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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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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

Installing and configuring Django and GeoDjango

Django, compared to Flask, is a batteries-included framework. It includes modules that allow for database backend support, without requiring a separate database code package (unlike Flask, which relies on SQLAlchemy). Django also includes an admin panel that allows for easy data editing and management through a web interface. This means fewer modules are installed and more code is included to handle database interactions and web processing.

There are some major differences between Flask and Django. Django separates URLs from views and models in a more structured manner than Flask. Django also uses Python classes for databases tables, but it has built-in database support. For geospatial databases, no extra module is required. Django also supports geometry columns in a wider range of databases, though PostgreSQL and PostGIS are used...

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