Some time ago I spent at least a day trying to find a good quality, free world political borders and coastlines dataset. Each product I found had some limitation – incomplete coverage or poor resolution. Today by happenstance a client pointed me to quite a good one.
The site hosting the dataset is www.gadm.org. These folks have obviously taken a lot of time and effort to create a top-notch dataset. The compressed world dataset weighs in at around 333mb in its zip for and around 1.5gb uncompressed. Needless to say, the first thing you will probably want to do is load it into postgis. Secondly, you will probably get best results using a simplified version of it at small scales and only enable it to render in QGIS at larger scales. The dataset is probably best used at scales up to 1:150 000 – at greater scales there is an obvious stair stepping effect showing what seems to be the raster origins of the dataset. It would be interesting to see if someone can come up with a smoothing algorithm to remove the step effect. That aside its a great dataset if used within its limitations. I’ve included a few screenshots below so you can get a better feel for it.

GADM - Scale 1:2.4 million - click to enlarge

GADM - Scale 1:144 000 - click to enlarge

GADM - Scale 1:18 000 with stair stepping noticable - click to enlarge
gamesbook
Thanks for sharing this Tim – very useful!
Now… do you know of any equivalent datasets to use as backdrop layers? For example, land cover or digital elevation images?
pcreso
OAM is trying, but has a long way to go. OSM is viable, if you are OK with road/feature backdrop rather than aerial imagery.
pcreso
Hi Tim,
I hadn’t found the GADM dataset before.
How does the coastline compare with the high res GSHHS one?
For non-GMT/netCDF users, there is a shpfile version at
http://www.soest.hawaii.edu/wessel/gshhs/
Brent
Tim Sutton
Hi Brent
See http://www.imagebanana.com/view/ja0y1iaa/id.jpeg for a comparison. Green dataset is GSHHS and outline dataset is GADM. GSHHS is only really usable at scales < 1:7.5million before generalisation becomes really obvious.
Regards
Tim