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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

Generating slope and aspect images from your DEM


Slope maps are very useful, for example, to help biologists identify habitat zones. Certain species only live in areas that are very steep—mountain goats, for instance. The slope raster can then quick identify potential habitat areas. To visualize this, we use QGIS to display our slope map, which will look similar to the following image. The area in white indicates the steeper area and the darker the color, the flatter the terrain:

Our aspect map displays the direction that the surface faces towards—such as north, east, south, and west—and this is expressed in degrees. In the screenshot, the orange area represents warm south-facing areas. The north-facing sides are cooler and are indicated in different hues of blues from our color spectrum. To achieve the colors, the QGIS singleband pseudocolor was classified into five continuous classes as shown in the following screenshot:

Getting ready

Ensure that your /ch07/geodata folder is downloaded and...

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