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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

Product type Paperback

Published in Sep 2019

Publisher

ISBN-13 9781789959277

Length 456 pages

Edition 3rd Edition

Languages

Python

Tools

QGIS

Concepts

Data Analysis

Author (1):

Joel Lawhead

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: The History and the Present of the Industry

2. Learning about Geospatial Analysis with Python FREE CHAPTER

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

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Further reading

Python provides a rich set of libraries for visualizing data. One of the most prominent is Matplotlib, which can produce numerous types of charts and maps and save them to PDF. Packt has several books on Matplotlib, including the Matplotlib 30 Cookbook: https://www.packtpub.com/big-data-and-business-intelligence/matplotlib-30-cookbook.

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Authors (1)

Joel Lawhead

Joel Lawhead

Joel Lawhead is a PMI-certified Project Management Professional (PMP), a certified GIS Professional (GISP), and vice president of NVision Solutions, Inc., an award-winning firm specializing in geospatial technology integration and sensor engineering for NASA, FEMA, NOAA, the US Navy, and many other commercial and non-profit organizations. Joel began using Python in 1997 and started combining it with geospatial software development in 2000. He has authored multiple editions of Learning Geospatial Analysis with Python and QGIS Python Programming Cookbook, both from Packt. He is also the developer of the open source Python Shapefile Library (PyShp) and maintains a geospatial technical blog.

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