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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

Working with projections, in my opinion, is not too exciting but they're very important, and your ability to deal with them in any application is crucial.

The goal of this chapter is to provide some common predata screening or transformation steps to get your data in shape or, better yet, in position for geospatial analysis. We cannot always perform analysis on multiple datasets that are in different coordinate systems without the risk of achieving inconsistent results, such as data positional inaccuracies. Therefore, it is a best practice to work on data in the same coordinate system, such as EPSG:4326, when working on a global scale, or use a local coordinate system for your region that will provide you the most accurate results.

European Petroleum Survey Group or EPSG codes have decided to give all coordinate systems a number code to simplify finding and sharing projection information. Coordinate systems are described by their definitions, which are stored in text files...

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