Book Image

Python Geospatial Development

By : Erik Westra
Book Image

Python Geospatial Development

By: Erik Westra

Overview of this book

<p>Open Source GIS (Geographic Information System) is a growing area with the explosion of applications such as Google Maps, Google Earth, and GPS. The GIS market is growing rapidly and as a Python developer you will find yourself either wanting grounding in GIS or needing to get up to speed to do your job. In today's location-aware world, all commercial Python developers can benefit from an understanding of GIS development gained using this book.</p> <p>Working with geo-spatial data can get complicated because you are dealing with mathematical models of the Earth's surface. Since Python is a powerful programming language with high-level toolkits, it is well suited to GIS development. will familiarize you with the Python tools required for geo-spatial development such as Mapnik, which is used for mapping in Python. It introduces GIS at the basic level with a clear, detailed walkthrough of the key GIS concepts such as location, distance, units, projections, datums, and GIS data formats. We then examine a number of Python libraries and combine these with geo-spatial data to accomplish a variety of tasks. The book provides an in-depth look at the concept of storing spatial data in a database and how you can use spatial databases as tools to solve a variety of geo-spatial problems. <br /><br />It goes into the details of generating maps using the Mapnik map-rendering toolkit, and helps you to build a sophisticated web-based geo-spatial map-editing application using GeoDjango, Mapnik, and PostGIS. By the end of the book, you will be able to integrate spatial features into your applications and build a complete mapping application from scratch.</p>
Table of Contents (19 chapters)
Python Geospatial Development
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Working with Shapely geometries


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

Task: Identify parks in or near urban areas

The U.S. 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 website provides lists of placenames and other details. Using these two datasources, we will identify any parks within or close to an urban area.

Tip

Because of the volume of data we are potentially dealing with, we will limit our search to California. Feel free to download the larger data sets if you want, though you will have to optimize the code or your program will take a very long time to check all the CBSA polygon/placename combinations.

  1. Let's start by downloading the necessary data. Go to the TIGER website at http://census.gov/geo/www/tiger...