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

Clipping images

Very rarely is an analyst interested in an entire satellite scene, which can easily cover hundreds of square miles. Given the size of satellite data, we are highly motivated to reduce the size of an image to our area of interest only. The best way to accomplish this reduction is to clip an image to a boundary that defines our study area. We can use shapefiles (or other vector data) as our boundary definition and basically get rid of all the data outside this boundary. The following image contains our stretched.tif image with a county boundary file layered on the top, visualized in Quantum GIS (QGIS):

In order to clip the image, our next example executes the following steps:

  1. Load the image in an array using gdal_array.

  2. Create a shapefile reader using PyShp.

  3. Rasterize shapefile into a georeferenced image (convert from a vector into raster).

  4. Turn the shapefile image into a binary mask or filter to grab only the image pixels that we want within the shapefile boundary.

  5. Filter the satellite...