Analyzing the stability of gun violence patterns during the COVID-19 pandemic in Syracuse, New York

Abstract

Gun violence is a leading cause of death in the United States. Understanding the geospatial patterns of gun violence and how the COVID-19 pandemic may have affected them is essential for developing evidence-based prevention strategies. This study investigates whether COVID-19 altered the geospatial patterns of gun violence in Syracuse, New York. To assess spatial and temporal trends, we analyzed the annual total gunshots (ATG) from 2009-2023 aggregated in census block groups and applied geospatial techniques including mean center, standard distance, Moran's I, and Getis-Ord Gi*. The ATG number was higher before the pandemic than during the pandemic, something not observed in other studies. Its geographic centers before and during the pandemic clustered within or near one census block and the associated standard distance remained similar between the two periods. Both global patterns and local clusters of ATG in the two periods not only showed similar patterns and consistent local hotspots located in similar areas, but also logarithmically related to the ATG number with statistical significance, suggesting that gun violence rates intensified within established areas rather than spreading citywide and demonstrated a similar distance-decay effect in both periods. This effect suggests that the incidence of gunshots diminished with increasing distance from the core concentrated zone, challenging assumptions of spatial spillover or contagion models in crime studies. These findings suggest that entrenched structural conditions, such as neighborhood-level socioeconomic disparities, are the primary drivers of gun violence patterns, rather than temporary pandemic-related policies. Methodologically, the study highlights the importance of long-term, meso-scale geospatial analyses to uncover persistent violence dynamics and guide preventive interventions. We argue that future violence prevention strategies should focus on enduring geospatial patterns of gun violence and their underlying structural determinants, rather than reacting solely to short-term fluctuations in incident frequency.

Keywords: And COVID-19 pandemic; Geospatial analysis; Gun violence; Hot spots; Spatial clustering.

Fig. 1

The geographic location of the study area and the gunshot data. (a) The location of New York State; (b) The location of the Onondaga County; and (c) The City of Syracuse and the compiled gunshot incidents in the study period from 2009 to 2023

Fig. 2

Illustration of geostatistical analysis based on the 2017 annual total gunshots (ATG) dataset. MC_2017 is the mean center of the 2017 ATGs, SD_2017 is the associated standard distance, and HS_2017 is the hotspot of the 2017 ATGs, termed as the Standardized Hotspot Area (SHA)

Fig. 3

Temporal trends of gunshot incidents (the gray bars) and geostatistical metrics including Standard Distance (blue), Moran’s I (black), and Hotspot area (orange), expressed as Standardized Hotspot Area (SHA)

Fig. 4

Locations of the mean centers for the ATGs in the selected 15 years at the CBG scale and the associated standard distances (i.e., the radii of the circles

Fig. 5

The relationships between geostatistical metrics and the annual total gunshots before (2009–2019) and during pandemic (2020–2023). (a) the Moran’s I; and (b) the SHA

Fig. 6

Variations of geospatial patterns of gun violence in Syracuse. (a) Spatial distributions of individual gunshot incidents in the four zones; (b) The total number of gunshot incidents in each of the four zones before (2009–2019) and during (2020–2023) pandemic; and (c) changes of the total number of gunshots along the four zones in the two periods