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Python Geospatial Development

Python Geospatial Development - Third Edition

By : Westra
4.3 (4)
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Python Geospatial Development

Python Geospatial Development

4.3 (4)
By: Westra

Overview of this book

Geospatial development links your data to locations on the surface of the Earth. Writing geospatial programs involves tasks such as grouping data by location, storing and analyzing large amounts of spatial information, performing complex geospatial calculations, and drawing colorful interactive maps. In order to do this well, you’ll need appropriate tools and techniques, as well as a thorough understanding of geospatial concepts such as map projections, datums, and coordinate systems. This book provides an overview of the major geospatial concepts, data sources, and toolkits. It starts by showing you how to store and access spatial data using Python, how to perform a range of spatial calculations, and how to store spatial data in a database. Further on, the book teaches you how to build your own slippy map interface within a web application, and finishes with the detailed construction of a geospatial data editor using the GeoDjango framework. By the end of this book, you will be able to confidently use Python to write your own geospatial applications ranging from quick, one-off utilities to sophisticated web-based applications using maps and other geospatial data.
Table of Contents (15 chapters)
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14
Index

Performing geospatial calculations


Shapely is a very capable library for performing various calculations on geospatial data. Let's put it through its paces with a complex, real-world problem.

Task – identifying parks in or near urban areas

The US Census Bureau makes available a shapefile containing something called Core Based Statistical Areas (CBSAs), which are polygons defining urban areas with a population of 10,000 or more. At the same time, the GNIS web site provides lists of place names and other details. Using these two data sources, we will identify any parks within or close to an urban area.

Because of the volume of data we are dealing with, we will limit our search to California. It would take a very long time to check all the CBSA polygon/place name combinations for the entire United States; it's possible to optimize the program to do this quickly, but this would make the example too complex for our current purposes.

Let's start by downloading the necessary data. We'll start by downloading...

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