bike-sharing

Data Collection

src/data-preprocessing

API requests to receive all current locations of bikes from nextbike, lidlbike and mobike in Berlin (inner circle) and store them into a single database.

Add config.py file to src/ with API Keys for Deutsche Bahn API (https://developer.deutschebahn.com/store/) and database credentials.

For access to lime bike API insert phone_no to config.py and follow steps in lime_access.py (three manual steps required).

Other open data on bikes system can be accessed on https://github.com/Liyubov/tidytuesday/tree/master/data/2019/2019-04-02

Data Analysis

src/analysis

Jupyter Notebook to analyse data.

• preprocess.ipynb contains the preprossing steps of the raw data to a usable format.

  • raw.csv contains the data from the database
  • preprocessed.csv contains the data with added columns and fixed lat / lng
  • routed.csv contains the data with distance and waypoints
  • cleaned.csv is the cleaned routed dataset (unplausible data is removed)
  • pseudonomysed.csv is the anonymized, cleaned data, following this standard
  • pseudonomysed_raw.csv ist the anonymized data (NOT cleaned).

• analysis.ipynb includes analysis about provider and bike specific data

• pseudonomysed.ipynb includes analysis using the anonymized dataset (without information on providers.)

In folder bike analysis trajectories we analyze bikes trajectories (work in progress).

Additional Jupyter Notebook on data analysis:

• analysis of trajectories is described in bikes_mobility_analysis_trajectories folder

The project on data analysis is updated on gitlab. Contact Liubov if you want to hear the details: liubov.tupikina (at) cri-paris.org

Main research question

1. Where to more optimally place stations to allow more regular distribution of bicycles?
2. How to design indicators of bicycle systems in order to monitor the bicycle sharing system on collective and individual level?