Open Data Bikes Sharing Stations Analysis
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bordeaux-map-n_clusters-4.html
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lyon-map-n_clusters-4.html
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README.md

Open Data Bikes Analysis

License: MIT

Analyze bikes sharing station data from Bordeaux and Lyon Open Data (French cities).

Use the Python 3 programming language in Jupyter notebooks and the following libraries: pandas, numpy, seaborn, matplotlib, scikit-learn, xgboost.

Environment & library

We use conda environment.

For OSX users

conda env create -f environment_osx.yml

For Linux users

conda env create -f environment_linux.yml

To activate environment

source activate python3DS

To desacticate environment

source deactivate python3DS

Clustering

Higly inspired by the Usage Patterns Of Dublin Bikes Stations article and his great notebook.

Analyze the daily profile and plot a map with a color for each usage pattern.

Example of pattern

You can see the percentage of available bikes for 4 different daily profiles. Note the analysis only keep job days.

  • Blue profile: people who take bikes in the morning, roll them into 'green' stations and go back home in the evening.
  • Green profile: opposite of the blue profile.
  • Orange profile: not very used stations. Sometimes too far from city center. Sometimes very close the tramway stations.
  • Red profile: stations where people go in the evening

Bordeaux-Pattern

Maps

Bordeaux Map Clustering

Bordeaux-Map

Lyon Map Clustering

Lyon-Map

Predict (draft)

Play with some different models to predict the number of available bikes (or a kind of availability).

Prediction Map

From history data (two weeks), prediction at T+30 minutes for every station in Lyon (France).

  • Blue means there are several available bikes
  • Red means there are just a few available bikes

Lyon-Prediction-Map

Data

See the lyon.tar.gz and bordeaux.tar.gz.