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Mastering Geospatial Analysis with Python

You're reading from   Mastering Geospatial Analysis with Python Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788293334
Length 440 pages
Edition 1st Edition
Languages
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Authors (3):
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Silas Toms Silas Toms
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Silas Toms
Paul Crickard Paul Crickard
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Paul Crickard
Eric van Rees Eric van Rees
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Eric van Rees
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Toc

Table of Contents (18) Chapters Close

Preface 1. Package Installation and Management 2. Introduction to Geospatial Code Libraries FREE CHAPTER 3. Introduction to Geospatial Databases 4. Data Types, Storage, and Conversion 5. Vector Data Analysis 6. Raster Data Processing 7. Geoprocessing with Geodatabases 8. Automating QGIS Analysis 9. ArcGIS API for Python and ArcGIS Online 10. Geoprocessing with a GPU Database 11. Flask and GeoAlchemy2 12. GeoDjango 13. Geospatial REST API 14. Cloud Geodatabase Analysis and Visualization 15. Automating Cloud Cartography 16. Python Geoprocessing with Hadoop 17. Other Books You May Enjoy

Summary

This introductory chapter discussed how to install and manage the code libraries that will be used in this book. We'll be working mainly with Anaconda, a freemium open source distribution of the Python programming language that aims to simplify package management and deployment. We discussed how to install Anaconda, and the options for Python package management using Anaconda Navigator, Anaconda Cloud, conda, and pip. Finally, we discussed virtual environments and how to manage these using Anaconda, conda, and virtualenv.

The recommended installation for this book is the Anaconda3 version, that will install not only a working Python environment, but also a large repository of local Python packages, the Jupyter Notebook application, as well as the conda package manager, Anaconda Navigator, and Cloud. In the next chapter, we will introduce the major code libraries used to process and analyze geospatial data.

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