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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

Python and Geospatial Algorithms

This chapter will focus on applying Python to algorithms that are typically performed by a geographic information system (GIS) such as QGIS or ArcGIS. An algorithm is like a recipe for a computer. Just like a recipe gives you step-by-step instructions on how to cook a dish, an algorithm gives a computer step-by-step instructions to solve a problem or complete a task. These algorithms are the heart and soul of geospatial analysis. We will continue to use as few external dependencies as possible outside of Python itself so that you have tools with maximum reusability in different environments. In this book, we separate GIS analysis and remote sensing from a programming perspective, which means that, in this chapter, we’ll mostly focus on vector data.

As with the other chapters in this book, the items presented here are core functions that serve as building blocks that you can recombine to solve challenges that you will encounter beyond this...

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