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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

By : Joel Lawhead
4.1 (8)
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Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

4.1 (8)
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 (12 chapters)
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11
Index

Creating histograms


A histogram shows the statistical frequency of data distribution within a data set. In the case of remote sensing, the data set 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 3-digit tuple with (0,0,0) being black, and (255,255,255) being white. We can graph the histogram of an image with the frequency of each value along the y-axis and the range of 255 possible pixel values along the x-axis.

Remember in Chapter 1, Creating the Simplest Possible Python GIS, when we used the Turtle graphics engine included with Python to create a simple GIS? Well we can also use it to easily graph histograms. Histograms are usually a one-off product that makes a quick script, like this example, great. Also histograms are typically displayed as a bar graph with the width of the bars representing the size of grouped data bins...

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