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Analyzing CitiBike Data for NY/NJ Metropolitan Area during COVID-19 pandemic (2020) using Tableau.

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Citi Bike Data Presentation - Tableau

Link to Tableau Public Project: https://public.tableau.com/profile/aaron.karpie#!/vizhome/Tableau-CitiBikeProject/Story1

Phenomenon #1: Peak Hours During

Peak Hours during Winter appear to be 1-6pm. Peak hours during Summer appear to be 6-7pm. The reason for the Sumemr peak hours could be that people are prone to renting Citi Bikes in the evenings when it's cooler, avoiding the high temperatures/humidity during the day.

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Phenomenon #2: Gender Gap for Subscribers and Customers

Gap in gender ridership among both Subscribers and Customers indicates little effectiveness in outreach to decrease gap. When filtered for "Customers" the gap does not appear to be as large amongst male and female riders. Customers are both "24 hour" and "3 day" pass riders, and are more likely to be tourists than residents or every day commuters. The reason for this phenomen could potentially be that most tourists are couples traveling together, so the count of customers (both male and female) would in turn grow or decrease at the same rate.

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Phenomenon #3: Avg Trip Duration by Age Group

Younger people, particularly age groups 15-17, 18-20, and 21-23 have the longest trip durations on average during the monitored period.

The reason for this phenomenon could be Covid restrictions in Jersey City/NY and the fact that older people are more risk averse when venturing out during the pandemic.

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Phenomenon #4: Change In Ridership Month to Month

Largest decrease in ridership occured from October to November for both males and female subscribers/customers. The reasoning for the drop in customers could be a substantial decrease in temperature on the East Coast in November.

The drop in subscribers could be attributed to Covid restrictions as the NY metro area experienced a surge beginning in November. My cool logo

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