Researcher uses data science to address homelessness

Data science and social work faculty member Hsun-Ta Hsu combines machine learning with data and community feedback.

In the U.S., more than 650,000 people don’t have homes — up 12% in 2023. That’s the largest jump seen since the government began collecting this data in 2007. The Triangle is no exception. More than 6,000 people identify as homeless in Raleigh and Wake County. Durham now has twice as many unsheltered individuals as in 2020.

These numbers drive Hsun-Ta Hsu, who’s spent the last decade working with some of the largest homeless populations in the country, in Los Angeles and St. Louis, using innovative tools to address this problem.

In July 2023, Hsu’s unique skillset led him to Carolina, where he is a professor in both the UNC School of Social Work and the new UNC School of Data Science and Society.

“Dr. Hsu is a prime example of how interdisciplinary data science can create insights that transform a seemingly intractable, multilevel social issue into something solvable,” SDSS Dean Stan Ahalt says.

Ramona Denby-Brinson, dean of social work, agrees about Hsu’s skills. “His work advances our understanding of neighborhood structures, the development of effective intervention programs and services, and how we can employ social networks in more practical terms to produce better health and behavioral outcomes for the unhoused.”

A human right

Hsu learned about social work in high school when his adviser recommended that he major in it based on his background and interests. In college, he earned bachelor’s, master’s and doctoral degrees in social work.

“I had relatives and people I was close to who were suffering with mental health-related issues, including suicide attempts and substance abuse,” he shares. “When I was younger, I didn’t know how to deal with it. So I was really thinking about that — and I wanted to do something about it.”

In 2010, at the start of his doctoral program at the University of Southern California, Hsu got his first look at the 50-block area of Los Angeles known as Skid Row. “I saw a young mother in a wheelchair breastfeeding her baby, surrounded by tents, bad smells and extreme poverty,” Hsu recalls. “That’s not OK. To me, housing is a human right.”

Hsu analyzed data from interviews with people housed by the Los Angeles Homeless Service Authority. He documented neighborhood characteristics for 50 blocks, a time-consuming, labor-intensive process that he thought technology could improve.

After a summer fellowship at the USC Center for Artificial Intelligence in Society, he started developing a mapping tool that uses machine learning to automate the identification of objects like garbage and broken-down cars.

Community-centered research

Since 2010, cities across the country have used another tool, the vulnerability index, to prioritize who gets housing.

“It’s a triage tool like we use in the emergency room,” Hsu explains. “We are measuring how vulnerable one is on the street and then bumping them up on the priority list to get them housing.”

In 2019, Hsu teamed up with CAIS researcher Eric Rice to improve this tool by combining demographic data with feedback from community stakeholders. They said they want to be considered for housing based on assets, not deficits. This “super important” feedback helped Hsu and Rice revise the vulnerability index survey to include questions focused on an individual’s positive traits.

Now Hsu is bringing this project model to rural communities, where nearly 87,000 Americans experience homelessness. Hsu believes his research in both rural and urban homeless populations will aid future projects in North Carolina and beyond.

“Homelessness is a national issue,” Ahalt stresses. “This research will create a replicable process that can be used in North Carolina and across the country.”

Read more about Hsun-Ta Hsu’s work.