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

Chapter 7. Python and Elevation Data

Elevation data is one of the most fascinating types of geospatial data. It represents many different types of data sources and formats. It can display properties of both vector and raster data resulting in unique data products. Elevation data can be used for terrain visualization, land cover classification, hydrology modeling, transportation routing, feature extraction, and many other purposes.

You can't perform all of these options with both raster and vector data but as elevation data is three-dimensional, containing x, y, and z coordinates, you can often get more out of this data than any other type.

In this chapter, you're going to learn to read and write elevation data in both raster and vector formats. We'll also create some derivative products. The topics that we'll cover are as follows:

  • Using ASCII Grid elevation data files

  • Creating shaded relief images

  • Creating elevation contours

  • Gridding LIDAR data

  • Creating a 3D mesh