NYC Bicyclists Life of Data Visualization 🚲 📈

7 min readMay 9, 2019

Introduction

This project is the extension from my previous 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 reach the needs of New York cyclists and create better bicycle living spaces in the city in the future. Thus, I decided to try to understand the experience of New York cyclists and bicycle environment from past to now by visualizing the general riders from 15 years ago and modern Citibike population that shows the user experience of exploring the city in recent years, based on open datasets “NYCDCP Manhattan Bike Counts — On Street Weekday”, “ Citi Bike Daily Ridership and Membership Data”, and “ Citi Bike Trip Histories “.

Let’s Play with the Data Results on Tableau Interactive Visualization Dashboards First 👋

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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 that help users find the information they want to see.

The overall data visualization on dashboards on Tableau

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

Findings & Recommendations

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

Bicyclist user numbers and non Citibike bicyclist users numbers

I collected data from each fall season in different 10 streets during 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 the Citibike share program was not very popular during this period. Since the 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.

Men aged 31 are the majority group of Citibike

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

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

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.

From the above picture, we can see that the majority of users from 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 an annual membership.

2019 Jan-May Citi Bike Daily Ridership and Membership

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 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.

I collected data from each fall season in different 10 streets during 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.

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.

Usability Testing

I recruited two participants who are all office workers to help me conduct it. In order to recruit participants to find the most useful feedback, only those who love to ride a bike in the city, or often ride Citibike were invited to join our research.

I asked them to look at and interact with Tableau workbook, and then I asked them 2 question:

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

Users expressed that the data show in charts are clear and they can understand in a short time. 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. 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 all believe that using such interactive sheets to visualize 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 time. They can select one of a specific data to see the result of visualization.

Regarding the filter on the sheet of “Bike lanes and higher volume locations” which I showed in the previous section, I designed the location filter that all options were checked and put locations in rows in Tableau. But one of the users told me that it cause the sheet too long to read since the page height depends on the filter length. Therefore, I changed my design and put the locations from rows to columns in Tableau so my chart could become more clear.

Reflection

Due to time limited, I hope in the future I can 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.