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Demoland engine

A predictive engine for the Alan Turing Institute project DemoLand

Install

Either clone and pip install or pip install from git.

Usage

See the notebooks in the docs folder.

How to get Demoland app for a new area?

Top level overview:

  1. Generate the required files using the Demoland pipeline Docker container
  2. Commit engine-related files to demoland-engine repository
  3. Commit app-related files to demoland-web repository

The rest should be automatic.

Generate data for a new area using the Demoland pipeline

Important

The pipeline requires a decent amount of RAM (about 12GB at peak but we've seen even 22GB when running on Apple Silicon under emulation) and a fast internet connect (it needs to download 1.3GB of OSM data). Depending on the size of the area and a machine it can take anywhere from 15 minutes to a few hours.

The process of generating data for a new area is provided in the form of a Docker container. You will need to provide four pieces of information:

  • AREA_NAME: Name that will be visible in the app to the user.
  • NAME: Name that is used as a key within the demoland_engine.
  • AOI_FILE_PATH: Path to a file relative to the current working directory (avoid ../) containing polygon geometries defining the extent of the area of interest. Can be any file readable by geopandas.read_file.
  • GTFS_FILE_PATH: Zip file with GTFS data covering the region of interest. Go to https://data.bus-data.dft.gov.uk/downloads/, register, and download timetable data for your region. Pass the file without any changes.

The best option is to create a folder with the two required files, navigate to the folder and run the container.

The container is not public, so you need to ensure you are logged in to ghcr.io within Docker. Follow the Github docs to do that. Note that to be able to pull the container, you need to have at least read permission of the demoland-engine repository.

Example:

docker pull ghcr.io/urban-analytics-technology-platform/demoland_pipeline:latest
docker run \
    --rm \
    -ti \
    -e AREA_NAME="Tyne and Wear" \
    -e NAME="tyne_and_wear_v1" \
    -e AOI_FILE_PATH="geography.geojson" \
    -e GTFS_FILE_PATH="itm_north_east_gtfs.zip" \
    -v ${PWD}:/app/data \
    --user=$UID \
    ghcr.io/urban-analytics-technology-platform/demoland_pipeline

The container generates two ZIP files. One shall be used in demoland_engine, and the other shall be used to deploy the app.

Commit engine-related files to demoland-engine repository

The file engine.zip contains files to be added to the data folder of the demoland-engine repository. Use the information in hashes.json to update data.py in the demoland_engine code. The result should look like the PR #7.

Commit app-related files to demoland-web repository

The file app.zip contains all the necessary files to generate the webapp. Note that the new version of demoland_engine with all the files from engine.zip and correct hashes needs to be deployed before the app. See the dedicated documenation on the app deployment. There's no need to pay a lot of attention to the contents of each file as all of them are autogenerated by the pipeline.

Building the container

To successfully build the container, navigate to the root of the repository and copy the necessary data files there as well. The required files:

  • air_quality_model.joblib
  • grid_adjacency_binary.parquet
  • grid_complete.parquet
  • house_price_model.joblib

All four need to be present in the repository at the time of building as they are copied to the container. You can then build the container and upload it to GHCR as:

docker build -t ghcr.io/urban-analytics-technology-platform/demoland_pipeline -f Dockerfile.pipe .
docker push ghcr.io/urban-analytics-technology-platform/demoland_pipeline:latest

Deployment to Azure Functions

The repository includes a GitHub Action which automatically deploys the main branch to Azure Functions.

For manual deployment steps, see the Developer Notes section in the DemoLand book for instructions.