Abstract
Objectives: This study uses two cluster detection techniques to identify clusters of violent crime during the 3 months of the 2020 COVID-19 lockdown in Miami-Dade County compared to that during an equivalent period in 2018 and 2019. Methods: Violent crime data from the Miami-Dade Central Records Bureau were analyzed. The Local Indicators of Spatial Association statistics and a space‐time permutation statistic were used to identify clusters of violent crimes and outliers, and Global Moran’s I tool was used to assess spatial patterning in violent crime. Neighborhood disadvantage data were obtained from the American Community Survey 5-year estimates linked with arrest locations. Results: Violent crime arrests fell by 7.1% in 2020. Arrests were concentrated in predominantly Black disadvantaged neighborhoods in the northern part, and similar results were produced for core clusters by the two cluster techniques with positive global Moran’s I for all study years. Although accounting for only 17% of the county population, nearly half of violent crime arrests were for Black or African American. Males comprised most violent crime arrests. Conclusions: Crime prevention and intervention efforts should be focused on both high-risk places and offenders.
| Original language | English |
|---|---|
| Pages (from-to) | 97-106 |
| Number of pages | 10 |
| Journal | Journal of Experimental Criminology |
| Volume | 19 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- COVID-19
- Geographic analysis
- GIS
- Miami-Dade County
- Neighborhood Health
- United States
- Violence
- Violent crime
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