Using the latter to help reduce the former is a wonderful idea, sketched out in this article from Fast Co-Exist.
With levels of homelessness at historic highs, New York City is now working to improve how it uses data in order to prevent families from entering the shelter system in the first place.
Much like how police departments around the country are now engaged in “predictive policing,” the Department of Homeless Services is now developing software that analyzes and visualizes the patterns of evictions that lead to family homelessness–and more importantly, predicts which neighborhoods, buildings, and even addresses to target its resources and outreach efforts.
“[Eviction] is a major cause of homelessness for families who come into shelters,” says Sara Zuiderveen, assistant commissioner of prevention services at DHS. “The challenge really comes in the targeting piece, making sure services are reaching the people who are most at risk.”
Among homeless families, about one in three first enter the shelter system after experiencing an eviction. But for the few hundred prevention and outreach staff at the NYC Department of Homeless Services, reaching these families before they show up at a shelter is like trying to find a needle in a haystack–only 5% of the some 200,000 eviction notices filed in New York each year cause families to become homeless.
Typically, evictions take several months from the first court filing to the point when a family gets booted out the door, so in theory that’s a vital window in which DHS and its network of partners has to act, says Zuiderveen. Between 2009 and 2013, about 4,800 families entered a homeless shelter in the months after an eviction filing against them.
DHS is working on the project with the SumAll Foundation, the charitable arm of a NY-based marketing analytics startup. Over the last few months, staff at the foundation spent time analyzing DHS files with shelter check-ins, where a family is asked to provide its most recent address, and matching these addresses to eviction court records. In that way, it could create the visualization (seen below) that highlights which evictions led to homelessness. Its next step was to use that data to find patterns and make predictions.