Big data can make a big difference in people’s lives.
For example, Mason Engineering’s Myeong Lee’s research shows how cities can learn to equalize services between poor and wealthy neighborhoods.
Lee is analyzing 311 data—information that people report about non-emergency issues in their neighborhood such as potholes, fallen trees, graffiti, abandoned cars, and trash—to identify disparities between the services provided to poor areas and those seen in wealthy communities.
“My goal is to use data analytics to increase the fair allocation of resources in cities,” says Lee, an assistant professor in the Department of Information Sciences and Technology.
His research shows that people in poor neighborhoods are less likely to report problems to the 311 service than those in wealthy neighborhoods, one of the reasons that poor areas often aren’t as well maintained.
Lee is analyzing data on multiple factors, including geographic isolation, poverty level, and broadband quality and accessibility.
Previous research has found that people in less affluent communities are more likely to report issues by phone, while wealthier citizens use the internet and apps.
Lee is currently studying 311 data from Boston but plans to build on his data by evaluating information from other big cities such as Washington, D.C., Baltimore, and New York City.
His ultimate goal: Create a product that will help policymakers identify information deserts and reduce inequality. He also plans to build a visualization tool to help track the differences between areas.
This project, funded by the National Science Foundation, is far different from his previous work as a software engineer. He was one of the founders of a company in South Korea that’s building emotional robots that act like pets and follow people around to keep them company.
That job was creative, but this one is even more challenging and meaningful. “This is part of smart city research because my work will help make sure everyone is getting good service.”