In this project we analyzed Citi Bike data from 2019 of New York Citi. The purpose of this analysis is to present visualized analysis that we made using Tableau for potential investors to launch bike share business in city of Des Moines.
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Tableau Public Citi Bike Story Page
https://public.tableau.com/app/profile/zarina.kossakova/viz/Citi_Bike__Story/CitiBikeStory -
Datasource used for this project
https://s3.amazonaws.com/tripdata/index.html -
Tableau Public 2021.3
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Python 3.7.10
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Pandas Library
Since the mechanics of making business work is quite different in a small city than in New York City, we first visualized New York City data to understand how the bike- sharing business works, then we applied our knowledge and presented visualization to potential investors. Here is our story in Tableau that consist of 4 dashboards and 10 worksheets.
We picked the month of August for a good reason; August is one of the busiest times of the year for the bike- sharing business. Our first page of story answers 3 main questions. First, there were total records of 2,344,224 rides in the month of August. 65 % of riders were male, 25% female and 10% unknown. Lastly, we determined that more than 80% of riders were subscribed customers.
This page of our story will help us see average trip count. Three bar charts represent peak hours, days of the week and days of the month as well as the customer/ subscriber ratio.
- The slowest times are between 12 am – 5 am, Wednesdays and Sundays.
- The busiest time is 7 am, between 4- 7 pm on Thursdays thru Saturdays.
The heatmaps page of story is equivalent to peak overview but this one also has a breakdown of gender rides. Just like the previous story page it shows us similar peak hours and days, additionally, male rides are significantly higher than females due to gender ratio.
Following line charts shows us the number of bikes used in 60 minutes. As we can see, most of the bikes' rides did not exceed 50 minutes, the significant amount returned in first 30 min. That gives us a good idea of the average ride time. Also, we provide a similar line chart with an additional gender breakdown.
Citi Bike Data of New York City of 2019 was used for this analysis. Our visualization helped us determine following patterns:
- Total ride count of the month of August is 2,344,224
- Majority of users are subscribers
- Majority of users are male
- Busiest days of the week are Thursdays thru Saturdays
- Slowest days of the week are Wednesdays and Sundays
- Peak hours of the day 7 am, 4-7 pm
- Slowest hours of the day 12-5 am
- Average ride time is less than 50 min
One of the crucial aspects to consider when planning to open a bike sharing business is maintenance. For this project we came up with additional visualization to determine what times will be the best to complete bike maintenance. Here are our findings:
- As we determined earlier in our heatmaps and bar charts, the best time will be Sundays thru Wednesday from 12 to 5 am. We provided a wide range of visualisation bars for each day of the month, weekday and hour of the day to present best times for maintenance work.
- Another additional visualization is our area graph. It helps determine what age range to focus on when promoting bike sharing business, as we can see in our graph there are almost 240k people that were born in 1969 that like to ride bikes, additionally, majority of users are 1980 thru 1998 year of birth.
