Best for local, regional, or high-accuracy mapping. Files are large and may slow down performance on weaker hardware.
The traditional format consisting of a cluster of files ( .shp , .dbf , .shx ). Best for older software versions. KMZ: Ideal for quick visualization in Google Earth.
Delimits hundreds of thousands of individual internal borders ranging from country-level down to local municipal layers.
This article is a complete guide to and ensuring it functions correctly in your projects. download gadm data version 36 work
library(sf) # Load a country-specific RDS file gadm_data <- readRDS("gadm36_USA_1_sf.rds") # Plot the data to verify plot(st_geometry(gadm_data)) Use code with caution. Using Python
Once you have downloaded the GADM data version 36, you can use it in a variety of applications, such as:
You have two main download options:
Complete Guide: How to Download and Work with GADM Data Version 3.6
While GADM frequently updates its database to newer versions, version 3.6 datasets are still accessible through official archives and historical download links. Step-by-Step Download Instructions
# Convert Shapefile to GMT ogr2ogr -f OGR_GMT gadm36_USA_0.gmt gadm36_USA_0.shp Best for local, regional, or high-accuracy mapping
Open your web browser and go to the official GADM website ( gadm.org ). Click on the tab.
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")
: Traditional format compatible with almost all GIS software. KMZ (.kmz) : Ideal for viewing in Google Earth. : Specialized format for use with R programming. 2. Administrative Levels GADM data is structured into hierarchical levels: : National boundaries (Country level). : Primary subdivisions (e.g., States, Provinces). : Secondary subdivisions (e.g., Districts, Counties). Level 3 & Higher Best for older software versions