https://github.com/dennis-nichols/clean-air-compass-mapping-api

An API to do spatial interpolation of Purple Air network sensor data and create polygons for display on web maps.
https://github.com/dennis-nichols/clean-air-compass-mapping-api

fastapi geopandas numpy pandas python scikit

Last synced: 15 days ago
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An API to do spatial interpolation of Purple Air network sensor data and create polygons for display on web maps.

• Host: GitHub
• URL: https://github.com/dennis-nichols/clean-air-compass-mapping-api
• Owner: dennis-nichols
• Created: 2023-02-19T07:20:51.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-24T20:14:49.000Z (over 1 year ago)
• Last Synced: 2024-01-25T22:17:22.361Z (12 months ago)
• Topics: fastapi, geopandas, numpy, pandas, python, scikit
• Language: Python
• Homepage:
• Size: 30.3 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Clean Air Compass - Mapping API

An API to handle location requests from the Clean Air Compass frontend and return an interpolated map of air pollution (PM 2.5) in the form of a list of georeferenced polygons.

## Details

The API conducts the following steps:

- Parses the location request to see if it is for a city or a US postal code

- Makes a request to the [LocationIQ API](https://locationiq.com/) and get back a bounding box of lat / lon coordinates

- Makes a request to the [Purple Air API](https://www2.purpleair.com/) to get back the list of sensors within the bounding box as well as their associated air pollution data

- Creates a spatial data frame from the sensor API response object using `geopandas`

- Creates a grid of polygons based on the spatial extent of the collection of sensor points and estimates the PM 2.5 pollution at each grid cell using the K-nearest neigbors (KNN) regressor method and the `scikit-learn` library.

- Returns the grid of polygons to the frontend as a GeoJSON object

## How to run

1. Clone the repo

2. Install all dependencies by running `pip install -r requirements.txt` in the console

3. Create your own environment file where you set the values for your own Location IQ API key (`LOC_IQ_KEY`) and Purple Air API key (`API_KEY`) respectively.

4. Run the API server locally using `uvicorn main:app --reload`