New York City Bike Sharing—A Case Study

The goal of this project was to build a database using MySQL, query the database and gain insights from the data using visualizations and tables.

Riding a bike is a greener alternative to driving a car or taking a taxi and often is a quicker option for getting around traffic-jammed cities. Bike-sharing can be a useful service not only for tourists but also for commuters and locals. In this case study, we used bike-sharing data from Citi Bike in New York City and Newark as well as weather data to analyze riding patterns. For simplicity and to minimize the data size, we used data from October 2021.

Implementation

We drew an entity-relationship diagram to guide the schema creation of our database.

We then queried the database and visualized the results to gain insight into both the stations and the rides. In this case study, we answered questions such as What are the most popular stations?

Do people rent bikes less often on rainy days?

A summary of our analysis can be found in presentation.pdf.

Instructions

Change the MySQL user credentials in the config dictionary in all Python scripts (database-setup.py, analysis-stations.py, and analysis-rides.py).

1. Run database-setup.py to setup the database with the schema defined in database-setup.sql, read the datasets, perform basic cleaning, and insert the data into the MySQL database.

   NOTE: The database is overwritten every time the setup file runs. This is so changes can be made more conveninetly without manually deleting the database for every change in the setup. To prevent this, comment out DROP DATABASE IF EXISTS nyc_bike_sharing; in database-setup.sql.

2. Run analysis-stations.py and analysis-rides.py to query the database using the respective SQL scripts (analysis-stations.sql and analysis-rides.sql). Both scripts each generate three visualizations in the \figures folder.

Libraries Used

We used several Python libraries to create this project:

• mysql.connector to connect to the MySQL database server (localhost) in Python
• pandas for basic data cleaning and transformation in Python
• matplotlib.pyplot and seaborn for data visualization
• GeoPandas to convert pandas DataFrames to geospatial data for map visualizations
• contextily to get map tiles used for the background map in the map visualizations

We used Python 3.9 throughout the project.

References

We used data from the following sources:

Menne, Matthew J., Imke Durre, Bryant Korzeniewski, Shelley McNeal, Kristy Thomas, Xungang Yin, Steven Anthony, Ron Ray, Russell S. Vose, Byron E.Gleason, and Tamara G. Houston (2012): Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. NOAA National Climatic Data Center. doi:10.7289/V5D21VHZdoi:10.7289/V5D21VHZ. Retrieved November 23, 2021, from https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00094728/detail.

NYC Bike Share, LLC & Jersey City Bike Share, LLC. (2021, November 5). JC-202110-citibike-tripdata [Dataset]. https://ride.citibikenyc.com/system-data