In this project, we used Tableau in our presentation to create our story and visualize bike-sharing data. We want to convince a potential investor who might be interested in providing seed funding to explore a bike-share program in Des Moines. The first step is to discover how the bike-share business actually works in New York City. In order to do so, we used trip data from the Citi Bike program in New York from August 2019 because there is likely more traffic during the summer months.
https://public.tableau.com/app/profile/tahereh5082/viz/NYC_Citibike_16229193503920/NYC_Citibike
-
Citi Bike Customers Overview shows about 2,344,224 total trips in August that majority of which were annual subscribers. Furthermore, 65% of the city bike users were male where only 25% were female.
-
The highest-traffic starting/ending locations are in the most touristic and busy areas. As we see here, Manhattan may have more bike rides because of the number of tourists.
3.we can see that most riders rent bikes for less than 60 minutes, with the highest duration of trips being about 5 minutes. The same trend exists for different genders as well.
4.The below dashboard shows the most popular time for rides during weekday. Most rides are from 7:00 AM to 9:00 AM and from 5:00 PM to 7:00 PM during working days. On the weekends most trips are from 9:00 AM to 6:00 PM.
5.This heatmap provides the number of bike trips by gender for every hour of the day of each week. This chart also reveals the same trend of male riders be the highest.
6.This heatmap gives us some information about the type of the users and their gender who use bike for trips. We can see the most users are Male Subscribers who tend to use bike sharing service on Thursdays and Fridays.
Above visualizations show that the vast majority of riders are male subscribers and the busiest time for rentals is early in the morning and early in the evening. Furthermore, most trips are less than an hour, indicating that Citi Bike service could be used as an alternative to public transportation by commuters.
Some additional visualizations :
- The number of trips by age groups during weekday.
- visualize data for different months and seasons of the year to find the correlation between the weather and renting bikes