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 cyber-dependent 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.

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.

Few researches have considered fear of crime as a context-specific experience. This article promotes a place-based theoretical framework for studying crime perceptions through presenting app-based and crowdsourcing measures of perception of crime and place as a robust methodological framework. A systematic review of published studies that use crowdsourced or app-based measures to explore perceptions of crime was conducted. From the 27 studies that met our inclusion criteria, reported strengths and limitations were synthesized to determine key developments and common issues, illustrated with data from three app-based studies. We found consensus that app-based and crowdsourcing measures of fear of crime capture more precise spatial and temporal data alongside auxiliary information about the individual and the environment. Practical benefits, such as reduced cost of data collection and implementable outputs that are useful to practitioners were also highlighted. However, limitations around sampling biases, generalizability of findings, and the under-representation of certain areas persist.

Much research has shown that the first lockdowns imposed in response to the COVID-19 pandemic were associated with changes in routine activities and, therefore, changes in crime. While several types of violent and property crime decreased immediately after the first lockdown, online crime rates increased. Nevertheless, little research has explored the relationship between multiple lockdowns and crime in the mid-term. Furthermore, few studies have analysed potentially contrasting trends in offline and online crimes using the same dataset. To fill these gaps in research, the present article employs interrupted time-series analysis to examine the effects on offline and online crime of the three lockdown orders implemented in Northern Ireland. We analyse crime data recorded by the police between April 2015 and May 2021. Results show that many types of traditional offline crime decreased after the lockdowns but that they subsequently bounced back to pre-pandemic levels. In contrast, results appear to indicate that cyber-enabled fraud and cyber-dependent crime rose alongside lockdown-induced changes in online habits and remained higher than before COVID-19. It is likely that the pandemic accelerated the long-term upward trend in online crime. We also find that lockdowns with stay-at-home orders had a clearer impact on crime than those without. Our results contribute to understanding how responses to pandemics can influence crime trends in the mid-term as well as helping identify the potential long-term effects of the pandemic on crime, which can strengthen the evidence base for policy and practice.

Private organizations suffer great losses due to cybersecurity incidents, and they invest increasing resources to prevent attacks, but little is known about the effectiveness of cybersecurity measures for prevention. Based on the framework of Routine Activity Theory, this paper analyzes the impact of companies’ online activities and cybersecurity measures on victimization. Our analysis of the UK Cybersecurity Breaches Survey shows that the most promising ways to minimize cyber-attacks and their impacts is to invest in in-house cybersecurity human resources and enhance the employees’ online self-protection by providing cybersecurity training, rather than just basic software protection and guidance about strong passwords.