Citi Bike Analysis

Based on the usage of Citi Bikes in New York available from Citi Bike website (https://www.citibikenyc.com/system-data) , I created a series of bubble maps for each hour of the day to show which stations are more frequently used than others.

You can watch the video in this link: https://www.youtube.com/watch?v=glH-pzARA28

● The data I used are in September and February of 2018

● Yellow bubbles show frequency of users starting their trips at the station

● Blue bubbles show frequency of users ending their trips at the station

● Size of bubbles is proportional to frequency of usage at each station (i.e. larger yellow bubbles at a station mean more users start their trips at the station)

● The snapshots were generated at each hour (i.e. snapshot at 9am show frequency of trips taken at each station between 9am and 10am)

● I used Python packages including Pandas, Numpy, and Folium in order to create data structures and bubble map

Snapshot at 9AM: