Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python

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. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. 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. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python Second Edition
About the Author
About the Reviewers

Routing along streets

Routing along streets uses a connected network of lines called a graph. The lines in the graph can have impedance values that discourage a routing algorithm from including them in a route. Examples of impedance values often include traffic volume, speed limit, or even distance. A key requirement for a routing graph is that all of the lines, known as edges, must be connected. Road datasets created for mapping will often have lines whose nodes do not intersect. In this example, we'll calculate the shortest route through a graph by distance. We'll use a start and end point which are not nodes in the graph, which means we'll have to first find the graph nodes closest to our start and destination.

To calculate the shortest route, we'll use a powerful pure Python graph library called NetworkX (NX). NX is a general network graphing library which can create, manipulate, and analyze complex networks including geospatial networks. If pip does not install NetworkX on your system...