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


This chapter covered the critical components of GIS analysis. You examined the challenges of measuring on the curved surface of the Earth using different approaches. You looked at the basics of coordinate conversion and full reprojection using OGR, the utm module with PyShp, and Fiona which simplifies OGR. We edited shapefiles and performed spatial and attribute selections. We created thematic maps from scratch using only Python. We imported data from spreadsheets. We parsed GPS data from NMEA streams. And we used geocoding to convert street addresses to locations and back.

As a geospatial analyst, you may be familiar with both GIS and remote sensing, but most analysts specialize in one field or the other. That is why, this book approaches the fields in separate chapters in order to focus on their differences. In Chapter 6, Python and Remote Sensing, we'll tackle remote sensing. In GIS, we have been able to explore the field using pure Python modules. In remote sensing, we'll become...