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Mastering Geospatial Analysis with Python

You're reading from  Mastering Geospatial Analysis with Python

Product type Book
Published in Apr 2018
Publisher Packt
ISBN-13 9781788293334
Pages 440 pages
Edition 1st Edition
Languages
Authors (3):
Silas Toms Silas Toms
Profile icon Silas Toms
Paul Crickard Paul Crickard
Profile icon Paul Crickard
Eric van Rees Eric van Rees
Profile icon Eric van Rees
View More author details

Table of Contents (23) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Package Installation and Management 2. Introduction to Geospatial Code Libraries 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 1. Other Books You May Enjoy Index

Summary


Django, with its batteries-included philosophy, creates complete applications with very few outside libraries required. This application performs data management and data analysis using only the Django built-in tools and the GDAL/OGR library. Enabling the GeoDjango functionality is a relatively seamless experience because it is an integral part of the Django project.

Creating web applications with Django allows for a lot of instant functionality, including the administrative panel. The LayerMapping makes it easy to import data from shapefiles. The ORM model makes it easy to perform geospatial filters or queries. The templating system makes it easy to add web maps as well as location intelligence to a website.

In the next chapter, we will use a Python web framework to create a geospatial REST API. This API will accept requests and return JSON encoded data representing geospatial features. 

 

 

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