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

Data access

As described in Chapter 2, Geospatial Data, geospatial datasets are typically large, complex, and varied. This challenge makes libraries, which efficiently read and in some cases, write this data essential to geospatial analysis. These libraries are also the most important. Without access to data, geospatial analysis doesn't begin. Furthermore, accuracy and precision are key factors in geospatial analysis. An image library that resamples data without permission, or a computational geometry library that rounds a coordinate even a couple of decimal places, can adversely affect the quality of analysis. Also, these libraries must manage memory efficiently. A complex geospatial process can last for hours or even days. If a data access library has a memory fault, it can delay an entire project or even an entire workflow involving dozens of people dependent on the output of that analysis.


The Geospatial Data Abstraction Library (GDAL) does the most heavy lifting task in the geospatial...