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https://github.com/mszell/bikenwgrowth

Source code for the paper "Growing urban bicycle networks", exploring algorithmically the limitations of urban bicycle network growth
https://github.com/mszell/bikenwgrowth

bicycle-network cycling gis network-analysis osmnx transportation-network urban-planning

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Source code for the paper "Growing urban bicycle networks", exploring algorithmically the limitations of urban bicycle network growth

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README 

        
# Growing Urban Bicycle Networks



This is the source code for the scientific paper [*Growing urban bicycle networks*](https://www.nature.com/articles/s41598-022-10783-y) by [M. Szell](http://michael.szell.net/), S. Mimar, T. Perlman, [G. Ghoshal](http://gghoshal.pas.rochester.edu/), and [R. Sinatra](http://www.robertasinatra.com/). The code downloads and pre-processes data from OpenStreetMap, prepares points of interest, runs simulations, measures and saves the results, creates videos and plots. 



**Paper**: [https://www.nature.com/articles/s41598-022-10783-y](https://www.nature.com/articles/s41598-022-10783-y)  

**Data repository**: [zenodo.5083049](https://zenodo.org/record/5083049)  

**Visualization**: [GrowBike.Net](https://growbike.net)  

**Videos & Plots**: [https://growbike.net/download](https://growbike.net/download)



[![Growing Urban Bicycle Networks](readmevideo.gif)](https://growbike.net/city/paris)



## Folder structure

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.



## Setting up code environment

### Conda yml

[Download `.yml`](env.yml)



### Manual procedure

```

conda create --override-channels -c conda-forge -n OSMNX python=3.8.2 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)



## Running the code on an HPC cluster with SLURM

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](https://slurm.schedmd.com/overview.html).  



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`

7. Recommended, run: `./fixresults.sh` (to clean up results in case of amended data from repeated runs)



## Running the code locally

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.



### Parameter sets 

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"` 



## Populating cities.csv

### Checking nominatimstring  

* Go to e.g. [https://nominatim.openstreetmap.org/search.php?q=paris%2C+france&polygon_geojson=1&viewbox=](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.



### Acquiring shape file  

* Go to [Overpass](overpass-turbo.eu), 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](https://gis.stackexchange.com/questions/98320/connecting-two-line-ends-in-qgis-without-resorting-to-other-software) and [Stackexchange answer 2](https://gis.stackexchange.com/questions/207463/convert-a-line-to-polygon))