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...

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:

• Client-server interactions
• Statelessness
• Cacheablitity
• Uniform interface 
• Layered system

The client (a web browser or a remote computer) will send a request to a server at a designated URL endpoint. The request can include parameters that limit the data objects returned, much like conditionals...

The REST API application

To enable access to a database of NBA Arenas, US States, US Counties, and US Congressional Districts, we will build a REST API. The API will allow for queries about tables and about specific table resources, that is, rows of data. It will also allow for geospatial queries.

Application components

The components of this application include:

• The database, created in Chapter 11,  Flask and GeoAlchemy2, which contains the tables for NBA Arenas, US States, US Counties, and US Congressional Districts
• Theapp.py file, which initiates the application when called by a Python executable
• Theapplication folder, which contains the application code and folders
• The__init__.py file, which makes the application folder into a module, defines the Flask object and connects to the database
• Theviews.py file, which defines the API endpoints, the view functions, and the return responses
• Themodels.py file, which defines the database table models as Python classes that subclass from SQLAlchemy
• The forms...

Deploying Flask to IIS

To deploy the new API application on a Microsoft Server with Internet Information Services (IIS) installed, we have to download some Python code, and an IIS module called FastCGI. Once configured, the application will respond to web requests from any allowed machine.  

Flask and web servers

While Flask includes a local web server for testing purposes, it is not designed for production deployments. Flask works best with web servers like Apache or IIS. While there is a lot of literature on how to deploy Flask with Apache, it is less common to find good instructions on how to deploy it using IIS. As most GIS professionals work with Windows servers or have access to them, these instructions will focus on deployment with IIS 7.

WSGI

The Web Server Gateway Interface (WSGI) is a Python specification that allows for a Python executable to be used to respond to web requests. WSGI is built into Python web frameworks such as Flask and Django.

To enable the use of the Flask web framework...

Summary

Creating an API with REST specifications is easy with Python web frameworks. Flask makes it simple to coordinate the URL endpoints with request methods and response types. With built-in JSON capabilities, and with the use of the SQLAlchemy and GeoAlchemy2 ORMs, Flask is a perfect framework for creating a geospatial REST API.

In the next chapter, we will cover the use of the CARTOframes module for cloud visualization of geospatial data.