Spatio-temporal stratified associations between urban human activities and crime patterns: a case study in San Francisco around the COVID-19 stay-at-home mandate

Chen, Tongxin; Bowers, Kate; Zhu, Di; Gao, Xiaowei; Cheng, Tao

Abstract

Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities has not been explored in depth in empirical studies. Here, we aim to quantitatively measure the spatio-temporal stratified associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the stratified associations between dynamic human activities and crimes in urban areas. In a case study of San Francisco, United States, we first identify human activity zones (HAZs) based on the similarity of daily footfall signatures on census block groups (CBGs). Then, we examine the spatial associations between crime spatial distributions at the CBG-level and the HAZs using spatial stratified heterogeneity statistical measurements. Thirdly, we use different temporal observation scales around the effective date of the SAH mandate during the COVID-19 pandemic to investigate the dynamic nature of the associations. The results reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits a higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centred around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights into that tailored crime intervention strategies need to consider human activity variables.

Citation

Chen, T., Bowers, K., Zhu, D., Gao, X., & Cheng, T. (2022). Spatio-temporal stratified associations between urban human activities and crime patterns: a case study in San Francisco around the COVID-19 stay-at-home mandate. Computational Urban Science, 2(1), Article 13. https://doi.org/10.1007/s43762-022-00041-2

• Journal Article Type: Article
• Acceptance Date: May 8, 2022
• Online Publication Date: Jun 6, 2022
• Publication Date: Jun 10, 2022
• Deposit Date: Nov 6, 2024
• Publicly Available Date: Nov 8, 2024
• Journal: Computational Urban Science
• Electronic ISSN: 2730-6852
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Issue: 1
• Article Number: 13
• DOI: https://doi.org/10.1007/s43762-022-00041-2
• Keywords: Spatio-temporal stratified association; Crime pattern analysis; Human activity; Social sensing; COVID-19
• Public URL: https://hull-repository.worktribe.com/output/4909966