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

Chapter 10. Putting It All Together

In this book we have touched all the important aspects of geospatial analysis. And we've used a variety of different techniques in Python to analyze different types of geospatial data. In this final chapter, we will draw on nearly all of the topics we have covered to produce one real-world product that has become very popular: A GPS route analysis report.

These reports are common to dozens of mobile-app services, GPS watches, in-car navigation systems, and other GPS-based tools. A GPS typically records location, time, and elevation. From these values we can derive a vast amount of ancillary information about what happened along the route on which that data was recorded. Fitness apps including RunKeeper.com, MapMyRun.com, Endomondo.com, and Nike Plus all use similar reports to present GPS-tracked exercise data from running, hiking, biking, or walking to users.

We will create one of these reports using Python. This program is nearly 500 lines of code, our...