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Python Geospatial Development

You're reading from   Python Geospatial Development Develop sophisticated mapping applications from scratch using Python 3 tools for geospatial development

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Product type Paperback
Published in May 2016
Publisher
ISBN-13 9781785288937
Length 446 pages
Edition 3rd Edition
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Author (1):
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Erik Westra Erik Westra
Author Profile Icon Erik Westra
Erik Westra
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Table of Contents (15) Chapters Close

Preface 1. Geospatial Development Using Python FREE CHAPTER 2. GIS 3. Python Libraries for Geospatial Development 4. Sources of Geospatial Data 5. Working with Geospatial Data in Python 6. Spatial Databases 7. Using Python and Mapnik to Generate Maps 8. Working with Spatial Data 9. Improving the DISTAL Application 10. Tools for Web-based Geospatial Development 11. Putting It All Together – a Complete Mapping System 12. ShapeEditor – Importing and Exporting Shapefiles 13. ShapeEditor – Selecting and Editing Features 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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