This analysis features Chicago Divvy bikeshare and weather data. When compared to my previously posted San Francisco Micromobility Supply & Demand Analysis, there are a few key differences:
- More complex datasets require more complex data prep. With the addition of demographic and to/from station information come additional steps in validating the data and extracting assumptions to make explicit.
- Kepler GL is awesome. To enable even more data visualization capabilities, I visualized the data in Kepler as well as Tableau.
- Constraints, such as fixed bikeshare stations, trigger new questions when compared to the dockless model. Since riders must start and end their trips from fixed locations, mixed use and combination transportation with public transportation becomes more important.
Here are some details about the data at hand:
- Chicago, IL city-specific dataset
- Vehicles are e-bikes only
- Vehicles must be docked at stations when not in use
See the full analysis below and see the Python Jupyter Notebook here:
Mari Bolton 