The purpose of this analysis is to create worksheets, dashboards, and stories from New York City bike-sharing data with Tableau to convince investors that a bike-sharing program in Des Moines is a solid business proposal.

• Software:

  • Tableau Public 2021.3.3
  • Jupyter notebook 6.4.3
    • Python
      • Pandas library (to convert datatype)

• Data source:

  • Citi Bike trip data in August 2019 (201908-citibike-tripdata.csv)

• Tableau Public Link
  • NYC Citi Bike Analysis >> Link to dashboard

• Story Point 1

  

  • User Analysis
    • The total rides are 2,344,224.
    • 81% of users are annual subscribers.
    • 65% of users are male.
    • The later the birth year, the longer the ride duration.

• Story Point 2

  

  • Popular Locations
    • The Popular Locations are the Manhattan Borough.

• Story Point 3

  

  • Trip Analysis: Peak Riding
    • Active rush hours during the day are
      • in the morning (8:00 a.m. - 9:00 a.m.)
      • in the evening (5:00 p.m. - 7:00 p.m.)
    • The most active weekday is Thursday.

• Story Point 4

  

  • Trip Analysis: by Gender
    • Most users are annual subscribers and male.
    • They are likely to use the service during rush hours on weekdays.

• Story Point 5

  

  • Trip Analysis: Checkout Times
    • Most users ride short distances.
    • They likely checked out within 4-6 minutes.

From the results, we can conclude that a bike-sharing program in Des Moines is a solid business proposal for investors because we have identified and guaranteed that over 80% are annual subscribers which means that we have customers who will use the service long term. Most of them seem to use this service for alternative transportation to go to their workplace during rush hours on weekdays.

• The additional visualizations for future analysis are
  • comparing data to see bigger points of view for trends by using data from the last 5 years to the current year.
  • having extra data about bike-sharing's profitability over the last 5 years.