import geopandas as gpd global_gdf = gpd.read_file("gadm36_levels.gpkg", layer="ADM_ADM_1") mexico = global_gdf[global_gdf["NAME_0"] == "Mexico"] mexico.to_file("mexico_adm1.gpkg") GADM 3.6 uses GID_0 , GID_1 , GID_2 as unique identifiers. Merge using these columns – more reliable than names (which may have spaces/case issues).
SELECT NAME_0, NAME_1, HASC_1, ISO FROM gadm36 WHERE ISO LIKE 'US%'; Here is how to work with the data after a successful download. Workflow A: Extract a single country from global Geopackage (fastest) If you downloaded the global Geopackage, you don’t need to re-download per country: download gadm data version 36 work
library(sf) library(dplyr) gadm <- st_read("gadm36_levels.gpkg", layer="ADM_ADM_1") pop_data <- read.csv("population_estimates.csv") # has GID_1 column merged <- left_join(gadm, pop_data, by="GID_1") GADM 3.6 boundaries are high-resolution (often >1 MB per province). Use simplification before serving tiles: import geopandas as gpd global_gdf = gpd
Example – add population data in R:
GADM (Database of Global Administrative Areas) is the gold standard for boundary data. Version 3.6, released in late 2020 (with minor updates into 2021), remains one of the most widely used versions due to its stability, licensing clarity, and compatibility with legacy systems. However, novice users often struggle with file formats, projection mismatches, and API changes. Workflow A: Extract a single country from global
Target Keyword: download gadm data version 36 work