Book Image

Learning Geospatial Analysis with Python - Fourth Edition

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python - Fourth Edition

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.
Table of Contents (18 chapters)
1
Part 1:The History and the Present of the Industry
5
Part 2:Geospatial Analysis Concepts
11
Part 3:Practical Geospatial Processing Techniques

Creating image histograms

A histogram shows the statistical frequency of data distribution within 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 that are used to store image information on computers.

In an RGB image, color is represented as a three-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 x axis and the range of 256 possible pixel values along the x axis.

Remember in Chapter 1, Learning about Geospatial Analysis with Python, in the Creating the simplest possible Python GIS section, 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. Also, histograms are typically displayed as a bar graph...