Chen Peng, Kurland Justin, Piquero Alexis, Borrion Herve
School for Informatics Cyber Security, People's Public Security University of China, Beijing, People's Republic of China.
School of Criminal Justice, Forensic Science, and Security, The University of Southern Mississippi, Hattiesburg, USA.
J Exp Criminol. 2021 Nov 9:1-28. doi: 10.1007/s11292-021-09486-7.
The study examines the variation in the daily incidence of eight acquisitive crimes: automobile theft, electromobile theft, motorcycle theft, bicycle theft, theft from automobiles, pickpocketing, residential burglary, and cyber-fraud before the lockdown and the duration of the lockdown for a medium-sized city in China.
Regression discontinuity in time (RDiT) models are used to test the effect of the lockdown measures on crime by examining the daily variation of raw counts and rate.
It is indicated that in contrast to numerous violent crime categories such as domestic violence where findings have repeatedly found increases during the COVID-19 pandemic, acquisitive crimes in this city were reduced during the lockdown period for all categories, while "cyber-fraud" was found more resilient in the sense that its decrease was not as salient as for most other crime types, possibly due to people's use of the internet during the lockdown period.
The findings provide further support to opportunity theories of crime that are contingent upon the need for a motivated offender to identify a suitable target in physical space.
本研究考察了中国一个中等规模城市在封锁前八类 acquisitive crimes（汽车盗窃、电动自行车盗窃、摩托车盗窃、自行车盗窃、汽车内盗窃、扒窃、住宅入室盗窃和网络诈骗）的每日发生率变化以及封锁持续时间。
采用时间回归断点（RDiT）模型，通过检查原始计数和发生率的每日变化来测试封锁措施对犯罪的影响。
结果表明，与众多暴力犯罪类别（如家庭暴力，研究反复发现其在 COVID-19 大流行期间有所增加）不同，该城市的所有 acquisitive crimes 类别在封锁期间均有所减少，而“网络诈骗”在某种意义上表现出更强的韧性，即其下降幅度不如大多数其他犯罪类型那么显著，这可能是由于人们在封锁期间使用互联网所致。
这些发现为犯罪机会理论提供了进一步支持，该理论取决于有动机的犯罪者在物理空间中识别合适目标的需求。
