Citi Bike Data Visualization for Bike Parking

Posted on Feb 29, 2016
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Citi Bike Data Visualization for Bike Parking Photograph: Mario Tama

Contributed by Yun-Ju Huang. She took NYC Data Science Academy 12 week full-time Data Science Bootcamp program from Jan 23th to Apr 1st, 2016. The post was based on her first class project (due the 4th week of the program).

Introduction: To Bike to feel the Vibe of NYC

According to "",  New York city is one of the top ten biker-friendly cities. Data shows there are more than 500,000 New Yorkers weaving among cars and trucks per month. The officers in the New York MTA and Citi Bike managing departments believe that the commuting trend of riding bikes has already become the crucial flow of transit, yet the trend is still growing!

The bike parking problem increased with the growing trend of bike riding. The urban Yuppie doesn't want to take the risk of parking their expensive bikes on the side of the New York streets. Thus, the young entrepreneur, Liza Perez came up with a business plan to create removable bike parking spaces, and provide a solution to the bike parking problem. The details of her proposal are in her Pedal2Park business plan. I would like to narrow down her removable parking locations to achieve her business goals.

Citi Bike Data Visualization for Bike Parking cited from Park2Pedal, Liza Perez

Dataset Description

Accumulating the user records in 2015 peak season( from August to October), I did some data analytics on Citi Bike. By analyzing the open data of Citi Bike's in NYC, I could provide more solid information to help Liza get the latest time bike parking market insights to plan their future marketing strategies.  I gathered the data from Citi bike website, which had 14581031 variables. The data contents include the Citi bike stop locations (longitude and latitude), riding times, gender, age etc.

Goals: Knowing Potential Customers

User Type:

Near 6/7 of the users are subscribers. Those subscribers are more likely to become private bike owners once they become regular bike users.

Citi Bike Data Visualization for Bike Parking

Age of Citi Bike Users:

We can predict the age of our target customers by knowing the age of the most frequent Citi Bike Users.

The age range of users is between 25 to 35 years-old.


Gender of Citi Bike Users:

The population of male users is larger than female user population, therefore, we may assume that the majority bike users are male.


Age Distribution:

The age range of male Citi bike users is between 15 to 80 with the highest concentration being between 25 to 30. However, the population of female users is far less than male users. To be more specific, the range of female users is narrower than male users. The age of female users is between 15 to 75 with the highest concentration being between 25-35.


Citi Bik Users  Hot Stops & Household Income Map:

We should choose an area with higher household income such as Brooklyn because the implementation fee will be lower than in Manhattan.


Predict Bikes Removable Containers Settle Locations:

           Popular Spots   perspective parking place


I discovered the most popular Citi Bike stops with high household income locations in Brooklyn. Most bike riders started or ended those stops frequently that it becomes their daily route. In other words, this place would be a great place to locate a removable bike parking space. In the first chart, the deep purple area is the optimal location for a container parking space. Liza should start Park2Pedal -- removable bike parking business in the deep purple location.

About Author

Yun-Ju Huang

A recent graduate of the NYU Integrated Marketing program, Claudia Huang specializes in marketing analysis. Immersed in a creative, trend-sensitive environment, she learned to integrate marketing channels, acquired data analytical skills, and forged a branding mindset. When she...
View all posts by Yun-Ju Huang >

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