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


In this chapter, you learned the hierarchy of geospatial analysis software. You learned a framework for approaching the hundreds of existing geospatial software packages and libraries by categorizing them into one or more major functions including data access, computational geometry, raster processing, visualization, and metadata management.

We also examined the commonly used foundation libraries including GDAL, OGR, PROJ.4, and GEOS found again and again in geospatial software. You can approach any new piece of geospatial software, trace it back to these core libraries and then ask, What is the value added? to better understand the package. If the software isn't using one of these libraries, you need to ask, Why are these developers going against the grain? to understand what that system brings to the table.

Python was only mentioned a few times in this chapter to avoid any distraction in understanding the geospatial software landscape. But, as we will see, Python is interwoven into...