Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

Context

I created a dataset to help people create choropleth maps of United States states.

Content

One geojson to plot the countries borders, and one csv from the Census Bureau for the us population per state.

Inspiration

I think the best way to use this dataset is in joining it with other data. For example, I used this dataset to plot police killings using the data from https://www.kaggle.com/jpmiller/police-violence-in-the-us

This dataset contains geometry data for the countries of the world together with their names and country codes in various formats. The primary use case is choropleths, color-coded maps. The data can be read as a pandas DataFrame with geopandas and plotted with matplotlib. See the starter notebook for an example how to do it.

The data was created by Natural Earth. It is in public domain and free to use for any purpose at the time of this writing; you might want to check their Terms of Use.

Photo by KOBU Agency on Unsplash

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

Space-time clusters of SARS-CoV-2 infection in household cats around Chicago city, United States, 2021–2023.

🧾 About Dataset

🌍 Global GDP by Country — 2024 Edition

📖 Overview

The Global GDP by Country (2024) dataset provides an up-to-date snapshot of worldwide economic performance, summarizing each country’s nominal GDP, growth rate, population, and global economic contribution.

This dataset is ideal for economic analysis, data visualization, policy modeling, and machine learning applications related to global development and financial forecasting.

📊 Dataset Information

• Total Records: 181 countries
• Time Period: 2024 (latest available global data)
• Geographic Coverage: Worldwide
• File Format: CSV
• File Size: ~10 KB
• Missing Values: None (100% complete dataset)

🎯 Target Use-Cases:
- Economic growth trend analysis
- GDP-based country clustering
- Per capita wealth comparison
- Share of world economy visualization

🧩 Key Features

📈 Statistical Summary

Population Overview

• Mean Population: 43.6 million
• Standard Deviation: 155.5 million
• Minimum Population: 9,816 (small island nations)
• Median Population: 9.1 million
• Maximum Population: 1.43 billion (China)

🌟 Highlights

💰 Top Economies (Nominal GDP):
United States, China, Japan, Germany, India

📈 Fastest Growing Economies:
India, Bangladesh, Vietnam, and Rwanda

🌐 Global Insights:
- The dataset covers 181 countries representing 100% of global GDP.
- Suitable for data visualization dashboards, AI-driven economic forecasting, and educational research.

💡 Example Use-Cases

• Build a choropleth map showing GDP distribution across continents.
• Train a regression model to predict GDP per capita based on population and growth.
• Compare economic inequality using population vs GDP share.

📚 Dataset Citation

Source: Worldometers — GDP by Country (2024)
Dataset compiled and cleaned by: Asadullah Shehbaz
For open research and data analysis.

PostGIS data for London and Greater London ward boundaries as of 2018.

This dataset is used in the london_votes sample Splitfile in which the 2017 General Election results and London Ward geodata are joined through the ONS UK Ward-Constituency lookup table to build a dataset of London constituencies and Conservative/Labour votes in each, ready for plotting as a Choropleth map.

https://data.london.gov.uk/dataset/statistical-gis-boundary-files-london

Contains National Statistics data © Crown copyright and database right 2012

Contains Ordnance Survey data © Crown copyright and database right 2012

This project provides a comprehensive dataset on intentional homicides in Mexico from 1990 to 2023, disaggregated by sex and state. It includes both raw data and tools for visualization, making it a valuable resource for researchers, policymakers, and analysts studying violence trends, gender disparities, and regional patterns.ContentsHomicide Data: Total number of male and female victims per state and year.Population Data: Corresponding male and female population estimates for each state and year.Homicide Rates: Per 100,000 inhabitants, calculated for both sexes.Choropleth Map Script: A Python script that generates homicide rate maps using a GeoJSON file.GeoJSON File: A spatial dataset defining Mexico's state boundaries, used for mapping.Sample Figure: A pre-generated homicide rate map for 2023 as an example.Requirements File: A requirements.txt file listing necessary dependencies for running the script.SourcesHomicide Data: INEGI - Vital Statistics MicrodataPopulation Data: Mexican Population Projections 2020-2070This dataset enables spatial analysis and data visualization, helping users explore homicide trends across Mexico in a structured and reproducible way.

Dataset info

The dataset contains Local Authority Boundaries for Great Britain (England, Scotland and Wales) as of December 2021. A total of 363 Local Authority objects are included. Created for future use in folium choropleth maps when combined with other datasets that contain the matching Local Authority Codes. Additionally, subsets were created for convenience holding the boundaries of local authorities in England and Wales together, and in each individual country, i.e., England, Scotland and Wales on their own.

Methodology

The original dataset was downloaded from ONS. Since the dataset was too large for most use cases (129.4MB) due to the level of detail, it was simplified with https://mapshaper.org/ using the default method (Visvalingam / weighted area) with 'prevent shape removal' enabled. The simplification was set to 1.4%, followed by intersection repair and export back to geojson. The shape coordinates were originally in British National Grid (BNG) format, which had to be converted to WGS84 (latitude and longitude) format. Finally, the coordinates were rounded to 6 decimal places, resulting in a file containing 2.2MB of uncompressed data with a sensible level of detail. The individual country data were extracted, based on the LAD21CD property, to create the additional files.

Licence

https://www.ons.gov.uk/methodology/geography/licences

Digital boundary products and reference maps are supplied under the Open Government Licence. You must use the following copyright statements when you reproduce or use this material:

• Source: Office for National Statistics licensed under the Open Government Licence v.3.0
• Contains OS data © Crown copyright and database right 2023

This project provides comprehensive information on the total number of suicides in Mexico from 1990 to 2024, categorized by sex and state. It includes the main dataset along with a Python script and supporting files that enable users to analyze suicide rates and trends across the country.The dataset follows the official government methodology, using year of registration and state of residence of the deceased as key variables. It includes:Total male and female populationsSuicide counts for males and femalesSuicide rates for each sexData SourcesSuicide Data: Extracted from the INEGI database of registered deathshttps://www.inegi.org.mx/programas/edr/#microdatosPopulation Data: Derived from Mexican government population projections for 2020–2070https://datos.gob.mx/dataset/proyecciones-de-poblacion/resource/de522924-f4d8-4523-a6fd-6b2efe73f3afIncluded Filesscript.py – Python script to generate choropleth maps of suicide rates by state for a selected yearrequirements.txt – Required Python packages to run the scriptmexico.json – GeoJSON file containing administrative boundaries of Mexico by stateSample Chart (2024) – Example visualization featuring suicide rates for 2024This project can be used by researchers, public health professionals, policymakers, journalists, and students interested in understanding suicide trends in Mexico. It allows users to explore long-term and state-level patterns, compare differences between males and females, generate spatial visualizations, and incorporate the data into broader statistical, geographic, or public health analyses.

This project features a Python script designed to visualize Mexico's trade relationships from 1993 to 2025. Using official trade data sourced from the DataMexico VizBuilder, the script generates:Bar Charts: Highlighting the top 30 export or import trade partners of Mexico for any given year.Choropleth Maps: Showing the trade values (exports or imports) for all countries, customizable by a specific year.The dataset included provides comprehensive trade figures for over three decades, broken down by country and trade flow type (exports or imports).Additionally, the project includes a requirements.txt file for easy dependency installation and sample visualizations to demonstrate the script's functionality.This tool aims to provide researchers, policymakers, and educators with a clear, customizable way to explore and analyze Mexico's trade dynamics over time.