In this project I was tasked with collecting and interpreting data from the NY Citi Bike Program (A bike share program).
Although much can be seen from the data, I was most curious with the degree of traffic among stations. Obviously, the stations closer to downtown would have higher trafiic, but what about on a relative basis?
To explore this, I manipulated the data with Python and was able to calculated the net inflow/outflow for each station. The results were quite clear, their is a pattern of bike(r) migration from the upper neighborhoods.
At first I believed this was because users would bike to work and taxi home. However, after looking at the busiest times that does not appear to be the case.
Because the weekend sees the greatest traffic, and most rides originate in the early afternoon, I hypothesize the flow of bikes to Downtown is a result of users wanting to meet up with friends and/or grab a drink. Because Downtown/Manhattan has the densest concentration of restaurants and bars, I believe users bike there (and then after heavy meal or a couple drinks!) decide it easier to taxi back to the Upper neighborhoods.