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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

Python markup and tag-based parsers


Tag-based data, particularly different XML dialects, have become a very popular way to distribute geospatial data. Formats which are both machine and human readable are generally easy to work with though they sacrifice storage efficiency for usability. These formats can become unmanageable for very large data sets but work very well in most cases.

While most formats are some form of XML (such as KML or GML), there is a notable exception. The Well-Known Text (WKT) format is fairly common but uses external markers and square brackets ("[]") to surround data instead of tags in angled brackets around data like XML does.

Python has standard library support for XML as well as some excellent third-party libraries available. Proper XML formats all follow the same structure so you can use a generic XML library to read it. Because XML is text-based, it is often easy to write it as a string instead of using an XML library. The vast majority of applications which output...

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