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This paper inspects changes in crime trends brought about by the COVID-19 pandemic in Tamil Nadu, India. Using Bayesian structural time-series models, the authors study the changes in two metrics of criminal activity – crime registration and distress calls. In addition to absolute changes, the authors analyse the relative changes in one metric with respect to the other during two stay-at-home orders across six categories of crimes: property offences; offences against the human body; cases of missing persons and unidentified dead bodies; vehicle- and traffic-related offences; crimes against women, children and elders; and emerging crimes. While there was an overall decrease in most types of crime, results show that each category was impacted by restrictions in human mobility in different ways; emerging crimes, in particular, increased significantly during both periods. The crime and call trends exhibited a synchronous pattern with respect to a majority of offences except in the category of crimes against women and missing persons, where a contrary trend of decreasing crime registrations and increasing distress calls was noticed during the partial lockdown period. This finding highlights inherent inadequacies such as under-reporting of crimes by victims and non-registration of crime by the police. Understanding these inadequacies could potentially enhance preparedness leading to improved public service delivery by reordering priorities. Breaking down these trends at a crime-specific level assists law enforcement practitioners in gaining insights on crime motivators and enablers, thus contributing towards more effective crime prevention under ordinary circumstances. [ABSTRACT FROM AUTHOR]

This study uses structured literature mapping to review worldwide trends in traffic safety following the phenomenon of the COVID-19 pandemic. Motivated by dissimilar findings globally and a lack of evidence from emerging nations which have been significantly more affected by road traffic crashes, the study examines the impact of the pandemic-induced lockdown on road traffic deaths and injuries in Tamil Nadu, India. Using a holistic approach, methods such as ARIMA, Holt-Winters, Bayesian Structural Time Series, and Generalized Additive Model are employed for counterfactual prediction, to draw a causal inference of lockdown on traffic safety. In line with global studies, a substantial reduction in traffic crashes, injuries, and fatalities during lockdowns has been found. However, the comparison of relative differences shows that the number of grievous injuries reduced more than minor injuries, crashes, or fatalities. Furthermore, these relative differences were sustained even when metrics returned to normalcy in the post-lockdown phases. Further spatial stratification at two levels (cities and districts) shows that the macroscopic state-level trends are also broadly seen in the sub-units. This validates the consistency of trends across rural-urban differences and shows that, despite variations in the degree of enforcement of the lockdown within Chennai city, contrary to expectation, increased police presence did not have a differential impact on road crashes. [ABSTRACT FROM AUTHOR]