Citi Bike Analytics

Background

Since 2013, the Citi Bike Program has implemented a robust infrastructure for collecting data on the program's utilization. Through the team's efforts, each month bike data is collected, organized, and made public on the Citi Bike Data webpage.

Data

• Citi Bike Trip Data (Jan 2018 - Jun 2018)
• Citi Bike Daily Ridership and Membership Data (Jan 2018 - Jun 2018)

Trip data was clean and processed by Python (trip_data_processing.ipynb). Ridership and membership data was processed by Excel.

Objectives

1. Design 2-5 visualizations for phenomena discovered between Jan 2018 and June 2018 in terms of the following questions:

• By what percentage has total ridership grown?
• How does the ridership change by gender, ticket type or age?
• What are the top 10 stations in the city for starting a journey?
• What are the top 10 stations in the city for ending a journey?

2. Use visualizations to design a dashboard for each phenomena. The dashboards should be accompanied with an analysis explaining why the phenomena may be occuring.

3. Create a dynamic map that shows how each station's popularity changes monthly with zip code data overlaid on the map. The map should also be accompanied by a write-up unveiling any trends that were noticed during analysis.

4. Create a Tableau story that brings together the visualizations, requested maps, and dashboards. Be sure to make it professional, logical, and visually appealing.

Tableau Public Website

https://public.tableau.com/profile/han1903#!/vizhome/NYC_Bike/Story1