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Introduction: In response to COVID-19, governments imposed various restrictions on movement and activities. According to the routine activity perspective, these should alter where crime occurs. For burglary, greater household occupancy should increase guardianship against residential burglaries, particularly during the day considering factors such as working from home. Conversely, there should be less eyes on the street to protect against non-residential burglaries. Methods: In this paper, we test these expectations using a spatio-temporal model with crime and Google Community Mobility data. Results: As expected, burglary declined during the pandemic and restrictions. Different types of burglary were, however, affected differently but largely consistent with theoretical expectation. Residential and attempted residential burglaries both decreased significantly. This was particularly the case during the day for completed residential burglaries. Moreover, while changes were coincident with the timing and relaxation of restrictions, they were better explained by fluctuations in household occupancy. However, while there were significant decreases in non-residential and attempted non-residential burglary, these did not appear to be related to changes to activity patterns, but rather the lockdown phase. Conclusions: From a theoretical perspective, the results generally provide further support for routine activity perspective. From a practical perspective, they suggest considerations for anticipating future burglary trends.
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Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities has not been explored in depth in empirical studies. Here, we aim to quantitatively measure the spatio-temporal stratified associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the stratified associations between dynamic human activities and crimes in urban areas. In a case study of San Francisco, United States, we first identify human activity zones (HAZs) based on the similarity of daily footfall signatures on census block groups (CBGs). Then, we examine the spatial associations between crime spatial distributions at the CBG-level and the HAZs using spatial stratified heterogeneity statistical measurements. Thirdly, we use different temporal observation scales around the effective date of the SAH mandate during the COVID-19 pandemic to investigate the dynamic nature of the associations. The results reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits a higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centred around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights into that tailored crime intervention strategies need to consider human activity variables.
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OBJECTIVES: Our objectives were (1) to systematically map the contours of the European evidence base on labour trafficking, identifying its key characteristics, coverage, gaps, strengths and weaknesses and (2) to synthesise key scientific research. METHODS: We took a two-phase approach: a systematic map followed by a detailed synthesis of key scientific research evidence. Our search strategy included 15 databases, hand searches of additional journals, backwards searches, snowball searches and expert recommendations. We identified and screened 6106 records, mapped 152 and synthesised eight. RESULTS: Overall, the literature was limited and fragmented. Reports produced by official agencies dominated; academic authorship and peer-reviewed outputs were comparatively rare. Few publications met minimum scientific standards. Qualitative designs outweighed quantitative ones. Publications typically described trafficking's problem profile and/or discussed interventions; they rarely assessed trafficking's impacts or evaluated interventions. Even among the key scientific research, the quality of evidence was variable and often low. Particular weaknesses included poor methods reporting, unclear or imprecise results and conclusions not properly grounded in the data. The synthesised studies were all exploratory, also sharing other design features. Common themes identified included: poor treatment of victims; diversity of sectors affected and commonalities among victims; inadequacies of current responses; and barriers to interventions. CONCLUSIONS: There is a lack of high-quality studies into European labour trafficking. Methodological opacity, insufficient rigour and publication in non-indexed locations impede the identification, assessment and synthesis of evidence. Adherence to higher reporting standards would further the field's development and particular research gaps should be addressed.
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OBJECTIVES: Decades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133-157, 2015) has recently termed the 'law of crime concentration at places'. Such findings suggest that the future locations of crime should-to some extent at least-be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed. METHODS: Using data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641-658, 2004)], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested. RESULTS: Findings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %). CONCLUSIONS: This study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research.
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BACKGROUND: Retailers routinely use security tags to reduce theft. Presently, however, there has been no attempt to systematically review the literature on security tags. Guided by the acronym EMMIE, this paper set out to (1) examine the evidence that tags are effective at reducing theft, (2) identify the key mechanisms through which tags are expected to reduce theft and the conditions that moderate tag effectiveness, and (3) summarise information relevant to the implementation and economic costs of tagging. METHODS: In this mixed-methods review, we performed systematic keyword searches of the published and unpublished literature, hand searched relevant journals, conducted forward and backward citation searches and consulted with four retailers. Studies were included if they reported an explicit goal of reducing the theft or shrinkage of items through the use of security tags in retail environments. RESULTS: We identified 50 eligible studies, eight of which reported quantitative data on the effectiveness of tags in retail environments. Across these eight studies, five showed positive results associated with the introduction of tags, but heterogeneity in the type of tag and reported outcome measures precluded a meta-analysis. We identified three mechanisms through which tags might plausibly reduce theft-increase the risks, reduce the rewards, increase the effort-which were found to vary by tag type, and their activation dependent on five broad categories of moderator: retail store and staff, customers (including shoplifters), tag type, product type, and the involvement of the police and criminal justice system. Implementation challenges documented in the literature related mainly to staffing issues and tagging strategy. Finally, although estimates are available on the costs of tagging, our searches identified no high-quality published economic evaluations of tagging. CONCLUSIONS: Through applying the EMMIE framework this review highlighted the complexity involved in security tagging in retail environments, whereby different kinds of tags are expected to reduce theft through different casual mechanisms which are dependent on a distinctive configuration of conditions. Based on the available evidence it is difficult to determine the effectiveness of tags as a theft reduction measure, albeit there is suggestive evidence that more visible tags are associated with greater reductions in theft than less visible tags.
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OBJECTIVES: Test the influence of darkness in the street robbery crime event alongside temperature. METHODS: Negative binomial regression models tested darkness and temperature as predictors of street robbery. Units of analysis were four 6-hr time intervals in two U.K. study areas that have different levels of darkness and variations of temperature throughout the year. RESULTS: Darkness is a key factor related to robbery events in both study areas. Traversing from full daylight to full darkness increased the predicted volume of robbery by a multiple of 2.6 in London and 1.2 in Glasgow. Temperature was significant only in the London study area. Interaction terms did not enhance the predictive power of the models. CONCLUSION: Darkness is an important driving factor in seasonal variation of street robbery. A further implication of the research is that time of the day patterns are crucial to understanding seasonal trends in crime data.
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This article explores the problem of wound-related pain. It provides an overview of the different types of pain associated with wounds, discusses approaches to pain assessment and considers a range of strategies that may be used to minimise wound-related pain.