A traditional format split into separate files for each administrative level (e.g., gadm36_USA_1.shp ).
Each administrative unit has several attributes stored in the data table. Understanding these attributes is essential for effective use: download gadm data version 36 work
import geopandas as gpd gadm = gpd.read_file("path/to/gadm36_country_level2.geojson") gadm.plot(column='NAME_1') # color by admin name A traditional format split into separate files for
You can also download layers 0 through 5 (country level down to smallest administrative units) as individual files to save processing time. 3. Download via Software (Programmatic Access) How to Work with the Data GADM data
library(sf) # Load GADM v3.6 Level 1 data for a country gadm_data <- readRDS("gadm36_NGA_1_sf.rds") # Plot the boundaries plot(st_geometry(gadm_data)) Use code with caution. Tips for Optimizing GADM 3.6 Workflows
: Smaller local administrative units (e.g., Communes, Taluks). PubMed Central (PMC) (.gov) 3. How to Work with the Data
GADM data includes the NAME_0 (country), NAME_1 (province), and TYPE_1 (type of area) columns. You can use the attribute table to filter data to a specific country or region. 3. Spatial Analysis You can use GADM polygons to: