 +São Miguel das Matas,Brazil,South America,-13.0478,-39.4558,10334 +São Miguel de Touros,Brazil,South America,-5.125,-35.6389,10221 +São Miguel do Araguaia,Brazil,South America,-13.275,-50.1628,21900 +São Miguel do Guamá,Brazil,South America,-1.6269,-47.4828,41366 +São Miguel do Guaporé,Brazil,South America,-11.6936,-62.7114,21635 +São Miguel do Iguaçu,Brazil,South America,-25.3478,-54.2378,29122 +São Miguel do Tapuio,Brazil,South America,-5.5036,-41.3233,17639 +São Miguel dos Campos,Brazil,South America,-9.7811,-36.0936,61797 +São Miguel d’Oeste,Brazil,South America,-26.725,-53.5178,44330 +São Paulo,Brazil,South America,-23.5504,-46.6339,23086000 +São Paulo de Olivença,Brazil,South America,-3.45,-68.95,32967 +São Paulo do Potengi,Brazil,South America,-5.895,-35.7628,16786 +São Pedro,Brazil,South America,-22.5486,-47.9139,34284 +São Pedro da Aldeia,Brazil,South America,-22.8392,-42.1028,106049 +São Pedro da Água Branca,Brazil,South America,-5.085,-48.4289,10927 +São Pedro do Ivaí,Brazil,South America,-23.865,-51.8558,8690 +São Pedro do Piauí,Brazil,South America,-5.9289,-42.7189,13755 +São Pedro do Sul,Portugal,Europe,40.75,-8.0667,16851 +São Raimundo Nonato,Brazil,South America,-9.015,-42.6989,38934 +São Raimundo das Mangabeiras,Brazil,South America,-7.0219,-45.4808,18672 +São Romão,Brazil,South America,-16.3689,-45.0689,10315 +São Roque,Brazil,South America,-23.5289,-47.135,79484 +São Sebastião,Brazil,South America,-23.804,-45.4051,58038 +São Sebastião da Boa Vista,Brazil,South America,-1.7178,-49.5408,25643 +São Sebastião da Grama,Brazil,South America,-21.7108,-46.8208,10441 +São Sebastião de Lagoa de Roça,Brazil,South America,-7.0828,-35.835,11040 +São Sebastião do Caí,Brazil,South America,-29.5869,-51.3758,24428 +São Sebastião do Curral,Brazil,South America,-20.2758,-45.005,8815 +São Sebastião do Maranhão,Brazil,South America,-18.0839,-42.5708,10079 +São Sebastião do Paraíso,Brazil,South America,-20.9169,-46.9908,71796 +São Sebastião do Passé,Brazil,South America,-12.5125,-38.4953,44430 +São Sebastião do Uatumã,Brazil,South America,-2.5719,-57.8708,11670 +São Sepé,Brazil,South America,-30.1608,-53.565,21219 +São Simão,Brazil,South America,-19.0127,-50.6748,17020 +São Tiago,Brazil,South America,-20.9128,-44.5089,10245 +São Tomé,Sao Tome and Principe,Africa,0.3361,6.7306,71868 +São Vicente,Brazil,South America,-23.9664,-46.3861,303551 +São Vicente Ferrer,Brazil,South America,-2.8939,-44.88,19498 +São Vicente Férrer,Brazil,South America,-7.5908,-35.4908,16677 +Säffle,Sweden,Europe,59.1333,12.9,8991 +Sätbayev,Kazakhstan,Asia,47.9,67.5333,61883 +Säter,Sweden,Europe,60.35,15.75,11161 +Sébaco,Nicaragua,North America,12.85,-86.1,32221 +Ségou,Mali,Africa,13.45,-6.2667,133501 +Séguéla,Côte d’Ivoire,Africa,7.9611,-6.6731,103980 +Sélestat,France,Europe,48.2594,7.4542,19523 +Sélibaby,Mauritania,Africa,15.167,-12.1833,26420 +Sérres,Greece,Europe,41.0833,23.55,58400 +Sétif,Algeria,Africa,36.19,5.41,288461 +Sêrro,Brazil,South America,-18.6044,-43.3794,21952 +Sítio do Mato,Brazil,South America,-13.085,-43.465,13408 +Sítio do Quinto,Brazil,South America,-10.35,-38.2169,14773 +Sîngerei,Moldova,Europe,47.6333,28.15,12465 +Sóc Trăng,Vietnam,Asia,9.6028,105.9736,221430 +Sông Cầu,Vietnam,Asia,13.55,109.2,105780 +Söderhamn,Sweden,Europe,61.3,17.05,11761 +Södertälje,Sweden,Europe,59.1978,17.6272,72704 +Söke,Turkey,Europe,37.7508,27.4103,123301 +Sömmerda,Germany,Europe,51.1617,11.1169,19052 +Söğüt,Turkey,Europe,40.0186,30.1814,13566 +Söğütlü,Turkey,Europe,40.9,30.4833,14724 +Søgne,Norway,Europe,58.0942,7.7725,11260 +Sønderborg,Denmark,Europe,54.9106,9.7928,28137 +Sühbaatar,Mongolia,Asia,50.2364,106.2064,19224 +Sürmene,Turkey,Europe,40.9142,40.1125,25950 +Sāmarrā’,Iraq,Asia,34.1959,43.8857,140400 +Sāveh,Iran,Asia,35.0278,50.3611,220762 +Sępólno Krajeńskie,Poland,Europe,53.45,17.53,8914 +Sīrjān,Iran,Asia,29.437,55.6802,175000 +Sīāhkal,Iran,Asia,37.1536,49.8711,19924 +Sındırgı,Turkey,Europe,39.24,28.175,32408 +Sławno,Poland,Europe,54.3667,16.6833,12028 +Słubice,Poland,Europe,52.35,14.5667,16495 +Słupca,Poland,Europe,52.3,17.8667,13353 +Słupsk,Poland,Europe,54.4658,17.0292,87660 +Sūhāj,Egypt,Africa,26.5606,31.6917,193931 +Sūrak,Iran,Asia,36.5939,53.2086,9208 +Sūrān,Iran,Asia,27.2883,61.9997,13580 +Sūsangerd,Iran,Asia,31.5569,48.1894,51431 +Sơn La,Vietnam,Asia,21.327,103.9141,106052 +Sơn Tây,Vietnam,Asia,21.1333,105.5,230577 +Taastrup,Denmark,Europe,55.6475,12.312,36193 +Tabapuã,Brazil,South America,-20.9639,-49.0319,11323 +Tabatinga,Brazil,South America,-4.2312,-69.9386,67182 +Tabio,Colombia,South America,4.9158,-74.0983,29731 +Tabira,Brazil,South America,-7.5908,-37.5394,27681 +Tabocas do Brejo Velho,Brazil,South America,-12.7058,-44.0069,11979 +Tabor,Slovenia,Europe,46.2225,15.0081,1669 +Tabora,Tanzania,Africa,-5.0167,32.8,308741 +Taboão da Serra,Brazil,South America,-23.6019,-46.7528,273542 +Tabrīz,Iran,Asia,38.0817,46.2992,1558693 +Tabuleiro do Norte,Brazil,South America,-5.2481,-38.13,30652 +Tabūk,Saudi Arabia,Asia,28.3972,36.5789,886036 +Tacaimbó,Brazil,South America,-8.3158,-36.2928,13738 +Tacaná,Guatemala,North America,15.2414,-92.0683,82079 +Tacaratu,Brazil,South America,-9.1058,-38.15,23902 +Tacheng,China,Asia,46.7517,82.9869,158098 +Tacloban,Philippines,Asia,11.24,125.0,251881 +Tacna,Peru,South America,-18.0147,-70.2489,286240 +Tacoma,United States,North America,47.2431,-122.4531,220482 +Tactic,Guatemala,North America,15.3167,-90.3511,41930 +Tacuarembó,Uruguay,South America,-31.7333,-55.9833,54755 +Tacuru,Brazil,South America,-23.6328,-55.0158,10808 +Tacámbaro de Codallos,Mexico,North America,19.2356,-101.4569,76661 +Tadjourah,Djibouti,Africa,11.7833,42.8833,14820 +Tadmur,Syria,Asia,34.5504,38.2833,51323 +Tadó,Colombia,South America,5.2667,-76.5667,18906 +Tafeng,China,Asia,25.3775,112.2034,150800 +Tafresh,Iran,Asia,34.6942,50.0211,16493 +Taft,Iran,Asia,31.7494,54.2089,18464 +Taftanāz,Syria,Asia,35.9983,36.7858,8540 +Tafí Viejo,Argentina,South America,-26.7333,-65.2667,39601 +Tagbilaran City,Philippines,Asia,9.65,123.85,104976 +Taguasco,Cuba,North America,22.005,-79.265,32968 +Taguatinga,Brazil,South America,-12.4061,-46.4339,14011 +Taguaí,Brazil,South America,-23.4519,-49.4089,12669 +Taguig City,Philippines,Asia,14.52,121.05,886722 +Tagum,Philippines,Asia,7.4478,125.8078,296202 +Tahoua,Niger,Africa,14.9,5.2599,117826 +Tahuna,Indonesia,Asia,3.6119,125.5008,35307 +Taibao,Taiwan,Asia,23.45,120.3333,38696 +Taicheng,China,Asia,38.7206,113.2437,40145 +Taichung,Taiwan,Asia,24.1439,120.6794,2850285 +Taihe,China,Asia,30.871,105.388,732380 +Taihecun,China,Asia,45.771,131.003,689611 +Tailai,China,Asia,46.3909,123.4161,75992 +Tainan,Taiwan,Asia,22.9833,120.1833,1856642 +Taiobeiras,Brazil,South America,-15.8078,-42.2328,33050 +Taipei,Taiwan,Asia,25.0375,121.5625,2494813 +Taitung,Taiwan,Asia,22.7583,121.1444,103260 +Taixing,China,Asia,32.159,120.029,990200 +Taiynsha,Kazakhstan,Asia,53.8478,69.7639,12418 +Taiyuan,China,Asia,37.8704,112.5497,3875053 +Taizhou,China,Asia,32.4567,119.9229,4512762 +Taió,Brazil,South America,-27.1158,-49.9978,16257 +Tajrīsh,Iran,Asia,35.8056,51.4381,86000 +Tajumulco,Guatemala,North America,15.0833,-91.8833,63405 +Tak,Thailand,Asia,16.8711,99.125,20290 +Tak Bai,Thailand,Asia,6.2592,102.0531,17317 +Takamatsu,Japan,Asia,34.35,134.05,417814 +Takeo,Cambodia,Asia,10.9833,104.7833,52000 +Takhli,Thailand,Asia,15.2667,100.35,24563 +Takua Pa,Thailand,Asia,8.8705,98.3438,8815 +Takāb,Iran,Asia,36.4008,47.1108,43702 +Tala,Mexico,North America,20.6667,-103.7,80365 +Talachyn,Belarus,Europe,54.4167,29.7,9666 +Talagante,Chile,South America,-33.6667,-70.8667,56990 +Talaigua Nuevo,Colombia,South America,9.3069,-74.5686,11350 +Talas,Turkey,Europe,38.6833,35.5667,169214 +Talca,Chile,South America,-35.4269,-71.6656,206069 +Taldyqorghan,Kazakhstan,Asia,45.0167,78.3667,80000 +Talghar,Kazakhstan,Asia,43.3,77.2333,47301 +Talipao,Philippines,Asia,5.976,121.1087,100088 +Talitsa,Russia,Asia,57.0125,63.7292,14547 +Taliwang,Indonesia,Asia,-8.7336,116.8667,57540 +Tall Abyaḑ,Syria,Asia,36.6965,38.9538,14825 +Tall Rif‘at,Syria,Asia,36.4723,37.0947,27086 +Tall Salḩab,Syria,Asia,35.2588,36.3846,35000 +Tall ‘Afar,Iraq,Asia,36.3792,42.4497,80000 +Tallaght,Ireland,Europe,53.2878,-6.3411,81022 +Tallahassee,United States,North America,30.4551,-84.2527,261271 +Tallbīsah,Syria,Asia,34.8407,36.7309,45853 +Tallinn,Estonia,Europe,59.4372,24.7453,638076 +Tallkalakh,Syria,Asia,34.6684,36.2599,35445 +Talpa de Allende,Mexico,North America,20.3806,-104.8222,14997 +Talsi,Latvia,Europe,57.2444,22.5867,8649 +Tam Kỳ,Vietnam,Asia,15.5667,108.4833,122374 +Tam Điệp,Vietnam,Asia,20.1556,105.9181,104175 +Tamahú,Guatemala,North America,15.3333,-90.1667,24444 +Tamalameque,Colombia,South America,8.8333,-73.5833,13862 +Tamale,Ghana,Africa,9.4075,-0.8533,1095808 +Tamandaré,Brazil,South America,-8.76,-35.105,23561 +Tamanrasset,Algeria,Africa,22.7889,5.5256,92635 +Tamarana,Brazil,South America,-23.7228,-51.0969,10707 +Tamazunchale,Mexico,North America,21.2667,-98.7833,96820 +Tambacounda,Senegal,Africa,13.7689,-13.6672,78800 +Tambaú,Brazil,South America,-21.705,-47.2739,21435 +Tambolaka,Indonesia,Asia,-9.4333,119.2333,35790 +Tamboril,Brazil,South America,-4.8319,-40.3208,24815 +Tambov,Russia,Asia,52.7231,41.4539,289701 +També,Brazil,South America,-7.41,-35.1128,34935 +Tame,Colombia,South America,6.4667,-71.7333,53266 +Tamiahua,Mexico,North America,21.2788,-97.4462,23120 +Tamiang Layang,Indonesia,Asia,-2.116,115.1691,10780 +Taminango,Colombia,South America,1.5667,-77.2833,21985 +Tampa,United States,North America,27.9945,-82.4447,2906035 +Tampamolón Corona,Mexico,North America,21.5667,-98.8167,13760 +Tampere,Finland,Europe,61.4981,23.76,334112 +Tampico,Mexico,North America,22.2553,-97.8686,297284 +Tamuín,Mexico,North America,22.0,-98.7833,35446 +Tanabi,Brazil,South America,-20.6258,-49.6489,25265 +Tanbei,China,Asia,35.088,112.951,447487 +Tandag,Philippines,Asia,9.0789,126.1986,62669 +Tandil,Argentina,South America,-37.3167,-59.1333,400 +Tando Allahyar,Pakistan,Asia,25.4617,68.7167,133487 +Tando Muhammad Khan,Pakistan,Asia,25.1239,68.5389,114406 +Tanga,Tanzania,Africa,-5.0742,39.0992,393429 +Tangdong,China,Asia,25.973,113.231,322990 +Tangdukou,China,Asia,27.0,111.2001,45617 +Tanghin-Dassouri,Burkina Faso,Africa,12.2667,-1.7167,43675 +Tangier,Morocco,Africa,35.7767,-5.8039,1275428 +Tangshan,China,Asia,39.6294,118.1739,3399231 +Tanguá,Brazil,South America,-22.73,-42.7139,31086 +Tangxing,China,Asia,35.7261,111.7108,86862 +Tanhaçu,Brazil,South America,-14.0208,-41.2478,21006 +Tanhuato de Guerrero,Mexico,North America,20.2819,-102.3319,15176 +Tanjung Selor,Indonesia,Asia,2.85,117.3833,63588 +Tanjungpandan,Indonesia,Asia,-2.75,107.75,103062 +Tanjungpinang,Indonesia,Asia,0.9188,104.4554,203008 +Tank,Pakistan,Asia,32.13,70.23,44996 +Tanque Novo,Brazil,South America,-13.5458,-42.4908,17158 +Tantoyuca,Mexico,North America,21.35,-98.2333,23893 +Taohuajiang,China,Asia,28.5239,112.1342,86800 +Taoyang,China,Asia,35.3754,103.8612,109300 +Taoyuan District,Taiwan,Asia,24.9913,121.3143,443273 +Taozhou,China,Asia,30.8956,119.411,499132 +Tapachula,Mexico,North America,14.9,-92.2667,217550 +Tapauá,Brazil,South America,-5.6278,-63.1828,19599 +Tapejara,Brazil,South America,-28.0678,-52.0139,24557 +Taperoá,Brazil,South America,-13.5378,-39.0989,18044 +Tapes,Brazil,South America,-30.6728,-51.3958,14695 +Taphan Hin,Thailand,Asia,16.2209,100.4198,13868 +Tapilula,Mexico,North America,17.25,-93.0167,13592 +Tapiramutá,Brazil,South America,-11.8469,-40.7908,15818 +Tapiratiba,Brazil,South America,-21.4678,-46.7489,11816 +Tapolca,Hungary,Europe,46.8828,17.4411,14006 +Taquara,Brazil,South America,-29.6506,-50.7806,57584 +Taquarana,Brazil,South America,-9.645,-36.4969,19032 +Taquari,Brazil,South America,-29.8,-51.8597,25198 +Taquaritinga,Brazil,South America,-21.4058,-48.505,52260 +Taquarituba,Brazil,South America,-23.5328,-49.2439,24350 +Taraclia,Moldova,Europe,45.9,28.6689,12355 +Taranto,Italy,Europe,40.4711,17.2431,188098 +Tarashcha,Ukraine,Europe,49.55,30.5,9689 +Tarauacá,Brazil,South America,-8.1608,-70.7658,26037 +Tarawa,Kiribati,Oceania,1.3382,173.0176,28802 +Taraz,Kazakhstan,Asia,42.9,71.3667,357795 +Tarazá,Colombia,South America,7.5881,-75.4028,39257 +Tarbes,France,Europe,43.23,0.07,44529 +Targovishte,Bulgaria,Europe,43.25,26.583,57264 +Tari,Papua New Guinea,Oceania,-5.8489,142.9506,15413 +Tarija,Bolivia,South America,-21.5333,-64.7333,268000 +Tarlac City,Philippines,Asia,15.4869,120.59,385398 +Tarnobrzeg,Poland,Europe,50.5833,21.6833,44901 +Tarnowskie Góry,Poland,Europe,50.4444,18.8583,61288 +Tarnów,Poland,Europe,50.0125,20.9886,106259 +Tarogong,Indonesia,Asia,-7.215,107.8856,115660 +Tarqui,Colombia,South America,2.1106,-75.8231,16108 +Tarrafal,Cabo Verde,Africa,15.2787,-23.7516,30000 +Tarragona,Spain,Europe,41.1175,1.2528,141151 +Tarsus,Turkey,Europe,36.9165,34.8951,350732 +Tartagal,Argentina,South America,-22.5,-63.8333,63196 +Tartarugalzinho,Brazil,South America,1.5058,-50.9119,12945 +Tartu,Estonia,Europe,58.38,26.7225,97435 +Tarumirim,Brazil,South America,-19.2808,-42.0069,14709 +Tarumã,Brazil,South America,-22.7469,-50.5769,14882 +Tarusa,Russia,Asia,54.7333,37.1833,9791 +Tarutung,Indonesia,Asia,2.0167,98.9667,44523 +Tarxien,Malta,Europe,35.8658,14.5119,8583 +Tarīm,Yemen,Asia,16.05,49.0,58523 +Tarīn Kōṯ,Afghanistan,Asia,32.6267,65.8733,71604 +Tashkent,Uzbekistan,Asia,41.3111,69.2797,3095498 +Tasīl,Syria,Asia,32.8339,35.9697,15985 +Tata,Hungary,Europe,47.65,18.3167,23549 +Tatabánya,Hungary,Europe,47.5862,18.3949,65861 +Tatahuicapan,Mexico,North America,18.25,-94.7667,15614 +Tataltepec de Valdés,Mexico,North America,16.3064,-97.5461,8869 +Tataouine,Tunisia,Africa,32.9306,10.45,70224 +Tatarbunary,Ukraine,Europe,45.8403,29.6125,10836 +Tatuí,Brazil,South America,-23.3556,-47.8569,116682 +Tatvan,Turkey,Europe,38.5022,42.2814,79214 +Taubaté,Brazil,South America,-23.0333,-45.55,317915 +Tauberbischofsheim,Germany,Europe,49.6225,9.6628,13621 +Taunggyi,Burma,Asia,20.7836,97.0354,264804 +Tauragė,Lithuania,Europe,55.2522,22.2897,21416 +Tauramena,Colombia,South America,5.0167,-72.75,27178 +Tausa,Colombia,South America,5.1964,-73.8875,8801 +Tauá,Brazil,South America,-6.0028,-40.2928,51948 +Tavares,Brazil,South America,-7.6358,-37.8783,14759 +Tavas,Turkey,Europe,37.5728,29.0714,41712 +Tavda,Russia,Asia,58.05,65.2667,32272 +Tavira,Portugal,Europe,37.1309,-7.6506,26167 +Tavşanlı,Turkey,Europe,39.5333,29.4833,73306 +Taxco de Alarcón,Mexico,North America,18.5564,-99.605,52217 +Taxisco,Guatemala,North America,14.0667,-90.4667,36750 +Taxtako‘pir,Uzbekistan,Asia,43.0225,60.2886,17100 +Tayshet,Russia,Asia,55.95,98.0167,34491 +Taywarah,Afghanistan,Asia,33.51,64.42,15000 +Taşköprü,Turkey,Europe,41.5097,34.2142,17048 +Taşova,Turkey,Europe,40.75,36.3167,11248 +Taşucu,Turkey,Europe,36.3333,33.8833,15184 +Ta‘izz,Yemen,Asia,13.5789,44.0219,458789 +Ta’ Xbiex,Malta,Europe,35.8992,14.4981,1804 +Tbeng Meanchey,Cambodia,Asia,13.8167,104.9667,24380 +Tbilisi,Georgia,Europe,41.7225,44.7925,1118035 +Tchibanga,Gabon,Africa,-2.9331,10.9831,30042 +Tczew,Poland,Europe,54.0875,18.7972,57990 +Teapa,Mexico,North America,17.5483,-92.953,29068 +Tearce,North Macedonia,Europe,42.0775,21.0519,3038 +Tecali,Mexico,North America,18.9,-97.9667,23625 +Tecamachalco,Mexico,North America,18.8667,-97.7167,77061 +Tecate,Mexico,North America,32.5722,-116.6403,81059 +Techiman,Ghana,Africa,7.5905,-1.9395,104212 +Tecoanapa,Mexico,North America,16.8,-99.15,42619 +Tecolotlán,Mexico,North America,20.1,-103.8333,16603 +Tecolutla,Mexico,North America,20.4797,-97.01,26431 +Tecomán,Mexico,North America,18.9089,-103.8747,116305 +Tecpán Guatemala,Guatemala,North America,14.7667,-91.0,112864 +Tectitán,Guatemala,North America,15.3333,-92.0333,11928 +Tecuala,Mexico,North America,22.4004,-105.46,14921 +Teculután,Guatemala,North America,14.9831,-89.5667,20272 +Tecámac,Mexico,North America,19.7131,-98.9683,364579 +Tefé,Brazil,South America,-3.3539,-64.7108,73669 +Tegucigalpa,Honduras,North America,14.1057,-87.204,1157509 +Tehran,Iran,Asia,35.6889,51.3897,14148000 +Tehuacán,Mexico,North America,18.4617,-97.3928,319375 +Tehuipango,Mexico,North America,18.5167,-97.05,26322 +Teixeira,Brazil,South America,-7.2228,-37.2539,14631 +Teixeira Soares,Brazil,South America,-25.3678,-50.4608,9547 +Teixeira de Freitas,Brazil,South America,-17.535,-39.7419,107486 +Teixeiras,Brazil,South America,-20.6508,-42.8569,12255 +Tejupilco,Mexico,North America,18.9058,-100.1528,71077 +Tejutla,Guatemala,North America,15.1225,-91.8053,44416 +Tejuçuoca,Brazil,South America,-3.9889,-39.5808,17154 +Tekeli,Kazakhstan,Asia,44.83,78.8239,30707 +Tekes,China,Asia,43.1903,81.7778,33740 +Tekirdağ,Turkey,Europe,40.9778,27.5153,186421 +Tekkeköy,Turkey,Europe,41.2125,36.4569,56318 +Tekman,Turkey,Europe,39.645,41.5083,23195 +Tel Aviv-Yafo,Israel,Asia,32.08,34.78,467875 +Telavi,Georgia,Europe,41.9169,45.4764,19509 +Teleneşti,Moldova,Europe,47.5028,28.3667,7227 +Telica,Nicaragua,North America,12.5167,-86.8667,23266 +Tello,Colombia,South America,3.0667,-75.1333,10273 +Telpaneca,Nicaragua,North America,13.5333,-86.2833,19025 +Telsen,Argentina,South America,-42.3833,-66.95,544 +Telêmaco Borba,Brazil,South America,-24.3239,-50.6158,75042 +Telšiai,Lithuania,Europe,55.9833,22.25,22261 +Temamatla,Mexico,North America,19.2028,-98.87,11206 +Temascalapa,Mexico,North America,19.8,-98.9,33063 +Temascalcingo,Mexico,North America,19.9147,-100.0036,58169 +Temascaltepec de González,Mexico,North America,19.0433,-100.0414,30336 +Tembagapura,Indonesia,Asia,-4.1428,137.0906,23022 +Temecula,United States,North America,33.4928,-117.1315,110404 +Temerin,Serbia,Europe,45.4167,19.8833,25780 +Teminabuan,Indonesia,Asia,-1.4333,132.0167,19876 +Temirtaū,Kazakhstan,Asia,50.05,72.95,185082 +Temixco,Mexico,North America,18.85,-99.2333,116143 +Temoaya,Mexico,North America,19.4686,-99.5933,105766 +Tempe,United States,North America,33.3881,-111.9319,186419 +Tempio Pausania,Italy,Europe,40.9015,9.1044,13196 +Temple,United States,North America,31.1068,-97.3891,123932 +Tempoal de Sánchez,Mexico,North America,21.5167,-98.3833,12237 +Temryuk,Russia,Asia,45.2667,37.3667,40994 +Temuco,Chile,South America,-38.7333,-72.6667,282415 +Tena,Ecuador,South America,-0.989,-77.8159,29724 +Tenancingo,Mexico,North America,18.9608,-99.5906,104677 +Tenango de Doria,Mexico,North America,20.3356,-98.2267,17503 +Tenango del Aire,Mexico,North America,19.1575,-98.8581,10578 +Tenente Ananias Gomes,Brazil,South America,-6.465,-38.18,10262 +Tenente Portela,Brazil,South America,-27.3708,-53.7578,14497 +Tenerife,Colombia,South America,9.8989,-74.8589,14320 +Tenggarong,Indonesia,Asia,-0.4,116.9667,114039 +Tengyue,China,Asia,25.0167,98.4833,620000 +Tenjo,Colombia,South America,4.9167,-74.1667,20269 +Tenkodogo,Burkina Faso,Africa,11.7833,-0.3667,58600 +Tenosique,Mexico,North America,17.4756,-91.4225,34946 +Tentena,Indonesia,Asia,-1.7822,120.6494,11233 +Teocelo,Mexico,North America,19.3833,-96.9667,16957 +Teocuitatlán de Corona,Mexico,North America,20.0167,-103.1833,10317 +Teodoro Sampaio,Brazil,South America,-22.5325,-52.1675,23273 +Teoloyucan,Mexico,North America,19.7442,-99.1811,63115 +Teopisca,Mexico,North America,16.55,-92.5,20044 +Teorama,Colombia,South America,8.4333,-72.2833,23381 +Teotitlán,Mexico,North America,18.1333,-97.0833,8675 +Tepalcatepec,Mexico,North America,19.1833,-102.85,42879 +Tepatitlán de Morelos,Mexico,North America,20.8139,-102.7639,141322 +Tepatlaxco,Mexico,North America,19.0667,-97.9667,17472 +Tepeapulco,Mexico,North America,19.7856,-98.5517,49850 +Tepechitlán,Mexico,North America,21.6667,-103.3333,8972 +Tepetlaoxtoc,Mexico,North America,19.5731,-98.8203,30680 +Tepetlixpa,Mexico,North America,19.0006,-98.8178,19843 +Tepetlán,Mexico,North America,19.6667,-96.8,9668 +Tepetzintla,Mexico,North America,21.1658,-97.8511,14736 +Tepic,Mexico,North America,21.5083,-104.8931,425942 +Teploklyuchenka,Kyrgyzstan,Asia,42.4995,78.527,14055 +Tepotzotlán,Mexico,North America,19.7161,-99.2236,67724 +Tepoztlán,Mexico,North America,18.9853,-99.0997,14130 +Tequila,Mexico,North America,20.8828,-103.8367,31115 +Tequisquiapan,Mexico,North America,20.5206,-99.8958,29799 +Tequixquiac,Mexico,North America,19.9097,-99.1417,22676 +Teramo,Italy,Europe,42.6589,13.7039,51548 +Terenos,Brazil,South America,-20.4419,-54.86,11662 +Teresina,Brazil,South America,-5.0903,-42.8167,868075 +Teresópolis,Brazil,South America,-22.4119,-42.9658,138081 +Termas de Río Hondo,Argentina,South America,-27.4833,-64.8667,32116 +Terme,Turkey,Europe,41.2092,36.9739,71092 +Termiz,Uzbekistan,Asia,37.2167,67.2833,182800 +Terneuzen,Netherlands,Europe,51.3333,3.8333,54589 +Terni,Italy,Europe,42.5667,12.65,106370 +Ternivka,Ukraine,Europe,48.5167,36.0,29226 +Ternopil,Ukraine,Europe,49.5667,25.6,225004 +Terra Alta,Brazil,South America,-1.0378,-47.9078,10400 +Terra Boa,Brazil,South America,-23.7678,-52.4439,17568 +Terra Nova,Brazil,South America,-8.23,-39.3758,8920 +Terra Rica,Brazil,South America,-22.7089,-52.6169,14842 +Terra Roxa d’Oeste,Brazil,South America,-24.1667,-54.0833,18119 +Terra Santa,Brazil,South America,-2.1039,-56.4869,18782 +Terra de Areia,Brazil,South America,-29.585,-50.0708,9878 +Terrabona,Nicaragua,North America,12.7333,-85.9667,12740 +Terrebonne,Canada,North America,45.7,-73.6333,119944 +Teruel,Spain,Europe,40.3436,-1.1072,36713 +Tesalia,Colombia,South America,2.4833,-75.7333,9767 +Tessalit,Mali,Africa,20.2011,1.0125,3612 +Tete,Mozambique,Africa,-16.1579,33.5898,152909 +Tetela de Ocampo,Mexico,North America,19.8167,-97.8,28139 +Tetela del Volcán,Mexico,North America,18.9633,-99.2533,10199 +Tetiiv,Ukraine,Europe,49.3708,29.69,12640 +Tetovo,North Macedonia,Europe,42.0103,20.9714,63176 +Teustepe,Nicaragua,North America,12.4167,-85.8,26265 +Teutônia,Brazil,South America,-29.4478,-51.8058,32797 +Texcaltitlán,Mexico,North America,18.9297,-99.9372,19206 +Texcatepec,Mexico,North America,20.5833,-98.3667,10934 +Texcoco,Mexico,North America,19.52,-98.88,105165 +Texistepec,Mexico,North America,17.9,-94.8167,20887 +Teyateyaneng,Lesotho,Africa,-29.1511,27.7425,75115 +Tezoatlán de Segura y Luna,Mexico,North America,17.65,-97.8167,11034 +Tezonapa,Mexico,North America,18.6,-96.6833,56956 +Tezontepec,Mexico,North America,19.8833,-98.8167,13032 +Tezoyuca,Mexico,North America,19.75,-99.1833,41333 +Teófilo Otoni,Brazil,South America,-17.8578,-41.505,137418 +Tešanj,Bosnia and Herzegovina,Europe,44.6119,17.9855,43063 +Tha Bo,Thailand,Asia,17.85,102.5814,18320 +Tha Chang,Thailand,Asia,9.2642,99.1922,11108 +Tha Luang,Thailand,Asia,15.0712,101.098,9003 +Tha Mai,Thailand,Asia,12.6214,102.0048,9843 +Tha Maka,Thailand,Asia,13.8865,99.801,9411 +Tha Muang,Thailand,Asia,13.9612,99.6412,10219 +Tha Yang,Thailand,Asia,12.9723,99.8827,28437 +Thaba-Tseka,Lesotho,Africa,-29.5333,28.6,5423 +Thakhèk,Laos,Asia,17.4,104.8,70000 +Thanh Hóa,Vietnam,Asia,19.8075,105.7764,850000 +Thap Khlo,Thailand,Asia,16.16,100.5966,9676 +Thap Than,Thailand,Asia,15.4606,99.8917,8931 +Thatta,Pakistan,Asia,24.7461,67.9244,220000 +The Bottom,"Bonaire, Sint Eustatius, and Saba",North America,17.6261,-63.2492,462 +The Hague,Netherlands,Europe,52.08,4.31,548320 +The Valley,Anguilla,North America,18.2208,-63.0517,3269 +The Woodlands,United States,North America,30.1738,-95.5134,116916 +Thebes,Greece,Europe,38.3208,23.3178,20038 +Thessaloníki,Greece,Europe,40.6403,22.9356,802572 +Thiers,France,Europe,45.8572,3.5483,11686 +Thimphu,Bhutan,Asia,27.4722,89.6361,114551 +Thinadhoo,Maldives,Asia,0.5302,72.9965,5230 +Thionville,France,Europe,49.3589,6.1692,42778 +Thiruvananthapuram,India,Asia,8.5241,76.9366,743691 +Thisted,Denmark,Europe,56.9588,8.6964,13534 +Thiès,Senegal,Africa,14.7916,-16.9249,115245 +Thoen,Thailand,Asia,17.6129,99.2161,15360 +Tholen,Netherlands,Europe,51.5322,4.2206,26085 +Thong Pha Phum,Thailand,Asia,14.7382,98.632,14141 +Thonon-les-Bains,France,Europe,46.3627,6.475,37689 +Thornton,United States,North America,39.9197,-104.9438,142878 +Thousand Oaks,United States,North America,34.1914,-118.8756,208302 +Thuin,Belgium,Europe,50.3398,4.2874,14671 +Thun,Switzerland,Europe,46.759,7.63,43734 +Thunder Bay,Canada,North America,48.3822,-89.2461,108843 +Thung Sai,Thailand,Asia,16.3149,99.8327,8971 +Thung Song,Thailand,Asia,8.154,99.7286,30500 +Thyolo,Malawi,Africa,-16.0667,35.1333,7029 +Thái Bình,Vietnam,Asia,20.4461,106.3422,268167 +Thái Nguyên,Vietnam,Asia,21.6,105.85,420000 +Thérmi,Greece,Europe,40.55,23.0167,55358 +Thạnh Phú,Vietnam,Asia,9.9539,106.5069,10331 +Thị Trấn Ngải Giao,Vietnam,Asia,10.6406,107.2478,16522 +Thủ Dầu Một,Vietnam,Asia,10.9667,106.65,321607 +Thủ Đức,Vietnam,Asia,10.8266,106.7609,1013795 +Tiachiv,Ukraine,Europe,48.0114,23.5722,8887 +Tianchang,China,Asia,32.6811,119.0137,602840 +Tianguá,Brazil,South America,-3.7322,-40.9917,76537 +Tianjin,China,Asia,39.1336,117.2054,10368000 +Tiaong,Philippines,Asia,13.95,121.32,106265 +Tiaret,Algeria,Africa,35.3667,1.3167,178915 +Tibagi,Brazil,South America,-24.5089,-50.4139,19961 +Tibaná,Colombia,South America,5.3167,-73.4,9186 +Tibasosa,Colombia,South America,5.8333,-72.9667,14063 +Tibau do Sul,Brazil,South America,-6.1869,-35.0919,16929 +Tibro,Sweden,Europe,58.419,14.158,8588 +Tibú,Colombia,South America,8.65,-72.7333,37286 +Ticuantepe,Nicaragua,North America,12.0167,-86.2,27008 +Tidjikja,Mauritania,Africa,18.55,-11.4166,15532 +Tiel,Netherlands,Europe,51.8833,5.4333,41920 +Tieli,China,Asia,46.95,128.05,225960 +Tielt,Belgium,Europe,51.0,3.3167,20500 +Tierra Colorada,Mexico,North America,17.1656,-99.5264,10502 +Tierralta,Colombia,South America,7.9167,-76.1667,78770 +Tietê,Brazil,South America,-23.1019,-47.715,37663 +Tifariti,Morocco,Africa,26.158,-10.567,3000 +Tigoa,Solomon Islands,Oceania,-11.5531,160.0647,613 +Tigre,Argentina,South America,-34.5,-58.6167,64867 +Tijuana,Mexico,North America,32.525,-117.0333,2002000 +Tijucas,Brazil,South America,-27.2408,-48.6339,51592 +Tijucas do Sul,Brazil,South America,-25.9278,-49.1989,12260 +Tikhoretsk,Russia,Asia,45.8667,40.1333,54582 +Tikhvin,Russia,Asia,59.65,33.5333,53932 +Tikrīt,Iraq,Asia,34.6,43.6833,105700 +Tiksi,Russia,Asia,71.6269,128.835,5700 +Tila,Mexico,North America,17.3,-92.4333,9609 +Tilburg,Netherlands,Europe,51.5572,5.0911,1944588 +Tillabéri,Niger,Africa,14.212,1.4531,19262 +Timaná,Colombia,South America,1.9833,-75.95,20463 +Timargara,Pakistan,Asia,34.8278,71.8417,58050 +Timashevsk,Russia,Asia,45.6167,38.9333,51398 +Timbaúba,Brazil,South America,-7.5053,-35.3183,52802 +Timbiquí,Colombia,South America,2.7719,-77.665,22077 +Timbiras,Brazil,South America,-4.255,-43.9408,26484 +Timbuktu,Mali,Africa,16.7735,-3.0074,35330 +Timbío,Colombia,South America,2.3445,-76.6839,33883 +Timbó,Brazil,South America,-26.8233,-49.2717,44977 +Timimoun,Algeria,Africa,29.2628,-0.2389,33060 +Timişoara,Romania,Europe,45.7597,21.23,250849 +Timon,Brazil,South America,-5.0939,-42.8369,174465 +Timrå,Sweden,Europe,62.4778,17.3069,10443 +Timóteo,Brazil,South America,-19.5828,-42.6439,81579 +Tinaquillo,Venezuela,South America,9.9167,-68.3,110000 +Tindouf,Algeria,Africa,27.6753,-8.1286,45610 +Tingloy,Philippines,Asia,13.65,120.87,19215 +Tinogasta,Argentina,South America,-28.0667,-67.5667,11485 +Tipasa,Algeria,Africa,36.5919,2.4494,25225 +Tipitapa,Nicaragua,North America,12.2,-86.1,147379 +Tiquisate,Guatemala,North America,14.2833,-91.3667,63971 +Tiquisio,Colombia,South America,8.5667,-74.2667,22041 +Tirana,Albania,Europe,41.3272,19.8186,418495 +Tiraspol,Moldova,Europe,46.85,29.6333,133807 +Tire,Turkey,Europe,38.0833,27.7333,87462 +Tirebolu,Turkey,Europe,41.0056,38.8167,20671 +Tirschenreuth,Germany,Europe,49.8833,12.3333,8580 +Tisma,Nicaragua,North America,12.0826,-86.0178,10681 +Tissemsilt,Algeria,Africa,35.6078,1.8111,75197 +Tiszafüred,Hungary,Europe,47.619,20.76,10642 +Tiszakécske,Hungary,Europe,46.9311,20.095,11690 +Tiszavasvári,Hungary,Europe,47.9511,21.3689,12276 +Tiszaújváros,Hungary,Europe,47.9228,21.0519,14700 +Titao,Burkina Faso,Africa,13.25,-2.05,48242 +Titel,Serbia,Europe,45.2,20.3,15738 +Titiribí,Colombia,South America,6.0667,-75.8,14092 +Tivat,Montenegro,Europe,42.43,18.7,9367 +Tixkokob,Mexico,North America,21.0022,-89.3936,10968 +Tixtla de Guerrero,Mexico,North America,17.5667,-99.4,21720 +Tizayuca,Mexico,North America,19.8333,-98.9833,119442 +Tizi Ouzou,Algeria,Africa,36.7167,4.05,135088 +Tizimín,Mexico,North America,21.1425,-88.1647,52593 +Tišina,Slovenia,Europe,46.6556,16.0933,408 +Tlachichilco,Mexico,North America,20.6217,-98.1994,11630 +Tlacoachistlahuaca,Mexico,North America,16.7833,-98.2,18055 +Tlacolula de Matamoros,Mexico,North America,16.9542,-96.4792,16510 +Tlacolulan,Mexico,North America,19.6667,-97.0,11431 +Tlacotepec,Mexico,North America,18.6882,-97.6489,52508 +Tlahualilo de Zaragoza,Mexico,North America,26.1083,-103.4431,9517 +Tlahuitoltepec,Mexico,North America,17.1,-96.0667,8922 +Tlajomulco de Zúñiga,Mexico,North America,20.4736,-103.4431,44103 +Tlalixcoyan,Mexico,North America,18.8031,-96.0611,37857 +Tlalixtac de Cabrera,Mexico,North America,17.0667,-96.65,10208 +Tlalmanalco,Mexico,North America,19.2044,-98.8025,46130 +Tlalnelhuayocan,Mexico,North America,19.5667,-96.9667,18715 +Tlalnepantla,Mexico,North America,19.5367,-99.1947,653410 +Tlalpujahua de Rayón,Mexico,North America,19.805,-100.1744,27788 +Tlaltenango de Sánchez Román,Mexico,North America,21.7815,-103.3059,21636 +Tlaltetela,Mexico,North America,19.3167,-96.9,15818 +Tlapa de Comonfort,Mexico,North America,17.5461,-98.5764,37975 +Tlapacoyan,Mexico,North America,19.9617,-97.2108,115000 +Tlaquepaque,Mexico,North America,20.6167,-103.3167,664193 +Tlaxcala,Mexico,North America,19.3125,-98.24,14692 +Tlayacapan,Mexico,North America,18.9556,-98.9811,14467 +Tlemcen,Algeria,Africa,34.8828,-1.3167,72200 +Tlumach,Ukraine,Europe,48.8667,25.0,8689 +Toamasina,Madagascar,Africa,-18.155,49.41,225116 +Toba Tek Singh,Pakistan,Asia,30.9711,72.4825,75943 +Tobelo,Indonesia,Asia,1.7319,128.0078,34150 +Tobias Barreto,Brazil,South America,-11.1839,-37.9978,50905 +Tobolsk,Russia,Asia,58.2,68.2667,99454 +Tobruk,Libya,Africa,32.0833,23.95,135832 +Toca,Colombia,South America,5.5667,-73.2,10157 +Tocaima,Colombia,South America,4.5,-74.6667,18790 +Tocancipá,Colombia,South America,4.9657,-73.9136,36439 +Tocantins,Brazil,South America,-21.175,-43.0178,16185 +Tocantinópolis,Brazil,South America,-6.3289,-47.4158,22615 +Tocopilla,Chile,South America,-22.0889,-70.1958,24247 +Tocumen,Panama,North America,9.0894,-79.3831,74952 +Tocuyito,Venezuela,South America,10.1144,-68.0656,173450 +Todos Santos Cuchumatán,Guatemala,North America,15.5167,-91.6167,39638 +Tofol,"Micronesia, Federated States of",Oceania,5.3258,163.0086,2591 +Tokat,Turkey,Europe,40.3139,36.5542,163405 +Tokmak,Ukraine,Europe,47.2556,35.7056,29573 +Toksun,China,Asia,42.7918,88.6536,29657 +Toktogul,Kyrgyzstan,Asia,41.8826,72.9372,20577 +Tokushima,Japan,Asia,34.0667,134.55,254510 +Tokyo,Japan,Asia,35.687,139.7495,37785000 +Tola,Nicaragua,North America,11.4333,-85.9333,22012 +Tolcayuca,Mexico,North America,19.95,-98.9167,11746 +Toledo,United States,North America,41.6638,-83.5827,492076 +Toli,China,Asia,45.9313,83.6039,28378 +Toliara,Madagascar,Africa,-23.35,43.6667,168756 +Tolimán,Mexico,North America,19.5333,-103.9,11219 +Tolmin,Slovenia,Europe,46.1857,13.7352,3525 +Tolna,Hungary,Europe,46.425,18.775,10640 +Toluca,Mexico,North America,19.2925,-99.6569,2476689 +Tolú Viejo,Colombia,South America,9.45,-75.45,20033 +Toma,Burkina Faso,Africa,12.7667,-2.8833,15851 +Tomar,Portugal,Europe,39.6,-8.4167,40677 +Tomar do Geru,Brazil,South America,-11.3728,-37.8408,12012 +Tomarza,Turkey,Europe,38.4472,35.7992,21100 +Tomaszów Lubelski,Poland,Europe,50.45,23.4167,18567 +Tomaszów Mazowiecki,Poland,Europe,51.5167,20.0167,57600 +Tomatlán,Mexico,North America,19.9369,-105.25,36316 +Tomaž pri Ormožu,Slovenia,Europe,46.4842,16.0836,254 +Tombos,Brazil,South America,-20.905,-42.0228,8609 +Tominian,Mali,Africa,13.2875,-4.5933,18130 +Tomohon,Indonesia,Asia,1.325,124.8389,96411 +Tomsk,Russia,Asia,56.5,84.9667,545391 +Tomé-Açu,Brazil,South America,-2.4189,-48.1519,47273 +Tonalá,Mexico,North America,20.6167,-103.2333,569913 +Tonantins,Brazil,South America,-2.8728,-67.8019,19247 +Tondela,Portugal,Europe,40.5164,-8.0808,28946 +Tonekābon,Iran,Asia,36.8153,50.8711,55434 +Tongchuan,China,Asia,34.897,108.945,698322 +Tongeren,Belgium,Europe,50.7807,5.4623,31142 +Tongjiang,China,Asia,47.65,132.5,176112 +Tongliao,China,Asia,43.654,122.243,2873168 +Tongshan,China,Asia,34.261,117.1859,8669000 +Tongyangdao,China,Asia,41.7676,109.9711,27476 +Tonya,Turkey,Europe,40.8856,39.2922,13457 +Topeka,United States,North America,39.0346,-95.6955,150343 +Topola,Serbia,Europe,44.2525,20.6761,22207 +Topoľčany,Slovakia,Europe,48.55,18.1833,25249 +Toprakkale,Turkey,Europe,37.0669,36.1464,11411 +Toqsu,China,Asia,41.5417,82.604,27454 +Torbalı,Turkey,Europe,38.1619,27.3583,207840 +Torbat-e Jām,Iran,Asia,35.2431,60.625,100449 +Torbat-e Ḩeydarīyeh,Iran,Asia,35.2872,59.2219,140019 +Torcy,France,Europe,48.8502,2.6508,22939 +Torgau,Germany,Europe,51.5603,13.0056,19683 +Torghay,Kazakhstan,Asia,49.626,63.499,5277 +Toribío,Colombia,South America,2.9581,-76.2714,26616 +Torit,South Sudan,Africa,4.4118,32.5705,17957 +Toritama,Brazil,South America,-8.0067,-36.0567,41137 +Tornio,Finland,Europe,65.85,24.15,21018 +Tornquist,Argentina,South America,-38.1,-62.2167,11759 +Toro,Colombia,South America,4.6117,-76.0814,16394 +Toronto,Canada,North America,43.7417,-79.3733,5647656 +Toropets,Russia,Asia,56.5,31.65,11441 +Tororo,Uganda,Africa,0.6928,34.1811,41906 +Torrance,United States,North America,33.8346,-118.3416,143499 +Torre de Moncorvo,Portugal,Europe,41.2,-7.1333,8572 +Torres Novas,Portugal,Europe,39.4833,-8.5333,36717 +Torres Vedras,Portugal,Europe,39.0833,-9.2667,79465 +Torreón,Mexico,North America,25.5419,-103.4083,66001 +Torrinha,Brazil,South America,-22.4258,-48.1689,9335 +Tortolì,Italy,Europe,39.9333,9.65,10953 +Tortum,Turkey,Europe,40.2981,41.6325,15259 +Toruń,Poland,Europe,53.0222,18.6111,198273 +Torzhok,Russia,Asia,57.0333,34.9667,41116 +Tosagua,Ecuador,South America,-0.78,-80.26,11317 +Toshloq,Uzbekistan,Asia,40.4808,71.7608,18600 +Tosno,Russia,Asia,59.55,30.9,32074 +Tostado,Argentina,South America,-29.2333,-61.7667,14582 +Tosya,Turkey,Europe,41.0172,34.0383,28963 +Totness,Suriname,South America,5.8775,-56.3292,2150 +Totogalpa,Nicaragua,North America,13.5667,-86.5,11927 +Totolapan,Mexico,North America,18.9869,-98.9197,10790 +Totonicapán,Guatemala,North America,14.9108,-91.3606,118960 +Totoró,Colombia,South America,2.5117,-76.4003,17611 +Tototlán,Mexico,North America,20.0833,-102.65,23171 +Totutla,Mexico,North America,19.2167,-96.9667,17428 +Touba,Côte d’Ivoire,Africa,8.2833,-7.6833,55013 +Tougan,Burkina Faso,Africa,13.0667,-3.0667,26347 +Touggourt,Algeria,Africa,33.1,6.0667,143270 +Tougué,Guinea,Africa,11.44,-11.67,25531 +Toul,France,Europe,48.675,5.8917,15570 +Toulon,France,Europe,43.1258,5.9306,180834 +Toulouse,France,Europe,43.6045,1.444,511684 +Toumodi,Côte d’Ivoire,Africa,6.552,-5.019,39005 +Tournai,Belgium,Europe,50.6056,3.3881,69554 +Tournon-sur-Rhône,France,Europe,45.0681,4.8333,11302 +Touros,Brazil,South America,-5.1989,-35.4608,33035 +Tours,France,Europe,47.3936,0.6892,138668 +Tovuz,Azerbaijan,Europe,40.9924,45.6167,13520 +Toyama,Japan,Asia,36.6959,137.2137,413028 +Toyloq Qishlog’i,Uzbekistan,Asia,39.6014,67.0908,13700 +Tozeur,Tunisia,Africa,33.9167,8.1333,41370 +Trabzon,Turkey,Europe,41.005,39.7225,293661 +Tracuateua,Brazil,South America,-1.0719,-46.8969,28595 +Tracunhaém,Brazil,South America,-7.805,-35.24,13867 +Traipu,Brazil,South America,-9.9706,-37.0033,27826 +Trairi,Brazil,South America,-3.2778,-39.2689,58415 +Trajano de Morais,Brazil,South America,-22.0628,-42.0658,10302 +Trakai,Lithuania,Europe,54.6333,24.9333,5912 +Tralee,Ireland,Europe,52.2675,-9.6962,26079 +Tramandaí,Brazil,South America,-29.9847,-50.1336,52632 +Trancas,Argentina,South America,-26.2172,-65.2831,3391 +Trancoso,Portugal,Europe,40.7833,-7.35,9878 +Trang,Thailand,Asia,7.5575,99.6103,59894 +Trani,Italy,Europe,41.2667,16.4167,54941 +Tranås,Sweden,Europe,58.05,14.9833,14197 +Trapani,Italy,Europe,38.0175,12.515,55559 +Trashi Yangtse,Bhutan,Asia,27.5833,91.4667,2735 +Trashigang,Bhutan,Asia,27.3326,91.5528,3037 +Trat,Thailand,Asia,12.2417,102.5125,10413 +Traunstein,Germany,Europe,47.8667,12.6333,21551 +Travnik,Bosnia and Herzegovina,Europe,44.2264,17.6597,53482 +Trbovlje,Slovenia,Europe,46.15,15.05,13822 +Trebinje,Bosnia and Herzegovina,Europe,42.7089,18.3217,31433 +Trebišov,Slovakia,Europe,48.6333,21.7167,22890 +Trebnje,Slovenia,Europe,45.9104,15.0086,3927 +Trece Martires City,Philippines,Asia,14.28,120.87,210503 +Treinta y Tres,Uruguay,South America,-33.2333,-54.3833,25477 +Trelleborg,Sweden,Europe,55.3667,13.1667,43359 +Tremedal,Brazil,South America,-14.9758,-41.4108,16296 +Tremembé,Brazil,South America,-22.9583,-45.5494,47714 +Trenque Lauquen,Argentina,South America,-35.9667,-62.7,46019 +Trento,Italy,Europe,46.0667,11.1167,118046 +Trenton,United States,North America,40.2237,-74.7641,362501 +Trenčín,Slovakia,Europe,48.8919,18.0367,54740 +Tres Arroyos,Argentina,South America,-38.3667,-60.2667,62835 +Tres Isletas,Argentina,South America,-26.35,-60.4167,24747 +Tres Valles,Mexico,North America,18.1667,-96.0167,44978 +Treviso,Italy,Europe,45.6722,12.2422,84607 +Treze Tílias,Brazil,South America,-27.0019,-51.4058,8787 +Trier,Germany,Europe,49.7567,6.6414,112737 +Triesen,Liechtenstein,Europe,47.1,9.5333,5275 +Triesenberg,Liechtenstein,Europe,47.1181,9.5433,2643 +Trieste,Italy,Europe,45.6503,13.7703,198417 +Trincomalee,Sri Lanka,Asia,8.5667,81.2333,99135 +Trindade,Brazil,South America,-16.6489,-49.4889,142431 +Trinidad,Bolivia,South America,-14.8292,-64.9014,130657 +Tripoli,Libya,Africa,32.8872,13.1914,1183000 +Triunfo,Brazil,South America,-29.9428,-51.7178,27498 +Trnava,Slovakia,Europe,48.3777,17.5862,63803 +Trnovska Vas,Slovenia,Europe,46.5167,15.9,369 +Trofa,Portugal,Europe,41.3374,-8.5596,38999 +Trogir,Croatia,Europe,43.5167,16.25,12393 +Trois-Rivières,Canada,North America,46.35,-72.55,139163 +Troitsk,Russia,Asia,54.0833,61.5667,69983 +Trollhättan,Sweden,Europe,58.2671,12.3,59210 +Tromsø,Norway,Europe,69.6517,18.9556,38980 +Trondheim,Norway,Europe,63.44,10.4,212660 +Trongsa,Bhutan,Asia,27.4994,90.5047,2695 +Troyes,France,Europe,48.2997,4.0792,62443 +Trstenik,Serbia,Europe,43.6186,20.9972,17180 +Trujillo,Peru,South America,-8.112,-79.0288,935147 +Trzebnica,Poland,Europe,51.305,17.0614,13631 +Trzin,Slovenia,Europe,46.1353,14.56,3925 +Trà Vinh,Vietnam,Asia,9.9333,106.35,160310 +Três Barras,Brazil,South America,-26.1058,-50.3219,19746 +Três Cachoeiras,Brazil,South America,-29.4558,-49.9239,10962 +Três Corações,Brazil,South America,-21.6947,-45.2553,75485 +Três Coroas,Brazil,South America,-29.5169,-50.7778,24425 +Três Lagoas,Brazil,South America,-20.7876,-51.7034,123281 +Três Marias,Brazil,South America,-18.2058,-45.2419,28895 +Três Passos,Brazil,South America,-27.4558,-53.9319,25436 +Três Pontas,Brazil,South America,-21.3669,-45.5128,51024 +Três Rios,Brazil,South America,-22.1169,-43.2089,78346 +Três de Maio,Brazil,South America,-27.7728,-54.24,24916 +Trëkhgornyy,Russia,Asia,54.8,58.45,30800 +Tríkala,Greece,Europe,39.55,21.7667,61608 +Trípoli,Greece,Europe,37.5167,22.3833,30383 +Tržič,Slovenia,Europe,46.3581,14.293,3670 +Trảng Bàng,Vietnam,Asia,11.033,106.367,183385 +Tsetserleg,Mongolia,Asia,47.4769,101.4503,21620 +Tshabong,Botswana,Africa,-26.02,22.4056,8939 +Tshikapa,Congo (Kinshasa),Africa,-6.4167,20.8,1006387 +Tsimlyansk,Russia,Asia,47.6478,42.0961,14731 +Tsiroanomandidy,Madagascar,Africa,-18.7698,46.05,44461 +Tsu,Japan,Asia,34.7331,136.5133,273267 +Tsévié,Togo,Africa,6.4333,1.2167,55775 +Tuapse,Russia,Asia,44.1044,39.0772,1392 +Tuban,Indonesia,Asia,-6.9,112.05,87947 +Tubará,Colombia,South America,10.8667,-74.9833,10602 +Tubarão,Brazil,South America,-28.4669,-49.0069,110088 +Tubbergen,Netherlands,Europe,52.4,6.7667,21315 +Tubmanburg,Liberia,Africa,6.8667,-10.8167,13114 +Tubod,Philippines,Asia,8.05,123.8,50073 +Tucacas,Venezuela,South America,10.7978,-68.3175,13901 +Tucano,Brazil,South America,-10.9628,-38.7869,48736 +Tuchola,Poland,Europe,53.6,17.85,13408 +Tuchín,Colombia,South America,9.1833,-75.55,41345 +Tucson,United States,North America,32.1541,-110.8787,879871 +Tucumã,Brazil,South America,-6.7519,-51.1539,39550 +Tucupita,Venezuela,South America,9.0592,-62.0681,86487 +Tucuruí,Brazil,South America,-3.7678,-49.6728,91306 +Tucurú,Guatemala,North America,15.3,-90.0667,50971 +Tufanbeyli,Turkey,Europe,38.2647,36.2225,16400 +Tuguegarao,Philippines,Asia,17.6133,121.7303,166334 +Tukums,Latvia,Europe,56.9688,23.1535,16318 +Tula,Russia,Asia,54.2,37.6167,466609 +Tulagi,Solomon Islands,Oceania,-9.1031,160.1506,1251 +Tulancingo,Mexico,North America,20.0833,-98.3667,151582 +Tulcea,Romania,Europe,45.1775,28.8036,65624 +Tulchyn,Ukraine,Europe,48.6744,28.8497,13896 +Tulcán,Ecuador,South America,0.8117,-77.7186,56719 +Tullamore,Ireland,Europe,53.2667,-7.5,14607 +Tulle,France,Europe,45.2678,1.7706,13602 +Tulsa,United States,North America,36.1283,-95.9042,740620 +Tultepec,Mexico,North America,19.685,-99.1281,131567 +Tultitlán de Mariano Escobedo,Mexico,North America,19.645,-99.1694,520557 +Tulum,Mexico,North America,20.2119,-87.4658,33374 +Tulun,Russia,Asia,54.5667,100.5667,38440 +Tuluá,Colombia,South America,4.0833,-76.2,219138 +Tumaco,Colombia,South America,1.8067,-78.7647,212692 +Tumba,Sweden,Europe,59.2,17.8167,37852 +Tumbes,Peru,South America,-3.5708,-80.4597,96946 +Tumen,China,Asia,42.9667,129.85,121000 +Tumpat,Malaysia,Asia,6.1978,102.171,183100 +Tunapuna,Trinidad and Tobago,North America,10.6333,-61.3833,26829 +Tunceli,Turkey,Europe,39.1064,39.5472,35161 +Tuncheng,China,Asia,19.3633,110.0978,86474 +Tunis,Tunisia,Africa,36.8064,10.1817,599368 +Tunja,Colombia,South America,5.5333,-73.3667,163894 +Tuntum,Brazil,South America,-5.2578,-44.6489,36251 +Tunuyán,Argentina,South America,-33.5667,-69.0167,49132 +Tuodian,China,Asia,24.6667,101.6333,49218 +Tupaciguara,Brazil,South America,-18.5928,-48.705,25470 +Tupanatinga,Brazil,South America,-8.7528,-37.34,20801 +Tupanciretã,Brazil,South America,-29.0808,-53.8358,20005 +Tupi Paulista,Brazil,South America,-21.3808,-51.5708,15854 +Tupã,Brazil,South America,-21.935,-50.5139,63928 +Tura,Russia,Asia,64.2833,100.2167,5444 +Turbaco,Colombia,South America,10.35,-75.3333,115000 +Turbaná,Colombia,South America,10.2833,-75.45,72168 +Turbat,Pakistan,Asia,26.0042,63.0606,268625 +Turbo,Colombia,South America,8.1,-76.7333,181000 +Turek,Poland,Europe,52.0167,18.5,24788 +Turgutlu,Turkey,Europe,38.5,27.7,175401 +Turhal,Turkey,Europe,40.39,36.09,62030 +Turiaçu,Brazil,South America,-1.6628,-45.3719,37491 +Turicato,Mexico,North America,19.05,-101.4167,31877 +Turin,Italy,Europe,45.0792,7.6761,841600 +Turinsk,Russia,Asia,58.0333,63.7,16594 +Turku,Finland,Europe,60.4517,22.2669,202250 +Turmalina,Brazil,South America,-17.2856,-42.73,20125 +Turmero,Venezuela,South America,10.2283,-67.4725,51235 +Turnhout,Belgium,Europe,51.3231,4.9483,44136 +Turnišče,Slovenia,Europe,46.6278,16.3203,1506 +Turpan,China,Asia,42.9512,89.1895,622903 +Tursunzoda,Tajikistan,Asia,38.5108,68.2303,298800 +Turukhansk,Russia,Asia,65.797,87.968,212 +Tururu,Brazil,South America,-3.5808,-39.4369,15412 +Turvo,Brazil,South America,-25.0428,-51.53,14231 +Tuscaloosa,United States,North America,33.2348,-87.5268,166253 +Tuta,Colombia,South America,5.7,-73.2333,9673 +Tutayev,Russia,Asia,57.8833,39.55,39837 +Tutin,Serbia,Europe,42.9875,20.3256,33053 +Tutong,Brunei,Asia,4.8067,114.6592,9883 +Tutrakan,Bulgaria,Europe,44.039,26.6194,8877 +Tuttlingen,Germany,Europe,47.985,8.8233,37784 +Tutóia,Brazil,South America,-2.7619,-42.2739,53356 +Tuusula,Finland,Europe,60.4333,25.05,41475 +Tuxpan,Mexico,North America,19.5539,-103.3756,37518 +Tuxtla,Mexico,North America,16.7528,-93.1167,578830 +Tuy Hòa,Vietnam,Asia,13.0819,109.295,242840 +Tuymazy,Russia,Asia,54.6,53.7,68349 +Tuyên Quang,Vietnam,Asia,21.8167,105.2167,104645 +Tuzi,Montenegro,Europe,42.3656,19.3314,4748 +Tuzla,Bosnia and Herzegovina,Europe,44.5384,18.6671,110979 +Tuzluca,Turkey,Europe,40.0494,43.6608,9619 +Tver,Russia,Asia,56.8625,35.9242,424969 +Tvrdošín,Slovakia,Europe,49.3369,19.5503,8952 +Tychy,Poland,Europe,50.1236,18.9867,124882 +Tyler,United States,North America,32.3184,-95.3062,131037 +Tynda,Russia,Asia,55.1667,124.7167,28160 +Tyre,Lebanon,Asia,33.2708,35.1961,60000 +Tyrnyauz,Russia,Asia,43.4,42.9167,17000 +Tysmenytsia,Ukraine,Europe,48.9008,24.8492,8958 +Tyumen,Russia,Asia,57.15,65.5333,861100 +Tzintzuntzán,Mexico,North America,19.6283,-101.5789,12259 +Tzitzio,Mexico,North America,19.5856,-100.9231,8966 +Tábua,Portugal,Europe,40.3667,-8.0333,12071 +Támesis,Colombia,South America,5.6667,-75.7167,15218 +Táriba,Venezuela,South America,7.8167,-72.2167,128590 +Tân An,Vietnam,Asia,10.5333,106.4167,215250 +Tân Châu,Vietnam,Asia,10.7739,105.2369,141211 +Tân Phú,Vietnam,Asia,11.272,107.4367,161000 +Tân Uyên,Vietnam,Asia,11.0508,106.7636,466053 +Târgovişte,Romania,Europe,44.9244,25.4572,66965 +Târgu Jiu,Romania,Europe,45.0342,23.2747,73545 +Târgu-Mureş,Romania,Europe,46.5456,24.5625,116033 +Tây Ninh,Vietnam,Asia,11.3678,106.1189,135254 +Täby,Sweden,Europe,59.5,18.05,65364 +Tébessa,Algeria,Africa,35.4,8.1167,194461 +Técpan de Galeana,Mexico,North America,17.25,-100.6833,14638 +Télimélé,Guinea,Africa,10.905,-13.043,30311 +Ténenkou,Mali,Africa,14.4556,-4.9181,11310 +Tórshavn,Faroe Islands,Europe,62.0,-6.7833,13326 +Tôlan̈aro,Madagascar,Africa,-25.0325,46.9833,67284 +Tôrres,Brazil,South America,-29.335,-49.7269,41751 +Törökszentmiklós,Hungary,Europe,47.1833,20.4167,19196 +Tønsberg,Norway,Europe,59.2981,10.4236,58561 +Túquerres,Colombia,South America,1.0833,-77.6167,41205 +Túxpam de Rodríguez Cano,Mexico,North America,20.95,-97.4,84750 +Tübingen,Germany,Europe,48.52,9.0556,93615 +Tüp,Kyrgyzstan,Asia,42.7276,78.3648,12355 +Türkistan,Kazakhstan,Asia,43.3019,68.2692,250000 +Türkmenabat,Turkmenistan,Asia,39.0833,63.5667,230861 +Türkoğlu,Turkey,Europe,37.3914,36.8522,78976 +Týrnavos,Greece,Europe,39.7333,22.2833,222280 +Tādif,Syria,Asia,36.3481,37.5309,41786 +Tākestān,Iran,Asia,36.0672,49.6958,80299 +Tāluqān,Afghanistan,Asia,36.7167,69.5167,258758 +Tāybād,Iran,Asia,34.7408,60.7792,56562 +Tīrān,Iran,Asia,32.7039,51.1525,15673 +Tūyserkān,Iran,Asia,34.5528,48.4417,50455 +Tərtər,Azerbaijan,Europe,40.3353,46.9303,12563 +Uarini,Brazil,South America,-2.99,-65.1083,13690 +Uauá,Brazil,South America,-9.8419,-39.4819,24665 +Ub,Serbia,Europe,44.45,20.0667,6191 +Ubaitaba,Brazil,South America,-14.3128,-39.3228,17596 +Ubajara,Brazil,South America,-3.8544,-40.9211,32767 +Ubalá,Colombia,South America,4.7439,-73.5347,10718 +Ubaporanga,Brazil,South America,-19.635,-42.1058,13017 +Ubatuba,Brazil,South America,-23.4339,-45.0708,66861 +Ubatã,Brazil,South America,-14.2139,-39.5228,16094 +Ubaí,Brazil,South America,-16.285,-44.7778,11708 +Ubaíra,Brazil,South America,-13.2678,-39.6628,18626 +Uberaba,Brazil,South America,-19.7478,-47.9319,252051 +Uberlândia,Brazil,South America,-18.9189,-48.2769,501214 +Ubon Ratchathani,Thailand,Asia,15.2281,104.8594,68477 +Ubá,Brazil,South America,-21.12,-42.9428,103365 +Ucar,Azerbaijan,Europe,40.5183,47.6542,14273 +Uch-Korgon,Kyrgyzstan,Asia,40.2288,72.0592,14708 +Uchaly,Russia,Asia,54.3167,59.3833,36175 +Uchoa,Brazil,South America,-20.9528,-49.175,10394 +Uchquduq Shahri,Uzbekistan,Asia,42.1567,63.5556,26800 +Uchqŭrghon Shahri,Uzbekistan,Asia,41.1214,72.0936,40000 +Uchtepa Qishlog’i,Uzbekistan,Asia,40.205,67.9011,10110 +Uchturpan,China,Asia,41.1517,79.225,27836 +Uddevalla,Sweden,Europe,58.3667,11.9333,34781 +Uden,Netherlands,Europe,51.6667,5.6167,41782 +Udine,Italy,Europe,46.0667,13.2333,97808 +Udomlya,Russia,Asia,57.8833,35.0,25950 +Udon Thani,Thailand,Asia,17.4167,102.75,130531 +Uelzen,Germany,Europe,52.9647,10.5658,33991 +Ufa,Russia,Asia,54.7261,55.9475,1128787 +Ugep,Nigeria,Africa,5.8,8.0833,420000 +Uglegorsk,Russia,Asia,49.0667,142.0333,7686 +Uglich,Russia,Asia,57.5333,38.3333,32057 +Uibaí,Brazil,South America,-11.3369,-42.1328,13432 +Uiraúna,Brazil,South America,-6.5183,-38.4122,15300 +Uitgeest,Netherlands,Europe,52.5333,4.7167,13632 +Uithoorn,Netherlands,Europe,52.2333,4.8333,30206 +Ukhta,Russia,Asia,63.5667,53.7,77187 +Ukmergė,Lithuania,Europe,55.25,24.7667,21048 +Ula,Turkey,Europe,37.1036,28.4147,26613 +Ulaan-Uul,Mongolia,Asia,44.3337,111.2333,3726 +Ulaanbaatar,Mongolia,Asia,47.9214,106.9055,1396288 +Ulaangom,Mongolia,Asia,49.9833,92.0667,26319 +Ulan Hua,China,Asia,41.53,111.7,36645 +Ulan-Ude,Russia,Asia,51.8333,107.6,439128 +Ulbroka,Latvia,Europe,56.9408,24.2861,5583 +Ulcinj,Montenegro,Europe,41.92,19.2,19921 +Uliastay,Mongolia,Asia,47.7428,96.8433,22006 +Ulm,Germany,Europe,48.4,9.9833,129942 +Ulricehamn,Sweden,Europe,57.7917,13.4186,11794 +Ulsan,"Korea, South",Asia,35.55,129.3167,1127553 +Ulubey,Turkey,Europe,40.8761,37.7406,16976 +Uludere,Turkey,Europe,37.446,42.852,12068 +Ulvila,Finland,Europe,61.4325,21.8825,12533 +Ulyanovsk,Russia,Asia,54.3167,48.3667,613334 +Umag,Croatia,Europe,45.4336,13.5172,12699 +Uman,Ukraine,Europe,48.75,30.2167,81525 +Umarizal,Brazil,South America,-5.9908,-37.8139,10078 +Umarkot,Pakistan,Asia,25.3631,69.7425,35059 +Umbaúba,Brazil,South America,-11.3828,-37.6578,23917 +Umburanas,Brazil,South America,-10.7328,-41.3258,13642 +Umeå,Sweden,Europe,63.8285,20.2706,94243 +Umirim,Brazil,South America,-3.6769,-39.35,17470 +Umm al Qaywayn,United Arab Emirates,Asia,25.5533,55.5475,59098 +Umuahia,Nigeria,Africa,5.5333,7.4833,359230 +Umuarama,Brazil,South America,-23.7658,-53.325,117095 +Umurlu,Turkey,Europe,37.85,27.9667,11953 +Una,Brazil,South America,-15.2928,-39.075,18131 +Unaí,Brazil,South America,-16.3639,-46.8944,86619 +Une,Colombia,South America,4.4019,-74.025,9196 +Unecha,Russia,Asia,52.8461,32.6767,24274 +Ungaran,Indonesia,Asia,-7.1381,110.405,164916 +Ungheni,Moldova,Europe,47.2167,27.8167,30804 +Ungoofaaru,Maldives,Asia,5.6681,73.0306,1531 +Unguía,Colombia,South America,8.05,-77.1,12192 +União,Brazil,South America,-4.5858,-42.8639,46119 +União da Vitória,Brazil,South America,-26.23,-51.0858,55033 +União dos Palmares,Brazil,South America,-9.1628,-36.0319,59280 +Unión de Reyes,Cuba,North America,22.8003,-81.5372,35021 +Unión de San Antonio,Mexico,North America,21.128,-102.006,17915 +Unión de Tula,Mexico,North America,19.957,-104.268,13799 +Unna,Germany,Europe,51.5347,7.6889,60223 +Upanema,Brazil,South America,-5.6419,-37.2578,10991 +Upata,Venezuela,South America,8.0204,-62.41,53685 +Upplands Väsby,Sweden,Europe,59.5167,17.9,48907 +Uppsala,Sweden,Europe,59.8498,17.6389,174982 +Uracoa,Venezuela,South America,8.9943,-62.352,9625 +Urandi,Brazil,South America,-14.7708,-42.655,15355 +Uray,Russia,Asia,60.1333,64.8,41315 +Uraí,Brazil,South America,-23.1978,-50.7958,10406 +Urbano Santos,Brazil,South America,-3.2078,-43.4039,32812 +Uren,Russia,Asia,57.45,45.7833,12450 +Ureña,Venezuela,South America,7.9183,-72.4467,37932 +Urganch,Uzbekistan,Asia,41.55,60.6333,145000 +Urgut Shahri,Uzbekistan,Asia,39.419,67.2612,99675 +Urgūn,Afghanistan,Asia,32.9006,69.1569,10665 +Uriangato,Mexico,North America,20.1333,-101.1667,52156 +Uribia,Colombia,South America,11.9167,-72.0,117674 +Urk,Netherlands,Europe,52.6653,5.6058,21227 +Urla,Turkey,Europe,38.3222,26.7647,74736 +Uropá,Brazil,South America,-11.1406,-62.3608,10725 +Urrao,Colombia,South America,6.3156,-76.1342,47128 +Uruana,Brazil,South America,-15.4978,-49.6878,13729 +Uruapan,Mexico,North America,19.4208,-102.0628,334749 +Uruará,Brazil,South America,-3.7178,-53.7369,43558 +Uruaçu,Brazil,South America,-14.525,-49.1408,42546 +Urubici,Brazil,South America,-28.015,-49.5919,10834 +Uruburetama,Brazil,South America,-3.625,-39.5078,20189 +Urucará,Brazil,South America,-2.5358,-57.76,16007 +Urucuia,Brazil,South America,-16.1328,-45.7419,17479 +Urucurituba,Brazil,South America,-3.1308,-58.155,23945 +Urucânia,Brazil,South America,-20.3508,-42.7389,10600 +Uruguaiana,Brazil,South America,-29.755,-57.0878,117210 +Urumita,Colombia,South America,10.5667,-73.0167,19629 +Uruoca,Brazil,South America,-3.3139,-40.5569,13746 +Urupês,Brazil,South America,-21.2019,-49.29,13744 +Urus-Martan,Russia,Asia,43.1333,45.55,65698 +Uruçuca,Brazil,South America,-14.5928,-39.2839,21420 +Uruçuí,Brazil,South America,-7.2333,-44.55,25203 +Uryupinsk,Russia,Asia,50.8,42.0167,36669 +Uryzhar,Kazakhstan,Asia,47.09,81.6228,17320 +Urânia,Brazil,South America,-20.2458,-50.6428,8833 +Ushuaia,Argentina,South America,-54.8019,-68.3031,82615 +Usiacurí,Colombia,South America,10.75,-74.9833,13573 +Usinsk,Russia,Asia,66.0,57.5333,31200 +Usmat Shaharchasi,Uzbekistan,Asia,39.7386,67.6433,10500 +Usol’ye-Sibirskoye,Russia,Asia,52.75,103.6333,74762 +Uspantán,Guatemala,North America,15.3458,-90.8694,74244 +Ussel,France,Europe,45.5489,2.31,9174 +Uster,Switzerland,Europe,47.3492,8.7192,36791 +Ustrzyki Dolne,Poland,Europe,49.4297,22.5867,8717 +Ust’-Ilimsk,Russia,Asia,58.0,102.6667,79570 +Ust’-Kamchatsk,Russia,Asia,56.2167,162.55,4939 +Ust’-Katav,Russia,Asia,54.9333,58.1833,22536 +Ust’-Kut,Russia,Asia,56.7833,105.6333,278 +Ust’-Maya,Russia,Asia,60.4566,134.5433,3062 +Ust’-Nera,Russia,Asia,64.5666,143.2,9148 +Ust’-Ordynskiy,Russia,Asia,52.8056,104.7622,14538 +Usulután,El Salvador,North America,13.35,-88.45,87078 +Usumatlán,Guatemala,North America,14.9489,-89.7767,13245 +Utena,Lithuania,Europe,55.5,25.6,25608 +Uthai Thani,Thailand,Asia,15.38,100.025,15434 +Uthal,Pakistan,Asia,25.8,66.6167,37071 +Utica,United States,North America,43.0962,-75.2261,114961 +Utinga,Brazil,South America,-12.0819,-41.0939,16277 +Utrecht,Netherlands,Europe,52.0908,5.1217,359370 +Utsunomiya,Japan,Asia,36.555,139.8825,518197 +Uttaradit,Thailand,Asia,17.6231,100.0958,33357 +Uusikaupunki,Finland,Europe,60.8,21.4167,14938 +Uvarovo,Russia,Asia,51.9833,42.2667,23584 +Uyar,Russia,Asia,55.8267,94.3153,12036 +Uyo,Nigeria,Africa,5.0333,7.9275,554906 +Uzda,Belarus,Europe,53.4661,27.2244,10619 +Uzhhorod,Ukraine,Europe,48.6239,22.295,115449 +Uzhur,Russia,Asia,55.3175,89.8225,14134 +Uzlovaya,Russia,Asia,53.9833,38.1667,49427 +Uzunköprü,Turkey,Europe,41.2667,26.6833,59351 +Uzynaghash,Kazakhstan,Asia,43.2297,76.31,46159 +Uíge,Angola,Africa,-7.6167,15.05,322531 +Uşak,Turkey,Europe,38.6778,29.4042,236366 +Užice,Serbia,Europe,43.85,19.85,59747 +Vaals,Netherlands,Europe,50.7694,6.0181,10084 +Vaasa,Finland,Europe,63.1,21.6167,69036 +Vacaria,Brazil,South America,-28.5119,-50.9339,57341 +Vacaville,United States,North America,38.3587,-121.9673,102085 +Vadsø,Norway,Europe,70.0733,29.7497,5593 +Vaduz,Liechtenstein,Europe,47.1406,9.5222,5668 +Vagos,Portugal,Europe,40.5553,-8.6814,22851 +Vahdat,Tajikistan,Asia,38.5531,69.0206,342700 +Vailoa,Samoa,Oceania,-13.7558,-172.3064,784 +Vakfıkebir,Turkey,Europe,41.0475,39.2797,27060 +Vakhsh,Tajikistan,Asia,37.7108,68.8311,14900 +Valandovo,North Macedonia,Europe,41.3169,22.5611,1992 +Valchedram,Bulgaria,Europe,43.7,23.45,11934 +Valday,Russia,Asia,57.9667,33.25,14074 +Valdivia,Chile,South America,-39.8139,-73.2458,150048 +Vale de Cambra,Portugal,Europe,40.85,-8.4,22864 +Valence,France,Europe,44.9333,4.8917,64288 +Valencia,Venezuela,South America,10.1833,-68.0,2585202 +Valenciennes,France,Europe,50.3581,3.5233,42979 +Valente,Brazil,South America,-11.4119,-39.4619,24362 +Valentim Gentil,Brazil,South America,-20.4219,-50.0878,14098 +Valenzuela,Philippines,Asia,14.7,120.98,714978 +Valença,Brazil,South America,-13.3703,-39.0731,97873 +Valença do Piauí,Brazil,South America,-6.4078,-41.7458,22279 +Valera,Venezuela,South America,9.32,-70.62,191167 +Valga,Estonia,Europe,57.7833,26.0333,11792 +Valinhos,Brazil,South America,-22.9708,-46.9958,82973 +Valjevo,Serbia,Europe,44.2708,19.8866,90312 +Valka,Latvia,Europe,57.7753,26.0214,4564 +Valkeakoski,Finland,Europe,61.2667,24.0333,20694 +Valkenswaard,Netherlands,Europe,51.35,5.4592,31221 +Valladolid,Spain,Europe,41.6528,-4.7236,300618 +Valle Hermoso,Mexico,North America,25.6736,-97.8144,68573 +Valle Nacional,Mexico,North America,17.7667,-96.3,21189 +Valle de Bravo,Mexico,North America,19.1925,-100.1306,61590 +Valle de La Pascua,Venezuela,South America,9.2033,-66.0103,105403 +Valle de Santiago,Mexico,North America,20.39,-101.26,150054 +Valledupar,Colombia,South America,10.4833,-73.25,544134 +Vallejo,United States,North America,38.1125,-122.2342,171414 +Vallenar,Chile,South America,-28.5708,-70.7581,45298 +Vallentuna,Sweden,Europe,59.5333,18.0833,29519 +Valletta,Malta,Europe,35.8983,14.5125,480134 +Valmiera,Latvia,Europe,57.5381,25.4231,22376 +Valongo,Portugal,Europe,41.1833,-8.5,93858 +Valozhyn,Belarus,Europe,54.0833,26.5167,10015 +Valparaíso,Chile,South America,-33.0461,-71.6197,296655 +Valpovo,Croatia,Europe,45.65,18.4167,9784 +Valuyki,Russia,Asia,50.2167,38.1,33032 +Van,Turkey,Europe,38.4942,43.38,525016 +Vanadzor,Armenia,Europe,40.8128,44.4883,90525 +Vancouver,Canada,North America,49.25,-123.1,2426160 +Vanimo,Papua New Guinea,Oceania,-2.6667,141.2833,11863 +Vanino,Russia,Asia,49.0873,140.2425,18618 +Vanj,Tajikistan,Asia,38.3731,71.4553,11217 +Vannes,France,Europe,47.6559,-2.7603,54955 +Vantaa,Finland,Europe,60.2944,25.0403,248199 +Varakļāni,Latvia,Europe,56.6078,26.755,1653 +Varash,Ukraine,Europe,51.3444,25.8508,41711 +Varaždin,Croatia,Europe,46.3081,16.3378,43782 +Varberg,Sweden,Europe,57.1086,12.2661,35782 +Varde,Denmark,Europe,55.6216,8.4807,14108 +Varese,Italy,Europe,45.8167,8.8333,78409 +Vareš,Bosnia and Herzegovina,Europe,44.1644,18.3283,9556 +Vargem,Brazil,South America,-22.8889,-46.4139,10512 +Vargem Alta,Brazil,South America,-20.6708,-41.0069,17376 +Vargem Grande,Brazil,South America,-3.5428,-43.9158,43261 +Vargem Grande Paulista,Brazil,South America,-23.6028,-47.0258,32683 +Vargem Grande do Sul,Brazil,South America,-21.8322,-46.8936,43110 +Vargem da Roça,Brazil,South America,-11.6069,-40.1369,13633 +Varginha,Brazil,South America,-21.5508,-45.43,136467 +Varjota,Brazil,South America,-4.1939,-40.4769,18105 +Varkaus,Finland,Europe,62.3167,27.8917,19727 +Varna,Bulgaria,Europe,43.2114,27.9111,350745 +Varre-Sai,Brazil,South America,-20.9308,-41.8689,10207 +Varto,Turkey,Europe,39.1731,41.4558,10914 +Varvarin,Serbia,Europe,43.7167,21.3667,17772 +Varzedo,Brazil,South America,-12.9708,-39.3939,9913 +Varzelândia,Brazil,South America,-15.7008,-44.0278,18840 +Varāmīn,Iran,Asia,35.3503,51.6364,225628 +Varėna,Lithuania,Europe,54.2167,24.5667,8042 +Vasilevo,North Macedonia,Europe,41.4758,22.6417,630 +Vaslui,Romania,Europe,46.6383,27.7292,63035 +Vassouras,Brazil,South America,-22.4039,-43.6628,33976 +Vasylivka,Ukraine,Europe,47.4431,35.2819,12567 +Vatican City,Vatican City,Europe,41.904,12.453,825 +Vaughan,Canada,North America,43.8333,-79.5,323103 +Vawkavysk,Belarus,Europe,53.1667,24.4667,41510 +Vazante,Brazil,South America,-17.9869,-46.9078,19975 +Vechta,Germany,Europe,52.7306,8.2886,34145 +Vecsés,Hungary,Europe,47.4108,19.2722,21383 +Veendam,Netherlands,Europe,53.1,6.8667,27616 +Veenendaal,Netherlands,Europe,52.025,5.555,66912 +Vega de Alatorre,Mexico,North America,19.9833,-96.675,20204 +Vegachí,Colombia,South America,6.7731,-74.7994,9966 +Veghel,Netherlands,Europe,51.6169,5.5481,38077 +Veinticinco de Mayo,Argentina,South America,-35.4167,-60.1667,35563 +Vejen,Denmark,Europe,55.4812,9.1379,10206 +Vejle,Denmark,Europe,55.709,9.535,61310 +Velasco Ibarra,Ecuador,South America,-1.0439,-79.6383,41778 +Veldhoven,Netherlands,Europe,51.42,5.405,45500 +Velenje,Slovenia,Europe,46.3625,15.1144,25594 +Veles,North Macedonia,Europe,41.7153,21.7753,40664 +Velika Gorica,Croatia,Europe,45.7,16.0667,61075 +Velika Plana,Serbia,Europe,44.3333,21.0833,40902 +Velika Polana,Slovenia,Europe,46.5719,16.3469,870 +Velike Lašče,Slovenia,Europe,45.8363,14.6377,724 +Velikiy Novgorod,Russia,Asia,58.55,31.2667,222340 +Velikiye Luki,Russia,Asia,56.3333,30.5333,85989 +Veliko Gradište,Serbia,Europe,44.75,21.5167,17610 +Veliko Tarnovo,Bulgaria,Europe,43.0787,25.6283,66943 +Velingrad,Bulgaria,Europe,42.0167,24.0,45803 +Velsk,Russia,Asia,61.0667,42.1167,21406 +Venadillo,Colombia,South America,4.75,-74.9167,19586 +Venceslau Brás,Brazil,South America,-23.8739,-49.8028,19188 +Vendas Novas,Portugal,Europe,38.678,-8.4555,11846 +Vendôme,France,Europe,47.7936,1.0664,15566 +Venecia,Colombia,South America,5.9167,-75.75,10280 +Venezuela,Cuba,North America,21.7375,-78.7934,28470 +Venice,Italy,Europe,45.4397,12.3319,250369 +Venlo,Netherlands,Europe,51.37,6.1681,37663 +Vennesla,Norway,Europe,58.3106,7.8569,15294 +Venray,Netherlands,Europe,51.5258,5.9747,43713 +Ventanas,Ecuador,South America,-1.45,-79.47,41531 +Ventania,Brazil,South America,-24.2458,-50.2428,9681 +Ventaquemada,Colombia,South America,5.4167,-73.5,15442 +Ventspils,Latvia,Europe,57.3906,21.5733,32634 +Venturosa,Brazil,South America,-8.5747,-36.8742,17251 +Venustiano Carranza,Mexico,North America,16.3294,-92.5625,16144 +Venâncio Aires,Brazil,South America,-29.6058,-52.1919,61234 +Venëv,Russia,Asia,54.35,38.2667,12668 +Vera,Argentina,South America,-29.4667,-60.2167,19185 +Vera Cruz,Brazil,South America,-12.96,-38.6089,29750 +Veracruz,Mexico,North America,19.1903,-96.1533,428323 +Veranópolis,Brazil,South America,-28.9358,-51.5489,24021 +Verbania,Italy,Europe,45.9228,8.5519,29945 +Vercelli,Italy,Europe,45.3256,8.4231,45206 +Verdal,Norway,Europe,63.7922,11.4817,15002 +Verdejante,Brazil,South America,-7.9256,-38.9717,9169 +Verden,Germany,Europe,52.9233,9.235,28453 +Verdun,France,Europe,49.162,5.3876,16610 +Vereshchagino,Russia,Asia,58.1,54.6667,22077 +Verkhnevilyuysk,Russia,Asia,63.45,120.3,6436 +Verkhneyarkeyevo,Russia,Asia,55.4458,54.3167,9384 +Verkhniy Ufaley,Russia,Asia,56.0667,60.2333,22548 +Verkhnodniprovsk,Ukraine,Europe,48.6561,34.3283,16976 +Verkhnyaya Pyshma,Russia,Asia,56.9761,60.5944,73727 +Verkhnyaya Salda,Russia,Asia,58.0456,60.5533,40474 +Verona,Italy,Europe,45.4386,10.9928,255588 +Versailles,France,Europe,48.805,2.135,83918 +Vertentes,Brazil,South America,-7.9028,-35.9878,21959 +Vertientes,Cuba,North America,21.2569,-78.1492,48821 +Verviers,Belgium,Europe,50.5835,5.8641,55198 +Veržej,Slovenia,Europe,46.5836,16.1653,900 +Vesele,Ukraine,Europe,47.0158,34.9122,9340 +Vesoul,France,Europe,47.6231,6.1558,15306 +Vespasiano,Brazil,South America,-19.6919,-43.9233,129765 +Veszprém,Hungary,Europe,47.1,17.9167,56029 +Vetlanda,Sweden,Europe,57.4333,15.0667,13050 +Veurne,Belgium,Europe,51.0667,2.65,12295 +Vevčani,North Macedonia,Europe,41.2403,20.5931,2359 +Veymandoo,Maldives,Asia,2.1881,73.0947,1321 +Vezirköprü,Turkey,Europe,41.1431,35.46,90308 +Veľký Krtíš,Slovakia,Europe,48.215,19.3381,10647 +Viamão,Brazil,South America,-30.0808,-51.0228,224112 +Viana,Brazil,South America,-20.3893,-40.4948,79500 +Viana do Castelo,Portugal,Europe,41.7,-8.8333,88725 +Vianden,Luxembourg,Europe,49.9,6.2,2161 +Vianen,Netherlands,Europe,51.9833,5.1,19967 +Vianópolis,Brazil,South America,-16.7419,-48.5158,14956 +Vibo Valentia,Italy,Europe,38.6753,16.0959,31092 +Viborg,Denmark,Europe,56.4333,9.4,42234 +Vicente Guerrero,Mexico,North America,23.75,-103.9833,15982 +Vicentinópolis,Brazil,South America,-17.735,-49.8058,8768 +Vicenza,Italy,Europe,45.55,11.55,109823 +Vichuga,Russia,Asia,57.2,41.9167,30694 +Vichy,France,Europe,46.1278,3.4267,25702 +Victoria,Canada,North America,48.4283,-123.3647,363222 +Victorica,Argentina,South America,-36.2167,-65.45,4458 +Victorville,United States,North America,34.5277,-117.3536,363596 +Vicência,Brazil,South America,-7.6569,-35.3269,26359 +Videira,Brazil,South America,-27.0078,-51.1519,55466 +Videm,Slovenia,Europe,45.8484,14.6944,590 +Videm pri Ptuju,Slovenia,Europe,46.3686,15.9064,451 +Vidin,Bulgaria,Europe,43.9931,22.8722,46269 +Vidnoye,Russia,Asia,55.55,37.7167,106222 +Viedma,Argentina,South America,-40.8,-63.0,59993 +Vieira do Minho,Portugal,Europe,41.6333,-8.1333,12997 +Vienna,Austria,Europe,48.2083,16.3725,2223236 +Vienne,France,Europe,45.5242,4.8781,31555 +Vientiane,Laos,Asia,17.98,102.63,948487 +Viersen,Germany,Europe,51.2561,6.3972,79250 +Vierzon,France,Europe,47.2225,2.0694,25254 +Vigan,Philippines,Asia,17.5747,120.3869,53935 +Vigia,Brazil,South America,-0.8578,-48.1419,50832 +Vihari,Pakistan,Asia,30.0419,72.3528,128034 +Vijes,Colombia,South America,3.7,-76.45,11010 +Vila Bela da Santíssima Trindade,Brazil,South America,-15.0078,-59.9508,16774 +Vila Franca de Xira,Portugal,Europe,38.95,-8.9833,137529 +Vila Nova de Cerveira,Portugal,Europe,41.94,-8.7444,8923 +Vila Nova de Gaia,Portugal,Europe,41.1333,-8.6167,303824 +Vila Pouca de Aguiar,Portugal,Europe,41.4833,-7.6333,13187 +Vila Real,Portugal,Europe,41.2953,-7.7461,51850 +Vila Real de Santo António,Portugal,Europe,37.2,-7.4167,19156 +Vila Rica,Brazil,South America,-10.0119,-51.1158,19888 +Vila Velha,Brazil,South America,-20.3364,-40.2936,472762 +Vila Verde,Portugal,Europe,41.6484,-8.4367,47888 +Vila do Conde,Portugal,Europe,41.35,-8.75,79533 +Vilhena,Brazil,South America,-12.7406,-60.1458,95832 +Viligili,Maldives,Asia,0.7569,73.4361,5370 +Viljandi,Estonia,Europe,58.3667,25.6,17407 +Vilkaviškis,Lithuania,Europe,54.6667,23.0333,10291 +Villa Aberastain,Argentina,South America,-31.65,-68.5833,8946 +Villa Aldama,Mexico,North America,19.65,-97.2333,9573 +Villa Berthet,Argentina,South America,-27.2667,-60.4167,12029 +Villa Canales,Guatemala,North America,14.4833,-90.5333,167779 +Villa Constitución,Argentina,South America,-33.2333,-60.3333,47374 +Villa Corona,Mexico,North America,20.2417,-103.6167,17824 +Villa Corzo,Mexico,North America,16.1848,-93.2677,10841 +Villa Donato Guerra,Mexico,North America,19.3083,-100.1422,29621 +Villa El Carmen,Nicaragua,North America,11.9833,-86.5167,27449 +Villa Gesell,Argentina,South America,-37.25,-56.9833,29593 +Villa González Ortega,Mexico,North America,22.5136,-101.9189,13208 +Villa Hayes,Paraguay,South America,-25.09,-57.53,57217 +Villa Hidalgo,Mexico,North America,21.6667,-102.6,20088 +Villa Juárez,Mexico,North America,27.1278,-109.8426,13153 +Villa Krause,Argentina,South America,-31.5833,-68.5333,107778 +Villa La Angostura,Argentina,South America,-40.7625,-71.6463,14246 +Villa Luvianos,Mexico,North America,18.92,-100.2983,27860 +Villa María,Argentina,South America,-32.4103,-63.2314,88600 +Villa Mercedes,Argentina,South America,-33.6667,-65.4667,131046 +Villa Nueva,Guatemala,North America,14.5269,-90.5875,492480 +Villa Ojo de Agua,Argentina,South America,-29.5167,-63.7,15000 +Villa Paranacito,Argentina,South America,-33.7,-58.6833,15321 +Villa Purificación,Mexico,North America,19.5667,-104.3833,11303 +Villa Rica,Colombia,South America,3.1736,-76.4631,14378 +Villa Sandino,Nicaragua,North America,12.05,-84.9833,13152 +Villa Unión,Argentina,South America,-29.3,-68.2,12263 +Villa Vicente Guerrero,Mexico,North America,19.119,-98.17,82688 +Villa Victoria,Mexico,North America,19.4333,-100.0,77819 +Villa de Cura,Venezuela,South America,10.0383,-67.4892,89364 +Villa de Guadalupe,Mexico,North America,23.3667,-100.75,9277 +Villa de Leyva,Colombia,South America,5.6333,-73.5333,21953 +Villa de San Diego de Ubaté,Colombia,South America,5.3072,-73.8144,39568 +Villa de Zaachila,Mexico,North America,16.9508,-96.7492,28003 +Villa de Álvarez,Mexico,North America,19.25,-103.7333,149762 +Villa del Carbón,Mexico,North America,19.7272,-99.5417,39587 +Villa del Rosario,Colombia,South America,7.8339,-72.4742,107991 +Villa Ángela,Argentina,South America,-27.5833,-60.7167,41403 +Villach,Austria,Europe,46.6167,13.85,65127 +Villacidro,Italy,Europe,39.4578,8.7424,13184 +Villaflores,Mexico,North America,16.2355,-93.2677,37546 +Villagarzón,Colombia,South America,1.0294,-76.6164,21507 +Villagrán,Mexico,North America,20.517,-100.983,27079 +Villaguay,Argentina,South America,-31.85,-59.0167,49445 +Villahermosa,Mexico,North America,17.9892,-92.9281,353577 +Villamaría,Colombia,South America,5.0,-75.5,60729 +Villanueva,Mexico,North America,22.3536,-102.8831,32835 +Villapinzón,Colombia,South America,5.2167,-73.6,21049 +Villarrica,Paraguay,South America,-25.75,-56.4333,56385 +Villavicencio,Colombia,South America,4.15,-73.6333,664148 +Villefranche-de-Rouergue,France,Europe,44.3533,2.035,11502 +Villefranche-sur-Saône,France,Europe,45.9833,4.7167,36224 +Villeneuve-sur-Lot,France,Europe,44.4081,0.705,22004 +Villeta,Colombia,South America,5.0083,-74.4738,43574 +Villingen-Schwenningen,Germany,Europe,48.0603,8.4586,88213 +Vilnius,Lithuania,Europe,54.6872,25.28,581475 +Vilvoorde,Belgium,Europe,50.9333,4.4167,44015 +Vilyeyka,Belarus,Europe,54.4972,26.9111,26625 +Vilyuysk,Russia,Asia,63.75,121.6333,10045 +Vimmerby,Sweden,Europe,57.6667,15.85,10934 +Vinces,Ecuador,South America,-1.55,-79.7333,35064 +Vinh,Vietnam,Asia,18.6667,105.6667,790000 +Vinhais,Portugal,Europe,41.8167,-7.0,9066 +Vinhedo,Brazil,South America,-23.03,-46.975,47215 +Vinica,North Macedonia,Europe,41.8828,22.5092,8584 +Vinkovci,Croatia,Europe,45.2833,18.8,30842 +Vinnytsia,Ukraine,Europe,49.2333,28.4833,356303 +Viotá,Colombia,South America,4.4381,-74.5239,12589 +Vipava,Slovenia,Europe,45.8476,13.9624,2101 +Viqueque,Timor-Leste,Asia,-8.8575,126.3647,6078 +Virac,Philippines,Asia,13.58,124.23,76520 +Viradouro,Brazil,South America,-20.8728,-48.2969,17414 +Viranşehir,Turkey,Europe,37.2306,39.7653,207315 +Vire,France,Europe,48.84,-0.89,10762 +Virgem da Lapa,Brazil,South America,-16.8039,-42.3428,11804 +Virginia Beach,United States,North America,36.7335,-76.0435,1431821 +Virginópolis,Brazil,South America,-18.8228,-42.7039,10314 +Virgínia,Brazil,South America,-22.3328,-45.0919,8908 +Virovitica,Croatia,Europe,45.8333,17.3833,19302 +Virton,Belgium,Europe,49.5677,5.5323,11323 +Visaginas,Lithuania,Europe,55.6,26.4333,19586 +Visalia,United States,North America,36.3276,-119.3269,166273 +Visby,Sweden,Europe,57.629,18.3071,26305 +Visconde do Rio Branco,Brazil,South America,-21.0103,-42.8406,39160 +Viseu,Portugal,Europe,40.6575,-7.9139,99274 +Visoko,Bosnia and Herzegovina,Europe,43.9894,18.1806,39938 +Vista Hermosa de Negrete,Mexico,North America,20.2717,-102.4749,18995 +Vistahermosa,Colombia,South America,3.1239,-73.7514,25461 +Vitanje,Slovenia,Europe,46.3825,15.2956,859 +Viterbo,Italy,Europe,42.4186,12.1042,65949 +Vitez,Bosnia and Herzegovina,Europe,44.1585,17.7885,25836 +Viti,Kosovo,Europe,42.3214,21.3583,46987 +Vitomarci,Slovenia,Europe,46.5275,15.9394,341 +Vitoria-Gasteiz,Spain,Europe,42.85,-2.6833,257968 +Vitorino,Brazil,South America,-26.2769,-52.7839,9706 +Vitry-le-François,France,Europe,48.7256,4.5853,11349 +Vitsyebsk,Belarus,Europe,55.1917,30.2056,358395 +Vittoriosa,Malta,Europe,35.8881,14.5225,2629 +Vitória,Brazil,South America,-20.3106,-40.2972,322869 +Vitória da Conquista,Brazil,South America,-14.8661,-40.8394,341128 +Vitória de Santo Antão,Brazil,South America,-8.1264,-35.3075,117609 +Vitória do Mearim,Brazil,South America,-3.4619,-44.8708,30805 +Vize,Turkey,Europe,41.5736,27.7653,15116 +Viçosa,Brazil,South America,-20.7539,-42.8819,79388 +Viçosa do Ceará,Brazil,South America,-3.5619,-41.0919,59712 +Viñales,Cuba,North America,22.6153,-83.7158,27771 +Viškovo,Croatia,Europe,45.3759,14.3789,16084 +Việt Trì,Vietnam,Asia,21.3,105.4333,415280 +Vlaardingen,Netherlands,Europe,51.9167,4.35,73924 +Vladikavkaz,Russia,Asia,43.04,44.6775,303597 +Vladimir,Russia,Asia,56.1286,40.4058,352347 +Vladimirci,Serbia,Europe,44.6167,19.7833,17462 +Vladivostok,Russia,Asia,43.115,131.8853,591628 +Vladičin Han,Serbia,Europe,42.7,22.0667,20938 +Vlasenica,Bosnia and Herzegovina,Europe,44.1833,18.9333,11467 +Vlasotince,Serbia,Europe,42.9667,22.1333,29669 +Vlissingen,Netherlands,Europe,51.45,3.5833,44358 +Vlorë,Albania,Europe,40.45,19.4833,130827 +Vobkent Shahri,Uzbekistan,Asia,40.0333,64.5167,17800 +Vodice,Croatia,Europe,43.7506,15.7789,8649 +Vodil,Uzbekistan,Asia,40.1789,71.7297,16200 +Voerendaal,Netherlands,Europe,50.8825,5.9272,12466 +Vogošća,Bosnia and Herzegovina,Europe,43.9,18.35,26343 +Voinjama,Liberia,Africa,8.4167,-9.75,26594 +Voitsberg,Austria,Europe,47.0333,15.15,9403 +Vojnik,Slovenia,Europe,46.2933,15.3052,2475 +Volda,Norway,Europe,62.15,6.1,10960 +Volendam,Netherlands,Europe,52.4994,5.0675,22715 +Volgodonsk,Russia,Asia,47.5167,42.15,163963 +Volgograd,Russia,Asia,48.7086,44.5147,1004763 +Volgorechensk,Russia,Asia,57.4439,41.1592,14355 +Volkhov,Russia,Asia,59.9167,32.3,37539 +Volnovakha,Ukraine,Europe,47.6022,37.4919,21166 +Volodarsk,Russia,Asia,56.2167,43.1833,9907 +Vologda,Russia,Asia,59.2167,39.9,312420 +Volosovo,Russia,Asia,59.4333,29.4833,11433 +Volta Redonda,Brazil,South America,-22.5231,-44.1042,242063 +Volzhsk,Russia,Asia,55.8703,48.3561,52164 +Volzhskiy,Russia,Asia,48.8056,44.7417,315220 +Voorschoten,Netherlands,Europe,52.1333,4.45,25650 +Vordingborg,Denmark,Europe,55.0104,11.9039,17868 +Vorkuta,Russia,Asia,67.5,64.0333,67547 +Voronezh,Russia,Asia,51.6717,39.2106,1051995 +Voskresensk,Russia,Asia,55.3167,38.7,17231 +Voss,Norway,Europe,60.7025,6.4231,16144 +Votkinsk,Russia,Asia,57.05,54.0,96037 +Votorantim,Brazil,South America,-23.5469,-47.4378,127923 +Votuporanga,Brazil,South America,-20.4228,-49.9728,96634 +Vouzela,Portugal,Europe,40.7231,-8.1122,10564 +Vovchansk,Ukraine,Europe,50.2878,36.9428,17459 +Voznesensk,Ukraine,Europe,47.5667,31.3,33442 +Voúla,Greece,Europe,37.85,23.75,31497 +Vranje,Serbia,Europe,42.5542,21.8972,55138 +Vranov nad Topľou,Slovakia,Europe,48.8806,21.6731,20702 +Vransko,Slovenia,Europe,46.172,14.8217,11 +Vrapčište,North Macedonia,Europe,41.8337,20.8851,4874 +Vratsa,Bulgaria,Europe,43.2121,23.5444,55451 +Vrbas,Serbia,Europe,45.5667,19.65,24112 +Vrbovec,Croatia,Europe,45.8833,16.4333,12981 +Vreed-en-Hoop,Guyana,South America,6.8076,-58.1815,469 +Vrhnika,Slovenia,Europe,45.9624,14.2937,8969 +Vrilíssia,Greece,Europe,38.0333,23.8333,32417 +Vrnjačka Banja,Serbia,Europe,43.6167,20.9,10065 +Vršac,Serbia,Europe,45.1206,21.2986,35701 +Vsevolozhsk,Russia,Asia,60.0333,30.6667,78011 +Vught,Netherlands,Europe,51.6533,5.2875,31669 +Vukovar,Croatia,Europe,45.35,19.0033,23175 +Vuktyl,Russia,Asia,63.7,57.3167,9001 +Vushtrri,Kosovo,Europe,42.8231,20.9675,69870 +Vuzenica,Slovenia,Europe,46.5967,15.1689,1522 +Vyatskiye Polyany,Russia,Asia,56.2239,51.0633,29742 +Vyazemskiy,Russia,Asia,47.525,134.7572,838 +Vyazma,Russia,Asia,55.2107,34.2912,50611 +Vyazniki,Russia,Asia,56.25,42.1667,36203 +Vyborg,Russia,Asia,60.7167,28.7667,71279 +Vyetka,Belarus,Europe,52.5667,31.1833,8599 +Vyksa,Russia,Asia,55.3167,42.1667,45240 +Vylgort,Russia,Asia,61.6275,50.7672,10289 +Vynohradiv,Ukraine,Europe,48.1497,23.025,25317 +Vyshhorod,Ukraine,Europe,50.5833,30.5,22933 +Vyshniy Volochëk,Russia,Asia,57.5833,34.5667,45830 +Vác,Hungary,Europe,47.7753,19.1311,34001 +Várpalota,Hungary,Europe,47.1972,18.1394,19395 +Várzea Alegre,Brazil,South America,-6.7889,-39.2958,38984 +Várzea Grande,Brazil,South America,-15.6469,-56.1328,300078 +Várzea Nova,Brazil,South America,-11.2589,-40.9419,13377 +Várzea Paulista,Brazil,South America,-23.2108,-46.8278,115771 +Várzea da Palma,Brazil,South America,-17.5978,-44.7308,33744 +Vänersborg,Sweden,Europe,58.3833,12.3167,40041 +Västerhaninge,Sweden,Europe,59.1167,18.1,15134 +Västervik,Sweden,Europe,57.75,16.6333,36747 +Västerås,Sweden,Europe,59.6173,16.5422,131643 +Växjö,Sweden,Europe,56.8837,14.8167,66275 +Vélez,Colombia,South America,6.0103,-73.6763,18808 +Véroia,Greece,Europe,40.5167,22.2,43212 +Vólos,Greece,Europe,39.3667,22.9333,85803 +Võru,Estonia,Europe,57.8486,26.9928,11865 +Vöcklabruck,Austria,Europe,48.0086,13.6558,12299 +Völkermarkt,Austria,Europe,46.6667,14.6333,10946 +Výronas,Greece,Europe,37.9617,23.7533,59134 +Vĩnh Châu,Vietnam,Asia,9.333,106.0,183918 +Vĩnh Long,Vietnam,Asia,10.25,105.9667,200120 +Vĩnh Yên,Vietnam,Asia,21.31,105.5967,152801 +Vũng Tàu,Vietnam,Asia,10.3833,107.1167,341552 +Vị Thanh,Vietnam,Asia,9.7833,105.4667,104244 +Wa,Ghana,Africa,10.0667,-2.5,107214 +Waalre,Netherlands,Europe,51.3867,5.4456,17544 +Waalwijk,Netherlands,Europe,51.6825,5.0694,48815 +Wabag,Papua New Guinea,Oceania,-5.4833,143.7,4072 +Waco,United States,North America,31.5599,-97.1882,197594 +Wad Medani,Sudan,Africa,14.4,33.51,345290 +Waddinxveen,Netherlands,Europe,52.05,4.65,31342 +Wadowice,Poland,Europe,49.8833,19.5,17905 +Wageningen,Netherlands,Europe,51.9644,5.6631,39635 +Wagner,Brazil,South America,-12.2869,-41.1678,9503 +Waiblingen,Germany,Europe,48.8303,9.3169,55917 +Waikabubak,Indonesia,Asia,-9.6358,119.4131,33821 +Waingapu,Indonesia,Asia,-9.65,120.2667,71218 +Waitangi,New Zealand,Oceania,-43.9514,-176.5611,200 +Wajir,Kenya,Africa,1.7472,40.0572,90116 +Wakayama,Japan,Asia,34.2333,135.1667,353299 +Wakiso,Uganda,Africa,0.3981,32.4781,87900 +Waldorf,United States,North America,38.6085,-76.9194,124186 +Waldshut-Tiengen,Germany,Europe,47.6231,8.2144,25114 +Wamena,Indonesia,Asia,-4.0975,138.9511,66080 +Wancheng,China,Asia,18.8,110.4,545992 +Wanderley,Brazil,South America,-12.12,-43.8878,12968 +Wanderlândia,Brazil,South America,-6.8489,-47.9628,10522 +Wang Nam Yen,Thailand,Asia,13.5033,102.1812,21301 +Wang Saphung,Thailand,Asia,17.301,101.7685,11992 +Wang Sombun,Thailand,Asia,13.3618,102.1911,10747 +Wangdue Phodrang,Bhutan,Asia,27.4833,89.9,8954 +Wangqing,China,Asia,43.3126,129.7572,88732 +Wanyuan,China,Asia,31.9833,107.9667,406685 +Waremme,Belgium,Europe,50.6967,5.2569,15168 +Warendorf,Germany,Europe,51.9539,7.9933,37847 +Warin Chamrap,Thailand,Asia,15.1932,104.8628,28154 +Warner Robins,United States,North America,32.5961,-83.6527,154327 +Warren,United States,North America,42.4934,-83.027,138128 +Warri,Nigeria,Africa,5.5167,5.75,363382 +Warsaw,Poland,Europe,52.23,21.0111,2028000 +Washington,United States,North America,38.9047,-77.0163,5146120 +Waspán,Nicaragua,North America,14.7333,-83.9667,47231 +Wassenaar,Netherlands,Europe,52.15,4.4,26949 +Watampone,Indonesia,Asia,-4.5386,120.3297,81629 +Waterbury,United States,North America,41.5582,-73.0361,199965 +Waterford,Ireland,Europe,52.2583,-7.119,48369 +Waterloo,Canada,North America,43.4667,-80.5167,121436 +Wau,South Sudan,Africa,7.7,27.99,163442 +Wavre,Belgium,Europe,50.7147,4.6107,34305 +Wałbrzych,Poland,Europe,50.7667,16.2833,104401 +Wałcz,Poland,Europe,53.2667,16.4667,24167 +Weert,Netherlands,Europe,51.251,5.709,50011 +Weesp,Netherlands,Europe,52.3,5.05,19334 +Weichanglu,China,Asia,37.1792,119.9333,824708 +Weifen,China,Asia,38.4633,111.1203,100615 +Weihai,China,Asia,37.5133,122.1205,2804771 +Weilheim,Germany,Europe,47.8333,11.15,23378 +Weimar,Germany,Europe,50.9811,11.3294,65611 +Weinan,China,Asia,34.5206,109.471,4688744 +Weinfelden,Switzerland,Europe,47.5698,9.112,11534 +Weishui,China,Asia,38.023,114.1458,64722 +Weiyuan,China,Asia,23.4683,100.6835,95795 +Weiz,Austria,Europe,47.2169,15.6331,11627 +Weißenburg,Germany,Europe,49.0306,10.9719,18931 +Wejherowo,Poland,Europe,54.6,18.25,46446 +Wellington,New Zealand,Oceania,-41.2889,174.7772,216200 +Wels,Austria,Europe,48.15,14.0167,65303 +Wenchang,China,Asia,31.0358,116.9367,593629 +Wencheng,China,Asia,19.617,110.7551,560894 +Wenlan,China,Asia,23.3961,103.3649,585976 +Weno,"Micronesia, Federated States of",Oceania,7.4417,151.8583,13856 +Wenping,China,Asia,27.1931,103.5273,71943 +Wenxian Chengguanzhen,China,Asia,32.9471,104.6814,31339 +Werkendam,Netherlands,Europe,51.8097,4.8928,26940 +Wesel,Germany,Europe,51.6586,6.6178,61277 +West Covina,United States,North America,34.0555,-117.9113,107436 +West Jordan,United States,North America,40.6024,-112.0008,116277 +West Palm Beach,United States,North America,26.7469,-80.132,119508 +West Valley City,United States,North America,40.6886,-112.0123,137955 +Westerstede,Germany,Europe,53.25,7.9167,23984 +Westervoort,Netherlands,Europe,51.9667,5.9667,15014 +Westminster,United States,North America,39.8837,-105.0624,115546 +Wete,Tanzania,Africa,-5.0567,39.7281,26450 +Wetzlar,Germany,Europe,50.5667,8.5,54629 +Wewak,Papua New Guinea,Oceania,-3.55,143.6333,25143 +Wexford,Ireland,Europe,52.3383,-6.4617,20188 +Whakatane,New Zealand,Oceania,-37.964,176.984,16850 +Whangarei,New Zealand,Oceania,-35.725,174.3236,50784 +Whitby,Canada,North America,43.8833,-78.9417,138501 +White Rock,Canada,North America,49.025,-122.8028,109167 +Whitehorse,Canada,North America,60.7029,-135.0691,28201 +Wiang Haeng,Thailand,Asia,19.55,98.65,8631 +Wiang Sa,Thailand,Asia,8.6358,99.3666,9587 +Wichian Buri,Thailand,Asia,15.6578,101.106,22995 +Wichita,United States,North America,37.6895,-97.3443,501760 +Wichita Falls,United States,North America,33.9072,-98.5291,102558 +Wicklow,Ireland,Europe,52.9779,-6.033,12957 +Wieliczka,Poland,Europe,49.9894,20.0661,26599 +Wieluń,Poland,Europe,51.2206,18.57,21270 +Wiener Neustadt,Austria,Europe,47.8089,16.2325,44461 +Wierden,Netherlands,Europe,52.3592,6.5931,24538 +Wieruszów,Poland,Europe,51.3,18.15,8514 +Wiesbaden,Germany,Europe,50.0825,8.24,285522 +Wijchen,Netherlands,Europe,51.8067,5.7211,41261 +Wijk bij Duurstede,Netherlands,Europe,51.9742,5.3417,23925 +Wil,Switzerland,Europe,47.4664,9.0497,23955 +Wilhelmshaven,Germany,Europe,53.5286,8.1056,76247 +Willemstad,Curaçao,South America,12.108,-68.935,150000 +Wilmington,United States,North America,34.2099,-77.8866,275128 +Wiltz,Luxembourg,Europe,49.9661,5.9325,5427 +Windhoek,Namibia,Africa,-22.57,17.0836,431000 +Windsor,Canada,North America,42.2833,-83.0,306519 +Winnipeg,Canada,North America,49.8844,-97.1464,758515 +Winsen,Germany,Europe,53.3667,10.2167,36499 +Winston-Salem,United States,North America,36.1029,-80.261,434231 +Winsum,Netherlands,Europe,53.3312,6.5157,13560 +Winter Haven,United States,North America,28.0118,-81.7014,296830 +Winterswijk,Netherlands,Europe,51.9667,6.7167,29022 +Winterthur,Switzerland,Europe,47.4992,8.7267,109775 +Wismar,Germany,Europe,53.9,11.4667,44022 +Wittlich,Germany,Europe,49.9869,6.8897,19718 +Wittmund,Germany,Europe,53.575,7.7808,20835 +Wiwilí,Nicaragua,North America,13.6167,-85.8333,57485 +Wiwilí de Jinotega,Nicaragua,North America,13.62,-85.8182,57485 +Wodzisław Śląski,Poland,Europe,50.0,18.45,45949 +Woerden,Netherlands,Europe,52.0833,4.8833,52694 +Wolfenbüttel,Germany,Europe,52.1622,10.5369,53034 +Wolfsberg,Austria,Europe,46.85,14.8333,25035 +Wolfsburg,Germany,Europe,52.4231,10.7872,127256 +Wolsztyn,Poland,Europe,52.1167,16.1167,12504 +Wolvega,Netherlands,Europe,52.8755,5.9969,13090 +Woodbridge,United States,North America,40.5611,-74.2943,103428 +Worcester,United States,North America,42.2705,-71.8079,487014 +Wormer,Netherlands,Europe,52.5,4.8167,11470 +Worms,Germany,Europe,49.6319,8.3653,85609 +Wote,Kenya,Africa,-1.7808,37.6288,56419 +Woudenberg,Netherlands,Europe,52.0806,5.4164,13639 +Woudrichem,Netherlands,Europe,51.8128,5.0003,14634 +Wołomin,Poland,Europe,52.35,21.2333,36311 +Wołów,Poland,Europe,51.3414,16.6283,12275 +Wrocław,Poland,Europe,51.11,17.0325,672929 +Września,Poland,Europe,52.3333,17.5833,31986 +Wschowa,Poland,Europe,51.8,16.3,13533 +Wuchang,China,Asia,44.924,127.158,897705 +Wudalianchi,China,Asia,48.75,126.1667,243283 +Wuhai,China,Asia,39.655,106.794,550000 +Wuhan,China,Asia,30.5934,114.3046,10251000 +Wuhu,China,Asia,31.3526,118.4331,3644420 +Wuji,China,Asia,38.179,114.9763,91952 +Wukari,Nigeria,Africa,7.8704,9.78,92933 +Wulan,China,Asia,36.5585,104.6765,86990 +Wuling,China,Asia,39.4421,114.23,121110 +Wunsiedel,Germany,Europe,50.0167,12.0167,9294 +Wuppertal,Germany,Europe,51.2667,7.1833,358938 +Wusong,China,Asia,30.945,117.811,1311726 +Wutong,China,Asia,30.6333,120.5333,815848 +Wuwei,China,Asia,37.929,102.638,1464955 +Wuxi,China,Asia,31.5667,120.2833,3245179 +Wuxue,China,Asia,29.844,115.561,644247 +Wuyi,China,Asia,37.7965,115.892,82526 +Wuyishan,China,Asia,27.7564,118.0353,238000 +Wuzhong,China,Asia,37.9978,106.1986,1284000 +Wuzhou,China,Asia,23.4767,111.279,2820977 +Wu’an,China,Asia,36.697,114.204,811631 +Wysokie Mazowieckie,Poland,Europe,52.9192,22.5144,9155 +Wyszków,Poland,Europe,52.5928,21.4584,26349 +Würzburg,Germany,Europe,49.7833,9.9333,128246 +Wąbrzeźno,Poland,Europe,53.2833,18.95,13317 +Wągrowiec,Poland,Europe,52.8,17.2,25773 +Węgorzewo,Poland,Europe,54.2167,21.75,10879 +Węgrów,Poland,Europe,52.4,22.0167,11993 +Włocławek,Poland,Europe,52.6592,19.0681,104705 +Włodawa,Poland,Europe,51.55,23.55,12500 +Włoszczowa,Poland,Europe,50.8542,19.9669,9750 +Wŏnsan,"Korea, North",Asia,39.1475,127.4461,363127 +Xagħra,Malta,Europe,36.0503,14.2675,4886 +Xai,Laos,Asia,20.6914,101.9861,79500 +Xai-Xai,Mozambique,Africa,-25.05,33.65,116343 +Xaignabouli,Laos,Asia,19.25,101.75,75700 +Xalapa,Mexico,North America,19.54,-96.9275,424755 +Xalatlaco,Mexico,North America,19.1811,-99.4164,29572 +Xam Nua,Laos,Asia,20.415,104.048,56900 +Xangda,China,Asia,32.2055,96.4781,32175 +Xanxerê,Brazil,South America,-26.8769,-52.4039,51607 +Xapuri,Brazil,South America,-10.6517,-68.5044,19596 +Xavantes,Brazil,South America,-23.0389,-49.7094,12484 +Xaxim,Brazil,South America,-26.9619,-52.535,31918 +Xaçmaz,Azerbaijan,Europe,41.4681,48.8028,39900 +Xewkija,Malta,Europe,36.0331,14.2583,3300 +Xexéu,Brazil,South America,-8.8019,-35.6269,11611 +Xgħajra,Malta,Europe,35.8864,14.5472,1732 +Xiamen,China,Asia,24.4796,118.0889,3707090 +Xiangcheng,China,Asia,25.4783,100.5551,140010 +Xianghe,China,Asia,39.7532,116.9814,103659 +Xiangjiaba,China,Asia,28.5627,104.3461,16198 +Xiangyang,China,Asia,32.01,112.122,5260951 +Xianshuigu,China,Asia,38.9786,117.3886,148602 +Xianyang,China,Asia,34.3299,108.7088,3959842 +Xiaoxita,China,Asia,30.692,111.287,4017607 +Xiaping,China,Asia,26.748,114.289,155976 +Xiaqiaotou,China,Asia,27.2167,100.15,15930 +Xiashi,China,Asia,30.5333,120.6833,806966 +Xiayang,China,Asia,26.4906,113.7708,50000 +Xibeijie,China,Asia,39.7334,98.4943,1055706 +Xichang,China,Asia,27.8944,102.2644,712434 +Xicheng,China,Asia,23.3667,103.15,419314 +Xico,Mexico,North America,19.417,-97.0,38198 +Xicoténcatl,Mexico,North America,22.9958,-98.9447,9593 +Xiedian,China,Asia,35.419,110.8281,81386 +Xigazê,China,Asia,29.25,88.8833,679771 +Xihuachi,China,Asia,35.8164,108.0125,43338 +Xiluodu,China,Asia,28.2442,103.6385,61668 +Xincheng,China,Asia,39.9883,112.4673,52903 +Xincun,China,Asia,27.6718,103.8699,9769 +Xingcheng,China,Asia,24.1347,115.73,779411 +Xinglong,China,Asia,40.4146,117.493,75381 +Xingsha,China,Asia,28.2573,113.1234,277034 +Xingtai,China,Asia,37.0717,114.5048,6645766 +Xinguara,Brazil,South America,-7.095,-49.9458,52893 +Xinhua,China,Asia,23.6243,105.6229,100792 +Xinhualu,China,Asia,34.3964,113.7402,1172237 +Xining,China,Asia,36.6224,101.7804,2208708 +Xinji,China,Asia,37.943,115.218,594628 +Xinjie,China,Asia,27.7049,103.3808,21252 +Xinpu,China,Asia,34.5966,119.2214,4393482 +Xinshi,China,Asia,31.1333,113.1167,544843 +Xinxing,China,Asia,39.8667,124.15,568566 +Xinyu,China,Asia,27.8186,114.9167,1186700 +Xinyuan,China,Asia,37.3,99.0,16585 +Xinzhou,China,Asia,38.416,112.734,2689668 +Xiongzhou,China,Asia,38.9653,116.0972,99693 +Xiping,China,Asia,25.6005,103.8166,652604 +Xique-Xique,Brazil,South America,-10.8229,-42.7281,44757 +Xireg,China,Asia,36.925,98.478,13723 +Xisa,China,Asia,23.45,104.7077,39648 +Xishan,China,Asia,27.6461,113.497,885987 +Xiva,Uzbekistan,Asia,41.3814,60.3611,115000 +Xiwanzi,China,Asia,40.9717,115.2737,38879 +Xi’an,China,Asia,34.2611,108.9422,12328000 +Xocalı,Azerbaijan,Europe,39.9131,46.7903,1397 +Xocavənd,Azerbaijan,Europe,39.795,47.1117,5700 +Xochistlahuaca,Mexico,North America,16.7914,-98.2419,28089 +Xonqa,Uzbekistan,Asia,41.4742,60.7822,39400 +Xo‘jaobod,Uzbekistan,Asia,40.6653,72.5667,20200 +Xo‘jayli Shahri,Uzbekistan,Asia,42.4,59.45,67800 +Xuddur,Somalia,Africa,4.12,43.8878,85500 +Xuejiawan,China,Asia,39.86,111.24,165000 +Xushan,China,Asia,30.1667,121.2333,1502000 +Xylókastro,Greece,Europe,38.0667,22.6333,12102 +Xánthi,Greece,Europe,41.1333,24.8833,58760 +Xırdalan,Azerbaijan,Europe,40.4486,49.7564,100900 +Xızı,Azerbaijan,Europe,40.9111,49.0694,1588 +Yabrūd,Syria,Asia,33.9692,36.6573,47136 +Yacha,China,Asia,19.2253,109.4512,37846 +Yacopí,Colombia,South America,5.4667,-74.3333,10887 +Yacuanquer,Colombia,South America,1.1167,-77.4,10579 +Yaguajay,Cuba,North America,22.3303,-79.2369,53375 +Yaguaraparo,Venezuela,South America,10.5667,-62.8333,15173 +Yaguará,Colombia,South America,2.6661,-75.5186,8952 +Yahualica de González Gallo,Mexico,North America,21.1781,-102.886,22586 +Yahyalı,Turkey,Europe,38.1,35.3608,35481 +Yahşihan,Turkey,Europe,39.8503,33.4536,31195 +Yajalón,Mexico,North America,17.1833,-92.3167,40285 +Yakacık,Turkey,Europe,36.75,36.2167,43919 +Yakakent,Turkey,Europe,41.6222,35.5314,8693 +Yakeshi,China,Asia,49.286,120.712,256396 +Yakima,United States,North America,46.5923,-120.5496,134003 +Yako,Burkina Faso,Africa,12.9667,-2.2667,38689 +Yakou,China,Asia,33.295,113.5269,300200 +Yakutsk,Russia,Asia,62.03,129.73,311760 +Yakymivka,Ukraine,Europe,46.7,35.1667,11069 +Yala,Thailand,Asia,6.5425,101.2831,60617 +Yalagüina,Nicaragua,North America,13.4833,-86.4833,9597 +Yalova,Turkey,Europe,40.6556,29.275,133109 +Yalutorovsk,Russia,Asia,56.65,66.2833,38882 +Yalvaç,Turkey,Europe,38.2956,31.18,22538 +Yalí,Colombia,South America,6.6767,-74.8411,8577 +Yamagata,Japan,Asia,38.2554,140.3396,247234 +Yamaguchi,Japan,Asia,34.1781,131.4739,191470 +Yambio,South Sudan,Africa,4.5705,28.4163,45685 +Yambol,Bulgaria,Europe,42.4833,26.5,83861 +Yamoussoukro,Côte d’Ivoire,Africa,6.8161,-5.2742,340234 +Yampil,Ukraine,Europe,48.24,28.2783,10679 +Yanaul,Russia,Asia,56.2833,54.9333,25908 +Yancheng,China,Asia,33.35,120.1569,6709629 +Yang Talat,Thailand,Asia,16.3998,103.3678,8666 +Yanghe,China,Asia,38.2727,106.2496,67901 +Yangi Mirishkor,Uzbekistan,Asia,38.8514,65.2778,17000 +Yangiariq,Uzbekistan,Asia,41.3628,60.6075,12200 +Yangirabot,Uzbekistan,Asia,40.0372,65.9653,17000 +Yangjiang,China,Asia,21.857,111.983,2555600 +Yangquan,China,Asia,37.8571,113.5804,1318505 +Yangsan,"Korea, South",Asia,35.3333,129.0333,297532 +Yangshe,China,Asia,31.8767,120.556,1447600 +Yanji,China,Asia,42.9,129.5,548700 +Yanjiang,China,Asia,30.129,104.627,2308631 +Yanqi,China,Asia,42.0586,86.5683,37649 +Yantzaza,Ecuador,South America,-3.85,-78.75,13335 +Yany Kapu,Ukraine,Europe,45.9675,33.8003,26268 +Yan’an Beilu,China,Asia,44.0144,87.2674,426253 +Yaofeng,China,Asia,35.1395,111.2174,86435 +Yaoundé,Cameroon,Africa,3.8667,11.5167,2440462 +Yara,Cuba,North America,20.2767,-76.9469,59415 +Yardımlı,Azerbaijan,Europe,38.9078,48.2406,7623 +Yaren,Nauru,Oceania,-0.5456,166.925,803 +Yaritagua,Venezuela,South America,10.0753,-69.1283,140256 +Yaroslavl,Russia,Asia,57.6167,39.85,567443 +Yartsevo,Russia,Asia,55.0667,32.7,40330 +Yarumal,Colombia,South America,7.0,-75.5,41542 +Yarīm,Yemen,Asia,14.2972,44.3803,46964 +Yasnogorsk,Russia,Asia,54.5,37.6667,15269 +Yasnyy,Russia,Asia,51.05,59.9667,15249 +Yasothon,Thailand,Asia,15.7972,104.1431,21134 +Yatağan,Turkey,Europe,37.3425,28.1394,45283 +Yatou,China,Asia,37.1653,122.4867,670251 +Yavoriv,Ukraine,Europe,49.9469,23.3931,12785 +Yavuzeli,Turkey,Europe,37.3192,37.5686,22762 +Yawatahama-shi,Japan,Asia,33.4667,132.4167,31385 +Yayladağı,Turkey,Europe,35.9025,36.0606,36803 +Yaypan,Uzbekistan,Asia,40.3758,70.8156,24900 +Yazd,Iran,Asia,31.8822,54.3397,529673 +Yazıhan,Turkey,Europe,38.5969,38.1878,11984 +Yecapixtla,Mexico,North America,18.8833,-98.865,39859 +Yecuatla,Mexico,North America,19.8667,-96.7833,12108 +Yefremov,Russia,Asia,53.15,38.1167,36545 +Yeghegnadzor,Armenia,Europe,39.7667,45.35,7633 +Yegoryevsk,Russia,Asia,55.3833,39.05,4779 +Yekaterinburg,Russia,Asia,56.8356,60.6128,1468833 +Yelets,Russia,Asia,52.6167,38.4667,99875 +Yelizovo,Russia,Asia,53.1833,158.3833,36199 +Yellowknife,Canada,North America,62.4709,-114.4053,20340 +Yelsk,Belarus,Europe,51.8167,29.15,8724 +Yemva,Russia,Asia,62.6,50.8667,10710 +Yenice,Turkey,Europe,41.2,32.3292,9038 +Yenimahalle,Turkey,Europe,39.9719,32.8119,704652 +Yenipazar,Turkey,Europe,37.8269,28.1975,11863 +Yeniseysk,Russia,Asia,58.4667,92.1333,17537 +Yenişehir,Turkey,Europe,37.9333,40.2333,219759 +Yeosu,"Korea, South",Asia,34.7607,127.6622,273761 +Yepocapa,Guatemala,North America,14.5,-90.95,42996 +Yerba Buena,Argentina,South America,-26.8167,-65.3167,50057 +Yerevan,Armenia,Europe,40.1814,44.5144,1106300 +Yerköy,Turkey,Europe,39.6381,34.4672,27084 +Yessentuki,Russia,Asia,44.0333,42.85,121534 +Yevlax,Azerbaijan,Europe,40.6172,47.15,59036 +Yevpatoriia,Ukraine,Europe,45.1939,33.3681,105719 +Yeysk,Russia,Asia,46.7111,38.2733,82172 +Yeşilhisar,Turkey,Europe,38.35,35.0867,15531 +Yeşilli,Turkey,Europe,37.3406,40.8258,13621 +Yeşilyurt,Turkey,Europe,38.2961,38.2489,341654 +Yi Xian,China,Asia,39.3537,115.496,94110 +Yian,China,Asia,47.8804,125.3,39924 +Yibin,China,Asia,28.752,104.643,4471896 +Yicheng,China,Asia,31.36,119.815,1082929 +Yichun,China,Asia,27.816,114.417,5007702 +Yilong,China,Asia,23.6914,102.487,108124 +Yima,China,Asia,34.7469,111.875,135819 +Yinchuan,China,Asia,38.485,106.225,1901793 +Yingcheng,China,Asia,24.21,113.401,941325 +Yingchuan,China,Asia,34.142,113.489,1109782 +Yinggen,China,Asia,19.0372,109.8283,41835 +Yingkou,China,Asia,40.625,122.219,2296540 +Yingshouyingzi,China,Asia,40.5451,117.656,29664 +Yinying,China,Asia,37.941,113.5602,70614 +Yishi,China,Asia,35.165,110.746,102090 +Yisuhe,China,Asia,27.7736,112.9558,60283 +Yitiaoshan,China,Asia,37.1889,104.0571,68952 +Yitong,China,Asia,43.3456,125.3039,115752 +Yiwu,China,Asia,29.3069,120.0753,1234015 +Yiyang,China,Asia,28.5549,112.356,3851564 +Ylivieska,Finland,Europe,64.075,24.5333,15369 +Ylöjärvi,Finland,Europe,61.55,23.5833,33653 +Yogyakarta,Indonesia,Asia,-7.8014,110.3644,422732 +Yokohama,Japan,Asia,35.4442,139.6381,3757630 +Yola,Nigeria,Africa,9.23,12.46,392854 +Yolombó,Colombia,South America,6.5978,-75.0122,25647 +Yomou,Guinea,Africa,7.566,-9.2533,29138 +Yomra,Turkey,Europe,40.9589,39.8469,47283 +Yondó,Colombia,South America,7.0,-73.9167,17597 +Yongbei,China,Asia,26.6462,100.7807,61932 +Yongqing,China,Asia,39.2958,116.4897,118494 +Yong’an,China,Asia,25.942,117.365,344793 +Yonkers,United States,North America,40.9466,-73.8673,209529 +Yopal,Colombia,South America,5.35,-72.41,106822 +York,United States,North America,39.9651,-76.7315,246769 +Yoro,Honduras,North America,15.1379,-87.1249,86665 +Yorosso,Mali,Africa,12.3581,-4.7769,22063 +Yoshkar-Ola,Russia,Asia,56.65,47.8833,274715 +Yotoco,Colombia,South America,3.8667,-76.3833,16263 +Youganning,China,Asia,34.7356,101.6114,21323 +Youngstown,United States,North America,41.0993,-80.6463,315751 +Youwarou,Mali,Africa,15.3683,-4.2628,23046 +Yovon,Tajikistan,Asia,38.3167,69.05,36700 +Yozgat,Turkey,Europe,39.8208,34.8083,92643 +Yozyovon,Uzbekistan,Asia,40.6619,71.7431,12800 +Ystad,Sweden,Europe,55.4167,13.8167,28985 +Yuanquan,China,Asia,40.5004,95.8,17886 +Yuanshan,China,Asia,24.3733,114.4856,78254 +Yuba City,United States,North America,39.1357,-121.6383,127854 +Yucheng,China,Asia,30.01,103.0415,1434603 +Yueyang,China,Asia,29.3647,113.1309,5051922 +Yuli,China,Asia,41.3351,86.2614,22474 +Yulin,China,Asia,22.654,110.181,5796766 +Yuma,United States,North America,32.5995,-114.5491,144509 +Yumbe,Uganda,Africa,3.465,31.245,47600 +Yumbo,Colombia,South America,3.585,-76.4958,95072 +Yumurtalık,Turkey,Europe,36.7678,35.7922,17654 +Yunak,Turkey,Europe,38.8172,31.7356,20991 +Yunfu,China,Asia,22.9152,112.0445,2383350 +Yunxian Chengguanzhen,China,Asia,32.8082,110.8136,133558 +Yunzhong,China,Asia,39.8279,113.1,326849 +Yupiltepeque,Guatemala,North America,14.2,-89.7833,18262 +Yuquan,China,Asia,40.4203,114.0864,67449 +Yuriria,Mexico,North America,20.2,-101.15,68741 +Yuryevets,Russia,Asia,57.3167,43.1,9300 +Yur’yev-Pol’skiy,Russia,Asia,56.5,39.6833,17276 +Yuscarán,Honduras,North America,13.9444,-86.8526,14144 +Yushan,China,Asia,31.322,120.985,1652159 +Yushu,China,Asia,44.826,126.55,836098 +Yutan,China,Asia,28.278,112.552,1263332 +Yuxi,China,Asia,24.347,102.527,2249502 +Yuzhno-Sakhalinsk,Russia,Asia,46.9667,142.7333,180085 +Yuzhnoukrainsk,Ukraine,Europe,47.8217,31.175,38560 +Yuzhnouralsk,Russia,Asia,54.45,61.25,37313 +Yverdon-les-Bains,Switzerland,Europe,46.7785,6.6408,30157 +Yélimané,Mali,Africa,15.1337,-10.5666,988 +Yên Bái,Vietnam,Asia,21.7,104.8667,100631 +Yüksekova,Turkey,Europe,37.569,44.283,84800 +Yüreğir,Turkey,Europe,36.9928,35.3764,399910 +Yāsūj,Iran,Asia,30.6672,51.5797,108505 +Yıldırım,Turkey,Europe,40.11,29.0821,655856 +Zaandam,Netherlands,Europe,52.4333,4.8333,78682 +Zabaykalsk,Russia,Asia,49.6514,117.3269,13154 +Zabok,Croatia,Europe,46.05,15.9667,8656 +Zabrze,Poland,Europe,50.3025,18.7781,158307 +Zabīd,Yemen,Asia,14.2,43.3167,50781 +Zacapa,Guatemala,North America,14.9667,-89.5333,66423 +Zacatecas,Mexico,North America,22.7736,-102.5736,149607 +Zacatecoluca,El Salvador,North America,13.5,-88.8667,75100 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+Zalău,Romania,Europe,47.1911,23.0572,52359 +Zamboanga City,Philippines,Asia,6.9042,122.0761,2100000 +Zambrano,Colombia,South America,9.75,-74.8333,11367 +Zambrów,Poland,Europe,52.9833,22.25,21351 +Zamora,Spain,Europe,41.4989,-5.7556,59506 +Zamość,Poland,Europe,50.7167,23.2528,60148 +Zandvoort,Netherlands,Europe,52.3667,4.5333,17168 +Zango,Nigeria,Africa,12.9333,8.5333,154743 +Zanhuang,China,Asia,37.6656,114.3861,66255 +Zanjān,Iran,Asia,36.6864,48.4953,386851 +Zanzibar,Tanzania,Africa,-6.165,39.199,219007 +Zaozërnyy,Russia,Asia,55.9667,94.7,9954 +Zapala,Argentina,South America,-38.9,-70.0667,40472 +Zapatoca,Colombia,South America,6.8167,-73.2667,8762 +Zapopan,Mexico,North America,20.7167,-103.4,1476491 +Zaporizhzhia,Ukraine,Europe,47.85,35.1175,710052 +Zapotiltic,Mexico,North America,19.627,-103.417,33713 +Zapotitlán,Guatemala,North America,14.1333,-89.8333,10573 +Zapotlanejo,Mexico,North America,20.6228,-103.0689,68519 +Zapotlán de Juárez,Mexico,North America,19.9667,-98.85,16493 +Zapotlán del Rey,Mexico,North America,20.4674,-102.9248,17893 +Zaprešić,Croatia,Europe,45.85,15.8,24133 +Zaqatala,Azerbaijan,Europe,41.6336,46.6433,31300 +Zara,Turkey,Europe,39.895,37.7531,11636 +Zaragoza,Spain,Europe,41.65,-0.8833,686986 +Zarand,Iran,Asia,30.8108,56.5658,57749 +Zaranj,Afghanistan,Asia,30.96,61.86,160902 +Zarasai,Lithuania,Europe,55.7306,26.2472,5942 +Zaraza,Venezuela,South America,9.3394,-65.3167,58200 +Zarbdor Shaharchasi,Uzbekistan,Asia,40.0747,68.1683,9110 +Zarechnyy,Russia,Asia,56.8167,61.3333,28517 +Zaria,Nigeria,Africa,11.0667,7.7,408198 +Zarrīn Shahr,Iran,Asia,32.3889,51.3733,55817 +Zaruma,Ecuador,South America,-3.6833,-79.6,10005 +Zarzal,Colombia,South America,4.3983,-76.0772,47062 +Zavitinsk,Russia,Asia,50.1281,129.4433,9253 +Zavodoukovsk,Russia,Asia,56.4833,66.5333,27100 +Zavolzhsk,Russia,Asia,57.4667,42.15,8896 +Zavyalovo,Russia,Asia,56.7922,53.3756,11066 +Zawiercie,Poland,Europe,50.5,19.4167,47689 +Zaysan,Kazakhstan,Asia,47.4667,84.8667,14389 +Zduńska Wola,Poland,Europe,51.6,18.9667,22904 +Zeewolde,Netherlands,Europe,52.3333,5.5167,22879 +Zehak,Iran,Asia,30.8933,61.6789,13357 +Zeist,Netherlands,Europe,52.0906,5.2331,65043 +Zelenikovo,North Macedonia,Europe,41.8867,21.5869,272 +Zelenogorsk,Russia,Asia,56.1,94.5833,54279 +Zelenogradsk,Russia,Asia,54.9667,20.4833,17133 +Zelenokumsk,Russia,Asia,44.4167,43.9167,10279 +Zell am See,Austria,Europe,47.3167,12.8,9762 +Zempoala,Mexico,North America,19.9153,-98.6689,57906 +Zemun,Serbia,Europe,44.85,20.4,161596 +Zenica,Bosnia and Herzegovina,Europe,44.2017,17.9039,70553 +Zequ,China,Asia,35.0376,101.4606,16666 +Zernograd,Russia,Asia,46.85,40.3167,24076 +Zest’aponi,Georgia,Europe,42.1092,43.036,20548 +Zevenaar,Netherlands,Europe,51.9167,6.0833,44096 +Zevenbergen,Netherlands,Europe,51.6453,4.5997,15859 +Zghartā,Lebanon,Asia,34.4,35.9,30000 +Zgierz,Poland,Europe,51.85,19.4167,55079 +Zgornja Hajdina,Slovenia,Europe,46.4061,15.8386,804 +Zgornja Kungota,Slovenia,Europe,46.6392,15.6156,1594 +Zgornje Gorje,Slovenia,Europe,46.3801,14.0684,532 +Zgornje Jezersko,Slovenia,Europe,46.3951,14.4966,558 +Zgorzelec,Poland,Europe,51.15,15.0083,29966 +Zhabinka,Belarus,Europe,52.2006,24.0233,14418 +Zhamog,China,Asia,29.8595,95.7681,11000 +Zhangatas,Kazakhstan,Asia,43.5705,69.7335,20731 +Zhangbei,China,Asia,41.158,114.7122,129338 +Zhanggu,China,Asia,30.9504,101.9167,70000 +Zhangjiajie,China,Asia,29.117,110.479,1517027 +Zhangjiakou,China,Asia,40.769,114.886,4118908 +Zhangjiakou Shi Xuanhua Qu,China,Asia,40.5944,115.0243,409745 +Zhangping,China,Asia,25.2902,117.42,242000 +Zhangye,China,Asia,38.9248,100.4499,1131016 +Zhangzhou,China,Asia,24.513,117.647,5054328 +Zhanjiang,China,Asia,21.2701,110.3575,6981236 +Zhaodong,China,Asia,46.051,125.962,666532 +Zhaoqing,China,Asia,23.05,112.4667,4113594 +Zhaoyu,China,Asia,37.3512,112.3193,31685 +Zhaozhou,China,Asia,37.7527,114.7775,127545 +Zharkent,Kazakhstan,Asia,44.1667,80.0,42617 +Zhatay,Russia,Asia,62.15,129.8167,11772 +Zhaxi,China,Asia,27.8508,104.9687,144259 +Zhedao,China,Asia,24.8193,98.2763,30246 +Zheleznogorsk,Russia,Asia,52.3333,35.3667,97038 +Zheleznogorsk-Ilimskiy,Russia,Asia,56.5833,104.1333,2000 +Zhemgang,Bhutan,Asia,27.2134,90.655,2332 +Zhengding,China,Asia,38.1464,114.5708,158599 +Zhengjiatun,China,Asia,43.5183,123.5027,317758 +Zhengzhou,China,Asia,34.764,113.684,5621593 +Zhenjiang,China,Asia,32.188,119.424,3113384 +Zhenzhou,China,Asia,32.2723,119.1848,560258 +Zhetisay,Kazakhstan,Asia,40.7753,68.3272,36494 +Zhezqazghan,Kazakhstan,Asia,47.7833,67.7,90661 +Zhigansk,Russia,Asia,66.7667,123.3833,3382 +Zhihe,China,Asia,36.7624,110.623,26218 +Zhijiang,China,Asia,27.4409,109.678,113907 +Zhirnovsk,Russia,Asia,50.9833,44.7833,15555 +Zhitiqara,Kazakhstan,Asia,52.1908,61.2011,34880 +Zhlobin,Belarus,Europe,52.9,30.0333,76844 +Zhmerynka,Ukraine,Europe,49.05,28.1,33754 +Zhob,Pakistan,Asia,31.3417,69.4486,46164 +Zhongba,China,Asia,31.771,104.755,731343 +Zhongcheng,China,Asia,28.5973,103.9773,77514 +Zhongshan,China,Asia,22.517,113.3925,2909633 +Zhongwei,China,Asia,37.5002,105.1968,1046000 +Zhosaly,Kazakhstan,Asia,45.4893,64.0915,18563 +Zhoukou,China,Asia,33.6367,114.7014,8677800 +Zhoushan,China,Asia,29.9856,122.2074,882932 +Zhuangyuan,China,Asia,37.3056,120.829,435405 +Zhubei,Taiwan,Asia,24.8333,121.0119,212695 +Zhugang,China,Asia,28.1358,121.2264,392800 +Zhuhai,China,Asia,22.2742,113.5719,2439585 +Zhuji,China,Asia,29.7167,120.2333,1157938 +Zhujiacun,China,Asia,26.3204,104.3957,41961 +Zhukovka,Russia,Asia,53.5333,33.7333,17628 +Zhukovo,Russia,Asia,55.0333,36.75,15656 +Zhukovskiy,Russia,Asia,55.5972,38.1203,110083 +Zhuolu,China,Asia,40.3753,115.215,72538 +Zhuozhou,China,Asia,39.485,115.974,603535 +Zhuzhou,China,Asia,27.829,113.133,3902738 +Zhydachiv,Ukraine,Europe,49.385,24.1444,10353 +Zhytkavichy,Belarus,Europe,52.2333,27.8667,15950 +Zhytomyr,Ukraine,Europe,50.25,28.6667,261624 +Zhänibek,Kazakhstan,Asia,49.4167,46.85,7460 +Ziarat,Pakistan,Asia,30.381,67.727,160422 +Zielona Góra,Poland,Europe,51.9333,15.5,216781 +Zierikzee,Netherlands,Europe,51.6497,3.9164,11360 +Ziguinchor,Senegal,Africa,12.5861,-16.2707,69646 +Zihuatanejo,Mexico,North America,17.6444,-101.5511,67408 +Zijinglu,China,Asia,34.7667,112.9667,785242 +Ziketan,China,Asia,35.5886,99.9867,10000 +Zile,Turkey,Europe,40.3,35.8833,33557 +Zima,Russia,Asia,53.9167,102.05,13200 +Zimatlán de Álvarez,Mexico,North America,16.8667,-96.7833,19849 +Zinacantepec,Mexico,North America,19.2833,-99.7333,136167 +Zinapécuaro,Mexico,North America,19.8603,-100.8275,16905 +Zinder,Niger,Africa,13.8014,8.9849,235605 +Ziniaré,Burkina Faso,Africa,12.5833,-1.3,33296 +Zinjibār,Yemen,Asia,13.1283,45.3803,19879 +Zipaquirá,Colombia,South America,5.0333,-74.0,196409 +Ziyodin Shaharchasi,Uzbekistan,Asia,40.0342,65.6547,11610 +Zlatoust,Russia,Asia,55.1667,59.6667,158597 +Zlaté Moravce,Slovakia,Europe,48.3781,18.3964,11946 +Zlín,Czechia,Europe,49.2331,17.6669,74255 +Znamianka,Ukraine,Europe,48.7136,32.6733,21221 +Zoetermeer,Netherlands,Europe,52.0667,4.5,125267 +Zoeterwoude,Netherlands,Europe,52.1333,4.5,8843 +Zográfos,Greece,Europe,37.9783,23.7717,69874 +Zolochiv,Ukraine,Europe,49.8075,24.9031,23912 +Zolotonosha,Ukraine,Europe,49.6686,32.0422,27206 +Zomba,Malawi,Africa,-15.386,35.3188,101140 +Zombo,Uganda,Africa,2.5131,30.9081,15000 +Zomin Shaharchasi,Uzbekistan,Asia,39.9631,68.3939,27077 +Zongolica,Mexico,North America,18.6667,-97.0,43871 +Zonguldak,Turkey,Europe,41.4564,31.7986,101749 +Zontecomatlán de López y Fuentes,Mexico,North America,20.7667,-98.3333,14620 +Zorgo,Burkina Faso,Africa,12.25,-0.6167,35406 +Zouerate,Mauritania,Africa,22.7333,-12.4667,62380 +Zozocolco de Hidalgo,Mexico,North America,20.1333,-97.5833,14254 +Zrenjanin,Serbia,Europe,45.3833,20.3894,67129 +Zreče,Slovenia,Europe,46.375,15.3853,2922 +Zrnovci,North Macedonia,Europe,41.8542,22.4442,1534 +Zubin Potok,Kosovo,Europe,42.9144,20.6897,15200 +Zug,Switzerland,Europe,47.1681,8.5169,31469 +Zugdidi,Georgia,Europe,42.5081,41.8725,41494 +Zuidhorn,Netherlands,Europe,53.2468,6.4077,18917 +Zuitou,China,Asia,34.0622,107.3128,26948 +Zumbagua,Ecuador,South America,-0.9558,-78.9017,11895 +Zumpango,Mexico,North America,19.7969,-99.0992,50742 +Zumpango del Río,Mexico,North America,17.65,-99.5,24719 +Zundert,Netherlands,Europe,51.4703,4.66,21988 +Zunhua,China,Asia,40.189,117.966,707047 +Zunil,Guatemala,North America,14.7833,-91.4833,15893 +Zunilito,Guatemala,North America,14.6167,-91.5167,8888 +Zutphen,Netherlands,Europe,52.14,6.195,47423 +Zuwārah,Libya,Africa,32.9333,12.0833,80310 +Zuyevka,Russia,Asia,58.4,51.15,9767 +Zvenyhorodka,Ukraine,Europe,49.0697,30.9678,16269 +Zverevo,Russia,Asia,47.0206,40.1225,19353 +Zveçan,Kosovo,Europe,42.9075,20.8403,16650 +Zvolen,Slovakia,Europe,48.5744,19.1533,39844 +Zvornik,Bosnia and Herzegovina,Europe,44.3833,19.1,63686 +Zwedru,Liberia,Africa,6.0667,-8.1333,23903 +Zweibrücken,Germany,Europe,49.25,7.3667,34613 +Zwettl,Austria,Europe,48.6033,15.1689,10722 +Zwickau,Germany,Europe,50.7167,12.5,87593 +Zwijndrecht,Netherlands,Europe,51.824,4.6126,44775 +Zwolle,Netherlands,Europe,52.5167,6.1,129840 +Zyryanka,Russia,Asia,65.736,150.89,3627 +Zyryanovsk,Kazakhstan,Asia,49.7453,84.2548,49658 +Zákynthos,Greece,Europe,37.8,20.75,41180 +Zárate,Argentina,South America,-34.0833,-59.0333,109443 +Zürich,Switzerland,Europe,47.3744,8.5411,448664 +Zābol,Iran,Asia,31.0339,61.4878,134950 +Zābolī,Iran,Asia,27.1283,61.6722,12245 +Zāhedān,Iran,Asia,29.5025,60.8558,587730 +Zākhū,Iraq,Asia,37.1436,42.6819,197000 +Ząbkowice Śląskie,Poland,Europe,50.5833,16.8167,14578 +Złotoryja,Poland,Europe,51.1333,15.9167,14656 +Złotów,Poland,Europe,53.3603,17.0408,18768 +Zərdab,Azerbaijan,Europe,40.2199,47.71,8450 +Àrvorezinha,Brazil,South America,-28.8719,-52.175,10322 +Ábrego,Colombia,South America,8.0,-73.2,38627 +Ágioi Anárgyroi,Greece,Europe,38.0267,23.7183,32283 +Ágios Dimítrios,Greece,Europe,37.9333,23.7333,71664 +Ágios Nikólaos,Greece,Europe,35.1833,25.7167,21562 +Água Azul do Norte,Brazil,South America,-6.7908,-50.4669,18080 +Água Boa,Brazil,South America,-17.9958,-42.3889,12589 +Água Branca,Brazil,South America,-9.2608,-37.9361,19008 +Água Clara,Brazil,South America,-20.45,-52.8667,16741 +Água Doce,Brazil,South America,-18.5464,-40.9828,10909 +Água Fria,Brazil,South America,-11.8669,-38.7669,14497 +Água Preta,Brazil,South America,-8.7,-35.35,26461 +Águas Belas,Brazil,South America,-9.1167,-37.1167,41548 +Águas Formosas,Brazil,South America,-17.0819,-40.9358,17845 +Águas Vermelhas,Brazil,South America,-15.7469,-41.46,14037 +Águas de Lindóia,Brazil,South America,-22.4758,-46.6328,16190 +Águeda,Portugal,Europe,40.5745,-8.448,47729 +Águia Branca,Brazil,South America,-18.9828,-40.74,9519 +Álamo,Mexico,North America,20.9167,-97.675,25159 +Álamos,Mexico,North America,27.0275,-108.94,10961 +Álimos,Greece,Europe,37.9167,23.7167,43174 +Álvares Machado,Brazil,South America,-22.0789,-51.4719,22661 +Ángel R. Cabada,Mexico,North America,18.5969,-95.4453,11689 +Áno Liósia,Greece,Europe,38.0833,23.7,35047 +Áno Sýros,Greece,Europe,37.4333,24.9167,21507 +Árgos,Greece,Europe,37.6167,22.7167,22085 +Árta,Greece,Europe,39.165,20.9875,21895 +Ávila,Spain,Europe,40.65,-4.6833,58111 +Älmhult,Sweden,Europe,56.55,14.1333,11003 +Änew,Turkmenistan,Asia,37.8833,58.5333,30000 +Ängelholm,Sweden,Europe,56.25,12.8667,42131 +Äänekoski,Finland,Europe,62.6,25.725,17971 +Åkersberga,Sweden,Europe,59.4833,18.3,28033 +Ålesund,Norway,Europe,62.4723,6.1549,52163 +Åmål,Sweden,Europe,59.05,12.7,9065 +Ås,Norway,Europe,59.6667,10.8,20652 +Åstorp,Sweden,Europe,56.1333,12.95,9488 +Çal,Turkey,Europe,38.0836,29.3989,17889 +Çaldıran,Turkey,Europe,39.1419,43.9139,58635 +Çameli,Turkey,Europe,37.0708,29.3453,17549 +Çan,Turkey,Europe,40.0275,27.0475,30970 +Çanakkale,Turkey,Europe,40.1519,26.4056,143622 +Çankaya,Turkey,Europe,39.9244,32.8856,942553 +Çankırı,Turkey,Europe,40.5986,33.6192,90564 +Çarşamba,Turkey,Europe,41.1992,36.7275,140664 +Çarşıbaşı,Turkey,Europe,41.0833,39.3833,15227 +Çat,Turkey,Europe,39.6111,40.9789,15556 +Çatak,Turkey,Europe,38.0086,43.0597,18462 +Çatalca,Turkey,Europe,41.1417,28.4631,77468 +Çatalpınar,Turkey,Europe,40.8789,37.4531,13025 +Çay,Turkey,Europe,38.5833,31.0333,14599 +Çaybaşı,Turkey,Europe,41.0175,37.0983,11889 +Çaycuma,Turkey,Europe,41.4267,32.0758,30255 +Çayeli,Turkey,Europe,40.75,41.0667,24583 +Çaykara,Turkey,Europe,40.7475,40.2419,13070 +Çayırova,Turkey,Europe,40.8,29.4167,150792 +Çağlayancerit,Turkey,Europe,37.7506,37.2931,22350 +Çekerek,Turkey,Europe,40.0731,35.4947,9471 +Çeltik,Turkey,Europe,39.0244,31.7906,9429 +Çerkezköy,Turkey,Europe,41.2889,28.0042,206829 +Çerkeş,Turkey,Europe,40.8139,32.8908,9634 +Çermik,Turkey,Europe,38.1372,39.4511,49644 +Çeşme,Turkey,Europe,38.3236,26.3028,48924 +Çifteler,Turkey,Europe,39.3831,31.0392,14906 +Çiftlikköy,Turkey,Europe,40.65,29.3333,50974 +Çilimli,Turkey,Europe,40.9,31.05,9936 +Çine,Turkey,Europe,37.6117,28.0614,48466 +Çivril,Turkey,Europe,38.3014,29.7386,59967 +Çobanlar,Turkey,Europe,38.7,30.7833,9190 +Çorlu,Turkey,Europe,41.15,27.8,300296 +Çorum,Turkey,Europe,40.5455,34.957,269595 +Çubuk,Turkey,Europe,40.2386,33.0331,95449 +Çukurca,Turkey,Europe,37.2469,43.6117,10252 +Çumra,Turkey,Europe,37.575,32.7747,67690 +Çüngüş,Turkey,Europe,38.2128,39.2869,10720 +Çınar,Turkey,Europe,37.7242,40.415,76966 +Çınarcık,Turkey,Europe,40.6333,29.1167,38600 +Ébolowa,Cameroon,Africa,2.9167,11.15,76885 +Édessa,Greece,Europe,40.8,22.05,17848 +Épernay,France,Europe,49.0403,3.96,22022 +Épinal,France,Europe,48.1744,6.4512,32296 +Érd,Hungary,Europe,47.3833,18.9167,71338 +Étampes,France,Europe,48.4343,2.1615,26601 +Évora,Portugal,Europe,38.5667,-7.9,53591 +Évosmos,Greece,Europe,40.6689,22.9083,79221 +Évreux,France,Europe,49.02,1.15,48335 +Évry,France,Europe,48.6238,2.4296,53641 +Ílhavo,Portugal,Europe,40.6,-8.6667,31383 +Ílion,Greece,Europe,38.0333,23.7,84004 +Íquira,Colombia,South America,2.65,-75.65,9064 +Ðakovo,Croatia,Europe,45.3083,18.4106,23577 +Óbidos,Brazil,South America,-1.9178,-55.5178,52229 +Ózd,Hungary,Europe,48.2192,20.2869,30550 +Ödemiş,Turkey,Europe,38.2311,27.9719,132740 +Ölgiy,Mongolia,Asia,48.9683,89.9686,40564 +Ömerli,Turkey,Europe,37.4025,40.9561,13740 +Öndörhaan,Mongolia,Asia,47.3167,110.65,20935 +Örebro,Sweden,Europe,59.2669,15.1965,128658 +Örkelljunga,Sweden,Europe,56.2833,13.2833,10300 +Örnsköldsvik,Sweden,Europe,63.294,18.7122,32953 +Öskemen,Kazakhstan,Asia,49.9833,82.6167,400142 +Östersund,Sweden,Europe,63.1833,14.65,49806 +Özalp,Turkey,Europe,38.6583,43.9894,59851 +Ørsta,Norway,Europe,62.2,6.15,10929 +Úmbita,Colombia,South America,5.2167,-73.4667,10314 +Úrsulo Galván,Mexico,North America,19.4,-96.3,29005 +Ústí nad Labem,Czechia,Europe,50.6583,14.0417,91342 +Ünye,Turkey,Europe,41.1272,37.2881,132432 +Ürgüp,Turkey,Europe,38.6314,34.9131,24647 +Ürümqi,China,Asia,43.8225,87.6125,4335017 +Üsharal,Kazakhstan,Asia,46.1697,80.9394,15991 +Üshtöbe,Kazakhstan,Asia,45.2422,77.9822,24895 +Ābyek,Iran,Asia,36.0411,50.5306,60107 +Ādaži,Latvia,Europe,57.0769,24.3236,7535 +Ālbū Kamāl,Syria,Asia,34.4504,40.9186,52020 +Āmol,Iran,Asia,36.4703,52.3467,237528 +Āqchah,Afghanistan,Asia,36.9114,66.1858,1012000 +Ārān Bīdgol,Iran,Asia,34.0589,51.4817,65404 +Āshkhāneh,Iran,Asia,37.5578,56.9258,25104 +Āshtīān,Iran,Asia,34.5222,50.0069,8763 +Āsosa,Ethiopia,Africa,10.0667,34.5167,20226 +Āstāneh-ye Ashrafīyeh,Iran,Asia,37.2658,49.9431,44941 +Āwasa,Ethiopia,Africa,7.05,38.4667,300100 +Āzādshahr,Iran,Asia,37.0869,55.1742,43760 +Āz̄arshahr,Iran,Asia,37.7628,45.9758,44887 +Āīzawl,India,Asia,23.7272,92.7178,293416 +Ćićevac,Serbia,Europe,43.7167,21.45,9446 +Ćuprija,Serbia,Europe,43.9231,21.3686,9609 +Čadca,Slovakia,Europe,49.4386,18.7883,22580 +Čajetina,Serbia,Europe,43.75,19.7167,14585 +Čakovec,Croatia,Europe,46.3833,16.4333,27122 +Čapljina,Bosnia and Herzegovina,Europe,43.11,17.7,28122 +Čačak,Serbia,Europe,43.8914,20.3497,71883 +Čelić,Bosnia and Herzegovina,Europe,44.7211,18.8203,10502 +Čepin,Croatia,Europe,45.5236,18.5633,9665 +České Budějovice,Czechia,Europe,48.9747,14.4747,97377 +Čitluk,Bosnia and Herzegovina,Europe,43.2286,17.7008,18140 +Čoka,Serbia,Europe,45.9333,20.15,4707 +Črenšovci,Slovenia,Europe,46.5744,16.2906,1151 +Črna na Koroškem,Slovenia,Europe,46.4697,14.849,2201 +Črnomelj,Slovenia,Europe,45.0,34.0,5496 +Čučer-Sandevo,North Macedonia,Europe,42.0975,21.3877,274 +Điện Biên Phủ,Vietnam,Asia,21.3833,103.0167,84672 +Đà Lạt,Vietnam,Asia,11.9417,108.4383,203710 +Đông Hà,Vietnam,Asia,16.8303,107.0972,164228 +Đồng Hới,Vietnam,Asia,17.4831,106.5997,133672 +Đồng Xoài,Vietnam,Asia,11.5169,106.8392,150052 +Đức Phổ,Vietnam,Asia,14.81,108.96,150927 +Ħamrun,Malta,Europe,35.8861,14.4894,9743 +Īnderbor,Kazakhstan,Asia,48.55,51.7833,13254 +Īrānshahr,Iran,Asia,27.1978,60.6836,113750 +İdil,Turkey,Europe,37.341,41.894,30271 +İkizce,Turkey,Europe,41.0531,37.0769,13276 +İmamoğlu,Turkey,Europe,37.2589,35.6728,27037 +İmişli,Azerbaijan,Europe,39.8697,48.06,31310 +İncesu,Turkey,Europe,38.6222,35.1847,29120 +İncirliova,Turkey,Europe,37.8547,27.7236,55848 +İnebolu,Turkey,Europe,41.9747,33.7608,10594 +İnegöl,Turkey,Europe,40.0806,29.5097,294485 +İpsala,Turkey,Europe,40.9167,26.3833,26148 +İskenderun,Turkey,Europe,36.5817,36.165,251682 +İskilip,Turkey,Europe,40.7361,34.4739,17612 +İslahiye,Turkey,Europe,37.025,36.6345,67650 +İsmayıllı,Azerbaijan,Europe,40.7847,48.1514,20660 +İspir,Turkey,Europe,40.4836,40.9953,14607 +İvrindi,Turkey,Europe,39.5833,27.4833,31512 +İzmir,Turkey,Europe,38.42,27.14,2965900 +İznik,Turkey,Europe,40.4292,29.7211,44236 +Ķekava,Latvia,Europe,56.8266,24.23,5039 +Łask,Poland,Europe,51.5903,19.1333,16576 +Łańcut,Poland,Europe,50.0667,22.2333,17768 +Łobez,Poland,Europe,53.6333,15.6167,9792 +Łomża,Poland,Europe,53.1764,22.0731,60848 +Łowicz,Poland,Europe,52.1,19.9333,26928 +Łuków,Poland,Europe,51.9272,22.3833,27906 +Łódź,Poland,Europe,51.7769,19.4547,670642 +Łęczna,Poland,Europe,51.3,22.8833,17826 +Łęczyca,Poland,Europe,52.05,19.2,13587 +Ōita,Japan,Asia,33.2333,131.6067,474804 +Ōsaka,Japan,Asia,34.6939,135.5022,15126000 +Ōtsu,Japan,Asia,35.0167,135.85,343991 +Śrem,Poland,Europe,52.0886,17.0147,28835 +Środa Wielkopolska,Poland,Europe,52.2333,17.2833,24225 +Środa Śląska,Poland,Europe,51.15,16.5833,9525 +Świdnica,Poland,Europe,50.85,16.4833,54792 +Świdnik,Poland,Europe,51.2167,22.7,37884 +Świdwin,Poland,Europe,53.7833,15.7667,14937 +Świecie,Poland,Europe,53.4167,18.4333,24796 +Świedbodzin,Poland,Europe,52.25,15.5333,21399 +Świnoujście,Poland,Europe,53.9167,14.25,39189 +Świętochłowice,Poland,Europe,50.2919,18.9178,46494 +Şabanözü,Turkey,Europe,40.4825,33.2836,8843 +Şafāshahr,Iran,Asia,30.6133,53.1928,26933 +Şahbuz,Azerbaijan,Europe,39.4073,45.5733,2283 +Şalkhad,Syria,Asia,32.492,36.7111,35000 +Şalpazarı,Turkey,Europe,40.9422,39.1942,10434 +Şalālah,Oman,Asia,17.0197,54.0897,331949 +Şanlıurfa,Turkey,Europe,37.1583,38.7917,596637 +Şarkîkaraağaç,Turkey,Europe,38.0803,31.3658,10128 +Şarköy,Turkey,Europe,40.6039,27.1064,33466 +Şarkışla,Turkey,Europe,39.35,36.4,23707 +Şaydnāyā,Syria,Asia,33.6947,36.3715,25194 +Şaḩneh,Iran,Asia,34.4819,47.69,35508 +Şaḩnāyā,Syria,Asia,33.4253,36.2255,13993 +Şa‘dah,Yemen,Asia,16.94,43.7592,51870 +Şebin Karahisar,Turkey,Europe,40.2886,38.4233,10695 +Şefaatlı,Turkey,Europe,39.5017,34.7522,8769 +Şemdinli,Turkey,Europe,37.308,44.571,18220 +Şenkaya,Turkey,Europe,40.5619,42.3464,16035 +Şereflikoçhisar,Turkey,Europe,38.9444,33.5419,33140 +Şile,Turkey,Europe,41.1764,29.6128,43464 +Şiran,Turkey,Europe,40.1894,39.1267,11539 +Şoldăneşti,Moldova,Europe,47.8161,28.7972,5883 +Şowme‘eh Sarā,Iran,Asia,37.3,49.3142,47083 +Ştefan Vodă,Moldova,Europe,46.5129,29.6619,7078 +Şuhut,Turkey,Europe,38.5333,30.55,15054 +Şuşa,Azerbaijan,Europe,39.7602,46.7504,4446 +Şāfītā,Syria,Asia,34.821,36.1177,32213 +Şırnak,Turkey,Europe,37.52,42.459,75932 +Şūrān,Syria,Asia,35.2919,36.7485,29100 +Şəki,Azerbaijan,Europe,41.1919,47.1706,68360 +Şəmkir,Azerbaijan,Europe,40.8297,46.0189,40600 +Šabac,Serbia,Europe,44.7558,19.6939,105432 +Šakiai,Lithuania,Europe,54.9556,23.0444,5440 +Šalovci,Slovenia,Europe,46.825,16.2981,392 +Šalčininkai,Lithuania,Europe,54.3111,25.3806,6841 +Šavnik,Montenegro,Europe,42.95,19.1,364 +Šaľa,Slovakia,Europe,48.15,17.8833,21183 +Šempeter pri Gorici,Slovenia,Europe,45.9284,13.6378,3694 +Šentjernej,Slovenia,Europe,45.8389,15.3361,1441 +Šentjur,Slovenia,Europe,46.2176,15.396,4940 +Šentrupert,Slovenia,Europe,45.9769,15.0897,310 +Šenčur,Slovenia,Europe,46.243,14.4181,3121 +Šiauliai,Lithuania,Europe,55.9281,23.3167,104300 +Šibenik,Croatia,Europe,43.7339,15.8956,42599 +Šid,Serbia,Europe,45.1283,19.2264,34188 +Šilalė,Lithuania,Europe,55.4833,22.1833,4699 +Šilutė,Lithuania,Europe,55.35,21.4833,16200 +Široki Brijeg,Bosnia and Herzegovina,Europe,43.3667,17.5833,28929 +Širvintos,Lithuania,Europe,55.05,24.95,5802 +Škocjan,Slovenia,Europe,45.9069,15.2914,233 +Škofja Loka,Slovenia,Europe,46.1672,14.303,11797 +Škofljica,Slovenia,Europe,45.9836,14.5761,3029 +Šmarje,Slovenia,Europe,46.2272,15.5192,1755 +Šmarješke Toplice,Slovenia,Europe,45.8622,15.2214,492 +Šmartno,Slovenia,Europe,46.0441,14.8466,1395 +Šoštanj,Slovenia,Europe,46.3798,15.0482,2933 +Štip,North Macedonia,Europe,41.7358,22.1914,42000 +Štore,Slovenia,Europe,46.2219,15.3111,2257 +Švenčionys,Lithuania,Europe,55.1333,26.1556,4480 +Ţabas,Iran,Asia,33.5961,56.9281,39676 +Ţanţā,Egypt,Africa,30.7833,31.0,253600 +Ţarţūs,Syria,Asia,34.8846,35.8866,89457 +Ţorqabeh,Iran,Asia,36.3142,59.3725,20998 +Ţūz Khūrmātū,Iraq,Asia,34.8881,44.6325,119000 +Żabbar,Malta,Europe,35.8772,14.5381,15404 +Żagań,Poland,Europe,51.6167,15.3167,24424 +Żary,Poland,Europe,51.6333,15.1333,36004 +Żebbuġ,Malta,Europe,35.8731,14.4419,11903 +Żejtun,Malta,Europe,35.8556,14.5333,11508 +Żnin,Poland,Europe,52.85,17.7,13268 +Żory,Poland,Europe,50.05,18.7,61793 +Żuromin,Poland,Europe,53.0667,19.9,8592 +Żurrieq,Malta,Europe,35.8292,14.4758,10823 +Żyrardów,Poland,Europe,52.05,20.4333,39374 +Żywiec,Poland,Europe,49.6892,19.2058,30334 +Žabalj,Serbia,Europe,45.3667,20.0667,9161 +Žabljak,Montenegro,Europe,43.155,19.1208,1937 +Žalec,Slovenia,Europe,46.251,15.1639,5004 +Železniki,Slovenia,Europe,46.2182,14.1592,3069 +Želino,North Macedonia,Europe,41.9794,21.0619,105 +Žepče,Bosnia and Herzegovina,Europe,44.4333,18.0333,31582 +Žiar nad Hronom,Slovakia,Europe,48.5919,18.8533,16879 +Žilina,Slovakia,Europe,49.2167,18.7333,82656 +Žiri,Slovenia,Europe,46.0469,14.1097,3657 +Žitište,Serbia,Europe,45.4833,20.55,13412 +Žitorađa,Serbia,Europe,43.1833,21.7167,16368 +Živinice,Bosnia and Herzegovina,Europe,44.4493,18.6498,57765 +Županja,Croatia,Europe,45.0667,18.7,9153 +Ḩadībū,Yemen,Asia,12.65,54.0167,8545 +Ḩadīthah,Iraq,Asia,34.1366,42.3773,46500 +Ḩajjah,Yemen,Asia,15.695,43.5975,53887 +Ḩalabjah,Iraq,Asia,35.1833,45.9833,65200 +Ḩamāh,Syria,Asia,35.1503,36.73,696863 +Ḩamīdīyeh,Iran,Asia,31.4811,48.435,22057 +Ḩarastā,Syria,Asia,33.5587,36.3652,45974 +Ḩārim,Syria,Asia,36.2118,36.5208,21934 +Ḩā’il,Saudi Arabia,Asia,27.5167,41.6833,310897 +Ḩīsh,Syria,Asia,35.5479,36.6436,8817 +‘Afrīn,Syria,Asia,36.5119,36.8695,36562 +‘Ajab Shīr,Iran,Asia,37.4833,45.8911,33606 +‘Ajlūn,Jordan,Asia,32.3325,35.7517,148870 +‘Ajmān,United Arab Emirates,Asia,25.4136,55.4456,490035 +‘Alīābād-e Katūl,Iran,Asia,36.9075,54.8667,52838 +‘Anah,Iraq,Asia,34.3686,41.9819,21000 +‘Anbarābād,Iran,Asia,28.4794,57.8414,18185 +‘Aqrah,Iraq,Asia,36.7604,43.8943,212000 +‘Ayn al ‘Arab,Syria,Asia,36.891,38.3535,44821 +‘Aynkāwah,Iraq,Asia,36.2306,43.9944,30000 +‘Āmūdā,Syria,Asia,37.1042,40.93,47580 +’s-Hertogenbosch,Netherlands,Europe,51.6833,5.3167,157486 diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css new file mode 100644 index 0000000..45ccae1 --- /dev/null +++ b/docs/stylesheets/extra.css @@ -0,0 +1,6 @@ +/* Extend the three-column layout to fill wider viewports. + Material's default max-width (~61 rem / ~976 px) leaves large whitespace + to the right of the TOC column on 1440 p+ screens. */ +.md-grid { + max-width: 90rem; /* 1440 px at browser default 16 px */ +} diff --git a/docs/usage.md b/docs/usage.md index 9687a47..c700747 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -35,9 +35,9 @@ X = np.random.randn(100, 20) y = np.random.randint(0, 5, size=100) # 2. Instantiate the model -# stage_1: Strategy to initialize the distance matrix ('computed' is usually default) +# parametrization: Strategy to initialize the distance matrix ('computed' is usually default) # manifold: The target shape (e.g., 'cluster', 'circular', 'chain', 'hierarchical') -model = SupervisedMDS(stage_1="computed", manifold="cluster", alpha=0.1) +model = SupervisedMDS(parametrization="computed", manifold="cluster", alpha=0.1) # 3. Fit to data model.fit(X, y) @@ -50,7 +50,7 @@ print(f"Projected shape: {X_projected.shape}") # (100, 2) ### Parameters - **`manifold`**: The name of the target shape (e.g., `"cluster"`, `"circular"`). See [Available Shapes](shapes.md) for a full list. -- **`stage_1`**: Strategy for the first stage. `"computed"` is standard. `"user_provided"` requires passing a pre-computed distance matrix. +- **`parametrization`**: Strategy for the first stage. `"computed"` is standard. `"user_provided"` requires passing a pre-computed distance matrix. - **`alpha`**: Regularization parameter (if applicable). ## 3. Automatic Manifold Discovery diff --git a/mkdocs.yml b/mkdocs.yml index 7c69596..7e20124 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,9 +1,13 @@ -site_name: smds +site_name: Supervised Multi-Dimensional Scaling site_url: https://ukplab.github.io/smds nav: - Home: index.md - Usage Walkthrough: usage.md - Available Shapes: shapes.md + - API Reference: + - Core Estimators: api/core.md + - Shapes: api/shapes.md + - Stress Metrics: api/stress.md - Examples: - Find Manifold Location: notebooks/find_manifold_location.ipynb - Family Tree Experiment: notebooks/family_tree.ipynb @@ -14,9 +18,23 @@ nav: theme: name: material + logo: assets/logo_white.png + favicon: assets/favicon.png features: - - navigation.sections # Groups items in sidebar nicely - - content.code.copy # Adds copy button to code blocks + - navigation.sections + - content.code.copy + - toc.follow + +extra_css: + - stylesheets/extra.css + +extra_javascript: + - javascripts/mathjax.js + - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js + +markdown_extensions: + - pymdownx.arithmatex: + generic: true plugins: - search @@ -28,6 +46,10 @@ plugins: docstring_style: numpy show_root_heading: true show_source: true + show_signature_annotations: true + separate_signature: true + show_bases: true + members_order: source - mkdocs-jupyter: execute: false include_source: true diff --git a/pyproject.toml b/pyproject.toml index cbb87e2..3d57efa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "smds" -version = "1.6.5" +version = "1.6.6" description = "A plug-and-play, scikit-learn compatible implementation of Supervised Multi-Dimensional Scaling (SMDS) for automatic feature manifold discovery in LLMs." authors = [ { name = "Federico Tiblias" }, diff --git a/smds/hsmds.py b/smds/hsmds.py index 0daa4d5..6e3efdb 100644 --- a/smds/hsmds.py +++ b/smds/hsmds.py @@ -25,16 +25,15 @@ class HybridSMDS(SupervisedMDS): sklearn-compatible reducer with fit(X, Y) and transform(X). bypass_mds : bool, default=False If True, treat y as target coordinates directly. - alpha, orthonormal, radius : inherited from SupervisedMDS + alpha, orthonormal : inherited from SupervisedMDS """ def __init__( self, manifold: Union[str, Callable[[NDArray[Any]], NDArray[np.float64]]] = "circular", n_components: int = 2, - alpha: float = 0.1, + alpha: float = 1.0, orthonormal: bool = False, - radius: float = 6371, reducer: BaseEstimator | None = None, bypass_mds: bool = False, ): @@ -47,31 +46,41 @@ def __init__( bypass_mds_bool = bool(arr_bypass.flat[0]) if bypass_mds_bool: - stage_1: SMDSParametrization | str = "user_provided" + parametrization: SMDSParametrization | str = "user_provided" manifold_for_super = "circular" elif isinstance(manifold, str): - stage_1 = "computed" + parametrization = "computed" manifold_for_super = manifold else: from smds.smds import ComputedSMDSParametrization - stage_1 = ComputedSMDSParametrization(manifold=manifold, n_components=n_components) + parametrization = ComputedSMDSParametrization(manifold=manifold, n_components=n_components) manifold_for_super = "circular" super().__init__( - stage_1=stage_1, + parametrization=parametrization, manifold=manifold_for_super, alpha=alpha, orthonormal=orthonormal, - radius=radius, ) self.n_components = n_components self.manifold = manifold # type: ignore[assignment] self.reducer = reducer self.bypass_mds = bypass_mds + def _resolve_n_components(self) -> int | None: + """ + Opt out of the base-class embedding-dimension override. + + For HybridSMDS ``n_components`` is a lower bound on the reducer target + (``Y_`` is zero-padded up to it in ``fit``), not a request to truncate the + manifold's natural embedding, so built-in manifolds keep their own + dimensionality here. + """ + return None + def _validate_data( - self, X: np.ndarray, y: np.ndarray, reset: bool = True, stage_1_model: SMDSParametrization | None = None + self, X: np.ndarray, y: np.ndarray, reset: bool = True, parametrization_model: SMDSParametrization | None = None ) -> tuple[np.ndarray, np.ndarray]: from sklearn.utils.validation import check_array @@ -95,7 +104,7 @@ def _validate_data( ) return X, y else: - return super()._validate_data(X, y, reset=reset, stage_1_model=stage_1_model) + return super()._validate_data(X, y, reset=reset, parametrization_model=parametrization_model) def fit(self, X: NDArray[np.float64], y: NDArray[np.float64]) -> "HybridSMDS": """ @@ -124,15 +133,15 @@ def fit(self, X: NDArray[np.float64], y: NDArray[np.float64]) -> "HybridSMDS": y_arr = y_arr.reshape(-1, 1) Y = y_arr else: - stage_1_model = self._resolve_stage_1() - stage_1_fitted = clone(stage_1_model) - stage_1_fitted.fit(y) + parametrization_model = self._resolve_parametrization() + parametrization_fitted = clone(parametrization_model) + parametrization_fitted.fit(y) try: - distances = stage_1_fitted.D_ + distances = parametrization_fitted.D_ except AttributeError: try: - distances = stage_1_fitted.compute_ideal_distances(y) + distances = parametrization_fitted.compute_ideal_distances(y) except (ValueError, TypeError) as e: if "dtype" in str(e).lower() or "type" in str(e).lower(): from sklearn.utils.multiclass import type_of_target # type: ignore[import-untyped] @@ -146,19 +155,19 @@ def fit(self, X: NDArray[np.float64], y: NDArray[np.float64]) -> "HybridSMDS": if isinstance(distances, np.ndarray) and np.any(distances < 0): raise ValueError("HybridSMDS does not support incomplete distance matrices.") - Y = stage_1_fitted.Y_ + Y = parametrization_fitted.Y_ if Y.shape[1] < self.n_components: pad = np.zeros((Y.shape[0], self.n_components - Y.shape[1]), dtype=np.float64) Y = np.hstack([Y, pad]) - self.stage_1_fitted_ = stage_1_fitted + self.parametrization_fitted_ = parametrization_fitted self.Y_ = Y if self.bypass_mds: - self.stage_1_fitted_ = UserProvidedSMDSParametrization(y=Y, n_components=self.n_components) - self.stage_1_fitted_.fit() + self.parametrization_fitted_ = UserProvidedSMDSParametrization(y=Y, n_components=self.n_components) + self.parametrization_fitted_.fit() self.reducer_ = clone(self.reducer) self.reducer_.fit(X, self.Y_) diff --git a/smds/pipeline/discovery_pipeline.py b/smds/pipeline/discovery_pipeline.py index a4bc06c..7bdcf0f 100644 --- a/smds/pipeline/discovery_pipeline.py +++ b/smds/pipeline/discovery_pipeline.py @@ -218,7 +218,7 @@ def make_scorer(m: StressMetrics) -> ScorerFunc: params = {} if isinstance(shape, BaseShape): - if shape.y_ndim != user_y_ndim: + if user_y_ndim not in np.atleast_1d(shape.y_ndim): continue shape_name = shape.__class__.__name__ @@ -290,13 +290,15 @@ def make_scorer(m: StressMetrics) -> ScorerFunc: if save_results and plots_dir is not None: try: - stage_1 = clone(parametrization) - stage_1.fit(y) - Y_ideal = stage_1.Y_ + parametrization_fitted = clone(parametrization) + parametrization_fitted.fit(y) + Y_ideal = parametrization_fitted.Y_ if n_folds >= 2: kf = KFold(n_splits=n_folds, shuffle=False) - X_embedded = np.full((X.shape[0], stage_1.n_components), np.nan, dtype=np.float64) + X_embedded = np.full( + (X.shape[0], parametrization_fitted.n_components), np.nan, dtype=np.float64 + ) for train_idx, test_idx in kf.split(X): X_embedded[test_idx] = ( SupervisedMDS(parametrization).fit(X[train_idx], y[train_idx]).transform(X[test_idx]) @@ -361,7 +363,8 @@ def make_scorer(m: StressMetrics) -> ScorerFunc: valid_shapes = [ h for h in shapes - if (isinstance(h, BaseShape) and h.y_ndim == user_y_ndim) or isinstance(h, UserProvidedSMDSParametrization) + if (isinstance(h, BaseShape) and user_y_ndim in np.atleast_1d(h.y_ndim)) + or isinstance(h, UserProvidedSMDSParametrization) ] if valid_shapes: create_plots( diff --git a/smds/pipeline/helpers/interactive_plots.py b/smds/pipeline/helpers/interactive_plots.py index 7729cd8..9c1c32e 100644 --- a/smds/pipeline/helpers/interactive_plots.py +++ b/smds/pipeline/helpers/interactive_plots.py @@ -36,16 +36,20 @@ def generate_interactive_plot( filename = f"{safe_name}.html" filepath = os.path.join(save_dir, filename) + # Coordinate labels (spherical, hierarchical, n-D euclidean) colour by their + # first axis, matching create_plots. + hue_vals = y[:, 0] if y.ndim > 1 else y + # Determine if y is discrete or continuous for coloring - unique_labels = np.unique(y) + unique_labels = np.unique(hue_vals) is_discrete = len(unique_labels) < 20 # Prepare DataFrame-like dict for Plotly - plot_data = {"x": X_embedded[:, 0], "y": X_embedded[:, 1], "label": y} + plot_data = {"x": X_embedded[:, 0], "y": X_embedded[:, 1], "label": hue_vals} # Adjust formatting for discrete labels to ensure categorical legend if is_discrete: - plot_data["label"] = y.astype(str) + plot_data["label"] = hue_vals.astype(str) if n_components == 3: plot_data["z"] = X_embedded[:, 2] @@ -62,7 +66,7 @@ def generate_interactive_plot( fig.update_traces(marker=dict(size=4)) # Enforce correct aspect ratio in 3D if Y_ideal is not None and Y_ideal.shape[1] >= 3: - order = np.argsort(y.ravel()) + order = np.argsort(hue_vals) line_x = Y_ideal[order, 0] line_y = Y_ideal[order, 1] line_z = Y_ideal[order, 2] @@ -96,7 +100,7 @@ def generate_interactive_plot( # Enforce 1:1 Aspect Ratio if Y_ideal is not None and Y_ideal.shape[1] >= 2: - order = np.argsort(y.ravel()) + order = np.argsort(hue_vals) line_x = Y_ideal[order, 0] line_y = Y_ideal[order, 1] fig.add_trace( diff --git a/smds/shapes/base_shape.py b/smds/shapes/base_shape.py index 3bfcea5..b657432 100644 --- a/smds/shapes/base_shape.py +++ b/smds/shapes/base_shape.py @@ -21,7 +21,7 @@ class BaseShape(BaseEstimator, ABC): # type: ignore[misc] Attributes ---------- - y_ndim : int + y_ndim : int or tuple of int Abstract property. The expected dimensionality of the input labels. normalize_labels : bool Abstract property. Whether to normalize inputs before computation. @@ -29,16 +29,20 @@ class BaseShape(BaseEstimator, ABC): # type: ignore[misc] @property @abstractmethod - def y_ndim(self) -> int: + def y_ndim(self) -> int | tuple[int, ...]: """ Get the required dimensionality of the input labels `y`. Returns ------- - int + int or tuple of int The number of dimensions expected for the input array. - 1: 1D array (e.g., time series, clusters). - 2: 2D array (e.g., lat/lon coordinates, hierarchical levels). + + A tuple declares that several are accepted, e.g. ``(1, 2)`` for a + shape defined for both scalar labels and coordinate arrays. Test it + with ``ndim in np.atleast_1d(shape.y_ndim)``, not for equality. """ pass @@ -149,9 +153,11 @@ def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: if y_proc.size == 0: raise ValueError("Input 'y' cannot be empty.") - if y_proc.ndim != self.y_ndim: + accepted = np.atleast_1d(self.y_ndim) + + if y_proc.ndim not in accepted: raise ValueError( - f"Input 'y' must be {self.y_ndim}-dimensional (n_samples,), " + f"Input 'y' must be {'- or '.join(map(str, accepted))}-dimensional, " f"but got shape {y_proc.shape} with {y_proc.ndim} dimensions." ) diff --git a/smds/shapes/continuous_shapes/circular.py b/smds/shapes/continuous_shapes/circular.py index 0b12656..071e842 100644 --- a/smds/shapes/continuous_shapes/circular.py +++ b/smds/shapes/continuous_shapes/circular.py @@ -44,8 +44,8 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: The labels are treated as fractions of a circle (0 to 1). The distance is calculated as: - $$ D_{ij} = 2 \\sin(\\pi \\cdot \\delta_{ij}) $$ - where $\\delta_{ij} = \\min(|y_i - y_j|, 1 - |y_i - y_j|)$ is the shortest + \\[ D_{ij} = 2 \\sin(\\pi \\cdot \\delta_{ij}) \\] + where \\(\\delta_{ij} = \\min(|y_i - y_j|, 1 - |y_i - y_j|)\\) is the shortest arc fraction between points. Parameters diff --git a/smds/shapes/continuous_shapes/euclidean.py b/smds/shapes/continuous_shapes/euclidean.py index f8dce78..d819f4a 100644 --- a/smds/shapes/continuous_shapes/euclidean.py +++ b/smds/shapes/continuous_shapes/euclidean.py @@ -1,15 +1,18 @@ import numpy as np from numpy.typing import NDArray +from scipy.spatial.distance import pdist, squareform # type: ignore[import-untyped] from smds.shapes import BaseShape class EuclideanShape(BaseShape): """ - Compute Euclidean (linear) distances for continuous 1D data. + Compute Euclidean (linear) distances for continuous data in n dimensions. - This shape models data lying on a flat, 1D line. The distance is simply the - absolute difference between values. + This shape models data lying in a flat Euclidean space of arbitrary + dimensionality. Inputs of shape (n_samples,) are treated as points on a + line, inputs of shape (n_samples, n_features) as points in + :math:`\\mathbb{R}^{n\\_features}`. Reference: Table 1 in "Shape Happens" paper (referred to as 'linear'). @@ -20,11 +23,20 @@ class EuclideanShape(BaseShape): Attributes ---------- - y_ndim : int - Dimensionality of input labels (1). Expects scalar values. + y_ndim : tuple of int + Accepted label dimensionalities, ``(1, 2)``: either scalar labels of + shape (n_samples,) or coordinate arrays of shape + (n_samples, n_features). + + Notes + ----- + :class:`~smds.SupervisedMDS` defaults ``n_components`` to 1 for this shape, + since scalar labels are the common case. With (n_samples, n_features) + labels you almost always want to pass ``n_components=n_features`` + explicitly; otherwise the embedding is truncated to a single dimension. """ - y_ndim = 1 + y_ndim = (1, 2) @property def normalize_labels(self) -> bool: @@ -34,57 +46,28 @@ def normalize_labels(self) -> bool: def __init__(self, normalize_labels: bool = True): self._normalize_labels = normalize_labels - def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: - """ - Validate input is 1D. - - Parameters - ---------- - y : NDArray[np.float64] - Input array. - - Returns - ------- - NDArray[np.float64] - Validated flat 1D array. - - Raises - ------ - ValueError - If `y` is empty or has more than one column. - """ - y_proc: NDArray[np.float64] = np.asarray(y, dtype=np.float64) - - if y_proc.size == 0: - raise ValueError("Input 'y' cannot be empty.") - - if y_proc.ndim > 1 and y_proc.shape[1] > 1: - raise ValueError( - f"Input 'y' for EuclideanShape must be 1-dimensional (n_samples,) " - f"or (n_samples, 1), but got shape {y_proc.shape}." - ) - - return y_proc.ravel() - def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ - Compute pairwise absolute differences. + Compute pairwise Euclidean distances. Calculates: - $$ D_{ij} = |y_i - y_j| $$ + + .. math:: + + D_{ij} = \\sqrt{\\sum_{k} (y_{ik} - y_{jk})^2} Parameters ---------- y : NDArray[np.float64] - Input 1D array of labels. + Input array of shape (n_samples,) or (n_samples, n_features). Returns ------- NDArray[np.float64] - Pairwise Euclidean distance matrix. + Pairwise Euclidean distance matrix of shape (n_samples, n_samples). """ - y_flat = y.ravel() + points = y.reshape(len(y), -1) - distance_matrix: NDArray[np.float64] = np.abs(y_flat[:, None] - y_flat[None, :]) + distance_matrix: NDArray[np.float64] = squareform(pdist(points)) return distance_matrix diff --git a/smds/shapes/continuous_shapes/klein_bottle.py b/smds/shapes/continuous_shapes/klein_bottle.py index 577a863..c53a901 100644 --- a/smds/shapes/continuous_shapes/klein_bottle.py +++ b/smds/shapes/continuous_shapes/klein_bottle.py @@ -44,7 +44,22 @@ def normalize_labels(self) -> bool: def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ - Validates input and ensures exactly 2 dimensions for (u, v) parameters. + Validate input and ensure exactly 2 dimensions for (u, v) parameters. + + Parameters + ---------- + y : NDArray[np.float64] + Input array of shape (n_samples,) or (n_samples, n_features). + + Returns + ------- + NDArray[np.float64] + Array of shape (n_samples, 2). + + Raises + ------ + ValueError + If ``y`` is empty or has more than 2 features. """ y_proc = np.asarray(y, dtype=np.float64) @@ -70,8 +85,21 @@ def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ - Computes geodesic distances on a Flat Klein Bottle. - Assumes y is normalized to [0, 1]. + Compute geodesic distances on a flat Klein bottle. + + Considers all four identification cases (direct, cylinder wrap, Möbius + twist, and combined wrap) and returns the minimum distance. + Assumes ``y`` is normalized to [0, 1] × [0, 1]. + + Parameters + ---------- + y : NDArray[np.float64] + Input array of shape (n_samples, 2) with columns (u, v). + + Returns + ------- + NDArray[np.float64] + Pairwise geodesic distance matrix of shape (n_samples, n_samples). """ u = y[:, 0] # Twist axis v = y[:, 1] # Cylinder axis diff --git a/smds/shapes/continuous_shapes/log_linear.py b/smds/shapes/continuous_shapes/log_linear.py index 5556d33..26c81a7 100644 --- a/smds/shapes/continuous_shapes/log_linear.py +++ b/smds/shapes/continuous_shapes/log_linear.py @@ -77,8 +77,8 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ Compute pairwise logarithmic distances. - Calculates $D_{ij} = |\\log(y_i + 1) - \\log(y_j + 1)|$. - A shift of 1.0 is added to avoid $\\log(0)$. + Calculates \\(D_{ij} = |\\log(y_i + 1) - \\log(y_j + 1)|\\). + A shift of 1.0 is added to avoid \\(\\log(0)\\). Parameters ---------- diff --git a/smds/shapes/continuous_shapes/semicircular.py b/smds/shapes/continuous_shapes/semicircular.py index 01ddd0f..e13058c 100644 --- a/smds/shapes/continuous_shapes/semicircular.py +++ b/smds/shapes/continuous_shapes/semicircular.py @@ -9,7 +9,7 @@ class SemicircularShape(BaseShape): Compute Euclidean (chord) distances for points mapped to a semicircle. This shape maps normalized 1D scalar values to angles on a unit semicircle - (ranging from 0 to $\pi$) and computes the straight-line (chord) distance + (ranging from 0 to \(\pi\)) and computes the straight-line (chord) distance between them. Parameters @@ -70,8 +70,8 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: Compute pairwise chord distances on the unit semicircle. The distance is calculated as: - $$ D_{ij} = 2 \\sin\\left(\\frac{\\pi}{2} |y_i - y_j|\\right) $$ - This corresponds to mapping $y$ (assumed in $[0, 1]$) to angles $\\theta \\in [0, \\pi]$ + \\[ D_{ij} = 2 \\sin\\left(\\frac{\\pi}{2} |y_i - y_j|\\right) \\] + This corresponds to mapping \\(y\\) (assumed in \\([0, 1]\\)) to angles \\(\\theta \\in [0, \\pi]\\) and finding the chord length. Parameters diff --git a/smds/shapes/continuous_shapes/spiral_shape.py b/smds/shapes/continuous_shapes/spiral_shape.py index 3a52cec..a72b0e5 100644 --- a/smds/shapes/continuous_shapes/spiral_shape.py +++ b/smds/shapes/continuous_shapes/spiral_shape.py @@ -10,7 +10,7 @@ class SpiralShape(BaseShape): Arrange points in an Archimedean spiral pattern. This class generates a shape where points are arranged along a spiral - trajectory defined by the equation $r = a + b\theta$. The distance metric + trajectory defined by the equation \\(r = a + b\\theta\\). The distance metric is computed based on the Euclidean distance between these points in Cartesian coordinates. @@ -18,14 +18,14 @@ class SpiralShape(BaseShape): ---------- initial_radius : float, optional The starting radius of the spiral (offset from the origin), corresponding - to $a$ in the Archimedean spiral equation. Default is 0.5. + to \\(a\\) in the Archimedean spiral equation. Default is 0.5. growth_rate : float, optional The rate at which the spiral expands away from the center for every radian - of rotation, corresponding to $b$ in the Archimedean spiral equation. + of rotation, corresponding to \\(b\\) in the Archimedean spiral equation. Default is 1.0. num_turns : float, optional The total number of complete rotations the spiral makes. This scales the - input labels mapping them to the angle $\theta$. Default is 2.0. + input labels mapping them to the angle \\(\\theta\\). Default is 2.0. normalize_labels : bool, optional Whether to normalize the input labels `y` to the range [0, 1] before computing the spiral coordinates. Default is True. @@ -92,10 +92,10 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ Compute the pairwise distance matrix for points mapped to the spiral. - The input labels `y` are first converted to polar coordinates $(r, \theta)$ + The input labels `y` are first converted to polar coordinates \\((r, \\theta)\\) using the class parameters: - $$ \theta = y \times 2\\pi \times \text{num\\_turns} $$ - $$ r = \text{initial\\_radius} + \text{growth\\_rate} \times \theta $$ + \\[ \\theta = y \\times 2\\pi \\times \\text{num\\_turns} \\] + \\[ r = \\text{initial\\_radius} + \\text{growth\\_rate} \\times \\theta \\] These polar coordinates are converted to Cartesian coordinates, and the Euclidean distances between all pairs of points are calculated. diff --git a/smds/shapes/continuous_shapes/torus.py b/smds/shapes/continuous_shapes/torus.py index 58c9b9d..e43945b 100644 --- a/smds/shapes/continuous_shapes/torus.py +++ b/smds/shapes/continuous_shapes/torus.py @@ -7,21 +7,30 @@ class TorusShape(BaseShape): """ - Manifold hypothesis representing a Flat Torus topology. - This shape assumes the data lies on the product of two circles (T1 x T1). - - Logic: - 1. Dimensionality Reduction: Projects input 'y' to 2 dimensions via PCA - (or padding) to capture the two independent cycles. - 2. Parametrization: Maps these 2 dimensions to the unit square [0, 1] x [0, 1]. - 3. Distance Calculation: Computes pairwise distances respecting the Torus - identifications (periodic boundary conditions in both directions): - - Top/Bottom edges match: (u, 0) ~ (u, 1) - - Left/Right edges match: (0, v) ~ (1, v) - - Hyperparameters: - radii: A tuple (r1, r2) weighting the two cyclic dimensions. - Default is (1.0, 1.0) (Flat Clifford Torus). + Compute geodesic distances on a flat torus (T\\ :sup:`1` × T\\ :sup:`1`) manifold. + + Models data lying on the product of two circles. Input coordinates are + mapped onto the unit square [0, 1] × [0, 1] with periodic boundary + conditions in both directions: + + - (u, 0) ~ (u, 1) — cylinder wrap in the v direction. + - (0, v) ~ (1, v) — cylinder wrap in the u direction. + + If the input has more than 2 dimensions it is first reduced to 2 via PCA; + if it has fewer than 2 dimensions zeros are padded. + + Parameters + ---------- + radii : tuple of (float, float), optional + Scaling weights (r1, r2) for the two cyclic dimensions. + Default is ``(1.0, 1.0)`` (flat Clifford torus). + normalize_labels : bool, optional + Whether to normalize labels to the range [0, 1]. Default is True. + + Attributes + ---------- + y_ndim : int + Dimensionality of input labels (2). Expects (u, v) parameters. """ def __init__(self, radii: tuple[float, float] = (1.0, 1.0), normalize_labels: bool = True): @@ -30,18 +39,37 @@ def __init__(self, radii: tuple[float, float] = (1.0, 1.0), normalize_labels: bo @property def y_ndim(self) -> int: - """Get the expected number of dimensions for the Torus topology.""" + """int: Expected input dimensionality (2).""" return 2 + @property + def radii(self) -> tuple[float, float]: + """Tuple of (float, float): Scaling weights for the two cyclic dimensions.""" + return self._radii + @property def normalize_labels(self) -> bool: - """Check whether labels should be normalized to the unit square.""" + """bool: Whether labels are normalized to the unit square.""" return self._normalize_labels_flag def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ - Validates input and reduces dimensions if necessary via PCA. - Matches KleinBottleShape logic. + Validate input and reduce or pad to exactly 2 dimensions. + + Parameters + ---------- + y : NDArray[np.float64] + Input array of shape (n_samples,) or (n_samples, n_features). + + Returns + ------- + NDArray[np.float64] + Array of shape (n_samples, 2) suitable for torus projection. + + Raises + ------ + ValueError + If ``y`` is empty. """ y_proc = np.asarray(y, dtype=np.float64) @@ -62,7 +90,6 @@ def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: else: pca = PCA(n_components=2) y_proc = pca.fit_transform(y_proc) - elif n_features < 2: zeros = np.zeros((n_samples, 2 - n_features)) y_proc = np.hstack([y_proc, zeros]) @@ -71,8 +98,27 @@ def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ - Computes geodesic distances on a Flat Torus. - Assumes y is normalized to [0, 1]. + Compute geodesic distances on the flat torus. + + Calculates the shortest-path distance respecting periodic boundaries + in both u and v directions: + + .. math:: + + D_{ij} = \\sqrt{(r_1 \\cdot \\delta u_{ij})^2 + (r_2 \\cdot \\delta v_{ij})^2} + + where :math:`\\delta u = \\min(|u_i - u_j|,\\, 1 - |u_i - u_j|)` and + analogously for :math:`\\delta v`. + + Parameters + ---------- + y : NDArray[np.float64] + Input array of shape (n_samples, 2), assumed normalized to [0, 1]. + + Returns + ------- + NDArray[np.float64] + Pairwise geodesic distance matrix of shape (n_samples, n_samples). """ u = y[:, 0] v = y[:, 1] diff --git a/smds/shapes/coordinates/cartesian_coordinates.py b/smds/shapes/coordinates/cartesian_coordinates.py index 90f7f6d..437470f 100644 --- a/smds/shapes/coordinates/cartesian_coordinates.py +++ b/smds/shapes/coordinates/cartesian_coordinates.py @@ -36,7 +36,7 @@ def compute_distances(self) -> NDArray[np.float64]: """ Compute the pairwise Euclidean distance matrix for the stored points. - Calculates $D_{ij} = ||x_i - x_j||_2$ using efficient vectorized operations. + Calculates \\(D_{ij} = ||x_i - x_j||_2\\) using efficient vectorized operations. Returns ------- diff --git a/smds/shapes/coordinates/polar_coordinates.py b/smds/shapes/coordinates/polar_coordinates.py index d1f9aa9..6b5e2d9 100644 --- a/smds/shapes/coordinates/polar_coordinates.py +++ b/smds/shapes/coordinates/polar_coordinates.py @@ -6,7 +6,7 @@ class PolarCoordinates(BaseCoordinates): """ - Represent points in Polar coordinates ($r, \theta$). + Represent points in Polar coordinates \\((r, \\theta)\\). Parameters ---------- @@ -25,8 +25,8 @@ def to_cartesian(self) -> CartesianCoordinates: Convert polar coordinates to 2D Cartesian coordinates. The transformation is defined as: - $$ x = r \\cos(\\theta) $$ - $$ y = r \\sin(\\theta) $$ + \\[ x = r \\cos(\\theta) \\] + \\[ y = r \\sin(\\theta) \\] Returns ------- diff --git a/smds/shapes/discrete_shapes/chain.py b/smds/shapes/discrete_shapes/chain.py index 46b7986..fe2d1b9 100644 --- a/smds/shapes/discrete_shapes/chain.py +++ b/smds/shapes/discrete_shapes/chain.py @@ -45,7 +45,7 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ Compute the thresholded cyclic distance matrix. - Calculates the shortest ring distance $d_{cycle}$. If $d_{cycle} < \\text{threshold}$, + Calculates the shortest ring distance \\(d_{cycle}\\). If \\(d_{cycle} < \\text{threshold}\\), the distance is preserved; otherwise, it is set to -1.0 to indicate no connection. Parameters diff --git a/smds/shapes/discrete_shapes/cluster.py b/smds/shapes/discrete_shapes/cluster.py index 28612fe..42103d9 100644 --- a/smds/shapes/discrete_shapes/cluster.py +++ b/smds/shapes/discrete_shapes/cluster.py @@ -45,8 +45,8 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: Returns ------- NDArray[np.float64] - A pairwise distance matrix where $D_{ij} = 0$ if $y_i = y_j$ and - $D_{ij} = 1$ otherwise. + A pairwise distance matrix where \\(D_{ij} = 0\\) if \\(y_i = y_j\\) and + \\(D_{ij} = 1\\) otherwise. """ distance_matrix: NDArray[np.float64] = (y[:, None] != y[None, :]).astype(float) diff --git a/smds/shapes/discrete_shapes/discrete_circular.py b/smds/shapes/discrete_shapes/discrete_circular.py index 7ef977e..d506226 100644 --- a/smds/shapes/discrete_shapes/discrete_circular.py +++ b/smds/shapes/discrete_shapes/discrete_circular.py @@ -49,8 +49,8 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: Compute the shortest ring distance (circular arc length) between points. Calculates the minimum distance along the cycle: - $$ D_{ij} = \min(|y_i - y_j|, C - |y_i - y_j|) $$ - where $C$ is the cycle length. + \[ D_{ij} = \min(|y_i - y_j|, C - |y_i - y_j|) \] + where \(C\) is the cycle length. Parameters ---------- diff --git a/smds/shapes/discrete_shapes/graph_geodesic.py b/smds/shapes/discrete_shapes/graph_geodesic.py index e645e61..b61863b 100644 --- a/smds/shapes/discrete_shapes/graph_geodesic.py +++ b/smds/shapes/discrete_shapes/graph_geodesic.py @@ -8,21 +8,25 @@ class GraphGeodesicShape(BaseShape): """ - A generic discrete shape hypothesis that approximates the manifold structure - using a K-Nearest Neighbors (KNN) Geodesic Graph (Isomap approach). + Approximate manifold distances via a k-nearest-neighbors geodesic graph. - Logic: - 1. Graph Construction: Connects each point to its 'k' nearest neighbors. - 2. Geodesic Distance: Defines distance between two points as the shortest - path along the graph edges, rather than the straight Euclidean line. + Constructs a KNN graph over the input coordinates and computes the shortest + path between all pairs (Isomap approach). This lets the model respect the + intrinsic geometry of curved manifolds without knowing their equation. - This allows the model to "unfold" arbitrary curved shapes (like Swiss Rolls - or folded paper) without knowing their equation beforehand. + Parameters + ---------- + n_neighbors : int, optional + Number of nearest neighbors to connect each point to. Default is 5. + Too small a value may leave the graph disconnected; too large a value + may introduce shortcuts that distort the geodesic distances. + normalize_labels : bool, optional + Whether to normalize labels using the base class logic. Default is True. - Hyperparameters: - n_neighbors (int): Number of neighbors to connect. Default is 5. - - Too small: Graph might be disconnected. - - Too large: Short-circuits the manifold (shortcuts). + Attributes + ---------- + y_ndim : int + Dimensionality of input labels (2). Expects (n_samples, n_features). """ def __init__(self, n_neighbors: int = 5, normalize_labels: bool = True): @@ -31,23 +35,54 @@ def __init__(self, n_neighbors: int = 5, normalize_labels: bool = True): @property def y_ndim(self) -> int: - """Dimensionality of the input labels.""" + """int: Dimensionality of the input labels (2).""" return 2 @property def normalize_labels(self) -> bool: - """Whether to normalize the input labels.""" + """bool: Whether to normalize the input labels.""" return self._normalize_labels_flag def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: + """ + Validate and coerce input to a 2D float64 array. + + Parameters + ---------- + y : NDArray[np.float64] + Input array of shape (n_samples,) or (n_samples, n_features). + + Returns + ------- + NDArray[np.float64] + Validated 2D array of shape (n_samples, n_features). + + Raises + ------ + ValueError + If ``y`` has more than 2 dimensions after reshaping. + """ y_proc = np.asarray(y, dtype=np.float64) if y_proc.ndim == 1: y_proc = y_proc.reshape(-1, 1) if y_proc.ndim != 2: - raise ValueError(f"GraphGeodesic expects 2D input (n_samples, n_features), got {y_proc.ndim}D") + raise ValueError(f"GraphGeodesicShape expects 2D input (n_samples, n_features), got {y_proc.ndim}D.") return y_proc def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: + """ + Build a KNN graph and return all-pairs shortest-path distances. + + Parameters + ---------- + y : NDArray[np.float64] + Input array of shape (n_samples, n_features). + + Returns + ------- + NDArray[np.float64] + Pairwise geodesic distance matrix of shape (n_samples, n_samples). + """ y_proc = np.asarray(y, dtype=np.float64) n_samples = y_proc.shape[0] @@ -59,5 +94,5 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: dist_matrix = shortest_path(csgraph=graph, method="auto", directed=False) - result = np.asarray(dist_matrix, dtype=np.float64) + result: NDArray[np.float64] = np.asarray(dist_matrix, dtype=np.float64) return result diff --git a/smds/shapes/discrete_shapes/hierarchical.py b/smds/shapes/discrete_shapes/hierarchical.py index 65c9f98..58ab47d 100644 --- a/smds/shapes/discrete_shapes/hierarchical.py +++ b/smds/shapes/discrete_shapes/hierarchical.py @@ -93,7 +93,7 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ Compute the pairwise distance matrix based on the first diverging level. - Identifies the index of the first column where $y_i$ and $y_j$ differ. + Identifies the index of the first column where \\(y_i\\) and \\(y_j\\) differ. The distance is then set to the value in `level_distances` at that index. If the rows are identical, the distance is 0.0. diff --git a/smds/shapes/discrete_shapes/polytope.py b/smds/shapes/discrete_shapes/polytope.py index 1b0afec..a2c0a6e 100644 --- a/smds/shapes/discrete_shapes/polytope.py +++ b/smds/shapes/discrete_shapes/polytope.py @@ -6,11 +6,29 @@ class PolytopeShape(BaseShape): """ - Manifold hypothesis representing a Polytope topology. - - This shape logic tries to push the n_clusters passed as an argument - as far away as possible from each other on an n-dimensional unit sphere - using iterative repulsion. + Arrange cluster centroids at maximally separated vertices of a unit polytope. + + Places ``n_clusters`` distinct points on the surface of an ``n_dim``-dimensional + unit sphere using iterative repulsion, then maps each input point to its + cluster centroid and computes Euclidean distances between centroids. + + Parameters + ---------- + n_dim : int, optional + Number of dimensions of the embedding sphere. Default is 3. + n_iter : int, optional + Number of repulsion iterations. Default is 500. + lr : float, optional + Step size for the repulsion update. Default is 0.1. + seed : int, optional + Random seed for reproducibility. Default is None. + normalize_labels : bool, optional + Whether to normalize labels using the base class logic. Default is False. + + Attributes + ---------- + y_ndim : int + Dimensionality of input labels (1). Expects a 1D array of cluster labels. """ def __init__( @@ -49,8 +67,17 @@ def _validate_input(self, y: NDArray[np.float64]) -> NDArray[np.float64]: def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ - Generates a distance matrix D between all points in y, where points with - different labels are maximally spread out in n_dim dimensions. + Place cluster centroids on a unit sphere via repulsion and return pairwise distances. + + Parameters + ---------- + y : NDArray[np.float64] + Input 1D array of cluster labels. + + Returns + ------- + NDArray[np.float64] + Pairwise Euclidean distance matrix between the repulsion-optimized centroids. """ rng = np.random.default_rng(self.seed) labels, inverse_indices = np.unique(y, return_inverse=True) diff --git a/smds/shapes/spatial_shapes/cylindrical.py b/smds/shapes/spatial_shapes/cylindrical.py index 36b74d8..258750b 100644 --- a/smds/shapes/spatial_shapes/cylindrical.py +++ b/smds/shapes/spatial_shapes/cylindrical.py @@ -72,9 +72,9 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ Map inputs to cylindrical coordinates and compute Euclidean norms. - Maps input (lat, lon) to $(x, y, z)$ where: - - $x, y$ are derived from `lon` (angle) and `radius`. - - $z$ is derived directly from `lat` (height). + Maps input (lat, lon) to \\((x, y, z)\\) where: + - \\(x, y\\) are derived from `lon` (angle) and `radius`. + - \\(z\\) is derived directly from `lat` (height). Parameters ---------- diff --git a/smds/shapes/spatial_shapes/spherical.py b/smds/shapes/spatial_shapes/spherical.py index 0884e45..3c9167a 100644 --- a/smds/shapes/spatial_shapes/spherical.py +++ b/smds/shapes/spatial_shapes/spherical.py @@ -71,10 +71,10 @@ def _compute_distances(self, y: NDArray[np.float64]) -> NDArray[np.float64]: """ Convert spherical coordinates to 3D Cartesian points and compute Euclidean norms. - Maps input (lat, lon) to $(x, y, z)$ using: - $$ x = r \\cos(lat) \\cos(lon) $$ - $$ y = r \\cos(lat) \\sin(lon) $$ - $$ z = r \\sin(lat) $$ + Maps input (lat, lon) to \\((x, y, z)\\) using: + \\[ x = r \\cos(lat) \\cos(lon) \\] + \\[ y = r \\cos(lat) \\sin(lon) \\] + \\[ z = r \\sin(lat) \\] Parameters ---------- diff --git a/smds/smds.py b/smds/smds.py index 1309f05..b1531c5 100644 --- a/smds/smds.py +++ b/smds/smds.py @@ -20,6 +20,7 @@ LogLinearShape, SemicircularShape, SpiralShape, + TorusShape, ) from smds.shapes.discrete_shapes import ( ChainShape, @@ -50,8 +51,23 @@ # result: X_proj -# smds stage 1 - for the manifold Y_ class SMDSParametrization(TransformerMixin, BaseEstimator, ABC): # type: ignore[misc] + """ + Abstract base class defining the interface for SMDS parametrization strategies. + + A parametrization maps labels or coordinates to an ideal pairwise distance + matrix and a low-dimensional embedding. Subclasses implement the specific + strategy for computing these (e.g., via a geometric manifold or user-provided + coordinates). + + Attributes + ---------- + D_ : ndarray of shape (n_samples, n_samples) + Ideal pairwise distance matrix, set after fitting. + Y_ : ndarray of shape (n_samples, n_components) + Low-dimensional embedding of the ideal distances, set after fitting. + """ + @property @abstractmethod def n_components(self) -> int | None: @@ -125,14 +141,25 @@ def compute_ideal_distances(self, y: NDArray[Any]) -> NDArray[np.float64]: class ComputedSMDSParametrization(SMDSParametrization): """ - Stage-1 parametrization that computes ideal distances using a manifold function. + Parametrization that computes ideal distances using a geometric manifold. + + Fits a classical MDS embedding from distances derived by applying a manifold + function to the input labels. Parameters ---------- manifold : Callable - Function that takes labels and returns a distance matrix. + A callable that accepts labels of shape (n_samples,) or (n_samples, k) + and returns a pairwise distance matrix of shape (n_samples, n_samples). n_components : int - Number of embedding dimensions. + Number of dimensions in the low-dimensional embedding. + + Attributes + ---------- + D_ : ndarray of shape (n_samples, n_samples) + Ideal pairwise distance matrix computed from the manifold. + Y_ : ndarray of shape (n_samples, n_components) + Classical MDS embedding of the ideal distances. """ def __init__(self, manifold: Callable[[NDArray[Any]], NDArray[np.float64]], n_components: int): @@ -241,20 +268,33 @@ def transform(self, X: NDArray[Any] | None = None) -> NDArray[np.float64]: class UserProvidedSMDSParametrization(SMDSParametrization): """ - Stage-1 parametrization using user-provided coordinates or template mapping. + Parametrization using user-provided coordinates or a template mapping. + + Instead of deriving distances from a built-in manifold, this class accepts + pre-computed embedding coordinates directly, or maps labels onto a fixed + template via a user-supplied function. Parameters ---------- - y : ndarray, optional - Pre-computed embedding coordinates. + y : ndarray of shape (n_samples, n_components), optional + Pre-computed embedding coordinates. If provided, these are used directly + without any fitting computation. n_components : int, optional - Number of embedding dimensions (inferred from y if not provided). + Number of embedding dimensions. Inferred from ``y`` if not provided. fixed_template : ndarray, optional - Fixed template coordinates for mapping. + Fixed reference coordinates used together with ``mapper``. mapper : Callable, optional - Function to map labels to template coordinates. + Function with signature ``(labels, template) -> coordinates`` that maps + input labels onto the fixed template. name : str, optional - Name for this parametrization. + Optional name for this parametrization instance. + + Attributes + ---------- + D_ : ndarray of shape (n_samples, n_samples) + Pairwise distance matrix computed from the stored coordinates. + Y_ : ndarray of shape (n_samples, n_components) + The stored or mapped embedding coordinates. """ def __init__( @@ -397,49 +437,98 @@ def transform(self, X: NDArray[Any] | None = None) -> NDArray[np.float64]: class SupervisedMDS(TransformerMixin, BaseEstimator): # type: ignore[misc] - _STAGE_1_OPTIONS = {"computed", "user_provided"} + """ + Learn a linear map W that projects high-dimensional data onto a hypothesis manifold. + + The manifold is defined by labels ``y`` via a parametrization strategy. + The projection matrix W is fitted in closed form (ridge regression or Procrustes) + when all ideal distances are fully defined, or via iterative optimization for + incomplete distance matrices. + + Parameters + ---------- + parametrization : str or SMDSParametrization, default="computed" + Strategy for computing the ideal manifold embedding. + + - ``"computed"``: derive distances from a built-in geometric manifold + selected by the ``manifold`` argument. + - ``"user_provided"``: accept coordinates or a template mapping directly. + - An ``SMDSParametrization`` instance: use a custom parametrization object. + manifold : str, default="circular" + Name of the built-in manifold to use when ``parametrization="computed"``. + Ignored when an ``SMDSParametrization`` instance is passed. + Valid options: ``chain``, ``circular``, ``cluster``, ``cylindrical``, + ``discrete_circular``, ``euclidean``, ``geodesic``, ``graph_geodesic``, + ``hierarchical``, ``klein_bottle``, ``log_linear``, ``polytope``, + ``semicircular``, ``spherical``, ``spiral``, ``torus``. + n_components : int or None, default=None + Dimensionality of the manifold embedding. If None, the natural embedding + dimension of the selected ``manifold`` is used (e.g. 2 for ``circular``, + 3 for ``spherical``, 4 for ``klein_bottle``). + + Only meaningful when ``parametrization="computed"``; ignored otherwise. + Note that classical MDS cannot produce more dimensions than the rank of + the manifold's distance matrix: requesting more components than a shape + can support yields zero-filled trailing columns rather than extra + geometry. It is most useful for the discrete shapes (``cluster``, + ``chain``, ``hierarchical``, ``graph_geodesic``), whose distance + matrices are typically full rank and therefore truncated by the default. + + ``euclidean`` is the one built-in manifold with no natural dimension of + its own: it accepts labels of shape (n_samples,) or + (n_samples, n_features) alike. Its default is fixed at 1 for the common + scalar-label case, so with (n_samples, n_features) labels pass + ``n_components=n_features`` explicitly to keep the full geometry. + alpha : float, default=1.0 + Ridge regularization strength for the closed-form solver. + Set to ``0`` for unregularized least squares. + orthonormal : bool, default=False + If True, solve for an orthonormal projection via Procrustes. + ``alpha`` is ignored when this is True. + gpu_accel : bool, default=False + If True, use a PyTorch-based solver for incomplete distance matrices. + Requires PyTorch to be installed. + + Attributes + ---------- + W_ : ndarray of shape (n_components, n_features) + Learned linear projection matrix. + Y_ : ndarray of shape (n_samples, n_components) + Ideal manifold embedding used during fitting. + parametrization_fitted_ : SMDSParametrization + Fitted parametrization object holding ``D_`` and ``Y_``. + """ + + _PARAMETRIZATION_OPTIONS = {"computed", "user_provided"} def __init__( self, - stage_1: str = "computed", + parametrization: str = "computed", manifold: str = "circular", - alpha: float = 0.1, + n_components: int | None = None, + alpha: float = 1.0, orthonormal: bool = False, - radius: float = 6371, gpu_accel: bool = False, ): - # todo: add string for stage_1 - # todo: add string for manifold - # todo: warn if stage_1 is UserProv -> manifold is ignored - """ - Parameters - ---------- - stage_1: - Stage 1 strategy. One of {"computed", "user_provided"}. - manifold: - Manifold type used by computed stage_1. - metric: - The metric to use for scoring the embedding. - gpu_accel: - If True, attempts to use PyTorch (and CUDA/MPS if available) - to solve sparse/incomplete manifold optimization. - """ - self.stage_1 = stage_1 + self.parametrization = parametrization + self.n_components = n_components self.manifold = manifold self.alpha = alpha self.orthonormal = orthonormal - self.radius = radius self.gpu_accel = gpu_accel @staticmethod - def _normalize_stage_1_name(stage_1: str) -> str: - if not isinstance(stage_1, str): - raise TypeError(f"stage_1 must be a string or SMDSParametrization instance, got {type(stage_1).__name__}") - stage_1_name = stage_1.strip().lower() - if stage_1_name not in SupervisedMDS._STAGE_1_OPTIONS: - valid = sorted(SupervisedMDS._STAGE_1_OPTIONS) - raise ValueError(f"Unknown stage_1: {stage_1!r}. Valid options are: {valid}") - return stage_1_name + def _normalize_parametrization_name(parametrization: str) -> str: + if not isinstance(parametrization, str): + raise TypeError( + f"parametrization must be a string or SMDSParametrization instance, " + f"got {type(parametrization).__name__}" + ) + parametrization_name = parametrization.strip().lower() + if parametrization_name not in SupervisedMDS._PARAMETRIZATION_OPTIONS: + valid = sorted(SupervisedMDS._PARAMETRIZATION_OPTIONS) + raise ValueError(f"Unknown parametrization: {parametrization!r}. Valid options are: {valid}") + return parametrization_name @staticmethod def _normalize_manifold_name(manifold: str) -> str: @@ -455,15 +544,15 @@ def _build_manifold(self, manifold_name: str) -> tuple[Callable[[NDArray[Any]], "discrete_circular": (lambda: DiscreteCircularShape(), 2), "hierarchical": (lambda: HierarchicalShape(level_distances=np.array([100.0, 10.0, 1.0])), 2), "circular": (lambda: CircularShape(), 2), - "cylindrical": (lambda: CylindricalShape(radius=self.radius), 3), - "spherical": (lambda: SphericalShape(radius=self.radius), 3), - "geodesic": (lambda: GeodesicShape(radius=self.radius), 3), + "cylindrical": (lambda: CylindricalShape(), 3), + "spherical": (lambda: SphericalShape(), 3), + "geodesic": (lambda: GeodesicShape(), 3), "spiral": (lambda: SpiralShape(), 2), "log_linear": (lambda: LogLinearShape(), 1), "euclidean": (lambda: EuclideanShape(), 1), "semicircular": (lambda: SemicircularShape(), 2), "klein_bottle": (lambda: KleinBottleShape(), 4), - "torus": (lambda: KleinBottleShape(), 3), + "torus": (lambda: TorusShape(), 3), "graph_geodesic": (lambda: GraphGeodesicShape(), 3), "polytope": (lambda: PolytopeShape(), 3), } @@ -473,20 +562,39 @@ def _build_manifold(self, manifold_name: str) -> tuple[Callable[[NDArray[Any]], factory, n_components = manifold_factories[manifold_name] return factory(), n_components - def _build_stage_1(self, stage_1_name: str, manifold_name: str) -> SMDSParametrization: - if stage_1_name == "computed": - manifold_obj, n_components = self._build_manifold(manifold_name) + def _resolve_n_components(self) -> int | None: + """ + Return the explicit embedding-dimension override, or None for the manifold default. + + Subclasses that give ``n_components`` a different meaning may override this. + """ + n_components = self.n_components + + if n_components is None: + return None + + if isinstance(n_components, bool) or not isinstance(n_components, (int, np.integer)) or n_components < 1: + raise ValueError(f"n_components must be a positive integer or None, got {n_components!r}.") + + return int(n_components) + + def _build_parametrization(self, parametrization_name: str, manifold_name: str) -> SMDSParametrization: + if parametrization_name == "computed": + manifold_obj, default_n_components = self._build_manifold(manifold_name) + n_components = self._resolve_n_components() + if n_components is None: + n_components = default_n_components return ComputedSMDSParametrization(manifold=manifold_obj, n_components=n_components) - warnings.warn("stage_1='user_provided': manifold value is ignored.", UserWarning, stacklevel=2) + warnings.warn("parametrization='user_provided': manifold value is ignored.", UserWarning, stacklevel=2) return UserProvidedSMDSParametrization() - def _resolve_stage_1(self) -> SMDSParametrization: - if isinstance(self.stage_1, SMDSParametrization): - return self.stage_1 - normalized_stage_1 = self._normalize_stage_1_name(self.stage_1) + def _resolve_parametrization(self) -> SMDSParametrization: + if isinstance(self.parametrization, SMDSParametrization): + return self.parametrization + normalized_parametrization = self._normalize_parametrization_name(self.parametrization) normalized_manifold = self._normalize_manifold_name(self.manifold) - return self._build_stage_1(normalized_stage_1, normalized_manifold) + return self._build_parametrization(normalized_parametrization, normalized_manifold) def _validate_and_convert_metric(self, metric: str | StressMetrics) -> StressMetrics: """ @@ -503,9 +611,9 @@ def _masked_loss(self, W_flat: np.ndarray, X: np.ndarray, D: np.ndarray, mask: n """ Compute the loss only on the defined distances (where mask is True). """ - n_components = self.stage_1_fitted_.n_components + n_components = self.parametrization_fitted_.n_components if n_components is None: - raise ValueError("stage_1_fitted_.n_components is not set.") + raise ValueError("parametrization_fitted_.n_components is not set.") W = W_flat.reshape((n_components, X.shape[1])) X_proj = (W @ X.T).T D_pred = np.linalg.norm(X_proj[:, None, :] - X_proj[None, :, :], axis=-1) @@ -514,12 +622,15 @@ def _masked_loss(self, W_flat: np.ndarray, X: np.ndarray, D: np.ndarray, mask: n return result def _validate_data( - self, X: np.ndarray, y: np.ndarray, reset: bool = True, stage_1_model: SMDSParametrization | None = None + self, X: np.ndarray, y: np.ndarray, reset: bool = True, parametrization_model: SMDSParametrization | None = None ) -> tuple[np.ndarray, np.ndarray]: """ Validate and process X and y based on the manifold's expected y dimensionality. """ - model = self.stage_1_fitted_ if hasattr(self, "stage_1_fitted_") else stage_1_model or self._resolve_stage_1() + if hasattr(self, "parametrization_fitted_"): + model = self.parametrization_fitted_ + else: + model = parametrization_model or self._resolve_parametrization() if isinstance(model, UserProvidedSMDSParametrization): X = check_array(X) @@ -533,22 +644,29 @@ def _validate_data( ) return X, y else: - expected_y_ndim = getattr(model.manifold, "y_ndim", 1) + accepted_ndim = np.atleast_1d(getattr(model.manifold, "y_ndim", 1)) - if expected_y_ndim == 1: - X, y = validate_data(self, X, y, reset=reset) + y_arr = np.asarray(y) + + # Shapes accepting several dimensionalities are dispatched on the y actually + # given; (n,) and (n, 1) carry the same labels, so both take the scalar path. + if 1 in accepted_ndim and y_arr.shape[1:] in ((), (1,)): + # Ravel first: a column vector carries the same labels, and passing it + # through unflattened only earns a DataConversionWarning from sklearn. + X, y = validate_data(self, X, y_arr.reshape(-1), reset=reset) type_of_target(y, raise_unknown=True) y = np.asarray(y).squeeze() if y.ndim == 0: y = y.reshape(1) else: - if y.ndim != expected_y_ndim: + y = y_arr + if y.ndim not in accepted_ndim: raise ValueError( - f"Input 'y' must be {expected_y_ndim}-dimensional, " + f"Input 'y' must be {'- or '.join(map(str, accepted_ndim))}-dimensional, " f"but got shape {y.shape} with {y.ndim} dimensions." ) - if hasattr(self.stage_1, "validate_y"): - self.stage_1.validate_y(y) + if hasattr(self.parametrization, "validate_y"): + self.parametrization.validate_y(y) if X.shape[0] != y.shape[0]: raise ValueError( f"X and y must have the same number of samples. " @@ -585,9 +703,9 @@ def _fit_pytorch(self, X: np.ndarray, D: np.ndarray, mask: np.ndarray) -> np.nda # Parameter Initialization n_features = X.shape[1] - n_components = self.stage_1_fitted_.n_components + n_components = self.parametrization_fitted_.n_components if n_components is None: - raise ValueError("Stage 1 n_components is not set") + raise ValueError("parametrization_fitted_.n_components is not set") W_t = torch.nn.Parameter(torch.randn(n_components, n_features, device=device, dtype=torch.float32) * 0.01) @@ -639,9 +757,9 @@ def _fit_scipy( Used when distances are incomplete (negative) and GPU accel is off/unavailable. """ rng = np.random.default_rng(42) - n_components = self.stage_1_fitted_.n_components + n_components = self.parametrization_fitted_.n_components if n_components is None: - raise ValueError("Stage 1 n_components is not set") + raise ValueError("parametrization_fitted_.n_components is not set") W0 = rng.normal(scale=0.01, size=(n_components, X.shape[1])) result = minimize(self._masked_loss, W0.ravel(), args=(X, D, mask), method="L-BFGS-B") @@ -650,23 +768,26 @@ def _fit_scipy( def fit(self, X: np.ndarray, y: np.ndarray) -> "SupervisedMDS": """ - Fit the linear transformation W to match distances induced by labels y. - Uses classical MDS + closed-form when all distances are defined, - and switches to optimization if some distances are undefined (negative). + Fit the linear projection W to match distances induced by labels y. + + Uses classical MDS and a closed-form solution when all ideal distances + are defined, and switches to iterative optimization when some distances + are undefined (negative). Parameters ---------- - X: array-like of shape (n_samples, n_features) - The input data to be transformed. - y: array-like of shape (n_samples,) or (n_samples, 2) - The labels or coordinates defining the ideal distances. + X : array-like of shape (n_samples, n_features) + Input data to be projected. + y : array-like of shape (n_samples,) or (n_samples, k) + Labels or coordinates defining the ideal manifold distances. Returns ------- - self: returns an instance of self. + self : SupervisedMDS + Fitted estimator. """ - stage_1_model = self._resolve_stage_1() - X, y = self._validate_data(X, y, stage_1_model=stage_1_model) + parametrization_model = self._resolve_parametrization() + X, y = self._validate_data(X, y, parametrization_model=parametrization_model) if X.shape[0] == 1: raise ValueError("Found array with n_samples=1. SupervisedMDS requires at least 2 samples.") @@ -677,21 +798,21 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "SupervisedMDS": y = np.asarray(y).squeeze() if ( - isinstance(stage_1_model, UserProvidedSMDSParametrization) - and stage_1_model.y is None - and stage_1_model.fixed_template is None + isinstance(parametrization_model, UserProvidedSMDSParametrization) + and parametrization_model.y is None + and parametrization_model.fixed_template is None ): y_arr = np.asarray(y) n_comp = y_arr.shape[1] if y_arr.ndim > 1 else 1 - if stage_1_model.n_components != n_comp: - stage_1_model = UserProvidedSMDSParametrization(y=None, n_components=n_comp) + if parametrization_model.n_components != n_comp: + parametrization_model = UserProvidedSMDSParametrization(y=None, n_components=n_comp) - self.stage_1_fitted_: SMDSParametrization = clone(stage_1_model) - self.stage_1_fitted_.fit(y) + self.parametrization_fitted_: SMDSParametrization = clone(parametrization_model) + self.parametrization_fitted_.fit(y) - self.Y_ = self.stage_1_fitted_.Y_ + self.Y_ = self.parametrization_fitted_.Y_ - D = self.stage_1_fitted_.D_ + D = self.parametrization_fitted_.D_ if np.any(D < 0): # Inform if any distances are negative @@ -748,17 +869,17 @@ def fit(self, X: np.ndarray, y: np.ndarray) -> "SupervisedMDS": def transform(self, X: np.ndarray) -> np.ndarray: """ - Apply the learned transformation to X. + Apply the learned projection to X. Parameters ---------- - X: array-like of shape (n_samples, n_features) - The input data to be transformed. + X : array-like of shape (n_samples, n_features) + Input data to project. Returns ------- - X_proj: array of shape (n_samples, n_components) - The transformed data in the low-dimensional space. + X_proj : ndarray of shape (n_samples, n_components) + Data projected into the low-dimensional manifold space. """ check_is_fitted(self) X = validate_data(self, X, reset=False) @@ -785,13 +906,13 @@ def inverse_transform(self, X_proj: np.ndarray) -> np.ndarray: Parameters ---------- - X_proj: array-like of shape (n_samples, n_components) - The low-dimensional representation of the input data. + X_proj : array-like of shape (n_samples, n_components) + Low-dimensional representation to invert. Returns ------- - X_reconstructed: array of shape (n_samples, original_n_features) - The reconstructed data in the original space. + X_reconstructed : ndarray of shape (n_samples, n_features) + Reconstructed data in the original feature space. """ check_is_fitted(self) X_proj = check_array(X_proj, ensure_2d=True) @@ -812,18 +933,19 @@ def inverse_transform(self, X_proj: np.ndarray) -> np.ndarray: def fit_transform(self, X: np.ndarray, y: np.ndarray) -> np.ndarray: """ - Fit the model and transform X in one step. + Fit the model and project X in one step. Parameters ---------- - X: array-like of shape (n_samples, n_features) - The input data to be transformed. - y: array-like of shape (n_samples,) or (n_samples, 2) + X : array-like of shape (n_samples, n_features) + Input data to project. + y : array-like of shape (n_samples,) or (n_samples, k) + Labels or coordinates defining the ideal manifold distances. Returns ------- - X_proj: array of shape (n_samples, n_components) - The transformed data in the low-dimensional space. + X_proj : ndarray of shape (n_samples, n_components) + Data projected into the low-dimensional manifold space. """ result: np.ndarray = self.fit(X, y).transform(X) return result @@ -838,7 +960,7 @@ def score( check_is_fitted(self) metric = self._validate_and_convert_metric(metric) X, y = self._validate_data(X, y, reset=False) - D_ideal = self.stage_1_fitted_.compute_ideal_distances(y) + D_ideal = self.parametrization_fitted_.compute_ideal_distances(y) # Compute predicted pairwise distances X_proj = self.transform(X) diff --git a/tests/shapes/conftest.py b/tests/shapes/conftest.py index 550cb43..390056b 100644 --- a/tests/shapes/conftest.py +++ b/tests/shapes/conftest.py @@ -51,15 +51,15 @@ def _generate_lat_lon(n_samples: int, seed: int = 42) -> NDArray[np.float64]: # CHAIN SETUP # ============================================================================= @pytest.fixture(scope="module") -def chain_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="chain") +def chain_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="chain") @pytest.fixture(scope="module") -def chain_engine_user_provided_stage1( +def chain_engine_user_provided( chain_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="chain") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -82,15 +82,15 @@ def chain_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDArray[ # CLUSTER SETUP # ============================================================================= @pytest.fixture(scope="module") -def cluster_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="cluster") +def cluster_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="cluster") @pytest.fixture(scope="module") -def cluster_engine_user_provided_stage1( +def cluster_engine_user_provided( cluster_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="cluster") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -112,15 +112,15 @@ def cluster_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDArra # DISCRETE CIRCULAR SETUP # ============================================================================= @pytest.fixture(scope="module") -def disc_circular_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="discrete_circular") +def disc_circular_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="discrete_circular") @pytest.fixture(scope="module") -def disc_circular_engine_user_provided_stage1( +def disc_circular_engine_user_provided( disc_circular_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="discrete_circular") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -140,15 +140,15 @@ def disc_circular_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], # HIERARCHICAL SETUP # ============================================================================= @pytest.fixture(scope="module") -def hierarchical_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="hierarchical") +def hierarchical_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="hierarchical") @pytest.fixture(scope="module") -def hierarchical_engine_user_provided_stage1( +def hierarchical_engine_user_provided( hierarchical_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="hierarchical") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -173,17 +173,17 @@ def hierarchical_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], N @pytest.fixture(scope="module") -def circular_engine_computed_stage1() -> SupervisedMDS: +def circular_engine_computed() -> SupervisedMDS: """Provides a default engine with Continuous Circular Shape.""" - return SupervisedMDS(stage_1="computed", manifold="circular") + return SupervisedMDS(parametrization="computed", manifold="circular") @pytest.fixture(scope="module") -def circular_engine_user_provided_stage1( +def circular_engine_user_provided( circular_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: """Provides a default engine with Continuous Circular Shape.""" - return SupervisedMDS(stage_1="user_provided", manifold="circular") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -203,17 +203,17 @@ def circular_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDArr # CYLINDRICAL SETUP # ============================================================================= @pytest.fixture(scope="module") -def cylindrical_engine_computed_stage1() -> SupervisedMDS: +def cylindrical_engine_computed() -> SupervisedMDS: # Cylinder is a 3D object - return SupervisedMDS(stage_1="computed", manifold="cylindrical", radius=1.0) + return SupervisedMDS(parametrization="computed", manifold="cylindrical") @pytest.fixture(scope="module") -def cylindrical_engine_user_provided_stage1( +def cylindrical_engine_user_provided( cylindrical_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: # Cylinder is a 3D object - return SupervisedMDS(stage_1="user_provided", manifold="cylindrical", radius=1.0) + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -235,17 +235,17 @@ def cylindrical_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], ND # SPHERICAL SETUP # ============================================================================= @pytest.fixture(scope="module") -def spherical_engine_computed_stage1() -> SupervisedMDS: +def spherical_engine_computed() -> SupervisedMDS: # Sphere is a 3D object - return SupervisedMDS(stage_1="computed", manifold="spherical", radius=1.0) + return SupervisedMDS(parametrization="computed", manifold="spherical") @pytest.fixture(scope="module") -def spherical_engine_user_provided_stage1( +def spherical_engine_user_provided( spherical_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: # Sphere is a 3D object - return SupervisedMDS(stage_1="user_provided", manifold="spherical", radius=1.0) + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -283,30 +283,30 @@ def spherical_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDAr # GEODESIC SETUP # ============================================================================= @pytest.fixture(scope="module") -def geodesic_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="geodesic", radius=1.0) +def geodesic_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="geodesic") @pytest.fixture(scope="module") -def geodesic_engine_user_provided_stage1( +def geodesic_engine_user_provided( spherical_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="geodesic", radius=1.0) + return SupervisedMDS(parametrization="user_provided") # ============================================================================= # SPIRAL SETUP # ============================================================================= @pytest.fixture(scope="module") -def spiral_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="spiral") +def spiral_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="spiral") @pytest.fixture(scope="module") -def spiral_engine_user_provided_stage1( +def spiral_engine_user_provided( spiral_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="spiral") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -334,17 +334,17 @@ def spiral_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDArray # LOG LINEAR SETUP # ============================================================================= @pytest.fixture(scope="module") -def log_linear_engine_computed_stage1() -> SupervisedMDS: +def log_linear_engine_computed() -> SupervisedMDS: # LogLinear is intrinsically 1D (a line where distance is log-scale) - return SupervisedMDS(stage_1="computed", manifold="log_linear", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="log_linear", alpha=0.1) @pytest.fixture(scope="module") -def log_linear_engine_user_provided_stage1( +def log_linear_engine_user_provided( log_linear_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: # LogLinear is intrinsically 1D (a line where distance is log-scale) - return SupervisedMDS(stage_1="user_provided", manifold="log_linear", alpha=0.1) + return SupervisedMDS(parametrization="user_provided", alpha=0.1) @pytest.fixture(scope="module") @@ -363,17 +363,17 @@ def log_linear_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDA # EUCLIDEAN SETUP # ============================================================================= @pytest.fixture(scope="module") -def euclidean_engine_computed_stage1() -> SupervisedMDS: +def euclidean_engine_computed() -> SupervisedMDS: # Euclidean maps to a 1D line - return SupervisedMDS(stage_1="computed", manifold="euclidean", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="euclidean", alpha=0.1) @pytest.fixture(scope="module") -def euclidean_engine_user_provided_stage1( +def euclidean_engine_user_provided( euclidean_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: # Euclidean maps to a 1D line - return SupervisedMDS(stage_1="user_provided", manifold="euclidean", alpha=0.1) + return SupervisedMDS(parametrization="user_provided", alpha=0.1) @pytest.fixture(scope="module") @@ -390,17 +390,17 @@ def euclidean_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDAr # SEMICIRCULAR SETUP # ============================================================================= @pytest.fixture(scope="module") -def semicircular_engine_computed_stage1() -> SupervisedMDS: +def semicircular_engine_computed() -> SupervisedMDS: # Semicircle is a 2D arc - return SupervisedMDS(stage_1="computed", manifold="semicircular", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="semicircular", alpha=0.1) @pytest.fixture(scope="module") -def semicircular_engine_user_provided_stage1( +def semicircular_engine_user_provided( semicircular_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: # Semicircle is a 2D arc - return SupervisedMDS(stage_1="user_provided", manifold="semicircular", alpha=0.1) + return SupervisedMDS(parametrization="user_provided", alpha=0.1) @pytest.fixture(scope="module") @@ -419,15 +419,15 @@ def semicircular_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], N # KLEIN BOTTLE SETUP # ============================================================================= @pytest.fixture(scope="module") -def klein_bottle_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="klein_bottle", alpha=0.1) +def klein_bottle_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="klein_bottle", alpha=0.1) @pytest.fixture(scope="module") -def klein_bottle_engine_user_provided_stage1( +def klein_bottle_engine_user_provided( klein_bottle_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="klein_bottle", alpha=0.1) + return SupervisedMDS(parametrization="user_provided", alpha=0.1) @pytest.fixture(scope="module") @@ -492,15 +492,15 @@ def torus_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], NDArray[ # GRAPH GEODESIC SETUP # ============================================================================= @pytest.fixture(scope="module") -def graph_geodesic_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="graph_geodesic") +def graph_geodesic_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="graph_geodesic") @pytest.fixture(scope="module") -def graph_geodesic_engine_user_provided_stage1( +def graph_geodesic_engine_user_provided( graph_geodesic_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="graph_geodesic") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") @@ -522,15 +522,15 @@ def graph_geodesic_data_10d() -> tuple[NDArray[np.float64], NDArray[np.float64], # POLYTOPE SETUP # ============================================================================= @pytest.fixture(scope="module") -def polytope_engine_computed_stage1() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="polytope") +def polytope_engine_computed() -> SupervisedMDS: + return SupervisedMDS(parametrization="computed", manifold="polytope") @pytest.fixture(scope="module") -def polytope_engine_user_provided_stage1( +def polytope_engine_user_provided( polytope_data_10d: tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]], ) -> SupervisedMDS: - return SupervisedMDS(stage_1="user_provided", manifold="polytope") + return SupervisedMDS(parametrization="user_provided") @pytest.fixture(scope="module") diff --git a/tests/shapes/optional_test_chain_shape_optimization.py b/tests/shapes/optional_test_chain_shape_optimization.py index 66b4d47..abeb56d 100644 --- a/tests/shapes/optional_test_chain_shape_optimization.py +++ b/tests/shapes/optional_test_chain_shape_optimization.py @@ -22,8 +22,9 @@ from smds import SupervisedMDS -def generate_large_synthetic_data(n_samples: int, n_features: int = 768, seed: int = 42) -> tuple[NDArray[np.float64], -NDArray[np.float64]]: +def generate_large_synthetic_data( + n_samples: int, n_features: int = 768, seed: int = 42 +) -> tuple[NDArray[np.float64], NDArray[np.float64]]: """ Generates a large synthetic dataset representing a latent cycle/chain projected into high-dimensional space with noise. diff --git a/tests/shapes/test_all_shapes_integration.py b/tests/shapes/test_all_shapes_integration.py index 738afcd..6f0ff11 100644 --- a/tests/shapes/test_all_shapes_integration.py +++ b/tests/shapes/test_all_shapes_integration.py @@ -24,61 +24,61 @@ # Format: (shape_name, engine_fixture_name, data_fixture_name, score_min, procrustes_max) SHAPE_TEST_CASES = [ - ("Chain_ComputedStage1", "chain_engine_computed_stage1", "chain_data_10d", 0.90, 0.2), - ("Chain_UserProvidedStage1", "chain_engine_user_provided_stage1", "chain_data_10d", 0.85, 0.2), - ("Cluster_ComputedStage1", "cluster_engine_computed_stage1", "cluster_data_10d", 0.80, 0.2), - ("Cluster_UserProvidedStage1", "cluster_engine_user_provided_stage1", "cluster_data_10d", 0.80, 0.2), + ("Chain_Computed", "chain_engine_computed", "chain_data_10d", 0.90, 0.2), + ("Chain_UserProvided", "chain_engine_user_provided", "chain_data_10d", 0.85, 0.2), + ("Cluster_Computed", "cluster_engine_computed", "cluster_data_10d", 0.80, 0.2), + ("Cluster_UserProvided", "cluster_engine_user_provided", "cluster_data_10d", 0.80, 0.2), ( - "DiscCircular_ComputedStage1", - "disc_circular_engine_computed_stage1", + "DiscCircular_Computed", + "disc_circular_engine_computed", "disc_circular_data_10d", 0.70, 0.2, ), # max score 0.87 without noise ( - "DiscCircular_UserProvidedStage1", - "disc_circular_engine_user_provided_stage1", + "DiscCircular_UserProvided", + "disc_circular_engine_user_provided", "disc_circular_data_10d", 0.70, 0.2, ), - ("Hierarchical_ComputedStage1", "hierarchical_engine_computed_stage1", "hierarchical_data_10d", 0.90, 0.1), - ("Hierarchical_UserProvidedStage1", "hierarchical_engine_user_provided_stage1", "hierarchical_data_10d", 0.90, 0.1), - ("Circular_ComputedStage1", "circular_engine_computed_stage1", "circular_data_10d", 0.80, 0.1), - ("Circular_UserProvidedStage1", "circular_engine_user_provided_stage1", "circular_data_10d", 0.80, 0.1), - ("Cylindrical_ComputedStage1", "cylindrical_engine_computed_stage1", "cylindrical_data_10d", 0.80, 0.1), - ("Cylindrical_UserProvidedStage1", "cylindrical_engine_user_provided_stage1", "cylindrical_data_10d", 0.80, 0.1), - ("Spherical_ComputedStage1", "spherical_engine_computed_stage1", "spherical_data_10d", 0.70, 0.2), - ("Spherical_UserProvidedStage1", "spherical_engine_user_provided_stage1", "spherical_data_10d", 0.70, 0.2), + ("Hierarchical_Computed", "hierarchical_engine_computed", "hierarchical_data_10d", 0.90, 0.1), + ("Hierarchical_UserProvided", "hierarchical_engine_user_provided", "hierarchical_data_10d", 0.90, 0.1), + ("Circular_Computed", "circular_engine_computed", "circular_data_10d", 0.80, 0.1), + ("Circular_UserProvided", "circular_engine_user_provided", "circular_data_10d", 0.80, 0.1), + ("Cylindrical_Computed", "cylindrical_engine_computed", "cylindrical_data_10d", 0.80, 0.1), + ("Cylindrical_UserProvided", "cylindrical_engine_user_provided", "cylindrical_data_10d", 0.80, 0.1), + ("Spherical_Computed", "spherical_engine_computed", "spherical_data_10d", 0.70, 0.2), + ("Spherical_UserProvided", "spherical_engine_user_provided", "spherical_data_10d", 0.70, 0.2), ( - "Geodesic_ComputedStage1", - "geodesic_engine_computed_stage1", + "Geodesic_Computed", + "geodesic_engine_computed", "spherical_data_10d", 0.70, 0.2, ), # max score 0.90 without noise - ("Geodesic_UserProvidedStage1", "geodesic_engine_user_provided_stage1", "spherical_data_10d", 0.70, 0.2), - ("Spiral_ComputedStage1", "spiral_engine_computed_stage1", "spiral_data_10d", 0.90, 0.1), - ("Spiral_UserProvidedStage1", "spiral_engine_user_provided_stage1", "spiral_data_10d", 0.90, 0.1), - ("LogLinear_ComputedStage1", "log_linear_engine_computed_stage1", "log_linear_data_10d", 0.70, 0.2), - ("LogLinear_UserProvidedStage1", "log_linear_engine_user_provided_stage1", "log_linear_data_10d", 0.70, 0.2), - ("Euclidean_ComputedStage1", "euclidean_engine_computed_stage1", "euclidean_data_10d", 0.90, 0.1), - ("Euclidean_UserProvidedStage1", "euclidean_engine_user_provided_stage1", "euclidean_data_10d", 0.90, 0.1), - ("Semicircular_ComputedStage1", "semicircular_engine_computed_stage1", "semicircular_data_10d", 0.70, 0.2), - ("Semicircular_UserProvidedStage1", "semicircular_engine_user_provided_stage1", "semicircular_data_10d", 0.70, 0.2), - ("KleinBottle_ComputedStage1", "klein_bottle_engine_computed_stage1", "klein_bottle_data_10d", 0.70, 0.2), - ("KleinBottle_UserProvidedStage1", "klein_bottle_engine_user_provided_stage1", "klein_bottle_data_10d", 0.70, 0.2), + ("Geodesic_UserProvided", "geodesic_engine_user_provided", "spherical_data_10d", 0.70, 0.2), + ("Spiral_Computed", "spiral_engine_computed", "spiral_data_10d", 0.90, 0.1), + ("Spiral_UserProvided", "spiral_engine_user_provided", "spiral_data_10d", 0.90, 0.1), + ("LogLinear_Computed", "log_linear_engine_computed", "log_linear_data_10d", 0.70, 0.2), + ("LogLinear_UserProvided", "log_linear_engine_user_provided", "log_linear_data_10d", 0.70, 0.2), + ("Euclidean_Computed", "euclidean_engine_computed", "euclidean_data_10d", 0.90, 0.1), + ("Euclidean_UserProvided", "euclidean_engine_user_provided", "euclidean_data_10d", 0.90, 0.1), + ("Semicircular_Computed", "semicircular_engine_computed", "semicircular_data_10d", 0.70, 0.2), + ("Semicircular_UserProvided", "semicircular_engine_user_provided", "semicircular_data_10d", 0.70, 0.2), + ("KleinBottle_Computed", "klein_bottle_engine_computed", "klein_bottle_data_10d", 0.70, 0.2), + ("KleinBottle_UserProvided", "klein_bottle_engine_user_provided", "klein_bottle_data_10d", 0.70, 0.2), ("Torus", "torus_engine", "torus_data_10d", 0.65, 0.2), - ("GraphGeodesic_ComputedStage1", "graph_geodesic_engine_computed_stage1", "graph_geodesic_data_10d", 0.35, 0.95), + ("GraphGeodesic_Computed", "graph_geodesic_engine_computed", "graph_geodesic_data_10d", 0.35, 0.95), ( - "GraphGeodesic_UserProvidedStage1", - "graph_geodesic_engine_user_provided_stage1", + "GraphGeodesic_UserProvided", + "graph_geodesic_engine_user_provided", "graph_geodesic_data_10d", 0.35, 0.95, ), - ("Polytope_ComputedStage1", "polytope_engine_computed_stage1", "polytope_data_10d", 0.35, 0.99), - ("Polytope_UserProvidedStage1", "polytope_engine_user_provided_stage1", "polytope_data_10d", 0.35, 0.99), + ("Polytope_Computed", "polytope_engine_computed", "polytope_data_10d", 0.35, 0.99), + ("Polytope_UserProvided", "polytope_engine_user_provided", "polytope_data_10d", 0.35, 0.99), ] @@ -103,14 +103,14 @@ def test_shape_smoke_execution( # The data fixture returns 3 values, we only need X and y for a smoke test X_high, y, x_original = data_tuple - y_for_fit = x_original if "UserProvidedStage1" in shape_name else y + y_for_fit = x_original if "UserProvided" in shape_name else y # Execution X_proj = smds_engine.fit_transform(X_high, y_for_fit) # Assertion n_samples = X_high.shape[0] - n_components = smds_engine.stage_1_fitted_.n_components + n_components = smds_engine.parametrization_fitted_.n_components assert X_proj.shape == (n_samples, n_components), ( f"[{shape_name}] Output shape is incorrect. Expected {(n_samples, n_components)}, but got {X_proj.shape}." @@ -142,7 +142,7 @@ def test_shape_recovers_structure_from_high_dim( data_tuple = request.getfixturevalue(data_name) X_high, y, X_original = data_tuple - y_for_fit = X_original if "UserProvidedStage1" in shape_name else y + y_for_fit = X_original if "UserProvided" in shape_name else y # Execution X_proj = smds_engine.fit_transform(X_high, y_for_fit) diff --git a/tests/shapes/test_circular.py b/tests/shapes/test_circular.py index b9dc183..78debfa 100644 --- a/tests/shapes/test_circular.py +++ b/tests/shapes/test_circular.py @@ -6,7 +6,7 @@ @pytest.fixture def engine() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="circular", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="circular", alpha=0.1) @pytest.fixture diff --git a/tests/shapes/test_cluster.py b/tests/shapes/test_cluster.py index 3743633..4c3cdcf 100644 --- a/tests/shapes/test_cluster.py +++ b/tests/shapes/test_cluster.py @@ -44,7 +44,7 @@ def test_cluster_distance_computation() -> None: @pytest.fixture def smds_engine() -> SupervisedMDS: """Provides a default SMDS engine configured with ClusterShape.""" - return SupervisedMDS(stage_1="computed", manifold="cluster") + return SupervisedMDS(parametrization="computed", manifold="cluster") @pytest.fixture diff --git a/tests/shapes/test_discovery_pipeline.py b/tests/shapes/test_discovery_pipeline.py index 0d2f216..687a075 100644 --- a/tests/shapes/test_discovery_pipeline.py +++ b/tests/shapes/test_discovery_pipeline.py @@ -4,6 +4,8 @@ sorts results, and identifies the correct winning shape for known data topologies. """ +from pathlib import Path + import numpy as np import pandas as pd # type: ignore[import-untyped] import pytest @@ -11,6 +13,7 @@ from smds import UserProvidedSMDSParametrization from smds.pipeline import discover_manifolds +from smds.pipeline.helpers.interactive_plots import generate_interactive_plot from smds.shapes.base_shape import BaseShape from smds.shapes.continuous_shapes import CircularShape, SpiralShape from smds.shapes.discrete_shapes import ClusterShape @@ -130,3 +133,15 @@ def test_discover_manifolds_bypass( assert col in results.columns for m in StressMetrics: assert f"mean_{m.value}" in results.columns + + +@pytest.mark.parametrize("y_shape", [(30,), (30, 2), (30, 5)]) +def test_interactive_plot_handles_coordinate_labels(y_shape: tuple[int, ...], tmp_path: Path) -> None: + """Coordinate labels (spherical, hierarchical, n-D euclidean) colour by their first axis.""" + rng = np.random.default_rng(0) + + filename = generate_interactive_plot( + rng.standard_normal((30, 2)), rng.standard_normal(y_shape), "EuclideanShape", str(tmp_path) + ) + + assert (tmp_path / filename).exists() diff --git a/tests/shapes/test_euclidean.py b/tests/shapes/test_euclidean.py index 9900a1d..e028b52 100644 --- a/tests/shapes/test_euclidean.py +++ b/tests/shapes/test_euclidean.py @@ -1,12 +1,17 @@ import numpy as np import pytest +from numpy.typing import NDArray +from scipy.spatial.distance import cdist # type: ignore[import-untyped] from smds import SupervisedMDS +from smds.shapes.continuous_shapes import EuclideanShape + +N_LATENT = 5 @pytest.fixture def engine() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="euclidean", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="euclidean", alpha=0.1) @pytest.fixture @@ -19,9 +24,113 @@ def y() -> np.typing.NDArray[np.float64]: return np.random.rand(100) * 10 +@pytest.fixture +def euclidean_data_5d() -> tuple[NDArray[np.float64], NDArray[np.float64]]: + """ + 20D observations carrying a latent 5D Euclidean point cloud. + + ``y`` holds the latent coordinates directly, so a well-fitted 5-component + embedding should reproduce the latent pairwise distances up to scale. + """ + rng = np.random.default_rng(42) + y_latent = rng.standard_normal((100, N_LATENT)) + + projection = rng.standard_normal((N_LATENT, 20)) + X_high = y_latent @ projection + rng.normal(scale=0.1, size=(100, 20)) + + return X_high, y_latent + + def test_euclidean_smoke( engine: SupervisedMDS, X: np.typing.NDArray[np.float64], y: np.typing.NDArray[np.float64] ) -> None: engine.fit(X, y) X_proj = engine.transform(X) assert X_proj.shape == (100, 1) + + +# ============================================================================= +# N-DIMENSIONAL LABELS +# ============================================================================= + + +def test_euclidean_accepts_scalar_and_coordinate_labels() -> None: + """``y_ndim`` declares both (n_samples,) and (n_samples, n_features).""" + assert EuclideanShape.y_ndim == (1, 2) + + +@pytest.mark.parametrize("n_features", [1, 3, 7]) +def test_shape_matches_cdist(n_features: int) -> None: + """The distance matrix is the true Euclidean metric in n dimensions.""" + y_nd = np.random.default_rng(0).random((15, n_features)) + shape = EuclideanShape(normalize_labels=False) + + assert np.allclose(shape(y_nd), cdist(y_nd, y_nd, metric="euclidean")) + + +def test_shape_treats_column_vector_as_scalar_labels() -> None: + """(n_samples,) and (n_samples, 1) describe the same points on a line.""" + y_1d = np.random.default_rng(0).random(15) + shape = EuclideanShape(normalize_labels=False) + + assert np.allclose(shape(y_1d), shape(y_1d.reshape(-1, 1))) + + +def test_shape_rejects_three_dimensional_labels() -> None: + shape = EuclideanShape() + + with pytest.raises(ValueError, match="must be 1- or 2-dimensional"): + shape(np.zeros((4, 2, 2))) + + +def test_smds_rejects_three_dimensional_labels(X: NDArray[np.float64]) -> None: + """The estimator rejects unusable label ranks before reaching the shape.""" + engine = SupervisedMDS(manifold="euclidean", alpha=0.1) + + with pytest.raises(ValueError, match="must be 1- or 2-dimensional"): + engine.fit(X, np.zeros((100, 2, 2))) + + +def test_smds_fits_coordinate_labels(euclidean_data_5d: tuple[NDArray[np.float64], NDArray[np.float64]]) -> None: + """The motivating case: 5D labels with a matching 5D embedding.""" + X_high, y_latent = euclidean_data_5d + engine = SupervisedMDS(manifold="euclidean", n_components=N_LATENT, alpha=0.1).fit(X_high, y_latent) + + assert engine.Y_.shape == (100, N_LATENT) + assert engine.W_.shape == (N_LATENT, 20) + assert engine.transform(X_high).shape == (100, N_LATENT) + + +def test_embedding_reproduces_latent_distances( + euclidean_data_5d: tuple[NDArray[np.float64], NDArray[np.float64]], +) -> None: + """ + ``Y_`` is a rigid re-embedding of the latent cloud, so its pairwise + distances match the latent ones (Euclidean labels are normalized by a + single global min-max factor, hence the shared rescaling). + """ + X_high, y_latent = euclidean_data_5d + engine = SupervisedMDS(manifold="euclidean", n_components=N_LATENT, alpha=0.1).fit(X_high, y_latent) + + d_embedded = cdist(engine.Y_, engine.Y_) + d_latent = cdist(y_latent, y_latent) + scale = d_latent.max() / d_embedded.max() + + assert np.allclose(d_embedded * scale, d_latent) + + +def test_coordinate_labels_beat_the_scalar_default( + euclidean_data_5d: tuple[NDArray[np.float64], NDArray[np.float64]], +) -> None: + """ + ``_build_manifold`` fixes the euclidean default at 1 for scalar labels, so + coordinate labels need an explicit ``n_components`` to avoid truncation. + """ + X_high, y_latent = euclidean_data_5d + + default = SupervisedMDS(manifold="euclidean", alpha=0.1).fit(X_high, y_latent) + widened = SupervisedMDS(manifold="euclidean", n_components=N_LATENT, alpha=0.1).fit(X_high, y_latent) + + assert default.Y_.shape == (100, 1) + assert np.all(widened.Y_.std(axis=0) > 1e-6) + assert widened.score(X_high, y_latent) > default.score(X_high, y_latent) diff --git a/tests/shapes/test_hierarchical.py b/tests/shapes/test_hierarchical.py index 8b0683e..994f7ce 100644 --- a/tests/shapes/test_hierarchical.py +++ b/tests/shapes/test_hierarchical.py @@ -17,7 +17,7 @@ def random_data() -> tuple[NDArray[np.float64], NDArray[np.float64]]: @pytest.fixture def smds_engine() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="hierarchical") + return SupervisedMDS(parametrization="computed", manifold="hierarchical") @pytest.mark.smoke @@ -36,7 +36,7 @@ def test_hierarchical_smoke_test( X_proj: NDArray[np.float64] = smds_engine.fit_transform(X, y) n_samples = X.shape[0] - n_components = smds_engine.stage_1_fitted_.n_components + n_components = smds_engine.parametrization_fitted_.n_components assert X_proj.shape == (n_samples, n_components), ( f"Output shape is incorrect. " @@ -77,7 +77,7 @@ def test_hierarchical_preserves_structure_in_2d( X_proj: NDArray[np.float64] = smds_engine.fit_transform(X, y) - assert X_proj.shape == (X.shape[0], smds_engine.stage_1_fitted_.n_components), ( + assert X_proj.shape == (X.shape[0], smds_engine.parametrization_fitted_.n_components), ( f"Output shape should be (n_samples, n_components), but got {X_proj.shape}." ) @@ -171,7 +171,7 @@ def test_hierarchical_recovers_structure_from_high_dim( X_proj: NDArray[np.float64] = smds_engine.fit_transform(X, y) - assert X_proj.shape == (X.shape[0], smds_engine.stage_1_fitted_.n_components), ( + assert X_proj.shape == (X.shape[0], smds_engine.parametrization_fitted_.n_components), ( f"Output shape should be (n_samples, n_components), but got {X_proj.shape}." ) diff --git a/tests/shapes/test_klein_bottle.py b/tests/shapes/test_klein_bottle.py index f1ee06f..5f1f0e7 100644 --- a/tests/shapes/test_klein_bottle.py +++ b/tests/shapes/test_klein_bottle.py @@ -6,7 +6,7 @@ @pytest.fixture def engine() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="klein_bottle", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="klein_bottle", alpha=0.1) @pytest.fixture diff --git a/tests/shapes/test_log_linear.py b/tests/shapes/test_log_linear.py index 9b4fb1d..441fe57 100644 --- a/tests/shapes/test_log_linear.py +++ b/tests/shapes/test_log_linear.py @@ -6,7 +6,7 @@ @pytest.fixture def engine() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="log_linear", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="log_linear", alpha=0.1) @pytest.fixture diff --git a/tests/shapes/test_semicircular.py b/tests/shapes/test_semicircular.py index 062cd97..f6dbef5 100644 --- a/tests/shapes/test_semicircular.py +++ b/tests/shapes/test_semicircular.py @@ -7,7 +7,7 @@ @pytest.fixture def engine() -> SupervisedMDS: # Semicircle is 2D - return SupervisedMDS(stage_1="computed", manifold="semicircular", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="semicircular", alpha=0.1) @pytest.fixture diff --git a/tests/shapes/test_spatial.py b/tests/shapes/test_spatial.py index 6b933cf..59d0af1 100644 --- a/tests/shapes/test_spatial.py +++ b/tests/shapes/test_spatial.py @@ -6,17 +6,17 @@ @pytest.fixture def engine_cylindrical() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="cylindrical", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="cylindrical", alpha=0.1) @pytest.fixture def engine_geodesic() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="geodesic", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="geodesic", alpha=0.1) @pytest.fixture def engine_spherical() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="spherical", alpha=0.1) + return SupervisedMDS(parametrization="computed", manifold="spherical", alpha=0.1) @pytest.fixture diff --git a/tests/shapes/test_spiral.py b/tests/shapes/test_spiral.py index fb9cfb2..0ca5783 100644 --- a/tests/shapes/test_spiral.py +++ b/tests/shapes/test_spiral.py @@ -10,7 +10,7 @@ @pytest.fixture def smds_engine() -> SupervisedMDS: - return SupervisedMDS(stage_1="computed", manifold="spiral") + return SupervisedMDS(parametrization="computed", manifold="spiral") @pytest.fixture @@ -94,7 +94,7 @@ def test_spiral_smoke_test( X_proj: NDArray[np.float64] = smds_engine.fit_transform(X, y) n_samples = X.shape[0] - n_components = smds_engine.stage_1_fitted_.n_components + n_components = smds_engine.parametrization_fitted_.n_components assert X_proj.shape == (n_samples, n_components), ( f"Output shape is incorrect. " diff --git a/tests/test_n_components.py b/tests/test_n_components.py new file mode 100644 index 0000000..b448b72 --- /dev/null +++ b/tests/test_n_components.py @@ -0,0 +1,217 @@ +""" +Tests for the ``n_components`` override on SupervisedMDS. + +``n_components=None`` (the default) keeps each built-in manifold's natural +embedding dimension; an explicit value overrides it. See the manifold factory +table in ``SupervisedMDS._build_manifold``. +""" + +from typing import Any + +import numpy as np +import pytest +from numpy.typing import NDArray +from sklearn.base import clone # type: ignore[import-untyped] +from sklearn.cross_decomposition import PLSRegression # type: ignore[import-untyped] + +from smds import HybridSMDS, SupervisedMDS + +N_SAMPLES = 80 +N_FEATURES = 10 + + +@pytest.fixture +def X() -> NDArray[np.float64]: + return np.random.default_rng(0).standard_normal((N_SAMPLES, N_FEATURES)) + + +@pytest.fixture +def y_scalar() -> NDArray[np.float64]: + return np.random.default_rng(1).random(N_SAMPLES) * 2 * np.pi + + +@pytest.fixture +def y_2d() -> NDArray[np.float64]: + return np.random.default_rng(2).random((N_SAMPLES, 2)) + + +@pytest.fixture +def y_clusters() -> NDArray[np.float64]: + """Six clusters: the distance matrix has rank 5, well above the default of 2.""" + return np.random.default_rng(3).integers(0, 6, N_SAMPLES).astype(float) + + +@pytest.fixture +def y_5d() -> NDArray[np.float64]: + return np.random.default_rng(4).random((N_SAMPLES, 5)) + + +# ============================================================================= +# DEFAULT (n_components=None) PRESERVES THE MANIFOLD'S NATURAL DIMENSION +# ============================================================================= + + +@pytest.mark.parametrize( + ("manifold", "expected", "y_fixture"), + [ + ("euclidean", 1, "y_scalar"), + ("log_linear", 1, "y_scalar"), + ("circular", 2, "y_scalar"), + ("cluster", 2, "y_clusters"), + ("spherical", 3, "y_2d"), + ("klein_bottle", 4, "y_2d"), + ], +) +def test_default_uses_manifold_dimension( + X: NDArray[np.float64], + manifold: str, + expected: int, + y_fixture: str, + request: pytest.FixtureRequest, +) -> None: + y = request.getfixturevalue(y_fixture) + engine = SupervisedMDS(manifold=manifold, alpha=0.1).fit(X, y) + + assert engine.n_components is None + assert engine.Y_.shape == (N_SAMPLES, expected) + assert engine.transform(X).shape == (N_SAMPLES, expected) + + +def test_default_is_none_and_unmodified_by_init() -> None: + """Parameters must be stored untouched by __init__, per the sklearn API.""" + assert SupervisedMDS().n_components is None + assert SupervisedMDS(n_components=5).n_components == 5 + + +# ============================================================================= +# EXPLICIT OVERRIDE +# ============================================================================= + + +@pytest.mark.parametrize("k", [1, 3, 5]) +def test_override_sets_embedding_dimension(X: NDArray[np.float64], y_clusters: NDArray[np.float64], k: int) -> None: + engine = SupervisedMDS(manifold="cluster", n_components=k, alpha=0.1).fit(X, y_clusters) + + assert engine.Y_.shape == (N_SAMPLES, k) + assert engine.W_.shape == (k, N_FEATURES) + assert engine.transform(X).shape == (N_SAMPLES, k) + + +def test_override_retains_geometry_the_default_truncates( + X: NDArray[np.float64], y_clusters: NDArray[np.float64] +) -> None: + """ + The motivating case: a discrete shape whose distance matrix out-ranks the + hard-coded default, so extra components carry real structure. + """ + default = SupervisedMDS(manifold="cluster", alpha=0.1).fit(X, y_clusters) + widened = SupervisedMDS(manifold="cluster", n_components=5, alpha=0.1).fit(X, y_clusters) + + # Every axis of the wider embedding is non-degenerate, not zero padding. + assert np.all(widened.Y_.std(axis=0) > 1e-6) + + # The first components agree up to sign; the extra ones are new information. + assert np.allclose(np.abs(widened.Y_[:, :2]), np.abs(default.Y_), atol=1e-8) + + # Retaining more of the distance matrix fits it better (higher score is better). + assert widened.score(X, y_clusters) > default.score(X, y_clusters) + + +def test_euclidean_override_matches_label_dimensionality(X: NDArray[np.float64], y_5d: NDArray[np.float64]) -> None: + """ + ``euclidean`` is the one manifold with no natural dimension: it takes both + scalar and coordinate labels, and its default of 1 suits only the former. + """ + default = SupervisedMDS(manifold="euclidean", alpha=0.1).fit(X, y_5d) + widened = SupervisedMDS(manifold="euclidean", n_components=5, alpha=0.1).fit(X, y_5d) + + assert default.Y_.shape == (N_SAMPLES, 1) + assert widened.Y_.shape == (N_SAMPLES, 5) + + # Random 5D labels span all five axes, so none of them is zero padding. + assert np.all(widened.Y_.std(axis=0) > 1e-6) + assert widened.score(X, y_5d) > default.score(X, y_5d) + + +def test_override_beyond_rank_pads_with_zeros(X: NDArray[np.float64], y_scalar: NDArray[np.float64]) -> None: + """ + Classical MDS cannot exceed the rank of the distance matrix. A circle spans + two dimensions, so the trailing components are numerically zero (residual + eigenvalues at the noise floor) rather than extra geometry. + """ + engine = SupervisedMDS(manifold="circular", n_components=4, alpha=0.1).fit(X, y_scalar) + + assert engine.Y_.shape == (N_SAMPLES, 4) + assert np.all(engine.Y_[:, :2].std(axis=0) > 1e-2) + assert np.all(np.abs(engine.Y_[:, 2:]) < 1e-6) + + +# ============================================================================= +# VALIDATION +# ============================================================================= + + +@pytest.mark.parametrize("bad", [0, -3, 1.5, "2", True, np.array([2])]) +def test_invalid_n_components_raises(X: NDArray[np.float64], y_scalar: NDArray[np.float64], bad: Any) -> None: + engine = SupervisedMDS(manifold="circular", n_components=bad, alpha=0.1) + + with pytest.raises(ValueError, match="n_components must be a positive integer or None"): + engine.fit(X, y_scalar) + + +def test_numpy_integer_is_accepted(X: NDArray[np.float64], y_clusters: NDArray[np.float64]) -> None: + # Annotated as int, but numpy scalars leak in from things like y.shape[1] or + # rng draws, so they are accepted at runtime. + engine = SupervisedMDS( + manifold="cluster", + n_components=np.int64(3), # type: ignore[arg-type] + alpha=0.1, + ).fit(X, y_clusters) + + assert engine.Y_.shape == (N_SAMPLES, 3) + + +# ============================================================================= +# SKLEARN COMPATIBILITY +# ============================================================================= + + +def test_get_params_and_clone_roundtrip() -> None: + engine = SupervisedMDS(manifold="cluster", n_components=5) + + assert engine.get_params()["n_components"] == 5 + assert clone(engine).get_params() == engine.get_params() + + +def test_set_params(X: NDArray[np.float64], y_clusters: NDArray[np.float64]) -> None: + engine = SupervisedMDS(manifold="cluster", alpha=0.1) + engine.set_params(n_components=4).fit(X, y_clusters) + + assert engine.Y_.shape == (N_SAMPLES, 4) + + +# ============================================================================= +# INTERACTION WITH OTHER PARAMETRIZATIONS AND SUBCLASSES +# ============================================================================= + + +def test_user_provided_parametrization_ignores_n_components( + X: NDArray[np.float64], y_scalar: NDArray[np.float64] +) -> None: + """``user_provided`` infers its dimensionality from the coordinates.""" + with pytest.warns(UserWarning, match="manifold value is ignored"): + engine = SupervisedMDS(parametrization="user_provided", n_components=7, alpha=0.1).fit(X, y_scalar) + + assert engine.Y_.shape == (N_SAMPLES, 1) + + +def test_hybrid_smds_keeps_lower_bound_semantics(X: NDArray[np.float64], y_2d: NDArray[np.float64]) -> None: + """ + Regression guard: HybridSMDS.n_components is a floor that Y_ is zero-padded + up to, not an override of the manifold's dimension, so a 3D spherical + embedding survives n_components=2. + """ + hybrid = HybridSMDS(manifold="spherical", n_components=2, reducer=PLSRegression(n_components=2)) + hybrid.fit(X, y_2d) + + assert hybrid.Y_.shape == (N_SAMPLES, 3) diff --git a/tests/test_sklearn_compatibility_smds.py b/tests/test_sklearn_compatibility_smds.py index 0badfe1..27245b7 100644 --- a/tests/test_sklearn_compatibility_smds.py +++ b/tests/test_sklearn_compatibility_smds.py @@ -6,7 +6,7 @@ from smds.smds import SupervisedMDS -# todo: add same for Stage1SMDSTransformer +# todo: add same for SMDSParametrization def dummy_manifold_func(y: NDArray[np.float64]) -> NDArray[np.float64]: @@ -25,7 +25,7 @@ def test_sklearn_compatibility() -> None: Tests SupervisedMDS with sklearn's check_estimator. This ensures that SupervisedMDS meets all sklearn compatibility requirements. """ - estimator = SupervisedMDS(stage_1="computed", manifold="circular") + estimator = SupervisedMDS(parametrization="computed", manifold="circular") try: check_estimator(estimator) diff --git a/uv.lock b/uv.lock index 12d9bf1..142f8b9 100644 --- a/uv.lock +++ b/uv.lock @@ -3516,7 +3516,7 @@ wheels = [ [[package]] name = "smds" -version = "1.6.5" +version = "1.6.6" source = { editable = "." } dependencies = [ { name = "matplotlib" },