Pretty much any language commonly used for data analysis (R, SAS, Python) can calculate the distance between two geographic coordinates with relative ease. But always having to pull your data out of your data warehouse any time you want to do some basic geographic analysis can be frustrating - sometimes it's nice to keep simple queries all in one system. If you've got a spatially enabled version of Postgres or SQL Server, you're in business. But if not, you'll need to roll your own SQL solution.
Because the earth is a sphere, the quickest route between two points is a "Great Circle," which may appear curved on flat maps...
In today's post, we're going to write our own code in vanilla SQL to calculate the distance between two latitude and longitude coordinates.
If you want to take a bunch of GIS data and rasterize it as a tiled image map for public consumption, the folks at ESRI would be happy to sell you an expensive solution. Of course, as with oh-so-many projects, you can accomplish the same thing for free with open-source software. In this case, we'll use Python and a library called Mapnik to render beautiful map layers, then display them on Google Maps, just like this demo rendering of my home county!
Ready to get started? Dust off your Python skills, and let's go!
Looking for a free geocoding solution and frustrated by the lack of options?
Though it’s poorly publicized (and documented), the US Census Bureau maintains a RESTful API for real-time and batch geocoding that’s free, fast, and accurate. It doesn’t even require an API key. Essentially, it’s everything you’d get out of a roll-your-own PostGIS Census/TIGER solution, without the hassle of having to set it up.
The only limitation is that the API mainly provides geocoding for residential addresses. Because the Census Bureau’s primary task is, well, conducting the national census, they’re a lot more interested in where people live than where they work. So, if you need to map business addresses, you may have to go elsewhere. But if you’re mapping customers (or students, or volunteers, or mailing list recipients, or whatever), the service is pretty on-point. In my work, I’ve seen about a 90% match rate for customer addresses when submitted to Census. Not too shabby.