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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

Raster analysis works similar to vector analysis but the spatial relation is determined by the position of the raster cell. Most of our raster data is collected through diverse remote sensing techniques. In this chapter, the goals are quite simple and focused on working with and around a digital elevation model (DEM). The DEM we are using is from Whistler, BC, Canada, home to the 2010 Winter Olympics. Our DEM is in the form of the USGS ASCII CDED (.dem) format. The DEM is our source data that is used to derive several new raster datasets. As with other chapters, we will leverage Python as our glue to run scripts to enable a processing pipeline for raster data. The visualization of our data will play out with matplotlib along with the QGIS desktop GIS.

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Programming languages
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Python Geospatial Analysis Cookbook
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