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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

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...

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