Spatial patterns of COVID-19 incidence in relation to crime rate across London

Sun, Y., Huang, Y., Yuan, K. , Chan, T.O. and Wang, Y. (2021) Spatial patterns of COVID-19 incidence in relation to crime rate across London. ISPRS International Journal of Geo-Information, 10 (2). ISSN 2220-9964

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COVID-19 containment policies are not only curbing the spread of COVID-19 but also changing human behavior. According to the routine activity theory, owing to lockdown, the closure of entertainment sites (e.g., pubs and bars), an increase in stay-at-home time, and an increase in police patrols are likely to influence chance of committing a crime. In this study, we aimed to further examine the spatial association of COVID-19 infection rate and crime rate. Particularly, we empirically validated the speculation that increase in COVID-19 cases is likely to reduce crime rate. In the empirical study, we investigated whether and how COVID-19 infection rate is spatially associated with crime rate in London. As the spatial data used are mainly areal data, we adopted a spatial regression mode (i.e., the “random effects eigenvector spatial filtering model”) to investigate the spatial associations after controlling for the socioeconomic factors. More specifically, we investigated the associations for all the four crime categories in three consequent months (March, April, and May of 2020). The empirical results indicate that 1) crime rates of the four categories have no statistically significant associations with COVID-19 infection rate in March; 2) violence-against-the-person rate has no statistically significant association with COVID-19 infection rate; and 3) robbery rate, burglary rate, and theft and handling rate have a statistically significant and negative association with COVID-19 infection rate in both April and May.

Additional Information:cited By 6
Keywords:crime rate, COVID-19 infection rate, containment policies, spatial regression
Subjects:L Social studies > L700 Human and Social Geography
Divisions:College of Science > School of Geography
ID Code:49388
Deposited On:23 May 2022 10:00

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