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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

Introduction

A spatial database is nothing but a standard database that can store geometry and execute spatial queries in their simplest forms. We will explore how to run spatial analysis queries, handle connections, and more, all from our Python code. Your ability to answer spatial questions such as "I want to locate all the hotels that are within 2 km of a golf course and less than 5 km from a park" is where PostGIS comes into play. This chaining of requests into a model is where the powers of spatial analysis shine.

We will work with the most popular and powerful open source spatial database called PostgreSQL, along with the PostGIS extension, including over 150 functions. Basically, we'll get a full-blown GIS with complex spatial analysis functions for both vectors and rasters, spatial data types, and diverse methods to move spatial data around.

If you are looking for more information on PostGIS and a good read, please check out PostGIS Cookbook by Paolo Corti (available...

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