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 Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Unleash the power of Python 3 with practical techniques for learning GIS and remote sensing

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837639175
Length 432 pages
Edition 4th 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 (18) Chapters Close

Preface 1. Part 1:The History and the Present of the Industry
2. Chapter 1: Learning about Geospatial Analysis with Python FREE CHAPTER 3. Chapter 2: Learning about Geospatial Data 4. Chapter 3: The Geospatial Technology Landscape 5. Part 2:Geospatial Analysis Concepts
6. Chapter 4: Geospatial Python Toolbox 7. Chapter 5: Python and Geospatial Algorithms 8. Chapter 6: Creating and Editing GIS Data 9. Chapter 7: Python and Remote Sensing 10. Chapter 8: Python and Elevation Data 11. Part 3:Practical Geospatial Processing Techniques
12. Chapter 9: Advanced Geospatial Modeling 13. Chapter 10: Working with Real-Time Data 14. Chapter 11: Putting It All Together 15. Assessments 16. Index 17. Other Books You May Enjoy

Summary

Congratulations! You created a complete report for a running route, similar to reports created by some of the leading commercial exercise apps! You combined terrain data, street mapping data, weather data, tracking data, geolocated photos, and charts into a single, comprehensive report. This type of synthesis is a perfect example of the extent to which you can turn raw geospatial data sources into truly useful information that can help shape future decisions. This kind of information product is the promise of geospatial analysis and the goal of this book was to teach you how to create this.

In this book, you pulled together the most essential tools and skills needed to be a modern geospatial analyst. Whether you use geospatial data occasionally or use it all the time, you will be better equipped to make the most of geospatial analysis. This book focused on using open source tools almost entirely found within the PyPI directory, for ease of installation and integration. However...

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