Crime Incidents Research Articles

Rapid nanomolar detection of ketamine in biofluids based on electrochemical aptamer-based sensor for drugged driving screening within 30 s

Effective drugged driving screening by the roadside requires simple-to-use sensing platforms that allow accurate measurement of drugs from biofluids. Achieving this goal is of vital importance to prevent and control the incidence of drug abuse, traffic accidents and crime. Given this, we designed a rapid, sensitive and reagentless electrochemical aptamer-based (EAB) sensor to detect ketamine. This platform produced nanomolar detection accuracy and reliable results were obtained within 30 s. To design a sensitive platform, circular dichroism (CD) was used to screen the aptamer which underwent the largest binding-induced conformational change. In addition, kinetic differential measurement (KDM) was utilized to improve the signal gain of the EAB sensor. The constructed sensing platform achieved sensitive and specific detection of ketamine in 50 % saliva and undiluted urine with detection limits as low as 10 nM, which was much lower than the physiological detection threshold. Hence the present EAB sensor is expected to be applied in the preliminary drugged driving screening in the real world.

Is your neighbor your friend? Scan methods for spatial social network hotspot detection

AbstractGIS analyses use moving window methods and hotspot detection to identify point patterns within a given area. Such methods can detect clusters of point events such as crime or disease incidences. Yet, these methods do not account for connections between entities, and thus, areas with relatively sparse event concentrations but high network connectivity may go undetected. We develop two scan methods (i.e., moving window or focal processes), EdgeScan and NDScan, for detecting local spatial‐social connections. These methods capture edges and network density, respectively, for each node in a given focal area. We apply methods to a social network of Mafia members in New York City in the 1960s and to a 2019 spatial network of home‐to‐restaurant visits in Atlanta, Georgia. These methods successfully capture focal areas where Mafia members are highly connected and where restaurant visitors are highly local; these results differ from those derived using traditional spatial hotspot analysis using the Getis–Ord Gi* statistic. Finally, we describe how these methods can be adapted to weighted, directed, and bipartite networks and suggest future improvements.

Auditing the fairness of place-based crime prediction models implemented with deep learning approaches

Place-based crime prediction models implemented with deep learning leverage the spatio-temporal patterns of historical crimes, together with built-environment factors, to predict aggregate volumes of crime incidents at specific locations. Nevertheless, related work has shown that crime incident data can suffer from under-reporting bias whereby certain social groups report more crimes than others and whereby certain crimes are recorded more often by the police. In addition, under the umbrella of crime opportunity theories that suggest that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based crime models implemented with deep learning. However, prior work has shown that human mobility data collected from cell phones can suffer from sampling bias because certain population groups are not equally represented due to lack of cell phone ownership. Despite these well reported data bias concerns, no related work has yet looked into the fairness of place-based crime prediction models implemented with deep learning; nor into the effect that adding human mobility data from cell phones as a predictor might have on their fairness. In this paper, we use publicly available fine-grained crime incident and human mobility data from the US to conduct a comprehensive fairness audit across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we explore potential root causes of the models' fairness metrics by looking into crime data bias, mobility data bias and algorithmic bias. Our results show that unfair predictions are pervasive across place-based crime prediction models implemented with deep-learning due to the inherent data bias in the crime incident data; that adding mobility features extracted from cell phone data decreases fairness under certain circumstances; and that part of the fairness loss can be explained by bias in the crime data, and in the predictive algorithms that might be exacerbating the data bias, with the mobility data bias having an insignificant effect on fairness loss.

The crime rate of five Latin American countries: Does income inequality matter?

Despite the Latin American region being the home to only 9 per cent of the global population, the region has the world's highest combined level of income inequality, with a measured net Gini coefficient of 48.3 (UNICEF, 2011) and has also registered approximately 40 per cent of the world's murders. Additionally, although the latest report published by ECLAC (2019) indicated that the level of income inequality has continued to trend downwards, the region's crime rate has remained persistently high. Thus, suggesting that while there is good reason to believe that income inequality could explain the incidence of crime in the region, the association between income inequality and the crime rate is not symmetrical. This paper aims to examine the asymmetric effect of income inequality on the crime rate of five Latin American countries. Utilising the nonlinear autoregressive distributed lag (NARDL) technique proposed by Shin et al. (2014) on the annual data from 1984 to 2017, this study found the presence of asymmetric cointegration between income inequality and crime rates in Argentina, Brazil, Chile and Columbia.

Beating the Gun—One Conversation at a Time? Evaluating the Impact of DC’s “Cure the Streets” Public Health Intervention Against Gun Violence

Violence interrupters (VI) operate as mediators after gang-involved shootings to stop retributory shootings. While some cities, like Chicago, have seen initial success, other cities, such as Boston, Newark, and Phoenix have seen little or mixed effects. This is the first evaluation of the Washington DC intervention. I use an interrupted time series model to measure the impact the intervention had on the rates of assaults with a gun using DC Crime Incident data. Recognizing that the Covid-19 pandemic is an important confounding event, I also run two time-series control models, a location control, and an outcome control. My findings indicate that the program was not effective in reducing gun violence.

Characterizing Asians in violence: a comparative analysis of English- and Chinese-language media’s crime news coverage during the pandemic

ABSTRACT This study examined variances in crime news involving Asian perpetrators and Asian victims in a mainstream English-language daily USA Today, and an ethnic newspaper Qiao Bao. Our quantitative content analysis focused on (a) the volume of crime stories published about Asians (as perpetrators and victims respectively), and (b) the news frames used to cover crime incidents involving Asians (as perpetrators and victims respectively). Our results revealed that relative to USA Today, the Chinese-language newspaper Qiao Bao not only reported more violent incidents involving Asians, but also placed less emphasis on thematic frames when covering violent incidents about Asians. On the other hand, the English-language newspaper USA Today published more articles emphasizing the consequences of violence when the crime incidents involved Asians (as either offenders or victims). We theorize that these differences arise due to the nature of ethnic news, as well as structural differences between the two outlets. Situated within the COVID-19 context, this study also found that the volume of crime news about Asian victims significantly grew during the pandemic in Qiao Bao’s coverage but remained unchanged in the USA Today, indicating that Asian victims are more newsworthy to ethnic media and their readers.

Micro-temporal analyses of crime related to alcohol outlets: A comparison of outcomes over weekday, weekend, daytime and nighttime hours.

Causal relationships between alcohol outlets and crime are inferred from their statistical associations across neighbourhoods. However, many unobserved covariates may confound these effects. Recognising that outlet sales vary by time of day and day of week, we assess whether areas with more bars/pubs, restaurants or off-premise outlets have more crime during days and times when alcohol sales are greatest. Annual administrative crime counts, sociodemographic data and other area characteristics of 336 Census block groups in Oakland, California, USA, were related to outlet densities from 2000 to 2015. Bayesian space-time Poisson models were used to measure associations between outlet densities and crime during: (i) weekday daytime; (ii) weekday nighttime; (iii) weekend daytime; and (iv) weekend nighttime periods (four seemingly unrelated equations). Comparisons of parameter estimates across equations provided an assessment of outlet effects on crime across days and times within the same analysis units using the same constellation of confounding covariates. Assault and driving under the influence crime incidents during weekend evening hours were more frequent in Census block group areas with greater numbers of bar/pubs. Burglaries were consistently greater in areas with greater densities of restaurants. The spatiotemporal signature relating densities of bars/pubs over weekend evening hours to assault and driving under the influence incidents suggests that these outlets are a critical source of these crimes across neighbourhoods. Prevention programs and policies that focus upon specific drinking establishments, days and times may be most effective in reducing assault and impaired driving incidents in neighbourhoods.

Effects of foreign residents on crime: Evidence from South Korea

Many countries worldwide are facing social change as they are increasingly becoming multicultural societies. South Korea has maintained a homogeneous ethnic identity for a long time but is experiencing an immense inflow of foreign residents. This study empirically examines the effects of foreign residents on crime. The employment permit system is adopted as an instrumental variable to seek a causal link. We found that increasing the number of foreign residents in the total population is positively related to the incidence of crime. The number of arrests also increases with the crime incidence, and we found little significant change in the ratio of arrests to crimes. Moreover, the increase in crimes falls largely within the category of violent crimes rather than felonies, property crimes, and intellectual crimes. Based on the results, we suggest that the government needs to promote a multicultural and accommodating culture and enable immigrants to settle stably in Korean society.

Duration of forensic psychiatric care and subsequent criminal recidivism in individuals sentenced in Sweden between 2009 and 2019.

The duration of forensic psychiatric care is in Sweden not determined at the time of sentencing; instead, offenders are regularly evaluated, often with regard to risk of criminal recidivism. The length and justifiability of such a sanction have been greatly debated; however, previous estimates of treatment duration based on datasets delimited to discharged patients-have provided an uncertain groundwork for these deliberations. The aim of this study was to use a more suitable approach to calculate average duration of forensic psychiatric care and to examine the relationship between length of treatment and subsequent recidivism after discharge. This retrospective cohort study focused on offenders sentenced to forensic psychiatric care in Sweden between 2009 and 2019 and registered in the Swedish National Forensic Psychiatric Register (n = 2064), with a follow-up period until May 2020. We used Kaplan-Meier estimator to calculate and visualize treatment duration including analyses comparing levels of relevant variables, and then evaluated criminal recidivism in patients discharged from treatment between 2009 and 2019 (n = 640), after stratification for the same variables and dichotomization by treatment duration. The median duration of forensic psychiatric care was estimated to 89.7 months (95% CI 83.2-95.8). Treatment was longer in offenders who committed violent crimes, suffered from psychosis, or had a history of substance use disorder, and in offenders whose sentences included special court supervision. The cumulative incidence of recidivism in patients discharged from treatment was estimated to 13.5% at 12 months (95% CI 10.6-16.2) and 19.5% at 24 months (95% CI 16.0-22.8). Corresponding cumulative incidence of violent crime post discharge was 6.3% at 12 months (95% CI 4.3-8.3) and 9.9% at 24 months (95% CI 7.3-12.4). Among other findings, in patients without a history of substance use disorder and patients whose sentences did not include special court supervision, recidivism was significantly higher in those with a shorter treatment duration. Using the entirety of a suitable, contemporary, prospectively enrolled cohort of mentally ill offenders, we were able to estimate-with greater accuracy than previous studies-the average duration of Swedish forensic psychiatric care and rate of subsequent criminal recidivism.

CRIME VICTIMIZATION OF NEUST CRIMINOLOGY STUDENTS LIVING IN BOARDING HOUSES AT SUMACAB ESTE, CABANATUAN CITY

Being a victim of crime is a phenomenon which may not be common to everyone, but is of great probability of happening anytime in our lives. This is because no one can actually predict and control the actions of other people especially those who intend to commit them. It is therefore important, that for us not be victimized, we have to create a certain kind of security within us, that is, we police ourselves. It is not enough that we depend on our police force for our security, but more importantly, we have to be responsible enough to keep ourselves secure and not put ourselves in a compromising situation that could lead to victimization. In the Philippines, the Philippine National Police (PNP) (2006) stated that they are committed in ensuring public safety and reducing the fear and the incidence of crime in the community. However, they acknowledge that there are many things that each and every community member can do to reduce their chances of becoming a victim or prevent the incidence of crime from happening.

Gender, sexuality and race: An intersectional analysis of racial consumption and exclusion in Birmingham’s gay village

Queer spaces have gained increasing attention academically with a range of studies exploring the construction of such spaces. This article addresses the spatial and social practices and processes that occur within these spaces that perpetuate exclusion based on race. Drawing on ethnographic data collected through semi-structured interviews and participant observations conducted in Birmingham’s ‘gay village’, we argue that gendered perceptions of racialised masculinities and femininities create unique experiences for men and women from minoritised ethnicities. We argue that queer spaces, which are often assumed to transgress and challenge social norms actually maintain, uphold and perpetuate white, patriarchal norms and can therefore be considered a microcosm of broader society. In doing so, we advance criminological thought by adopting a zemiological framework that centres social harm rather than relying on legally defined incidents of crime.

Criminal Behaviour After Diagnosis of a Neurocognitive Disorder: A Nationwide Finnish Register Study

ObjectiveTo explore criminal behavior of individuals with Alzheimer's disease (AD), frontotemporal dementia (FTD), or Lewy body dementias (LBD) after the diagnosis. DesignNationwide register study. SettingInformation on diagnoses and criminality was received from Finnish registers. Crime types and incidences were compared between disorders and the general population. ParticipantsAll Finnish individuals diagnosed with AD, LBD, or FTD (n = 92 189) during 1998–2015. MeasurementsTypes of crimes and incidences, the standardized criminality ratio (SCR, number of actual crimes per number of expected crimes), numbers of observed cases, and person-years at risk counted in 5-year age groups and for both sexes and yearly. ResultsAmong men, at least one crime was committed by 2.8% of AD, 7.2% of FTD, and 4.8% of LBD patients. Among women, the corresponding figures were 0.4%, 2.0%, and 2.1%. The most frequent type of crime was traffic offence, followed by property crime. After age adjustment, the relative number of crimes between groups did not differ, except that men with FTD and LBD committed more crimes than those with AD. The SCR (95% CI) among men were 0.40 (0.38–0.42) in AD, 0.45 (0.33–0.60) in FTD, and 0.52 (0.48–0.56) in LBD. Among women, these were 0.34 (0.30–0.38), 0.68 (0.39–1.09), and 0.59 (0.51–0.68). ConclusionsThe diagnosis of a neurocognitive disorder does not increase criminal behavior, but rather reduces it by up to 50%. Differences in crime activity are present between different neurocognitive disorders and between the sexes.

Antecedent mechanisms of crime

Crime has always been a topical issue in society. Because crime can cause significant damage and harm to individuals and groups, researchers have researched the antecedents of crime to reduce the crime at its source. This paper reviews previous research from individual, environmental, and social perspectives and makes suggestions for future research. At the individual level, researchers have found that the MAOA gene is associated with criminal behavior from the perspective of genetic polymorphism and that criminal behavior is hereditary; in addition, antisocial personality is closely related to crime. At the environmental level, parenting styles and traditional education in the family environment can lead to delinquency. Poor parenting styles can cause individuals to develop poor attachment patterns, leading to sexual delinquency. Community organization and order are associated with delinquency among young people at the social level. In addition, social media coverage of violent crime-related information gives stimulus to aggression leading to crime, and the researcher used information processing theory to explain this behavioral arousal process. The results of previous studies are comprehensive, but some of the results are controversial, which requires a combination of multidisciplinary and modern technological tools to strictly control the variables and thus design more rigorous experiments to produce more accurate results, which can effectively reduce the incidence of crime.

Promoting Education Quality in Curbing Business Crime Costs: A Quantile Analysis

Studies have concentrated on numerous strategies and measures to curb the crime rate. Out of these, interventions via education are most promising as teaching children has a multiplier effect on crime reduction. Empirically there is a debate on the effect of education on the incidence of crime, and it is reasoned that education with opportunities, relevance, and ethics plays a decisive role in reducing crime. This study has used more comprehensive indicators and a robust distribution-free approach to estimate the effect of education on the crime rate. The contour plot of Quantile on Quantile regression helps assess each level of education quality on each level of the business cost of crime. This model does not require the assumption of normality and linearity of variables. This study points out that primary and science education reduces crime rates at low levels, while management studies and research help reduce crime at high levels. This study provides a robust assessment of how education may influence the cost of crime at their different corresponding levels. These estimates can help determine how the marginal effects will affect and change in response to the change in the cost of crime. This study is an instrument in for assessing the role of education on crime at each quantile data position.

Analyzing physical and socio-economic factors for property crime incident in Addis Ababa, Ethiopia

Property crime has become a challenge in major cities of developing countries including Addis Ababa, Ethiopia. However, factors contributing to property crime have not been carefully examined. Therefore, this paper presents the physical and socio-economic factors that clearly have a substantial impact on property crime. In this study, both primary and secondary data were used. Recorded property crime was collected from Addis Ababa police commission. Property crime incidence locations were georefrenced using Google Earth as well as handheld Global Positioning System. Then, a property crime surface map and pattern were created using point pattern analysis and inverse distance weighted interpolation technique. The study area was then classified into low crime and hotspot areas based on the frequency of crime events as well as the created crime surface map. A total of 200 respondents (100 in high crime area and 100 in low crime area) were selected using purposive sampling techniques. Five key informants were selected purposely from senior police officers. Data were collected using questionnaires and in-depth interview. A binary logistic regression model was employed to analyze the collected data. The data analysis showed that physical features mainly commercial center, road with no street light and river courses were positively correlated at significant level with the committing of property crime. On the other hand, public participation in crime prevention, witness and crime events reporting influenced negatively the committing of property crime. In addition, unemployment, family background, education level, age, time, season and police patrolling activities determine the occurrence of property crime events. The finding depicts that the identified physical and socio-economic factors can influence the patterns and rate of crime incidents in the study area. Therefore, the law enforcement officers should consider these influential factors to deploy police officers and to reduce property crime problems.

Nonlinear relationships and interaction effects of an urban environment on crime incidence: Application of urban big data and an interpretable machine learning method

While environmental criminology suggests that crime and the urban environment are closely related, some studies suggest a nonlinear relationship. This study analyzed the relationship between crime incidence and the urban environment using urban big data such as points-of-interest (POI), smart civil complaint data, and street image data from Naver Street View in Seoul, Korea. For analysis, the Light Gradient Boosting Machine (LightGBM) model and SHapley Additive exPlanation (SHAP) method have been used. The analysis results confirmed a nonlinear relationship comprising inflection points between crime incidence and the urban environment. Also, this study identified the interaction effects of urban environmental variables on crime incidence. Finally, the hierarchical clustering method was used to identify the contributions of various aspects of the urban environments to crime incidence. Then, this study provides policy implications to prevent potential criminal activities and promote public safety for sustainable cities and societies.

Pengaruh Knalpot Racing Pada Kendaraan Roda Dua Yang Bising Di Kota Bima

In the midst of a pluralistic community life as problems occur that require legal resolution, so that the settlement has certainty. The complexity of legal issues becomes very dilemmatic. Even the law lags far behind when compared to the current incidence of crimes and offenses. However, amid the complexity of violations and crimes today, the law must provide legal certainty and a sense of security in society. One of the violations that concerns violations of technical requirements and roadworthiness is the use of exhaust racing / not stanfdar which directly shows an attitude of not having traffic ethics, where the use of "exhaust racing" violates Law Number 22 of 2009 concerning Road Transport Traffic. By conducting raids on the control of two-wheeled motorized vehicles with noise, it is hoped that there will be legal awareness of the public to return to using standard knalopt so that it can reduce pollution that arises and can realize the comfort of fellow road users.

Exploring the influence of cultural participation on the subjective well-being of victims in Mexico.

Considering the increasing incidence of crime in Mexico, it is necessary to understand the strategies that individuals utilize in response to victimization and the effects of this on their subjective well-being. A generalized structural equation modeling (GSEM) analysis with data from the 2012 Self-reported Well-Being Survey (BIARE, n = 10,654); dependent variables: subjective well-being (i.e., cognitive well-being and affective balance); independent variables: self-reported victimization (i.e., by domestic violence, community violence, and structural violence) and cultural participation (i.e., cultural attendance, engagement, and consumption). Results show an overall positive and statistical influence of the cultural participation activities on the subjective well-being of victims of community and structural violence (but not of domestic violence), because, for those who reported higher levels of cultural participation, the probability of better subjective well-being were higher. Victims potentially coped and adapted to stressful and traumatic situations (i.e., experiences of victimization) via cultural participation activities. However, there are distinctive effects according to different forms of violence, which may be accounted for in formulating public policies related to victims. This has implications for scholars, policymakers, and practitioners in improving the general quality of life of victims and the general population.

Blue-Collar Crime and Finance

We examine the reputational and persistent costs of blue-collar crime against firms. Blue-collar crime negatively affects firms' reputation regarding credit risk, which persists over time and worsens future access to, and the conditions of, external financing (even if firms are financially healthy again in the future, and even if current crime events are unrelated to future crime incidence). Blue-collar crime does not need to be disclosed to lenders, but revelation is more likely among firms with more employees and in smaller communities, due to potential information leakages. However, the CEO's work experience mitigates the impact of blue-collar crime on future financing conditions.

Semantic Reasoning for Geolocalized Assessment of Crime Risk in Smart Cities

The increasing number of crimes affecting urban areas requires the adoption of countermeasures to tackle this problem from different perspectives, including the technological one. Currently, there are many research initiatives with the goal of applying machine or deep learning techniques leveraging historical data to predict the occurrence of crime incidents. Conversely, there is a lack of tools aiming at crime risk assessment, in particular, by supporting the police in conceiving what could be the crime incidents affecting a given city area. To this purpose, we propose the Crime Prevention System, a modular software application for qualitative crime risk assessment. This consists of an ontology of crime risk, a module to retrieve contextual data from OpenStreetMap, semantics reasoning functionalities, and a GIS interface. We discuss how this system can be used through a case study related to the Italian city of Syracuse.