An Exploratory Study on Murders in the Chaos of COVID-19: An Analysis of Changes in Murder Rates and Patterns in Trinidad and Tobago

Abstract

The study assessed the changes in murder counts and patterns under COVID-19 conditions in The Republic of Trinidad and Tobago. Initial research indicates that crime rates and patterns have changed under the COVID-19 pandemic possibly because of government implemented restrictions. The specific impact of these responses on murder has not been examined. To fill this gap in the literature the study utilized several interrupted time-series analyses to assess the change(s) in murder trends and their possible relationship to restrictions in movement associated with COVID-19. Overall, murder decreased while under COVID-19 conditions. However, the change was not consistent across different classifications of murder, with some increasing while others continued a downward trend that started before COVID-19 restrictions. The findings suggest that the change in murder trends under COVID-19 was not based on a linear relationship with restrictions in movement. Rather the results suggest that the data contains nonlinear components, which are associated with an initial but inconsistent change in murder counts among different classifications of murder. Which was followed by a phase of diminishing return on the effect of restriction on movement on the murder count. Further, in support of the findings the forecasting of murder was found to be better achieved by an algorithm which supports continuous non-linear functions (Artificial Neural Networks) when compared to a linear approach (Bayesian Structural Time-Series). Supplementary Information The online version contains supplementary material available at 10.1007/s43576-022-00075-w.

Bayesian Structural Time Series and Geographically Weighted Logistic Regression Modelling Impacts of COVID-19 Lockdowns on the Spatiotemporal Patterns of London’s Crimes

“…In line with this approach, our study utilises monthly average temperatures (both highest and lowest) and the count of holiday days from March 2011 to March 2021 in London as covariates for the BSTS model. However, existing research has a notable limitation in generating a single counterfactual prediction curve for the entire lockdown period; for example, in Figure 4, by Troy Smith et al, the blue-dashed line represents the counterfactual murder crime prediction generated by the BSTS model [37]. In this sense, the prediction may be biased to sudden changes incurred by the first lockdown, whilst it is well acknowledged that London had indeed experienced three lockdowns, with each posed by distinct policies; it further made the universal modelling risky in overlooking potential inter-lockdown disparities.…”