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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

PIL


The PIL was originally developed for remote sensing but has evolved as a general image editing library for Python. Like NumPy, it is written in C for speed, but is designed specifically for Python. In addition to image creation and processing, it also has a useful raster drawing module. PIL is also available via PyPI.

In this example, we'll combine PyShp and PIL to rasterize the hancock shapefile from previous examples and save it as an image. We'll use a "world to pixel" coordinate transformation similar to our SimpleGIS from Chapter 1, Learning Geospatial Analysis with Python. We'll create an image to use as a canvas in PIL and then use the PIL ImageDraw module to render the polygon. Finally we'll save it as a PNG image:

>>> import shapefile
>>> import Image, ImageDraw
>>> r = shapefile.Reader("hancock.shp")
>>> xdist = r.bbox[2] - r.bbox[0]
>>> ydist = r.bbox[3] - r.bbox[1]
>>> iwidth = 400
>>> iheight = 600
>>&gt...