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Python Geospatial Analysis Cookbook

You're reading from   Python Geospatial Analysis Cookbook Over 60 recipes to work with topology, overlays, indoor routing, and web application analysis with Python

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781783555079
Length 310 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (15) Chapters Close

Preface 1. Setting Up Your Geospatial Python Environment FREE CHAPTER 2. Working with Projections 3. Moving Spatial Data from One Format to Another 4. Working with PostGIS 5. Vector Analysis 6. Overlay Analysis 7. Raster Analysis 8. Network Routing Analysis 9. Topology Checking and Data Validation 10. Visualizing Your Analysis 11. Web Analysis with GeoDjango A. Other Geospatial Python Libraries
B. Mapping Icon Libraries
Index

Executing a PostGIS ST_Buffer analysis query and exporting it to GeoJSON

Let's start by executing our first spatial analysis query from Python against our already running PostgreSQL and PostGIS database. The goal is to generate a 100 m buffer around all schools and export the new buffer polygon to GeoJSON, including the name of a school. The end result will be shown on this map, available (https://github.com/mdiener21/python-geospatial-analysis-cookbook/blob/master/ch04/geodata/out_buff_100m.geojson) on GitHub.

Tip

Quick visualizations of GeoJSON data using GitHub is a fast and simple way to create a web map without coding a single line. Note that the data is then free for everyone else to download if you are using a public and free GitHub account. Private GitHub accounts mean the data, that is, GeoJSON, will also remain private if data privacy or sensitivity is an issue.

Executing a PostGIS ST_Buffer analysis query and exporting it to GeoJSON

Getting ready

To get started, we'll use our data in the PostGIS database. We will begin by accessing our schools...

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