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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

Splitting polygons with lines


Typically, in GIS, we work with data that influences other data in some form due to their inherit spatial relationship. This means that we need to work with one dataset to edit, update, and even delete another dataset. A typical example of this is an administrative boundary, which is a polygon that you cannot see on a physical surface but that influences feature information it crosses such as a lake. If we have a lake polygon and an administrative boundary, we might want to know how many square meters of lake belongs to each administrative boundary.

Another example could be a forest polygon that contains one species of trees that crosses a river. We might want to know the area on either side of the river. In the first scenario, we need to transform our administrative boundaries into LineStrings and then perform the cut.

To see what this looks like, take a look at this spoiler on how the results will look up front since we all like a good visual.

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