Impact Assessment Through Interrupted Time Series: Divergent Influences of Stay-at-Home Order on Socioeconomically Disadvantaged Areas in NYC Shooting Incidents.

Abstract

This study examines the impact of the COVID-19 pandemic and stay-at-home (SAH) orders on gun violence in New York City (NYC), with a focus on variations across neighborhood demographic and socioeconomic characteristics. Using a 4-year longitudinal and geospatial analysis, we investigate the relationship between socioeconomic factors (e.g., poverty, unemployment, and minority presence) and shooting incidents, as well as how SAH orders shaped these trends. A detailed heatmap visualizes the distribution of shooting incidents, revealing concentrations in the Upper Bronx and Central Brooklyn. Hypothesis testing was conducted with a negative binomial regression model using interrupted time series analysis. We found neighborhoods with higher proportions of Black, Hispanic, unemployed, and low-income residents experienced more shootings. Shooting incidents were unusually low at the begining of the SAH order but increased steadily over time, peaking dramatically after the order were lifted. Unemployment drove a sharp rise in shooting incidents during the SAH period, while poverty contributed to a more sustained impact on violence in the post-SAH period. The findings highlight the disproportionate burden of COVID-19 pandemic on vulnerable groups and the evolving influence of SAH orders on gun violence.