Divvy is a bike-sharing system boasting hundreds of stations and over 3000 bicycles across Chicago. People can keep a bike out for 30 minutes. There are more than 4,000 rental records everyday.
To analyze Divvy’s traffic pattern and the system’s user demographics, I visualized the data on Openstreetmap. Moreover, I developed a visualization system for the Divvy company that helps them dispatch their bikes more efficiently.
I sorted and analyzed data using Python, developing the visualization interface with D3.js.
In the task, I visualized the flow of Divvy Bikes and each station's user composition. Every circle on the map stands for a station, while the size of the circle is proportional to the total number of trips made. Users can select different hours of a day to see the total average of bikes in and out of stations on the map. They can also select circles on the map to see the character of the various stations in the hourly pie chart on the right panel. Besides, they can see the overview of station traffic by day below the map.
Firstly, I discovered that Clinton St & Washington Blvd Station has the most subscribers and Streeter Dr & Illinois St Station has the most customers. Besides, I found that weekends are the peak time for customers while weekdays are the peak time for subscribers. 5 pm is the peak time for commuters on a daily basis. On the other hand, there were more subscribers in December.
I found that some bikes are frequently ridden because they are only ridden in some area. Frequently being used can cause bikes or their components to be more susceptible to damage.
To maintain these bikes and reduce the amount of time they are used, I developed this system with visualization methods. The Divvy company can use the system to see which bikes are most frequently ridden, and which bikes are used least frequently. The system recommends exchanges between two bikes' location and the Divvy company can re-dispatch their bikes by referencing to the given suggestions.