Different lockdowns and theft: a Bayesian analysis of COVID-19's impact on urban crime in ZG City, China

Abstract The COVID-19 pandemic and restrictions had an unprecedented impact on human society. Recent studies have shown that the negative effects of pandemic lockdown measures on crime are significant. However, the impact of strict lockdown measures on the spatial distribution of crime at the community level remains underexplored. This study employs Bayesian Structural Time Series to estimate the community-level impact of a complete lockdown (from January 21 to February 24, 2020) and a refined lockdown (from May 21 to June 24, 2021) on theft in ZG City, China. Additionally, based on social disorganization theory and crime pattern theory, we utilize a logistic regression model to explore how social characteristics influence the significant reduction in theft observed in the BSTS analyses. The key findings are: (1) Complete lockdowns achieved a 57% reduction in theft, compared to 15% for refined lockdowns, indicating a stronger suppressive effect of complete lockdowns. (2) Communities experienced significant reductions in theft during the complete and refined lockdowns, while thefts in some communities have experienced a marked increase during the refined lockdown. (3) Under complete lockdowns, the proportion of migrant population significantly positively impacted the reduction in theft. The main contribution of this study is that it provides a comprehensive assessment of the contrasting impacts of the complete and refined lockdown on the temporal and spatial patterns of theft. These insights provide a scientific basis for effective crime prevention and urban governance strategies during emergencies.