Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python

4 (1)
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. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. 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. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python
About the Author
About the Reviewers


GDAL is the dominant geospatial library. Its raster capability is so significant that it is a part of virtually every geospatial toolkit in any language and Python is no exception. To see the basics of how GDAL works in Python, download the following sample raster satellite image as a ZIP file and unzip it:

Let's open this image and see how many bands it has and how many pixels along each axis:

>>> from osgeo import gdal
>>> raster = gdal.Open("SatImage.tif")
>>> raster.RasterCount
>>> raster.RasterXSize
>>> raster.RasterYSize

So we see this image has three bands, 2,592 columns of pixels, and 2,693 rows of pixels, as shown in OpenEV:

GDAL is an extremely fast geospatial raster reader and writer within Python. It can also reproject images quite well plus a few other tricks. However, the true value of GDAL comes from its interaction with the next Python module that we'll examine...