NYC CitiBike Sharing Analysis

Use Tableau to create effective visualizations from the Citi Bike data to potential investors.

Data Sources

Citi Bike Data

Start analysing the data with following questions:

  - how many bike trips were recorded during the month of August?
  - how many of short-term customers and annual subscribers to the Citi Bike service?
  - What Are Peak Riding Hours in the Month of August?
  - what are the highest-traffic locations? What Are the Top Bike Stations in the City for Starting a Journey?
  - What Are the Top Bike Stations for Ending a Journey?
  - What Is the Gender Breakdown of Active Riders?
  - What Is the Average Trip Duration by Age?
  - How Variable Is Bike Utilization?
  - How is length of time that bikes are checked out for all riders and genders?
  - What is number of bike trips for all riders and genders for each hour of each day of the week?
  - How long bikes are checked out for all riders and genders?
  - How many trips are taken by the hour for each day of the week, for all riders and genders?

Results

there are two deliverables were deployed in Tableau for the analysis

Links are: Deliverable2, Deliverable3;

New York Citi Bike data visualizations for August 2019

• There were over 2.3 million rides for the month of August 2019.
• 81% of the users were subscribers. 65% of the users were confirmed males and 25% were confirmed females.

Top starting stations and August Peak Hours

• Top ride starting locations are in the most touristic and busy areas, as we see here in Manhattan.
• Highest activity hours are from 5:00 PM to 7:00 PM and require the most resources mobilized.

Check out time by Gender and by weekdays

• Male users take approximately 3 times more rides than the female users.

Trips by gender and weekdays per hour

• Most weekday rides are around 7:00 AM to 9 AM and 5:00 PM to 7:00 PM.
• Weekend rides are highest from 10:00 AM to 7:00 PM.

Summary

The visualized data by this project is for the bike sharing in New York during the month of August 2019. The far majority of the rides were taken by male users during morning and evening rush hours and shows that Manhattan Island is the most busy area for stating location. Additional analysis would be beneficial by :

• comparing data for different months to determine trends across the year,
• including weather data to find the correlation between the weather and the rides.