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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783281138
Length 364 pages
Edition 1st Edition
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (12) Chapters Close

Preface 1. Learning Geospatial Analysis with Python 2. Geospatial Data FREE CHAPTER 3. The Geospatial Technology Landscape 4. Geospatial Python Toolbox 5. Python and Geographic Information Systems 6. Python and Remote Sensing 7. Python and Elevation Data 8. Advanced Geospatial Python Modelling 9. Real-Time Data 10. Putting It All Together Index

Dot density calculations


A dot density map shows concentrations of subjects within a given area. If an area is divided up into poylgons containing statistical information, you can model that information using randomly distributed dots within that area using a fixed ratio across the data set. This type of map is commonly used for population density maps. The cat map in Chapter 1, Learning Geospatial Analysis with Python, is a dot density map. Let's create a dot density map from scratch using pure Python. For this example, we'll use a US Census Bureau Tract shapefile along the US Gulf Coast which contains population data. We'll also use the point in polygon algorithm to ensure the randomly distributed points are with the proper census tract. Finally, we'll use the PNGCanvas module to write out our image.

The PNGCanvas module is excellent and fast. However, it doesn't have the ability to fill in polygons beyond simple rectangles. You can implement a fill algorithm but it is very slow in pure...

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