A review of crime trends in Hong Kong during COVID-19: Empirical analysis based on ARIMA model

Abstract

Abstract This study uses the crime dynamics in Hong Kong as the research object and builds an autoregressive integrated moving average model to compare the expected crime rates of common criminal activities with the observed crime rates in order to analyse the impact of COVID-19 on criminal activities in detail. Findings reveal distinct patterns in crime rates, with significant decreases observed in serious assault, burglary, and theft from vehicles, alongside increases in fraud-related crimes and incidents of child abuse. These trends underscore the relationship between changes in routine activities, availability of potential targets, and negative emotions experienced by individuals during the pandemic. The study highlights the importance of considering both Routine Activities Theory and General Strain Theory when analysing the multifaceted impacts of societal changes on crime dynamics. The findings offer valuable implications for researchers, policymakers, and law enforcement agencies in understanding changes of criminal behaviours and developing proactive strategies for crime prevention, and promoting societal well-being in the post-pandemic era.