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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

Visualizing DEM data with Three.js


You have a great 3D Digital Elevation Model (DEM) that you may want to view on a web page, so your choices are limited only to your imagination and programming skills. In this little example based on the great work of Bjorn Sandvik, we will explore the methods needed to manipulate a DEM to load a Three.js HTML-based web page.

Tip

A great plugin that I would highly recommend for QGIS is the qgis2threejs plugin, written by Minoru Akagi. The Python plugin code is available on GitHub at https://github.com/minorua/Qgis2threejs where you can find a nice gdal2threejs.py converter.

The resulting 3D DEM mesh can be viewed in your browser:

Getting ready

We need Jinja2 as our template engine (installed in the first section of this chapter) to create our HTML. The remaining requirements include JavaScript and our 3D DEM data. Our DEM data is from Chapter 7, Raster Analysis, and is located in the /ch07/geodata/dem_3857.dem folder, so if you have not already downloaded all...

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