Tableau, Pandas
The purpose of this analysis was to utilize NYC Citi Bike data to make visualizations in order to prepare a business proposal to create a new bike sharing program, similar to the one in NYC, in Des Moines, Iowa. Vizualization were created using Tableau to create a visual representation of the data from NYC Citi Bike program. Variables that were used in the analysis were designed to examine bike sharing data by gender, weekday, time of the day, and trip duration among others to give an overview of trends in the bike sharing program in NYC.
- The results of the Top Starting Locations for users can be seen in the visualization. The larger the circles and darker the circles the more starting times there were for Bike Sharing trips there. Areas the were central in NYC and in the city were much more heavily used areas. The areas that were far more inland and off the main area had lower usage rates as seen by the smaller circles and lighter colors. This could indicate where bikes should be initially placed and where more bikes should be placed around the area to ensure there are enough bikes for riders.
- The number Hour of Starttime for Bike Rides is seen in the second visualization represents the most popular hours that bike rides are started. 5-7PM were some of the most heavily used hours for starting bike rides in addition to 7-8AM. This could coincide with work time travel and commute. Some of the lower utilized Starttimes were late at night between 12AM and 5AM.
- The next visualization shows the Number of Bike Ride Counts per Weekday and Hour by Stoptime. During Monday throuhg Friday, the stoptimes are mostly entered around 7-9AM and 5-7PM. This could be good data to utilize along with the starttimes to make repairs and maintenance on bikes involved in the bike sharing program.
- This visualization represents a line graph of the Number of Bike Rides by Trip duration. The greatest number of trips are between 0-20 minutes and there is a steep drop off in tripduration after around 10 minutes. Most trips do not last longer than 20 minutes as seen in the visualization here.
- This next visualization is a filtered version of the previous data with a gender breakdown. There are many more Male bike riders than females and represent a vast majority of the utilization of Bike Sharing Program. Males also have a longer trip duration than females.
- This next visualization represents the User Trips by Gender for Weekday per Hour of usage. It shows similar data to the Bike Ride Counts Per Weekday and Hour by Stoptime. It also shows continually that males have a much higher utilization than females. Usership is the highest in the same hours as seen previously as well between 7-8AM and 5-7PM. The weekends have a much more spread out Bike Ride Count.
- This final visualization represents the User Trips by Gender by Usertype and Weekday. One of the large things that can be seen here is that males represent a much larger number of bike rides that are subscribrs that utilize the bike sharing program. And in general, users that are Subscribers use the bikesharing service much more often than those that are not subscribers and are just customers.
There a few major points that can be used from the NYC Citi Bike data when designing a program in Des Moines, Iowa. One of those is the start times and stop times. The bike sharing program is much more utilized during 7-9AM and 5-7PM especially on the weekdays. Bikes should be readily available at this time to ensure the highest utilization of the program. In addition, bike maintenance and repairs shoudl be done late at night or early in the morning to make sure bikes are available and ready to be used when needed. Starting location will also be importants, bikes are utilized much more in certain areas than not. The population and number of tourists in a ceratin area could affect the utilization and when looking at Des Moines, there should be special attention payed to where bikes will be placed around the city. Finally, males appear to represent the largest group of users and subscribers to the bike share program. This could be an area for growth, by paying special attention to Females and catering toward them more to increase utilization of the program. Another vizualization that could be helpful in this analysis would be seeing what age groups utilize the bike share program more often. When designing a program in Des Moines bikes could be placed in areas where there is a higher population of those age groups with higher utilization of the program. Another vizualization could be made for BikeID and usercount to see which bikes are being utilized the most and where bike maintenance should be done most often.