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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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Author (1):
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 Diener Diener
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Diener
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Toc

Table of Contents (20) Chapters Close

Python Geospatial Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
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 Other Geospatial Python Libraries
Mapping Icon Libraries
Index

Converting an ESRI ASCII DEM to an image height map


To end this chapter with a bang, here is the most complicated conversion we have seen so far and the most fun as well. Input is an elevation dataset that's stored in ASCII format, more specifically, Arc/Info ASCII Grid, for short with the AAIGrid with the (.asc) file ending. Our output is a heightmap image (http://en.wikipedia.org/wiki/Heightmap). A heightmap image is an image that stores height elevation as a pixel value. A heightmap is also simply known as a digital elevation model (DEM). The benefit of using an image to store elevation data is that it is web compatible and we can use this in a 3D visualization with threejs, for example, as shown in Chapter 10, Visualizing Your Analysis.

We need to be careful with regard to the output image format because simply storing an 8-bit image limits us to only storing 0 to 255 height values, which is typically not enough. The output image should store a minimum of 16-bits, giving us a range from...

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