Summary
In this chapter, we surveyed the Python-specific tools for geospatial analysis. We looked at an easier way to install third-party modules using Anaconda. We reviewed the libraries needed for networking including urllib, requests, and ftplib. We experimented with bundling, compressing, and decompressing geospatial files with the zipfile and tarfile modules. We parsed and created tag-based data with minidom and ElementTree. We manipulated WKT data strings with shapely. We worked with JSON-based data using JSON and GeoJSON.
Later, we also learned about the most powerful vector library, OGR, and the pure-Python shapefile library PyShp. We learned how to access geospatial data using the Pythonic fiona library. We examined the powerful GDAL raster data library and its connection to Python’s large array math library NumPy. We learned how to create map images with PIL and PNGCanvas as well as GeoPandas. We created PDF maps using PyFPDF. We made a geospatial database using...
