This is the source code for the scientific paper Growing Urban Bicycle Networks by M. Szell, S. Mimar, T. Perlman, G. Ghoshal, and R. Sinatra. The code downloads and pre-processes data from OpenStreetMap, prepares points of interest, runs simulations, measures and saves the results, creates videos and plots.

Preprint: arXiv:2107.02185
Data repository: [insert link]
Visualization: GrowBike.Net
Videos & Plots: http://growbike.net/download

The main folder/repo is bikenwgrowth, containing Jupyter notebooks (code/), preprocessed data (data/), parameters (parameters/), result plots (plots/), HPC server scripts and jobs (scripts/).

Other data files (network plots, videos, results, exports, logs) make up many GBs and are stored in the separate external folder bikenwgrowth_external due to Github's space limitations.

conda create --override-channels -c conda-forge -n OSMNX python=3 osmnx=0.16.2 python-igraph watermark haversine rasterio tqdm geojson
conda activate OSMNX
conda install -c conda-forge ipywidgets
pip install opencv-python
conda install -c anaconda gdal
pip install --user ipykernel
python -m ipykernel install --user --name=OSMNX

Run Jupyter Notebook with kernel OSMNX (Kernel > Change Kernel > OSMNX)

For multiple, esp. large, cities, running the code on a high performance computing cluster is strongly suggested as the tasks are easy to paralellize. The shell scripts are written for SLURM.

1. Populate parameters/cities.csv, see below.
2. Run 01 and 02 once locally to download and prepare all networks and POIs (The alternative is server-side sbatch scripts/download.job, but OSMNX throws too many connection issues, so manual supervision is needed)
3. Upload code/*.py, parameters/*, scripts/*
4. Run: ./mastersbatch_analysis.sh
5. Run, if needed: ./mastersbatch_export.sh
6. After all is finished, run: ./cleanup.sh

Single (or few/small) cities could be run locally but require manual, step-by-step execution of Jupyter notebooks:

1. Populate parameters/cities.csv, see below.
2. Run 01 and 02 once to download and prepare all networks and POIs
3. Run 03,04,05 for each parameter set (see below), set in parameters/parameters.py
4. Run 06 or other steps as needed.

1. prune_measure = "betweenness", poi_source = "railwaystation"
2. prune_measure = "betweenness", poi_source = "grid"
3. prune_measure = "closeness", poi_source = "railwaystation"
4. prune_measure = "closeness", poi_source = "grid"
5. prune_measure = "random", poi_source = "railwaystation"
6. prune_measure = "random", poi_source = "grid"

• Go to e.g. https://nominatim.openstreetmap.org/search.php?q=paris%2C+france&polygon_geojson=1&viewbox= and enter the search string. If a correct polygon (or multipolygon) pops up it should be fine. If not leave the field empty and acquire a shape file, see below.

• Go to Overpass, to the city, and run: relation["boundary"="administrative"]["name:en"="Copenhagen Municipality"]({{bbox}});(._;>;);out skel;
• Export: Download as GPX
• Use QGIS to create a polygon, with Vector > Join Multiple Lines, and Processing Toolbox > Polygonize (see Stackexchange answer 1 and Stackexchange answer 2)