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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

To get the most out of this book

This book assumes you have basic knowledge of the Python programming language. You will require the Anaconda 2.4.0 or higher with Python (3.10 or higher); a minimum hardware requirement of a 300-MHz processor, 128 MB of RAM, and 1.5 GB of available hard disk; and a Windows, Linux, or macOS X operating system.

Software/hardware covered in the book

Operating system requirements

Anaconda 2.4.0 or higher

Windows – 64-bit x86; macOS – 64-bit x86 and M1; or Linux – 64-bit x86, 64-bit aarch64 (AWS Graviton2), 64-bit Power8/Power9, or s390x (Linux on IBM Z and LinuxONE)

Python 3.10 or higher

8 GB of RAM

ECMAScript 11

6 GB of disk space or more

Follow the instructions for the latest Anaconda installation. All scripts in this book assume you will run the code from the command line within an Anaconda Python environment. If you try to use Jupyter Notebooks, you may get unpredictable results.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

The GitHub repository contains folders for each chapter that include all code in the book, as well as color images and geospatial data files needed for the examples.

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