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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

Creating a hillshade raster from your DEM with ogr


Our DEM can be the basis for many types of derived raster datasets. One of these derivatives is the so called hillshade raster dataset. A hillshade raster represents a 2D view of 3D elevation data, assigning gray raster shades and giving them a 3D effect by enabling you to see the highs and lows of your terrain. The hillshade is a pure visualization helper to create a nice looking map and show relief on a 2D map.

The pure Python solution to creating a hillshade is written by Roger Veciana i Rovira and you can find it at http://geoexamples.blogspot.co.at/2014/03/shaded-relief-images-using-gdal-python.html. There is also a nice solution by Joel Lawhead in Chapter 7, Python and Elevation Data of Learning Geospatial Analysis with Python. For those of you looking for a detailed description of the hillshade from ESRI, check this page out at http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=How%20Hillshade%20works. The gdaldem hillshade...

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