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

Choropleth maps

Choropleth maps also show concentration, however, they use different shades of color to show concentration. Darker colors have higher concentration and lighter colors have lower concentration. This method is useful if related data spans multiple polygons. For example, in a worldwide population density map by country, many countries have disconnected polygons (for example, Hawaii is an island state of the US). In this example, we'll use the PIL discussed in Chapter 3, The Geospatial Technology Landscape. PIL is not purely Python but is designed specifically for Python. We'll recreate our previous dot density example as a choropleth map. We'll calculate a density ratio based on the number of people (population) per square kilometer and use that value to adjust the color. Dark is more densely populated and lighter is less:

import math
import shapefile
import Image
import ImageDraw

def world2screen(bbox, w, h, x, y):
  """convert geospatial coordinates to pixels"""