Geospatial Validations Using FastAPI and PostGIS

Introduction

Geospatial workflows and tooling mostly fall into two (often related) categories: analysis and visualization. The first is hypothesis testing in support of research; the second info-design for knowledge transfer. We will look at an often overlooked third category: adding geospatial dimensions to input validation and business logic, with a specific focus on web applications (FastAPI + PostgreSQL).

This project accompanies my PyCon 2024 talk of the same name.

Setup

To run this project, you will need to have Docker installed on your machine. Once you have Docker installed, you can run the following command to start the application:

docker-compose up

The docker-compose file will start a FastAPI application and a PostgreSQL/PostGIS database as well as run the alembic migrations to load the necessary data into the database.

There is a Dockerfile_postgis file that is used to build the PostGIS image. At the time of writing, the official PostGIS image does not support ARM64 architecture. This Dockerfile is taken directly from the official PostGIS image repository and builds successfully on ARM64 architecture.

Notes: To run this project without Docker, the database urls in the database.py and alembic.ini files will need to be updated. These may also need to be updated if you are using a linux machine.

Project Structure

The project is structured as follows:

. 
├── Dockerfiles*
├── README.md
├── app
│   ├── __init__.py
│   ├── __pycache__
│   ├── alembic
│   ├── alembic.ini
│   ├── data_access_layer.py
│   ├── database.py
│   ├── main.py
│   ├── models.py
│   └── schemas.py
├── poetry.lock
├── pyproject.toml

The main.py file contains the FastAPI application and endpoints.
The models.py file contains the SQLAlchemy models for the database tables.
The schemas.py file contains the Pydantic models for the request and response bodies.
The data_access_layer.py file contains the functions used to query the database.
The alembic directory contains the migration scripts as well as the *.sql file used to load the data into the database.

Resources

Below are some resources that you will find useful as you continue your journey into utilizing geospatial data to extend this project.

Visualization

There are many ways to visualize geospatial data. Database GUIs such as pgAdmin and DBeaver support viewing geospatial data. The two non-database-GUI ways I view geospatial data most often to are:

QGIS - An open-source GIS software
Plotly - A Python graphing library

Data

There are many free sources of geospatial data available online. A good place to start is with government agencies as they provide free access to their geospatial data. A large number of datasets can be found on the United States Open Data Site website. The data for this project was sourced from the Pittsburgh Open Data site.