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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 an elevation profile


Creating an elevation profile is very helpful when trying to visualize a 3D terrain cross-section or simply to see the elevation gain of a bike tour. In this example, we will define our own LineString geometry and extract the elevation values from the DEMs that are located every 20 m along our line. The analysis will generate a new CSV file that we can open in Libre Office Calc or Microsoft Excel to visualize the new data as a line chart.

The 2D view of our line plotted on top of the elevation model as seen inside QGIS looks like this:

Getting ready

This recipe calls for GDAL and Shapely. Make sure that you have them installed and are running them inside your python virtual environment that you set up earlier. To visualize your final CSV file, you must also install Libre Office Calc or some other charting software. The code to execute this is located at /ch07/code/ch07-02_elev_profile.py.

How to do it...

Running the script directly from your command line will generate...

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