Analyzing the Stability of Gun Violence Patterns During the COVID-19 Pandemic in Syracuse

Gun violence is a leading cause of death in the US. Understanding the spatial patterns of gun violence and how they may have been affected by the COVID-19 pandemic is essential for developing evidence-based prevention strategies. This study investigates whether the COVID-19 pandemic altered the spatial patterns of gun violence in Syracuse, New York. Using data from 2009 to 2023, we analyzed the annual incidence of gunshots at the census block group level and applied geospatial techniques including mean center, standard distance, Moran’s I, and Getis-Ord Gi* to assess its temporal trends and spatial clustering. Despite a reversal in the linear trends of annual gunshot counts, which declined before and rose during the pandemic, various spatial patterns of gun violence remained remarkably stable. The geographic center and dispersion of gun violence showed minimal variation, and gun violence continued to cluster within consistent “hot spot” neighborhoods throughout the entire study period of 14 years. Moreover, the intensity of clustering increased logarithmically with gunshot counts, suggesting that higher gun violence rates intensified within established areas rather than spreading citywide. A distance-decay effect further revealed that incidence of gunshots diminished with increasing distance from core hot spots, challenging assumptions of spatial spillover or contagion models. These findings suggest that entrenched structural conditions, such as racial segregation and neighborhood-level socioeconomic disparities, may be primary drivers of gun violence patterns, rather than temporary disruptions like pandemic-related policies. Methodologically, the study highlights the importance of long-term, fine-scale geospatial analyses to uncover persistent violence dynamics and guide preventive interventions. We argue that future violence prevention strategies should focus on enduring spatial patterns of gun violence and their underlying structural determinants, rather than reacting solely to short-term fluctuations in incident frequency.