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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


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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