CitiBike-Analysis: https://public.tableau.com/app/profile/kyle.carlson/viz/TableauChallenge_16358842449910/Novemberstory
Some conclusions that I can make about this data set from Citibike from New York City, New York in November of 2020. The first question was more about demographics and focused on breaking the data down to show the gender, age and status of the riders using this service. Regarding gender of riders, I found that 67% were males and 33% were females and that most users were subscribed to the service. Nothing too shocking from these discovers as men typically are more inclined to go out on a bike as they may feel safer doing so in New York. This is seen even more following my second question.
My second question revolved around daily hourly breakdowns and looking at usage from men and women and average duration per trip. Average trip duration didn’t add too much to my analysis but did reveal which days the service was used the most, which were weekends (Saturday and Sunday). Hourly breakdown per day did however reveal a lot, in the early morning and night hours the riders trended to be males making up 80% of all riders almost every day. This further supports my thoughts that women aren’t using the service as much due to safety being that men would use these during these hours then women. Women used the service most between 8am-9pm when there would be more foot traffic in the city.
The final question I had unrelated to the first two dashboards was looking at which starting and ending stations were used the most. I ranked starting and ending stations into the top 15 for each to see what the difference was and then had a map that changed colors and size based on amount of starts or ends for each station. My maps showed that many stations that had strong start station also were strong end stations leading me to the conclusion people are using these as hubs. These stations must be near large employment centers or entertainment areas as they are ending and starting their trips at these stations. Further analysis could be done to see what are close to these stations whether there are bars, stadiums, or employment building leading to these large number of users that these spots.
