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


Elevation data can often provide a complete dataset for analysis and derivative products without any other data. In this chapter, you learned to read and write ASCII Grids using only NumPy. You also learned to create shaded reliefs, slope grids, and aspect grids. We created elevation contours using a little-known feature of the GDAL library available for Python. We transformed LIDAR data into an easy-to-manipulate ASCII Grid. We experimented with different ways to visualize the LIDAR data with the PIL. Finally, we created a 3D surface or TIN by turning a LIDAR point cloud into a 3D shapefile of polygons.

In the next chapter, we'll combine the building blocks from the previous three chapters to perform some advanced modeling and actually create some information products.