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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

By : Diener
4.4 (5)
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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

4.4 (5)
By: Diener

Overview of this book

Geospatial development links your data to places on the Earth’s surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems. This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. This is followed with network routing analysis focused on constructing indoor routes within buildings, over different levels. Finally, we put several recipes together in a GeoDjango web application that demonstrates a working indoor routing spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements.
Table of Contents (15 chapters)
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12
A. Other Geospatial Python Libraries
13
B. Mapping Icon Libraries
14
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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