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Python Geospatial Analysis Cookbook

You're reading from   Python Geospatial Analysis Cookbook Over 60 recipes to work with topology, overlays, indoor routing, and web application analysis with Python

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781783555079
Length 310 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (15) Chapters Close

Preface 1. Setting Up Your Geospatial Python Environment FREE CHAPTER 2. Working with Projections 3. Moving Spatial Data from One Format to Another 4. Working with PostGIS 5. Vector Analysis 6. Overlay Analysis 7. Raster Analysis 8. Network Routing Analysis 9. Topology Checking and Data Validation 10. Visualizing Your Analysis 11. Web Analysis with GeoDjango A. Other Geospatial Python Libraries
B. Mapping Icon Libraries
Index

Splitting LineStrings at intersections using ST_Node

Working with road data is usually a tricky business because the validity of the data and data structure plays a very important role. If you want to do anything useful with your road data, such as building a routing network, you will need to prepare the data first. The first task is usually to segmentize your lines, which means splitting all lines at intersections where LineStrings cross each other, creating a base network road dataset.

Note

Be aware that this recipe will split all lines on all intersections regardless of whether, for example, there is a road-bridge overpass where no intersection should be created.

Getting ready

Before we get into the details of how to do this, we will use a small section of the OpenStreetMap (OSM) road data for our example. The OSM data is available in your /ch04/geodata/folder called vancouver-osm-data.osm. This data was simply downloaded from the www.openstreetmap.org home page using the Export button...

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