  New York City faces the challenge of an ever-increasing homeless population
with almost 60,000 people currently living in city shelters. In 2015,
approximately 25% of families stayed longer than 9 months in a shelter, and 17%
of families with children that exited a homeless shelter returned to the
shelter system within 30 days of leaving. This suggests that "long-term"
shelter residents and those that re-enter shelters contribute significantly to
the rise of the homeless population living in city shelters and indicate
systemic challenges to finding adequate permanent housing. Women in Need (Win)
is a non-profit agency that provides shelter to almost 10,000 homeless women
and children (10% of all homeless families of NYC), and is the largest homeless
shelter provider in the City. This paper focuses on our preliminary work with
Win to understand the factors that affect the rate of readmission of homeless
families at Win shelters, and to predict the likelihood of re-entry into the
shelter system on exit. These insights will enable improved service delivery
and operational efficiencies at these shelters. This paper describes our recent
efforts to integrate Win datasets with city records to create a unified,
comprehensive database of the homeless population being served by Win shelters.
A preliminary classification model is developed to predict the odds of
readmission and length of shelter stay based on the demographic and
socioeconomic characteristics of the homeless population served by Win. This
work is intended to form the basis for establishing a network of "smart
shelters" through the use of data science and data technologies.
