Build a tableau visualization to identify unexpected phenomena in CitiBike usage.
The following visualization looks at the CitiBike Trip History logs for 2019 in New York City.
The visuals look at:
- The usage of bikes across all the stations of New York City to find out the most popular stations where most bikes have been rented as well as the stations where bikes are rented for the longest duration.
- The age ranges of riders to identify which age groups rent the most number of bikes as well as the age group that rent bikes for the longest time.
- The difference, if any, in the number of bikes rented by male vs female riders. As well as the gender that rents bikes for the longest time.
- The most popular hours of the day to rent bikes as well as the times when bikes are rented for the longest time.
- The most popular days of the week to rent bikes as well as the days when bikes are rented for the longest time.
Notes:
- Ages up to 100 only were considered in the dataset.
- Only Male and Female was included in gender.
Please use full screen mode for optimal viewing.
Citi Bike Data Collated Citibike usage data for 2019
- Downloaded all Citibike logs for 2019 for New York City and concetenated all the csvs into one dataframe.
- Converted date columns to datetime objects
- Grouped data by month and station ID to get the top 10 stations.
- Created bins for ages [10, 19, 29, 39, 49, 59, 69, 100] and grouped the data by month, age ranges, gender and user type to find the number of riders for each group and average trip duration.
- Grouped data by month and hour
- Grouped data by month and day of week
- Created csvs for all the above aggregations and used that for the tableau visualization.
Finding: The majority of the top bike stations are in the central Manhattan area. The 2 most popular stations are 'Pershing Square North' and '8 Ave & W 31 ST'. It is notable that they are situated close to major train stations: Grand Central Terminal and Penn Station respectively.
The majority of long duration rides originate from the Brooklyn borough.
Finding: The least number of riders are in the below 20 age group, but they ride for a longer duration on average. The most number of riders are in the 20-40 age group.
Opportunity: Introduce a school/college-friendly marketing campaign targetted at the below 20 age group to increase their usage.
During the winter months, the number of riders decreases. Introduce a promotion to encourage more riders during the colder months and a summer promotion to encourage longer use of bikes.
Finding: There are more male riders than female riders, though males and females ride for almost the same time across the age groups.
Opportunity: Introduce a marketing strategy to increase adoption by female riders.
Finding: Throughout the year more subscribers use bikes than customers. However, customers use bikes for a longer period of time.
The largest group of subscribers are in the 30-39 age group. The largest group of customers are in the 50-59 age group.
Opportunity: Target customers to increase bike usage and subscribers to use bikes for longer.
Finding: More people use bikes during peak office hours. However, bikes are used for a longer period in the early hours of the day.
Opportunity: Introduce a promotion for off-peak hours to increase the number of riders.
Finding: Weekends have less riders than weekdays. However, the weekend rider uses the bikes for a longer time.
Opportunity: Weekend riders are possibly tourists. Introduce a tourist promotion to increase ridership during the week.
- Python
- Pandas
- Tableau
Created by @deepavadakan