“Impressive in the timeliness along with it being a beautiful interactive piece that supports a much-needed untold story with data and presenting it in a digestible way.” —Aruna Mall
“I thought this was a great presentation of data regarding how MTA ridership was affected by the pandemic. I particularly liked how it deliberately walked users through the content before allowing them to dive in on their own.” —Jill Toloza
Overview: MTA Ridership Changes due to COVID-19 recounts what happened to New York City during the first wave of the coronavirus pandemic. Data visualization agency TWO-N began the project wanting to create a data-driven narrative that would engage New Yorkers and urban data enthusiasts to examine the ways different communities were affected by the unfolding pandemic. The MTA subway system, by being such a ubiquitous vector of mobility throughout the five boroughs, provided a unique lens through which viewers could understand the pandemic’s effects on the city’s populace, especially in terms of privilege and who is most at risk.
MTA Ridership Changes due to COVID-19 went through several iterations in order to find the most compelling perspectives, such as the relationship between the pandemic, socioeconomic status and changes in ridership—or one’s ability to stay home.
The site was built in JavaScript and leverages D3.js, Scrollama and Webpack.
The project took TWO-N approximately three months to complete, from mid-May to mid-August, as the reality of working remotely began to set in.
Comments by Aucher Serr:
What was the most challenging aspect of the project? “Deciding what data would best tell a story about people in New York during this pandemic. After opting to use MTA ridership data, we grappled with finding a metric that would clearly articulate pandemic-related changes in an accessible yet expressive way. Ultimately, we focused on ‘percent still riding’ to ground our analyses to the people affected.”
How does the design of the site help communicate the information? “An important design mechanism we used was maintaining the visual constancy of each MTA station as the site transitions through various representations. This was critical for us to be able to connect the story through multiple geographic and socioeconomic dimensions.”
Did you learn anything new during the development process? “Most illuminating were the significant differences in ridership across the city. This prompted us to ask, Who were the people still riding? To answer this question, we pulled in census demographic data from the area surrounding each station. We observed a startling relationship between socioeconomic dimensions and relative ridership. Notably, residents of less-affluent neighborhoods—many of whom are frontline workers—were riding in higher proportions, though many of them lacked basic protections such as health insurance.”
