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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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Author (1):
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 Diener Diener
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Diener
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Toc

Table of Contents (20) Chapters Close

Python Geospatial Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
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 Other Geospatial Python Libraries
Mapping Icon Libraries
Index

Calculating 3D ground distance and total elevation gain


We've finished finding points on lines and returning points on a line, so now, it is time to calculate the true ground 3D distance that we actually ran or biked along a real 3D road. It is also possible to calculate the elevation profile and we will see this in the Chapter 7, Raster Analysis.

Calculating the ground distance sounds easy, but 3D calculations are more complicated to calculate than 2D. Our 3D LineString has a z-coordinate for each vertex that makes up our LineString. Therefore, we need to calculate the 3D distance between each set of coordinates, —that is, from vertex to vertex in our input LineString.

The mathematics to calculate the distance between two 3D Cartesian coordinates is relatively simple and uses the 3D form of the Pythagoras formula:

3d_distance = square root √ ( (x2 – x1) 2 + (y2 – y1) 2 + (z2 -z1)2)

Here it is in Python:

import math
3d_dist = math.sqrt((x2 – x1)**2 + (y2 – y1)**2 + (z2 – z1)**2 )

Getting ready...

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