In this chapter, we looked at a number of important libraries for developing geo-spatial applications using Python. We learned that:
- GDAL is a C++ library for reading (and sometimes writing) raster-based geo-spatial data.
- OGR is a C++ library for reading (and sometimes writing) vector-based geo-spatial data.
- GDAL and OGR include Python bindings that are easy to use, and support a large number of data formats.
- The PROJ.4 library and its Pythonic
pyprojwrapper allow you to convert between geographic coordinates (points on the Earth's surface) and cartographic coordinates (x,y coordinates on a two-dimensional plane) using any desired map projection and ellipsoid. - The
pyprojGeod class allows you to perform various geodetic calculations based on points on the Earth's surface, a given distance, and a given angle (azimuth). - A geo-spatial data manipulation library called the Java Topology Suite was originally developed for Java. This was then rewritten in C++ under the name GEOS, and there is now a Python interface to GEOS called Shapely.
- Shapely makes it easy to represent geo-spatial data in the form of Points, LineStrings, LinearRings, Polygons, MultiPoints, MultiLineStrings, MultiPolygons, and GeometryCollections.
- As well as representing geo-spatial data, these classes allow you to perform a variety of geo-spatial calculations.
- Mapnik is a tool for producing good-looking maps based on geo-spatial data.
- Mapnik can use an XML stylesheet to control which elements appear on the map, and how they are formatted. Styles can also be created by hand if you prefer.
- Each Mapnik style has a list of Rules that are used to identify features to draw onto the map.
- Each Mapnik Rule has a list of Symbolizers that control how the selected features are drawn.
While these tools are very powerful, you can't do anything with them unless you have some geo-spatial data to work with. Unless you are lucky enough to have access to your own source of data, or are willing to pay large sums to purchase data commercially, your only choice is to make use of the geo-spatial data that is freely-available on the Internet. These freely-available sources of geo-spatial data are the topic of the next chapter.
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