Book Image

Learning Geospatial Analysis with Python - Fourth Edition

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python - Fourth Edition

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.
Table of Contents (18 chapters)
1
Part 1:The History and the Present of the Industry
5
Part 2:Geospatial Analysis Concepts
11
Part 3:Practical Geospatial Processing Techniques

Putting It All Together

Throughout the book, we’ve touched upon 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 a 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, MapMyRun, Strava, and Nike Plus, all use similar reports to present GPS-tracked exercise data from running, hiking, biking, and walking.

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