NYC bicyclists life visualization

Final Projects


This project is the extension from my previous lab report “Explore Green in NY” which is an interactive bike map for helping bicyclists to explore nature in New York. When I was creating this map, I was very curious about bicycle riders’ experience. I think if we can use past data and visualize it, we can help more people understand the needs of New York cyclists and create better bicycle living space in the city in the future. Thus, I decided to try to understand the bicycle rider data of more than 10 years ago and the Citibike population which can represent modern cyclists image.

Based on these datasets “NYCDCP Manhattan Bike Counts – On Street Weekday”, “Citi Bike Daily Ridership and Membership Data“, and “Citi Bike Trip Histories“, I focus on exploring the bicycle environment and the composition of modern cyclists in New York from the past to now.

Design process

In order to tell a story through data, I chose to use Tableau to visualize the data. For making the label and interpretation of the data clearer, I used open refine at the beginning to delete some redundant and unnecessary data to produce a more effective dataset. With a better dataset, I put them into Tableau for data visualization. Tableau has a powerful image system to help highlight the focus. In order to make it easy for users to view, there are filters or data nodes in every form to find the information they want to see.!/vizhome/Bike0508_2/Totalusernumbers

I collected data from the past 15 years to see where were the most important locations that riders usually went and how bicycle riders population were growth. I also collected data from January 2019 to May 2019 to visualize who are the Citibike main user.

Findings & Recommendations

Finding 1: The number of bikers in New York City has grown steadily over the past 15 years

I collected data from each fall season in different 10 streets durning 2005-2015 to see the bicycle volume. According to the total user numbers above, we can see that the bicycle culture in New York has become more and more popular, and more people choose to ride bicycles in New York City. By comparing the yellow line with the grey line in the figure below, we can see that Citibike share program was not very popular during this period. Since Citibike share program was established from 2013, there is not much data in this period.

Finding 2: From January to May in 2019, men who born in 1988 are the main users of Citibike share program.

Citibike users data collection from January 2019 to May 2019.

I analyzed the data of Citibike users from January to May in 2019. In the open detaset I found, it listed each Citibike users’ age and gender, which can help people understand clearly the age and gender of borrowers. Hence, people are able to get the basic information of the main users during this period. I integrated the data and visualized it, and the result are as shown above.

If we make a more in-depth analysis of the above figure, we can get more detailed data visualization as follows.

Citibike users data collection from January 2019 to May 2019.

As we can see from the above figures, men ride Citibike more often than women during the period from January to May in 2019 in New York.

Citibike users data collection from January 2019 to May 2019.

From the above picture, we can see that the majority of users of January to May in 2019 were born in 1988, and the secondary groups were born in 1987, 1986 and 1990.

Finding 3: From January to May in 2019, most of the people who borrow Citibike might have annual membership.

Citibike users data collection from January 2019 to May 2019.

I also analyzed the data of Citibike ridership and membership from January to May in 2019. Because Citibike stipulates that people who want to rent Citibike must buy a temporary pass or join an annual fee member. Through the visualization of the data above, we can know that Citibike has a tons of annual members and most of the users have annual membership qualifications.

In addition, if we assume that most of the members who are usually willing to apply for annual membership are long-term residents, we can speculate that the emergence of Citibike share program has stimulated resident’s bicycle hobby rather than tourists, and accelerated the bicycle-riding trend throughout the city.

Finding 4: Second Avenue at E7 St., Sixth Avenue at W23 St., and Eighth Avenue at W28 St. had higher bicycle volume during 2005-2015and more bike lane.

Data collection from each fall season during2005-2015.

I collected data from each fall season in different 10 streets durning 2005-2015. These 10 streets above have more bicycle traffic, and are also places where most bicyclists often go. For cyclists, traffic safety should be the first consideration. Thus, I looked up the bike lanes data for these streets, and I wanted to use the data to see if there were safety plans for bicycle riders.

Data collection from each fall season during 2005-2015.

Through data visualization, the bike lanes of these three regions are much more perfect than those of the other seven regions. But the bike sidewalk section is less than the other seven regions.


In the usability testing, I recruited two participants to help me conduct it. In order to recruit participants to give the most useful feedback, only those who love to ride bike in the city, or often ride Citibike or his or her bike. The participants both are office workers.

I asked them to look at and interact with my tableau workbook, and then I ask them 2 question:

  1. Is this similar to the chart you expected?
  2. Some charts have filters next to them. Are these filters helpful to you?


Users all think that they can know the appearance of the data more clearly through these charts. One of the users preferred to look at the horizontal bars. He thought that the horizontal bars was simple, easy to compare and easy to see in a short time. Another user thought the packed bubbles were an interesting way to describe data. Packed bubbles can help him quickly grasp the proportion of different data without knowing how much actual data have.

In addition, users believe that using such interactive sheets to describe data are more efficient than ordinary charts. Because the filters in the sheets can help users find the data they want in a short period of time. Regarding the filter on the sheet of “Bike lanes and higher volume locations”, I designed the location filter that all options were checked and put locations in rows in tableau. But one of the user told me that would cause the sheet too long to read. Therefore, I changed my design. I put the locations from rows to columns in tableau so my chart became more clear. If users are interested in finding fewer items, they can also use filter to select by themselves.


I hope, in the future, I will be able to make a more in-depth discussion of bicycle bikers in New York, such as what the purpose of using bike in the city, how to use bicycles, where to ride more frequently, where to ride more popular places, and how long they ride each time. If we can get a deeper understanding of users’ needs through data visualization, I think we will be able to provide users with better products and create a better bicycle city.