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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

Shapely


Shapely was mentioned in the WKT section for import and export ability. But its true purpose is a generic geometry library. Shapely is a high-level, pythonic interface to the GEOS library for geometric operations. In fact, Shapely intentionally avoids reading or writing files. It relies completely on data import and export and maintains focus on geometry manipulation.

Let's do a quick Shapely demonstration in which we'll define a single WKT polygon and then import it into Shapely. Then we'll measure the area. Our computational geometry will consist of buffering that polygon by a measure of 5 which will return a new, bigger polygon for which we'll measure the area:

>>> from shapely import wkt, geometry
>>> wktPoly = "POLYGON((0 0,4 0,4 4,0 4,0 0))"
>>> poly = wkt.loads(wktPoly)
>>> poly.area
16.0
>>> buf = poly.buffer(5.0)
>>> buf.area
174.41371226364848

We can then do a difference on the area of the buffer and the original polygon...

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