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

Creating histograms

A histogram shows the statistical frequency of the data distribution in a dataset. In the case of remote sensing, the dataset is an image; the data distribution is the frequency of pixels in the range of 0 to 255, which is the range of 8-byte numbers used to store image information on computers. In an RGB image, color is represented as a three-digit tuple with (0,0,0, 0, 0) as black and (255,255,255) as white. We can graph the histogram of an image with the frequency of each value along the y axis and the range of 256 possible pixel values along the x axis.

In Creating the simplest possible Python GIS section of Chapter 1, Learning Geospatial Analysis with Python, we used the turtle graphics engine included with Python to create a simple GIS. Well, we can use it to easily graph histograms as well. Histograms are usually a one-off product that makes a quick script great. Additionally, histograms are typically displayed as a bar graph with the width of the bars representing...