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


The PIL was originally developed for remote sensing, but has evolved as a general image editing library for Python. Like NumPy, it is written in C for speed, but is designed specifically for Python. In addition to image creation and processing, it also has a useful raster drawing module. PIL is also available via PyPI; however, in Python 3, you may want to use the Pillow module which is an upgraded version of PIL. As you'll see in the example below, we use a Python try statement to import PIL using two possible variations depending on how you installed it.

In this example, we'll combine PyShp and PIL to rasterize the hancock shapefile from previous examples and save it as an image. We'll use a world to pixel coordinate transformation similar to our SimpleGIS from Chapter 1, Learning Geospatial Analysis with Python. We'll create an image to use as a canvas in PIL, and then we'll use the PIL ImageDraw module to render the polygon. Finally, we'll save it as a PNG image, as you can see in...