Authors - Sudharsan Vasudevan, Venkata Durga Kavya Bhatta, Krishna Mohan Bathula Abstract - New York City is experiencing continued population growth, driven by both documented and undocumented immigration. With over 8.8 million residents and a population density exceeding 29,000 individuals per square mile, this surge has intensified pressure on the city’s housing and shelter systems. The resulting overcrowding has contributed to a decline in living conditions and a rise in homelessness, especially among vulnerable and underserved populations. One particularly affected group is runaway and homeless youth (RHY), who face heightened risks such as trauma, exploitation, and limited access to essential services like education and healthcare. This study explores trends within the New York City Department of Homeless Services (DHS) shelter system, analysing key factors that influence shelter occupancy, exit patterns, and disparities based on demographics, education, and race or ethnicity. The research applies data science and machine learning techniques to forecast occupancy trends and identify variables associated with successful exits from shelters. It examines the operational efficiency, accessibility, and equity of the shelter system to uncover systemic challenges and areas for improvement. This study aims to provide actionable insights by utilizing a data-driven approach that can support informed decision-making, enhance service delivery, and guide long-term policy development. The findings are intended to help optimize resource allocation and promote more effective housing strategies. Overall, this work seeks to contribute to sustainable solutions for reducing homelessness in New York City and improving outcomes for those experiencing housing instability.
