This is a really useful dataset, especially when combined with tools like mapbox tile mill[0] and qgis[1]. Hours of fun!

[0] https://www.mapbox.com/tilemill/

[1] http://www.qgis.org/

Works great with d3.js too. Couldn't've made this map of the early days of the Ukraine crisis[0] without Natural Earth.

Also helpful to that end: the Global Administrative Areas[1] database.

Finally, it would've been impossible to even start getting my head around how to go about these things if it wasn't for this amazing tutorial by Mike Bostock[2].

[0] http://www.the-american-interest.com/blog/2014/03/03/the-noo...

[1] http://www.gadm.org

[2] http://bost.ocks.org/mike/map/

I used it for exactly this when playing around with the data I exported from my Google location history. As a relative newcomer to GIS the process was pretty simple.

http://i.imgur.com/fXMc7nc.png

Aaaargh, holy distortion. Try a projection like OSGB36/EPSG:27700 for a less weird image. ;)

Ah thanks, that looks much better!

We're using some of the vector data from there for printablegeography.com and so far have been impressed with the quality of the data when compared to other free outline datasets out there.

There's a Wikipedia article with a little more info about this dataset including a reference to the Null Island: https://en.wikipedia.org/wiki/Natural_Earth

Earlier this week I wanted to write a function that takes geographic coordinates and returns whether that coordinate is on land or in water. I might be able to use the raster data here to do that.

Depending on the resolution you need, the OpenStreetMap derived world coastline may work better:

http://openstreetmapdata.com/data/land-polygons

You could also use the World Vector Shorelines (http://shoreline.noaa.gov/data/datasheets/wvs.html) or if you need lakes, the GSHHG (http://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html) from NOAA.

I recently implemented similar functionality in Python, I haven't open sourced it (yet) but I can share some code if you want.