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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7

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Product type Paperback
Published in Sep 2019
Publisher
ISBN-13 9781789959277
Length 456 pages
Edition 3rd 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 (15) Chapters Close

Preface 1. Section 1: The History and the Present of the Industry FREE CHAPTER
2. Learning about Geospatial Analysis with Python 3. Learning Geospatial Data 4. The Geospatial Technology Landscape 5. Section 2: Geospatial Analysis Concepts
6. Geospatial Python Toolbox 7. Python and Geographic Information Systems 8. Python and Remote Sensing 9. Python and Elevation Data 10. Section 3: Practical Geospatial Processing Techniques
11. Advanced Geospatial Python Modeling 12. Real-Time Data 13. Putting It All Together 14. Other Books You May Enjoy

Understanding desktop tools (including visualization)

Geospatial analysis requires the ability to visualize output. This fact makes tools that can visualize data absolutely critical to the field. There are two categories of geospatial visualization tools.

The first is geospatial viewers and the second is geospatial analysis software. The first category – geospatial viewers—allows you to access, query, and visualize data, but not to edit it in any way. The second category allows you to perform those tasks, and edit the data, too. The main advantage of viewers is that they are typically lightweight pieces of software that launch and load data quickly.

Geospatial analysis software requires far more resources to be able to edit complex geospatial data, so it loads more slowly and often renders data more slowly, in order to provide dynamic editing functionality.

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