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AbstractMuch 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.

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.

Abstract
Crime data are problematic: Crimes that are never reported undermine its validity and differences in police recording practices affect its reliability. However, the true extent of these problems is not well known, with existing studies suffering from a number of methodological limitations. We examine the quality of police recorded crime data and survey-based crime estimates recorded in England and Wales using a robust latent trait model that effectively represents the competing sources of error. We find that whilst crime rates derived from police data systematically underestimate the true extent of crime, they are substantially more reliable than estimates from survey data. Reliability is lower for violence and criminal damage and is getting worse over time.

AbstractIt is well known that police recorded crime data is susceptible to substantial measurement error. However, despite its limitations, police data is widely used in regression models exploring the causes and effects of crime, which can lead to different types of bias. Here, we introduce a new R package ('rcme': Recounting Crime with Measurement Error) that can be used to facilitate sensitivity assessments of the impact of measurement error in analyses using police recorded crime rates across a wide range of settings. To demonstrate the potential of such sensitivity analysis, we explore the robustness of the effect of collective efficacy on criminal damage across Greater London's neighbourhoods. We show how the crime reduction effect attributed to collective efficacy appears robust, even when most criminal damage incidents are not recorded by the police, and if we accept that under-recording rates are moderately affected by collective efficacy.

Crime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice, statistics-driven decision-making and predictive analytics. The fuel that has driven this growth is data – and one of its most pressing challenges - is the lack of research on the use and interpretation of data sources. This accessible, engaging book closes that gap for researchers, practitioners and students. International researchers and crime analysts discuss the strengths, perils and opportunities of the data sources and tools now available and their best use in informing sound public policy and criminal justice practice