The Impact of Urban Facilities on Crime during the Pre- and Pandemic Periods: A Practical Study in Beijing

Xinyu Zhang and Peng Chen ()
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Xinyu Zhang: School for Informatics Engineering and Cyber Security, People’s Public Security University of China, Beijing 100038, China
Peng Chen: School for Informatics Engineering and Cyber Security, People’s Public Security University of China, Beijing 100038, China

IJERPH, 2023, vol. 20, issue 3, 1-16

Abstract: The measures in the fight against COVID-19 have reshaped the functions of urban facilities, which might cause the associated crimes to vary with the occurrence of the pandemic. This paper aimed to study this phenomenon by conducting quantitative research. By treating the area under the jurisdiction of the police station (AJPS) as spatial units, the residential burglary and non-motor vehicle theft that occurred during the first-level response to the public health emergencies (pandemic) period in 2020 and the corresponding temporal window (pre-pandemic) in 2019 were collected and a practical study to Beijing was made. The impact of urban facilities on crimes during both periods was analyzed independently by using negative binomial regression (NBR) and geographical weight regression (GWR). The findings demonstrated that during the pandemic period, a reduction in the count and spatial concentration of both property crimes were observed, and the impact of facilities on crime changed. Some facilities lost their impact on crime during the pandemic period, while other facilities played a significant role in generating crime. Additionally, the variables that always kept a stable significant impact on crime during the pre- and pandemic periods demonstrated a heterogeneous impact in space and experienced some variations across the periods. The study proved that the strategies in the fight against COVID-19 changed the impact of urban facilities on crime occurrence, which deeply reshaped the crime patterns.

Keywords: COVID-19; residential burglary; non-motor vehicle theft; urban facility; negative binomial regression; geographical weight regression; Beijing (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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