Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Least cost path analysis


Calculating driving directions is the most commonly used geospatial function in the world. Typically these algorithms calculate the shortest path between point A and B or they may take into account the speed limit of the road or even current traffic conditions to choose a route by drive time.

But what if your job is to build a new road? Or what if you are in charge of deciding where to run power transmission lines or water lines across a remote area? In a terrain-based setting, the shortest path might cross a difficult mountain or run through a lake. In this case we need to account for obstacles and avoid them if possible. However, if avoiding a minor obstacle takes us too far out of our way, the cost of implementing that route may be more expensive than just going over a mountain.

This type of advanced analysis is called Least Cost Path analysis. We search an area for the route that is the best compromise of distance versus the cost of following the route. The algorithm...