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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

Flask and its component modules

Flask, as opposed to Django and GeoDjango (covered in Chapter 12, GeoDjango), does not include batteries. Instead, it allows a number of supporting modules to be installed as needed. This gives more freedom to you as the programmer, but it also makes it necessary to install the required components separately.

I've chosen some modules for this chapter that will allow us to create a Flask application with a geospatial component. The following sections will detail how to set up, install, and utilize these modules to generate a website, using a demonstration site with a PostGIS database backend (as covered in Chapter 7, Geoprocessing with Geodatabases) and the ability to perform spatial queries through a web-based interface.

Setup

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