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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

Writing a REST API in Python

To understand the components of a REST API with JSON response, we will utilize the Flask web framework, a PostgreSQL/PostGIS database, and SQLAlchemy and GeoAlchemy2 for ORM queries. Flask will be used to create the URL endpoints for the API. PostGIS will store the data in tables defined by SQLAlchemy models, which define the column types for all columns except the geometry columns, which are defined by GeoAlchemy2 column types.

REST

REST is a standard for web services, designed to accept requests and parameters and return a representation of that data, usually in a JSON format but sometimes in XML or HTML format. APIs that use REST architecture must meet these architectural constraints:

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