This is the source code for the scientific project Quality assessment of a country-wide bicycle node network with loop census analysis. The code assesses the quality of a Bicycle Node Network via loop census analysis.
Output from running the code on Denmark, showing round trip options for a family with small children

First clone the repository:

git clone https://github.com/mszell/bikenwloops

Go to the cloned folder and create a new virtual environment via mamba using the environment.yml file:

mamba env create -f environment.yml

Then, install the virtual environment's kernel in Jupyter:

mamba activate bikenwloops
ipython kernel install --user --name=bikenwloops
mamba deactivate

You can now run jupyter lab -kernel bikenwloops.

Data of the knudepunkter network comes from BikeNodePlanner: Data for Denmark and BikeNodePlanner.

• Use BikeNodePlanner: Data for Denmark
• Uncomment the municipalities of your study area in config-municipalities.yml. Several config files are already prepared for copy-pasting in the parameters/dataretrieval/ folder for large study areas like Jutland or Zealand.
• Set all values in config-layers-polygon.yml to ignore. This file is already prepared for copy-pasting.
• Run the run.sh script
• Copy all subfolders of /input-for-bike-node-planner/ into the /data/input/ folder of bike-node-planner

This step is needed to add elevation data (from dem/dem.tif) to the edges, creating an edges_slope.gpkg file.

• Use BikeNodePlanner
• Run scripts 01 to 04
• Let's call loopspath the data/input path to your project, for example bikenwloops/data/input/funen/
• Copy the file edges_slope.gpkg from bike-node-planner/data/output/elevation into loopspath/network/processed/

├── code                    <- Jupyter notebooks and py scripts
├── data                    <- Data (not saved on github)
│   ├── input               <- Original, immutable data
│   └── processed           <- Modified data
├── parameters              <- Parameters and config files
│   └── dataretrieval       <- Config files for retrieving data
├── plots                   <- Generated figures
├── .gitignore              <- Files and folders ignored by git
├── .pre-commit-config.yaml <- Pre-commit hooks used
├── LICENSE.txt
├── README.md
└── environment.yml         <- Environment file to set up the environment using conda/mamba