Creating a story in Tableau analyzing New York City bike sharing data. This presentation aims to provide key points about the business and clients to key stakeholders.
- This chart shows the length of time that bikes are checked out for all riders; generally bikes are checked out for several hours, peaking at 5 hour rentals.
- This chart shows the length of time that bikes are checked out by gender; males use the service significantly more frequently, and usage still peaks at 5 hours for both genders.
- This chart shows the number of bike trips for all riders for each hour of each day of the week; on weekdays the bikes are most often checked out during commute hours, while on weekends the bikes are most often used in the middle of the day.
- This chart shows the number of bike trips by gender for each hour of each day of the week; the ridership trends are the same, but males rent bikes more frequently.
- This chart shows the number of bike trips for each type of user and gender for each day of the week; for subscribers, ridership is higher during the weekdays, but for customers it is higher during the weekends, suggesting that most one-time customers are tourists
- This chart shows the busiest start locations; it appears that Manhattan is the busiest, likely because it's a tourist destination and has heavy traffic congestion.
- This chart shows the busiest ending locations; it appears that Manhattan is the busiest, likely because it's a tourist destination and has heavy traffic congestion.
Overall, the majority of bike rentals are taken by males, those with subscriptions, and during commuter hours during the weekdays, with the majority of rentals being made in Manhattan.
Further analysis can be done on the usage habits of tourists, by plotting a count of trip duration with different usertypes. Additionally, analysis can be done on the ages of tourists that like bikesharing by plotting a count of birth years with different usertypes.