The economic impact of illicit trade is in the trillions of dollars per year, with a proportion of this trade concealed within cargo containers. The interdiction of this trade relies upon efficient and effective external screening of cargo containers, typically using x rays. The present work introduces a technique of x-ray screening that aims to increase the efficiency and effectiveness of x-ray screening. Traditional X-ray screening of cargo containers is performed using high-energy (MV) transmission imaging or low-energy (kV) Compton scatter imaging to provide two-dimensional images. Two-dimensional images can contain complex, overlapping objects and require significant experience and time to interpret. Time-of-Flight information can be used in conjunction with Compton scatter imaging to provide information about the depth of each Compton scatter interaction, leading to three-dimensional images, reducing false positives and image analysis time. The expected Time-of-Flight from photons scattered back from a set of objects is well defined when the photons are produced with a delta-type (infinitely narrow) pulse duration, however, commercially available linear accelerators used for cargo screening typically have pulse widths of the order of 1 μs. In the present work, the possible use of linear accelerators for Time-of-Flight Compton scatter imaging is investigated using a mixture of analytic and Monte Carlo methods. Ideal data are obtained by convolving a number of wide x-ray pulses (up to 5 μs) with the expected Time-of-Flight from a set of objects using a delta-type pulse. Monte Carlo simulations, using Geant4, have been performed to generate x-ray spectra produced by a linear accelerator. The spectra are then used as the input for detailed Monte Carlo simulations of the Time-of-Flight of photons produced by a single linear accelerator pulse scattering back from a set of objects. Both ideal and Monte Carlo data suggest that Time-of-Flight information can be recovered from a wide linear accelerator pulse, provided that the leading and falling edge of the pulse are sharp. In addition, it has been found that using a linear accelerator leads to double the amount of Time-of-Flight information as both the leading and falling edge are utilised (unlike for a delta-type pulse).
More than 63,000 cars were reported stolen in Los Angeles in 2003–04. However, the distribution of thefts across car types is very uneven. Some cars types such as the Honda Civic were stolen at much higher frequencies than the majority of car types. Charnov’s classic prey selection model suggests that such uneven targeting should be related to variations in the environmental abundance, expected payoffs, and handling costs associated with different car types. Street-based surveys in Los Angeles suggest that differences in abundance explain the majority of thefts. Cars stolen despite being rare may reflect offender preference based on differential payoffs, probably in some non-monetary currency such as prestige or excitement. Differential handling costs play a more ambiguous role in target selection, but may underlie thieves’ decisions to ignore some cars common in the environment. The unspecialized nature of car theft in Los Angeles suggests that the behavioral and cognitive capacities needed to be a successful car thief are generic. The evolved capacity to solve foraging problems in boundedly-rational ways, mixed with small amounts of trial-and-error and/or social learning, are sufficient to produce experts from inexperienced thieves.
To test the accuracy of various methods previously proposed (and one new method) to estimate offence times where the actual time of the event is not known.
For 303 thefts of pedal cycles from railway stations, the actual offence time was determined from closed-circuit television and the resulting temporal distribution compared against commonly-used estimated distributions using circular statistics and analysis of residuals.
Aoristic analysis and allocation of a random time to each offence allow accurate estimation of peak offence times. Commonly-used deterministic methods were found to be inaccurate and to produce misleading results.
It is important that analysts use the most accurate methods for temporal distribution approximation to ensure any resource decisions made on the basis of peak times are reliable.
This paper describes the work undertaken over many years by the author and colleagues concerning the role of opportunity in crime. The work began in the early 1970s in the Home Office Research Unit, the British government’s criminological research department.
The work supported a preventive approach – situational crime prevention – that was highly contentious in the criminology of the day because it sought to reduce opportunities for crime, rather than to modify offender propensities. Critics claimed that situational crime prevention would displace rather than reduce crime because they assumed that opportunity merely determines the time and place of crime, but does not cause it.
This paper describes the difficulties in establishing that opportunity is cause of crime and why this took so long. It reviews the research that was undertaken to this end, and it summarizes the benefits for criminology and crime policy of accepting that opportunity does cause crime.
Sexual violence is a major public health, societal, and judicial problem worldwide. Studies investigating the characteristics of its perpetrators often rely on samples of convicted offenders, which are biased by low reporting and conviction rates. Based on a self-report study in the Belgian general population aged 16 to 69 (n = 4687), we provide lifetime and past-year prevalence rates of sexual aggression and report the characteristics of the events, including type, target, and the applied coercion strategies. Future research should use behaviourally specific questions that take the perpetrator’s perspective into account to limit interpretation ambiguity which could reduce unintentional non-disclosure of sexual aggression.
The opportunity for web camera theft increased globally as institutions of higher education transitioned to remote learning during COVID-19. Given the thousands of cameras currently installed in classrooms, many with little protection, the present study tests the effectiveness of anti-theft signage for preventing camera theft.
Examined web camera theft at a southern, public university located in the United States of America by randomly assigning N = 104 classrooms to receive either anti-theft signage or no signage. Camera theft was analyzed using Blaker’s exact test.
Classrooms not receiving anti-theft signage (control) were 3.42 times more likely to exhibit web camera theft than classrooms receiving anti-theft signage (medium effect size).
Using classrooms as the unit of analysis presents new opportunities for not only future crime prevention experiments, but also improving campus safety and security. Also, preventing web camera theft on campus is both fiscally and socially responsible, saving money and ensuring inclusivity for remote learners.
Abstract The spread of the coronavirus has led to containment policies in many places, with concomitant shifts in routine activities. Major declines in crime have been reported as a result. However, those declines depend on crime type and may differ by parts of a city and land uses. This paper examines burglary in Detroit, Michigan during the month of March, 2020, a period of considerable change in routine activities. We examine 879 block groups, separating those dominated by residential land use from those with more mixed land use. We divide the month into three periods: pre-containment, transition period, and post-containment. Burglaries increase in block groups with mixed land use, but not blocks dominated by residential land use. The impact of containment policies on burglary clarifies after taking land use into account.
Confronted by rapidly growing infection rates, hospitalizations and deaths, governments around the world have introduced stringent containment measures to help reduce the spread of COVID-19. This public health response has had an unprecedented impact on people’s daily lives which, unsurprisingly, has also had widely observed implications in terms of crime and public safety. Drawing upon theories from environmental criminology, this study examines officially recorded property crime rates between March and June 2020 as reported for the state of Queensland, Australia. We use ARIMA modeling techniques to compute 6-month-ahead forecasts of property damage, shop theft, residential burglary, fraud, and motor vehicle theft rates and then compare these forecasts (and their 95% confidence intervals) with the observed data for March through to June. We conclude that, with the exception of fraud, all property offence categories declined significantly. For some offence types (shop stealing, other theft offences, and residential burglary), the decrease commenced as early as March. For other offence types, the decline was lagged and did not occur until April or May. Non-residential burglary was the only offence type to significantly increase, which it did in March, only to then decline significantly thereafter. These trends, while broadly consistent across the state’s 77 local government areas still varied in meaningful ways and we discuss possible explanations and implications.
Restrictions resulting from the COVID-19 pandemic interrupted people's daily routine activities. Rooted in crime pattern and routine activity theories, this study tests whether the enactment of a Safer-at-Home mandate was associated with changes in the distance between individuals' home addresses and the locations of where they committed crimes (i.e., residence-to-crime distance). Analyses are based on violent (N = 282), property (N = 1552), and disorder crimes (N = 1092) reported to one police department located in a United States' Midwest suburb. Multilevel models show that residence-to-crime distances were significantly shorter during the Safer-at-Home order, compared to the pre- and post-Safer-at-Home timeframes, while controlling for individual and neighborhood characteristics. Additionally, these relationships varied by crime type. Consistent with the literature, the findings support the argument that individuals tend to offend relatively near their home address. The current findings extend the state of the literature by highlighting how disruptions to daily routine activities stemming from the COVID-19 pandemic led to alterations in crime patterns, in which analyses indicated shorter distances between home address and offense locations.
The online version contains supplementary material available at 10.1186/s40163-022-00172-1.
The existing empirical evidence suggests a reduction in aggregate crime as a consequence of the COVID-19 lockdown. However, what happens when lockdown measures are relaxed? This paper considers how the COVID-19 pandemic affects crime rates throughout Mexico when the stay-at-home orders end. We use national crime data from Mexico’s National Public Security System, which reports municipality-level rates on assault & battery, theft & property crime, fraud, drug crimes & extortion, and homicides. Our results show that the majority of crimes follow a U-shaped trend—when the lockdown ends—crimes rise back to pre-pandemic levels.
The covid-19 disease has a large impact on life across the globe, and this could potentially include impacts on crime. The present study describes how crime has changed in Sweden during ten weeks after the government started to implement interventions to reduce spread of the disease. Sweden has undertaken smaller interventions than many other countries and is therefore a particularly interesting case to study. The first major interventions in Sweden were implemented in the end of week 11 (March 12th) in the year 2020, and we analyze police reported crimes through week 21 (ending May 24th). Descriptive statistics are provided relative to expected levels with 95% confidence intervals for eight crime types. We find that total crime, assaults, pickpocketing and burglary have decreased significantly, personal robberies and narcotics crime are unchanged. Vandalism possibly increased somewhat but is hard to draw any firm conclusions on. The reductions are fairly small for most crime types, in the 5-20% range, with pickpocketing being the biggest exception noting a 59% drop relative to expected levels.
Despite the immense impact of wildlife trafficking, comparisons of the profits, costs, and seriousness of crime consistently rank wildlife trafficking lower relative to human trafficking, drug trafficking and weapons trafficking. Using the published literature and current events, we make the case, when properly viewed within the context of COVID-19 and other zoonotic diseases transmitted from wildlife, that wildlife trafficking is the most costly and perhaps the most serious form of trafficking. Our synthesis should raise awareness of the seriousness of wildlife trafficking for humans, thereby inducing strategic policy decisions that boost criminal justice initiatives and resources to combat wildlife trafficking.
The spread of COVID-19 has prompted Governments around the world to impose draconian restrictions on business activity, public transport, and public freedom of movement. The effect of these restrictions appears to vary from country to country and, in some cases, from one area to another within a country. This paper examines the impact of the COVID-19 restrictions imposed in New South Wales (NSW) by the State Government. We examine week-to-week changes in 13 categories of crime (and four aggregated categories) from 2 January 2017 to 28 June 2020. Rather than using the pre-intervention data to make a forecast and then comparing that with what is actually observed, we use a Box-Jenkins (ARIMA) approach to model the entire time series. Our results are broadly in accord with those of other studies, but we find no effect of the lockdown (upward or downward) on domestic assault.
The online version contains supplementary material available at 10.1186/s40163-021-00160-x.
Research on pandemic domestic abuse trends has produced inconsistent findings reflecting differences in definitions, data and method. This study analyses 43,488 domestic abuse crimes recorded by a UK police force. Metrics and analytic approaches are tailored to address key methodological issues in three key ways. First, it was hypothesised that reporting rates changed during lockdown, so natural language processing was used to interrogate untapped free-text information in police records to develop a novel indicator of change in reporting. Second, it was hypothesised that abuse would change differentially for those cohabiting (due to physical proximity) compared to non-cohabitees, which was assessed via a proxy measure. Third, the analytic approaches used were change-point analysis and anomaly detection: these are more independent than regression analysis for present purposes in gauging the timing and duration of significant change. However, the main findings were largely contrary to expectation: (1) domestic abuse did not increase during the first national lockdown in early 2020 but increased across a prolonged post-lockdown period, (2) the post-lockdown increase did not reflect change in reporting by victims, and; (3) the proportion of abuse between cohabiting partners, at around 40 percent of the total, did not increase significantly during or after the lockdown. The implications of these unanticipated findings are discussed.
The online version contains supplementary material available at 10.1186/s40163-023-00190-7.
Worry about COVID-19 is a central topic of research into the social and economic consequences of the COVID-19 pandemic. In this paper, we present a new way of measuring worry about catching COVID-19 that distinguishes between worry as a negative experience that damages people’s quality of life (dysfunctional) and worry as an adaptive experience that directs people’s attention to potential problems (functional). Drawing on work into fear of crime, our classification divides people into three groups: (1) the unworried, (2) the functionally worried (where worry motivates proactive behaviours that help people to manage their sense of risk) and (3) the dysfunctionally worried (where quality of life is damaged by worry and/or precautionary behaviour). Analysing data from two waves of a longitudinal panel study of over 1000 individuals living in ten cities in England, Scotland and Wales, we find differing levels of negative anxiety, anger, loneliness, unhappiness and life satisfaction for each of the three groups, with the dysfunctionally worried experiencing the most negative outcomes and the functionally worried experiencing less negative outcomes than unworried. We find no difference between groups in compliance and willingness to re-engage in social life. Finally, we show a difference between the dysfunctionally worried compared with functional and unworried groups in perceptions of risk (differentiating between likelihood, control and consequence). This finding informs what sort of content-targeted messaging aimed at reducing dysfunctional worry might wish to promote. We conclude with some thoughts on the applicability of our measurement scheme for future research.
COVID-19 impacts the daily lives of millions of people. This radical change in our daily activities affected many aspects of life, but acted as well as a natural experiment for research into the spatial distribution of 911 calls. We analyse the impact of the COVID-19 measures on the spatial pattern of police interventions. Crime is not uniformly distributed across street segments, but how does COVID-19 affect these spatial patterns? To this end, Gini coefficients are calculated and a proportion differences spatial point pattern test is applied to compare the similarity of the patterns of incidents before, during, and after the first lockdown in Antwerp, Belgium. With only essential mobility being allowed, the emergency call pattern has not significantly changed before, during or after this lockdown, however, a qualitative shift in police officer’s daily work may have had an effect on the daily operation of the Antwerp police force.
This study aimed to determine whether crime patterns in Mexico City changed due to the COVID-19 pandemic, and to test whether any changes observed were associated with the disruption of routine activities, as measured by changes in public transport passenger numbers.
The first objective was assessed by comparing the observed incidence of crime after the COVID-19 pandemic was detected in the country with that expected based on ARIMA forecasts based on the pre-pandemic trends. The second objective was assessed by examining the association between crime incidence and the number of passengers on public transport using regressions with ARIMA errors.
Results indicated that most crime categories decreased significantly after the pandemic was detected in the country or after a national lockdown was instituted. Furthermore, the study found that some of the declines observed were associated with the reductions seen in public transport passenger numbers. However, the findings suggested that the changes in mobility explain part of the declines observed, with important variations per crime type.
The findings contribute to the global evaluation of the effects of COVID-19 on crime and propose a robust method to explicitly test whether the changes observed are associated with changes in routine activities.
Illegal dumping of household and business waste, known as fly-tipping in the UK, is a significant environmental crime. News agencies reported major increases early in the COVID-19 pandemic when waste disposal services were closed or disrupted. This study examines the effect of lockdowns on illegal dumping in the UK.
A freedom of information request was sent to all local authorities in the UK asking for records of reported incidents of fly-tipping for before and after the first national lockdown. ARIMA modelling and year-on-year comparison was used to compare observed and expected levels of fly-tipping. Urban and rural local authorities were compared.
A statistically significant decline in fly-tipping during the first lockdown was followed by a similar increase when lockdown ended. The effects largely cancelled each other out. There was pronounced variation in urban–rural experience: urban areas, with higher rates generally, experienced most of the initial drop in fly-tipping while some rural authorities experienced an increase.
Waste services promote compliance with laws against illegal dumping. When those services were disrupted during lockdown it was expected that fly-tipping would increase but, counter-intuitively, it declined. This enhanced compliance effect was likely due to increased perceived risk in densely populated urban areas. However, as lockdown restrictions were eased, fly-tipping increased to clear the backlog, indicating temporal displacement.
Drawing upon seven years of police calls for service data (2014-2020), this study examined the effect of the COVID-19 pandemic on calls involving persons with perceived mental illness (PwPMI) using a Bayesian Structural Time Series. The findings revealed that PwPMI calls did not increase immediately after the beginning of the pandemic in March 2020. Instead, a sustained increase in PwPMI calls was identified in August 2020 that later became statistically significant in October 2020. Ultimately, the analysis revealed a 22% increase in PwPMI calls during the COVID-19 pandemic than would have been expected had the pandemic not taken place. The delayed effect of the pandemic on such calls points to a need for policymakers to prioritize widely accessible mental health care that can be deployed early during public health emergencies thus potentially mitigating or eliminating the need for increased police intervention, as was the case here.
The online version contains supplementary material available at 10.1186/s40163-021-00157-6.
Lone-wolf terrorism is often more difficult to detect through intelligence due to limited communication between plotters. This study addresses this problem by spelling out an alternative method for impeding such attacks. It combines crime scripts, situational crime prevention and rational planning to study how to impede attacks such as the 2011 Norway attacks. Analyzing the transport issues in these attacks demonstrates that some sort of entry control and measures facilitating the evacuation in case of prolonged attacks might reduce the harm.
Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. Estimations in the literature are mostly based on border seizures, but in this paper, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective. Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015. The findings suggest that darknet markets harbor similar counterfeit product types to those found in seizures but that the share of watches is higher while the share of electronics, clothes, shoes, and Tobacco is lower on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the equivalent products on the surface web appear to be advertised for a multiple of the prices found for darknet markets. We provide some suggestions on how information about darknet market counterfeits could be used by companies and authorities for preventative purposes, showing that insight gathering from the dark web is valuable and could be a cost-effective alternative (or compliment) to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better.
Geostatistical methods currently used in modern epidemiology were adopted in crime science using the example of the Opole province, Poland, in the years 2015-2019. In our research, we applied the Bayesian spatio-temporal random effects models to detect 'cold-spots' and 'hot-spots' of the recorded crime numbers (all categories), and to ascertain possible risk factors based on the available statistical population (demographic), socio-economic and infrastructure area characteristics. Overlapping two popular geostatistical models in the analysis, 'cold-spot' and 'hot-spot' administrative units were detected which displayed extreme differences in crime and growth rates over time. Additionally, using Bayesian modeling four categories of possible risk factors were identified in Opole. The established risk factors were the presence of doctors/medical personnel, road infrastructure, numbers of vehicles, and local migration. The analysis is directed toward both academic and police personnel as a proposal for an additional geostatistical control instrument supporting the management and deployment of local police based on easily available police crime records and public statistics.
The online version contains supplementary material available at 10.1186/s40163-023-00189-0.
A new body of research that focuses on crime harm scores rather than counts of crime incidents has emerged. Specifically in the context of spatial analysis of crime, focusing on crime harm suggests that harm is more concentrated than counts, at the level of crime hot spots. It remains presently unclear what drives the concentration distributions, and whether the count-based model should be abandoned.
Cross-sectional and longitudinal analysis of 6 year of spatiotemporal crime data in Toronto, Canada, to compare patterns and concentration of crime harm (measured in terms of the Crime Severity Index (CSI) against crime counts. Conditional probabilities, trajectory analyses, power few concentrations, and spatial Global Moran’s I are used to infer generalised trends from the data.
Overall CSI and crime counts tend to exhibit similar concentrations at the spatial micro levels, except against-the-body crimes such as violence which seems to drive nearly all the variations between the two measurement types. Violence harm spots tend to be more dispersed citywide and often do not remain constant year-to-year, whereas overall crime hotspots are more stable over time. Nevertheless, variations in disproportionally high crime hot spots are associated with total variations in crime, with as little as 1% increase in crime levels in these hot spots translating into substantial overall gains in recorded crime citywide.
Abandoning count-based models in spatial analysis of crime can lead to an incomplete picture of crime concentrations. Both models are needed not just for understanding spatial crime distributions but also for cost-effective allocation of policing resources.