Connect CSV files containing statistical data to the spatial boundaries. Summary of Benefits High Resolution: Superior boundary detail. Global Coverage: Includes every country. Free for Research: Ideal for academic projects.
If you are looking for more up-to-date boundary changes (post-2020), consider checking the latest GADM version, but for robust, historically consistent research, version 3.6 is an excellent choice. Pro-Tip Checklist for GADM Data
Example – add population data in R:
md5sum gadm36_USA_0_gpkg.zip
If you want, I can produce exact code examples for a specific format (GeoJSON, shapefile, or RData), a particular country, or an automated script to batch-download multiple countries from GADM v3.6. Which would you like? download gadm data version 36 work
Smaller localized divisions (Communes, Wards, Parishes—available only for select countries).
import geopandas as gpd # Load Level 1 (Provinces/States) from a GADM 3.6 Geopackage gadm_path = "gadm36_levels.gpkg" states = gpd.read_file(gadm_path, layer="level1") # Filter for a specific country using its ISO 3-letter code france_states = states[states['GID_0'] == 'FRA'] # Plot the map france_states.plot() Use code with caution. 3. Working in R Connect CSV files containing statistical data to the
GADM data is typically in WGS84 (EPSG:4326). If you are calculating areas (km²), you must reproject the layer to a projected coordinate system (e.g., UTM or Mercator).
import geopandas as gpd
If you are running global models, download the complete world database. Note: Global files are large and require significant RAM to process. Understanding the GADM Layer Hierarchy