The COVID-19 outbreak and the far-reaching lockdown measures are having direct and indirect effects on complex social domains, including opportunities for crime offline and online. This paper presents preliminary analyses about the short-term effect of COVID-19 and lockdown measures on cyberdependent crime and online fraud in the UK. Time series analyses from data about crimes known to police between May 2019 and May 2020 are used to explore the extent to which cybercrime has been affected by the COVID-19 outbreak. More specifically, we examine whether cybercrime has suffered an increase during the months with the strictest lockdown restrictions, as an effect of the displacement of crime opportunities from physical to online environments. Results indicate that reports of cybercrime have increased during the COVID-19 outbreak, and these were remarkably large during the two months with the strictest lockdown policies and measures. In particular, the number of frauds associated with online shopping and auctions, and the hacking of social media and email, which are the two most common cybercrime categories in the UK, have seen the largest increases in the number of incidents. The increase in cyber-dependent crimes has mainly been experienced by individual victims rather than organisations.

The unprecedented changes in routine activities brought about by COVID-19 and the associated lockdown measures contributed to a reduction in opportunities for predatory crimes in outdoor physical spaces, while people spent more time connected to the internet, and opportunities for cybercrime and fraud increased. This article applies time-series analysis to historical data on cybercrime and fraud reported to Action Fraud in the United Kingdom to examine whether any potential increases are beyond normal crime variability. Furthermore, the discrepancies between fraud types and individual and organizational victims are also analyzed. The results show that while both total cybercrime and total fraud increased beyond predicted levels, the changes in victimization were not homogeneous across fraud types and victims. The implications of these findings on how changes in routine activities during COVID-19 have influenced cybercrime and fraud opportunities are discussed in relation to policy, practice, and academic debate.

Objectives Police-recorded crimes are used by police forces to document community differences in crime and design spatially targeted strategies. Nevertheless, crimes known to police are affected by selection biases driven by underreporting. This paper presents a simulation study to analyze if crime statistics aggregated at small spatial scales are affected by larger bias than maps produced for larger geographies. Methods Based on parameters obtained from the UK Census, we simulate a synthetic population consistent with the characteristics of Manchester. Then, based on parameters derived from the Crime Survey for England and Wales, we simulate crimes suffered by individuals, and their likelihood to be known to police. This allows comparing the difference between all crimes and police-recorded incidents at different scales. Results Measures of dispersion of the relative difference between all crimes and police-recorded crimes are larger when incidents are aggregated to small geographies. The percentage of crimes unknown to police varies widely across small areas, underestimating crime in certain places while overestimating it in others. Conclusions Micro-level crime analysis is affected by a larger risk of bias than crimes aggregated at larger scales. These results raise awareness about an important shortcoming of micro-level mapping, and further efforts are needed to improve crime estimates.

For decades, criminologists have been aware of the severe consequences of the dark figure of police records for crime prevention strategies. Crime surveys are developed to address the limitations of police statistics as crime data sources, and estimates produced from surveys can mitigate biases in police data. This paper produces small area estimates of crimes unknown to the police at local and neighbourhood levels from the Crime Survey for England and Wales to explore the geographical inequality of the dark figure of crime. The dark figure of crime is larger not only in small cities that are deprived but also in wealthy municipalities. The dark figure is also larger in suburban, low-housing neighbourhoods with large concentrations of unqualified citizens, immigrants and non-Asian minorities.

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