Update: New versions of All GCCs and USCENTCOM available as of 20 Aug 2015, previous versions did not include Kyrgyzstan.
- All Geographic Combatant Commands
- United States Africa Command (USAFRICOM)
- United States Central Command (USCENTCOM)
- United States European Command (USEUCOM)
- United States Northern Command (USNORTHCOM)
- United States Pacific Command (USPACOM)
- United States Southern Command (USSOUTHCOM)
How It's Made
I needed the land areas for the Geographic Combatant Commands (GCCs) for a work project, but the data was not openly available through the traditional means (Google, Data.gov). So I decided to make my own.
I started with the 10m resolution Admin 0 - Countries data provided as part of Natural Earth's Culutral Data Collection. I imported this layer (which comes as a Shapefile) into QGIS and began to segment the countries into these groups.
GCCs are complex regions that are segmented based on both geographic and cultural aspects of the region. Looking at USAFRICOM in particular, it includes pretty much the entire African continent, including six island nations, and exclusing Egypt, which is aligned with USCENTCOM because of cultural considerations. Similarly, Israel, which is at the very heart of USCENTCOM, is actually aligned with USEUCOM, again because of cultural reasons. I began by manually chunking out (i.e. saving to dedicated layers) the country polygons into their respective GCCs, referencing this map on Wikipedia as I went.
There was still a good bit of complexity in the Natural Earth data to deal with after chunking out the countries. First, some countries — namely France, England, and the United States — control territories in multiple GCCs, and the polygons associated with those nations, which include the territories, must be edited to remove the polygons that do not belong. Second, the Natural Earth data uses the Prime Meridian as its central longitude and splits the data at the International Date Line (IDL). To address these issues I used a combination of GDAL and QGIS to: (1) remove the incorrect polygons by spliting the multi-part polygons into singlepart features, removing them, and merging the single-part features back together using the ADMIN field; and (2) shift the features to the correct sides of the IDL using ogr2ogr clipping, SpatiaLite shifting, and merging (see this entry on GIS Stack Exchange).
Finally, I simplified the vector layers using the built-in QGIS tool (set to a tolerance of 0.1), and exported the layers as GeoJSON. So, while these aren't supremely accurate, they get the job done for small-scale maps (zoom level 0-8 for those familiar with Leaflet, OSM, or Google Maps).