Geospatial Data Pack for Visualization
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zipUpdated Oct 21, 2025
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EmailClick to copy linkLink copiedCiteVega Datasets (2025). Geospatial Data Pack for Visualization [Dataset]. https://www.kaggle.com/datasets/vega-datasets/geospatial-data-packzip(1422109 bytes)Available download formatsDataset updatedOct 21, 2025Dataset authored and provided byVega DatasetsDescriptionGeospatial Data Pack for Visualization πΊοΈ
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 π. 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-datasetsproject serves as a common repository for example datasets used across these visualization libraries and related projects.Why Use This Dataset? π€
- Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
- Vector Data: Includes points (like
airports.csv), lines (likelondonTubeLines.json), and polygons (likeus-10m.json). - Raster-like Data: Work with gridded datasets (like
windvectors.csv,annual-precip.json).
- Vector Data: Includes points (like
- 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 ποΈ
This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.
1. BASE MAP BOUNDARIES (Topological Data)
Dataset File Size Format License Description Key Fields / Join Info US Map (1:10m) us-10m.json627 KB TopoJSON CC-BY-4.0 US state and county boundaries. Contains statesandcountiesobjects. Ideal for choropleths.id(FIPS code) property on geometriesWorld Map (1:110m) world-110m.json117 KB TopoJSON CC-BY-4.0 World country boundaries. Contains countriesobject. Suitable for world-scale viz.idproperty on geometriesLondon Boroughs londonBoroughs.json14 KB TopoJSON CC-BY-4.0 London borough boundaries. properties.BOROUGHN(name)London Centroids londonCentroids.json2 KB GeoJSON CC-BY-4.0 Center points for London boroughs. properties.id,properties.nameLondon Tube Lines londonTubeLines.json78 KB GeoJSON CC-BY-4.0 London Underground network lines. properties.name,properties.color2. GEOGRAPHIC REFERENCE POINTS (Point Data) π
Dataset File Size Format License Description Key Fields / Join Info US Airports airports.csv205 KB CSV Public Domain US airports with codes and coordinates. iata,state, `l...
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FacebookTwitterEmailGeospatial Data Pack for Visualization
Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets
Geospatial Data Pack for Visualization πΊοΈ
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 π. 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? π€
- Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
- Vector Data: Includes points (like
airports.csv), lines (likelondonTubeLines.json), and polygons (likeus-10m.json). - Raster-like Data: Work with gridded datasets (like
windvectors.csv,annual-precip.json).
- Vector Data: Includes points (like
- 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 ποΈ
This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.
1. BASE MAP BOUNDARIES (Topological Data)
| Dataset | File | Size | Format | License | Description | Key Fields / Join Info |
|---|---|---|---|---|---|---|
| US Map (1:10m) | us-10m.json | 627 KB | TopoJSON | CC-BY-4.0 | US state and county boundaries. Contains states and counties objects. Ideal for choropleths. | id (FIPS code) property on geometries |
| World Map (1:110m) | world-110m.json | 117 KB | TopoJSON | CC-BY-4.0 | World country boundaries. Contains countries object. Suitable for world-scale viz. | id property on geometries |
| London Boroughs | londonBoroughs.json | 14 KB | TopoJSON | CC-BY-4.0 | London borough boundaries. | properties.BOROUGHN (name) |
| London Centroids | londonCentroids.json | 2 KB | GeoJSON | CC-BY-4.0 | Center points for London boroughs. | properties.id, properties.name |
| London Tube Lines | londonTubeLines.json | 78 KB | GeoJSON | CC-BY-4.0 | London Underground network lines. | properties.name, properties.color |
2. GEOGRAPHIC REFERENCE POINTS (Point Data) π
| Dataset | File | Size | Format | License | Description | Key Fields / Join Info |
|---|---|---|---|---|---|---|
| US Airports | airports.csv | 205 KB | CSV | Public Domain | US airports with codes and coordinates. | iata, state, `l... |