Module 14 of Data Analytics Bootcamp
The purpose of this analysis is to view some of the parameters of Citi Bike's business in New York that we believe may be useful for determining the success of starting a similar bike share program in Des Moines, Iowa. The data was taken from the month of August, as that was determined to be an optimal month for bike riding in general. Our analysis was completed using Tableau's visualization tools.
Using the visualizations you have in your Tableau Story, describe the results of each visualization underneath the image.
- This image shows the most demanded times for Citi Bikes are during the rush hours on weekdays. Specifically, bikes are most in demand between 7am - 9am and 5pm - 7pm on weekdays. They are also somewhat heavily used during the daytime on weekends. The only strange finding highlighted in this visual is that Wednesday evenings, for whatever reason, don't see nearly as much bike usage as the other weekday evenings.
- It appears there is very little difference in the high and low demand times for bikes. Each gender seems to want to ride bikes at the same times. However, it is very clear from this visualization that males utilize these bikes far more than any other gender.
- It is clear that far more Citi Bike subscribers use the bikes more than one-time users. Additionally, as we have already noted, there is much higher usage among males compared to other genders.
- From this visualization we can tell that almost all bike rides are less than an hour long. The most common bike ride duration is only around 5 minutes long, and most rides are under 30 minutes.
- There doesn't appear to be much of a difference between the genders regarding the duration of bike rides. Males, females, and all those unknown seem to mostly take bike rides under 20 minutes, with almost all rides still lasting under an hour.
- It is difficult to distinguish trends from this visualization without knowing much about New York, however, you can see that there is a much higher density of starting points toward the southwest side of the map.
- This map, despite being the ending locations instead of the starting locations, looks practically identical to the map with the starting locations. This does make sense when you consider that these bikes are usually picked up and dropped off from predetermined locations. Therefore, this map along with the starting points map show us that most of the travel on these bikes stays within the high density area of the city on the southwest portion of the map.
Provide a high-level summary of the results and two additional visualizations that you would perform with the given dataset.
Based on our findings from the visualizations above, it seems like there could be some good potential for success using a bike sharing program in Des Moines, Iowa. Some considerations for starting the program would be to incentivise people to subscribe to the system early and often, as our data suggests subscribers utilize the bikeshare service far more frequently than non-subscribers (which is great, because this suggests it's not all tourists using the bikes in New York). Additionally, it might be worth considering focusing on densely populated areas and putting bike stations relatively close together. This is because most of the data in New York came from very densely populated areas, and most trips were very short, suggesting that users were not traveling large distances between their starting point and ending point.
For future analysis, I would consider the following two additional vizualizations in order to better determine whether or not this investment will be a success in Des Moines, Iowa.
- The amount of bikeshare users that are also car owners in NYC. It may be helpful to know if car owners are using bikeshare services, as I would suspect there are far more car owners per capita in Des Moines relative to NYC, which could cut out a significant portion of our potential users.
- It is also worth looking at bikeshare data in a colder month, such as Febraury, to see how frequently the bikes are used when the weather is far less ideal to ride a bike in. If bike usage significantly declines in the winter months, we may need to adjust our business strategy, as Iowa can get just as cold as New York, especially with the winds that the midwest can experience.