Book Image

Mastering Geospatial Analysis with Python

By : Silas Toms, Paul Crickard, Eric van Rees
Book Image

Mastering Geospatial Analysis with Python

By: Silas Toms, Paul Crickard, Eric van Rees

Overview of this book

Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis. You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Packt Upsell
Geoprocessing with Geodatabases

Chapter 13. Geospatial REST API

Publishing data for consumption on the web is a major component ofmodern GIS. To transfer data from remote servers to remote clients, most geospatial publishing software stacks use Representational State Transfer (REST) web services. In response to web requests for specific data resources, REST services return JavaScript Object Notation (JSON)-encoded data to the requesting client machine. The web services are combined in an application programming interface, or API, which will contain the endpoints that represent each data resource available for querying.

By combining a Python web framework with object-relational mapping (ORM) and a PostGIS backend, we can create a custom REST API that will respond to web requests with JSON. For this exercise, we will use the Flask web framework and the SQLAlchemy module with GeoAlchemy2 providing spatial ORM capabilities.

In this chapter, we will learn about the following:

  • REST API components
  • JSON response formatting
  • How to process...