In this project I was tasked with collecting and interpreting data from the NY Citi Bike Program (A bike share program) to convince investors that a bike-sharing program in Des Moines is a solid business proposal We used Tableau as a tool to do POC on the data.In this interactive dashboard there are additional filters for the user to explore, gender and usertype.
Citi Bike Tableau Public: link to dashboard
The Statistics in The Dashboard Shows you the Overall Picture of Data for Month of August. The Top Down Dasboard shows overall Trips Followed by Peak Hours and the Maps Shows most used Start and End Station. This Visual helps to see what are age group and gender breakdown and how much the bike Utization is.
This Page Shows use the overall usage of Bike in the Month of August and a breakdown by Gender Type (Male, Female, Unknown). This visual will help to see what time are best and on how much average consumer uses the bike and helps to depicts what different marketing strategy can be developed to attract customer.
Trips By Weekday and Gender Based Page Helps us to analysis which are the busiest day of the week and which Gender Type uses the most service. As in the second heat map depicts Men uses the most service.
Overall, it appears for a Des Moines ride share to be successful.Breaking down the trips by usertype and gender, clearly shows that subscribers make up a large number of the trips, however customers tend to make longer trips. This makes sense because subscribers are every day commuters who probably commute to work only during the weekdays, whereas customers are most likely travelers who like to explore the city. However, it must be noted that within subscribers the gap between the number of trips made by each gender is large. Female subscribers make less trips than male subscribers. Therefore something must be done to attract more female riders. Looking over to the time breakdown, there is no difference between the usertype or genders. The popular time is between 4pm and 7pm, as concluded by previous phenomena analysis. The map shows an overlay of 2018 Male/Female Ratio data from the Census. It can be seen that majority of the stations exist in areas that are pretty balanced, so it really comes down to marketing and appealing the female riders. It may be possible to move the less popular stations to higher female population areas as well.
- The First visual will be good to see what is price consumer is paying by the customer type (Subscriber VS Customer)
- The Bike repair and loss of assest value.
- How much companies payout is out of business.
These some visual helps to see bigger picture.