This project is used as a backend for the web map application of the Green Paths route planner: green-paths.web.app / DigitalGeographyLab/hope-green-path-ui.
Green Paths is an open source route planner being developed by Digital Geography Lab, University of Helsinki, for the project HOPE – Healthy Outdoor Premises for Everyone funded by Urban Innovative Action. Its goal is to inform people on clean air and quite routes for walking and cycling in Helsinki region. It utilizes Air Quality Index (AQI) data from the FMI-ENFUSER modelling system (by the Finnish Meteorological Institute) and modelled traffic noise data from the city of Helsinki. AQI is based on real-time hourly data as a composite measure of NO2, PM2.5, PM10 and O3.
Currently implemented features include calculation of walkable unpolluted and quiet paths with respect to real-time air quality and typical traffic noise levels. The exposure-based routing method (and application) is based on an MSc thesis.
See docs/green_paths_api.md for detailed documentation of the green paths routing API.
- Green Paths project website
- UIA HOPE project
- FMI-Enfuser model
- SYKE - Traffic noise modelling data from Helsinki urban region
- Traffic noise zones in Helsinki 2017
- OpenStreetMap
- See also CONTRIBUTING.md
- Please bear in mind that the current objective of the project is to develop a proof-of-concept of a green path route planner rather than a production ready service
- You are most welcome to add feature requests or bug reports in the issue tracker
- When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change ([email protected])
- Simple typo fixes etc. can be sent as PRs directly, but for features or more complex bug fixes please add a corresponding issue first for discussion
- Python 3.8
- igraph
- GeoPandas
- Shapely
- Flask & Gunicorn
$ git clone [email protected]:DigitalGeographyLab/hope-green-path-server.git
$ cd hope-green-path-server/src
# create Python environment with Conda
$ conda env create -f conda-env.yml
$ conda activate gp-env
$ export GRAPH_SUBSET=True
$ gunicorn --workers=1 --bind=0.0.0.0:5000 --log-level=info --timeout 450 green_paths_app:app
# or
$ sh start-application.sh
WIP