Different Types Of Crime Research Articles

The drop in worry about crime and its gender gap: Trends in England and Wales from 1998 to 2019/2020

Using data from the Crime Survey for England and Wales, this research explores trends in worry about crime and its gender gap between 1998 and 2019/20. Worry about different types of crime follows a downwards trajectory, with worries about car-related crimes showing the strongest decline. Although both men’s and women’s worries decline over time, the gender gap in worry about crime reveals a narrowing trend for crimes that involve physical interaction with the offender (e.g. assault or rape) due to women’s worries decreasing more rapidly than men’s. Potential reasons for the general downwards trend include the steady decline in crime rates, the rise in security standards and a shift in worry from traditional to newer forms of crime (e.g. cybercrime). Rising gender equality and women’s empowerment over the last few decades may explain the narrowing trend in the gender gap in worry about personal crime.

Assessment of Social Damage from Crime and Proportionality of Punishment to Damage: Legal Economics Approach

The article considers the principle of proportionality of the severity of punishment to public damage from an offense in terms of law and economics and criminology. It presents an overview of various criminological approaches to assessing social damage from different types of crimes, concluding that it is not possible to measure this damage precisely in each specific case. The methodology proposed by Sellin and Wolfgang (Sellin & Wolfgang 1964), which rates the severity of crimes based on sociological surveys, is highlighted as an effective basis for determining the severity of punishment proportional to public damage, though it has its drawbacks. The conclusion offers proposals for synthesizing all the approaches considered in the article to assess public damage from crime and establish the socially optimal severity of punishment for offenses.

The relationships between Urban Tree Canopy Cover and Crime in São Paulo City, Brazil

Prior research has indicated lower crime rates in areas with greater tree canopy cover predominantly in the Global North. There are few studies from the Global South, and more specifically in Latin America. Given the high prevalence of crime in Latin American cities, the need to address social and environmental inequalities using nature-based solutions is urgent. This study examines crime and tree canopy cover through a series of spatial analyses for São Paulo city, Brazil, while controlling for potential confounders. We tested six levels of data aggregation that were combined with three different crime types and two denominators of crime, to account for spurious findings from Modifiable Areal Unit Problems (MAUP). Of the 36 models, a majority of 27 models (75 %) indicate a negative relationship between crime and tree cover canopy, while 4 models (11 %) show a positive correlation, and 5 models (14 %) suggest no statistically significant relationship. Our best models allow us to infer that 10 % greater tree canopy cover is associated with a 1.20 % fewer property, personal and total reported crimes, with statistical significance at the p < 0.001 level. These findings support our hypothesis that there is a negative relationship between tree canopy cover and crime, with a stronger association observed for property crime than personal or total crime. The negative relationship persisted independently of the level of data aggregation, crime types, crime denominator and spatial models specifications (lag and SARMA), avoiding statistical bias of MAUP. The results follow the literature that have observed tree cover associated with lower crime rates, for different types of crime, while adding a new climatic and cultural context to the evidence base.

The association between depression and crime outcomes: A Swedish population-based study

BackgroundResearch on the association between depression and crime is limited regarding different types of crime and severity of crime. AimUsing Swedish national register data, we explored the association between depression and various crime outcomes and the severity of crime. MethodsWe identified 72,057 individuals with an outpatient depression diagnosis between 2001 and 2013. Individuals with depression were age-, sex-, and socioeconomic status (SES)-matched to undiagnosed population controls (n = 1,080,855). Logistic regression models estimated odds of any, violent, property, drug-related and non-violent crime, as well as the severity of crime. ResultsIn fully adjusted models, individuals with depression had significantly higher odds of any conviction (adjusted OR [adjOR] 1.66 [95 % CI 1.60–1.71]). This pattern persisted across all crime types (violent, property, drug-related, non-violent), and for crime severity, including increased odds for longer sentences and multiple convictions. ConclusionsIndividuals with depression had an increased risk for convictions across all crime types and more severe crimes. Our results emphasize the importance of early identification of risk factors and targeted treatments, and the necessity for clarification of the underlying mechanisms of our findings.

Issues of Determining the Systematicity in the Qualification of Crimes

The article analyzes the issues of determining the systematicity in the qualification of criminal offenses. Based on the conducted research, the author concludes that there are no uniform criteria for determining the evaluation feature — systematicity, quantitative and temporal characteristics. The author shows that the systematicity is determined differently in different types of crimes, in particular in the article, in the note to the article or in the explanations of the Resolutions of the Plenum of the Supreme Court of the Russian Federation. The examples given in the paper show striking differences in criteria. So, systematic can be suitable: at least once, more than twice, or three or more times. By time, this may be at least once a quarter, during the year, during the period of the prescribed measures, or there are no specific time limits. The use of analogy according to the norms of the Criminal Code of the Russian Federation is prohibited, and there are not always norms defining the criteria for the qualification of evaluative concepts. The conducted research allows us to conclude that it is necessary to develop unified approaches to the interpretation of the norms of legislation on systematization and their consolidation in criminal legislation.

Piracy and the broader ‘gun business’ in the Niger Delta

Abstract Piracy cannot be understood in isolation from other (criminal) engagements, but how can we explore such linkages and why is it relevant to understand their implications, both for piracy and counterpiracy? Appreciating that Niger Delta-based piracy has dropped to historically low levels, this article explores what pirates do when they do not sustain themselves by engaging in piracy. As such, the article offers novel empirical insights into underexplored dimensions of piracy. Moreover, we use this case-study to demonstrate the relevance of an infrastructure-inspired conceptualization much beyond analyses of piracy and counterpiracy. Showing how piracy is linked to broader and often violent infrastructures that interventions leave unaddressed, and stressing key implications of the continuity and change in such linkages, is a first step towards appreciating the centrality of a broader dilemma that intervention actors confront: how to conceptualize the necessity to delimit a problem, while appreciating the significance of problem-dimensions left unaddressed? Our suggestion is that an infrastructure-inspired approach offers a helpful starting-point, a suggestion that we demonstrate in an analysis of how Niger Delta-based piracy forms part of a broader ‘gun business’ landscape, which does not only entail different types of crime but also various types of victims.

Income mobility and crime: a hierarchical cluster analysis on principal components for 27 OECD countries

PurposeThe aim of this paper is to investigate the linkages between intergenerational income mobility and crime for 27 OECD countries, considering different types of crime, family ties, enforcement, in terms of punishment rate and perceived quality of the legal system, redistributive outcomes and government expenditure.Design/methodology/approachUsing the Global Database of Intergenerational Mobility (GDIM), the empirical analysis is conducted by coupling the principal components analysis with the hierarchical clustering. The variance tests verify the robustness of the clusters.FindingsIncome mobility is higher in those countries where there is high public investment devoted to education and high perception of rule of law to buffer the adverse effects of crime on income mobility. The redistributive policies must be oriented to better the wealth distribution and not only income equality opportunity to decrease crime and to increase income mobility. A plausible existence of “hidden” income mobility emerges from the linkages between income mobility and frauds.Social implicationsMore redistributive policies for education, income and wealth equality should be applied in those countries with low income mobility and high violent crime rates; higher punishment rates should be applied to reduce the rates of thefts and frauds in high income mobility countries.Originality/valueThe main contribution is the identification of what type of crime leads to downward income mobility, as well as the role of perceived quality of the legal system, government and family ties in the association between income mobility and crime, suggesting also the potential existence of “hidden” income mobility.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0520

Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations

Crime prediction is a crucial yet challenging task within urban computing, which benefits public safety and resource optimization. Over the years, various models have been proposed, and spatial-temporal hypergraph learning models have recently shown outstanding performances. However, three correlations underlying crime are ignored, thus hindering the performance of previous models. Specifically, there are two spatial correlations and one temporal correlation, i.e., (1) co-occurrence of different types of crimes (type spatial correlation), (2) the closer to the crime center, the more dangerous it is around the neighborhood area (neighbor spatial correlation), and (3) the closer between two timestamps, the more relevant events are (hawkes temporal correlation). To this end, we propose Hawkes-enhanced Spatial-Temporal Hypergraph Contrastive Learning framework (HCL), which mines the aforementioned correlations via two specific strategies. Concretely, contrastive learning strategies are designed for two spatial correlations, and hawkes process modeling is adopted for temporal correlations. Extensive experiments demonstrate the promising capacities of HCL from four aspects, i.e., superiority, transferability, effectiveness, and sensitivity.

A Study of Original and Tempered Images for Real-Fake Image

The widespread use of image editing technologies in the digital age has raised concerns about the authenticity of visual content. This study delves into the field of image forensics, specifically analyzing original and tempered photos to determine their graphical behavior. The major goal is to develop solid algorithms for distinguishing between authentic and fraudulent photos based on an in-depth assessment of their visual properties. The study makes use of a large data collection that includes both original and manipulated photographs from a variety of sources and contexts. To reveal small differences between authentic and modified pictures, image processing techniques such as noise analysis, color profile investigation, and geometric feature extraction are used. Machine learning algorithms are critical in automating the analysis process and increasing the efficiency and scalability of the proposed methodology. Picture security is an issue for every company that employs digital images. Suspect data has long been used in forensics and public safety pictures, images from crime scenes, biometric photos, and other types of images. In this discipline, the usage of digital photographs has increased dramatically with the advancement of digital imaging. Digital image processing has made picture manipulation easier, but it has also aided in the development of several novel techniques in forensic investigation. Digital picture authenticity is becoming an issue due to the public availability of several programs for cropping and manipulating images. It serves as compelling evidence in many different types of crimes write for a variety of purposes. This development is picture processing or edits of two are also simplifies and editing photos. The most typical kinds of Conducted.

The Emergence of the Institution of Extreme Necessity in the RF (Russian) Legislation

In this study, the milestones (stages) through which the norms regarding extreme necessity have emerged and developed in the legislation of Russia, specifically its criminal legislation, were considered. A historical and legal analysis was performed to shed light on the processes that have influenced the establishment of the institution of extreme necessity in Russia. The evolution of the above norms was conditionally divided into different periods based on a number of criteria. The high relevance of extreme necessity and its justified position among the current norms of the Criminal Code of the Russian Federation were emphasized. The general terminology was elaborated as a conceptual apparatus. In line with the modern legal understanding and interpretation of criminal law canons, it was logically deduced that certain monuments of law from the studied periods are more progressive compared to others. When investigating the legal and legislative approach employed by “the legislator from the era under survey” in order to formulate provisions in legal acts, a distinctive aspect of placing norms according to different types of crime was highlighted. For the generalization and systematization of the legislative processes that occurred in Russia and influenced the emergence of extreme necessity, the development of this institution should be perceived within a historical retrospective and perspective of criminal law. Therefore, the views of a wide range of researchers were examined here.

Коррупционные преступления: тенденции методико-криминалистического обеспечения расследования

Рассмотрены состояние и перспективы развития криминалистической науки в аспекте разработки методик расследования преступлений. Акцентировано внимание на имеющейся тенденции формирования методик, содержанием которых являются общие методические рекомендации, предназначенные для расследования нескольких различных видов преступлений, объединенных в единую группу на основании схожих признаков. Представляется, что наличие общих признаков у разных видов преступлений определяет возможность построения укрупненных методик расследования. Их конструирование предполагает первоначальное выделение общих уголовно- правовых и криминалистических классификационных признаков преступлений. Актуализация интереса научного сообщества в части формирования теоретических знаний и построения укрупненных методик расследования не способствовала выработке единства в их наименовании и структурном наполнении. Сложилось понимание необходимости проведения классификационногоанализа группы преступлений, в отношении которых разрабатывается укрупненная (общая, базовая, родовая и другие наименования) методика их расследования. Для построения аналогичной методики расследования коррупционных преступлений предлагается основываться на признаках субъектов деяний (должностных лиц) и сферы служебной деятельности, что позволяет рассматривать их в качестве базы криминалистической классификации коррупционных преступлений. Значимость укрупненной (общей, базовой, групповой и др.) методики расследования коррупционныхпреступлений видится в том, что она послужит основанием для разработки новых и совершенствования имеющихся частных методик расследования отдельных видов коррупционных преступлений.

A Bayesian Analysis of a Cognitive-Behavioral Therapy Intervention for High-Risk People on Probation.

This analysis employs a Bayesian framework to estimate the impact of a Cognitive-Behavioral Therapy (CBT) intervention on the recidivism of high-risk people under community supervision. The study relies on the reanalysis of experimental datal using a Bayesian logistic regression model. In doing so, new estimates of programmatic impact were produced using weakly informative Cauchy priors and the Hamiltonian Monte Carlo method. The Bayesian analysis indicated that CBT reduced the prevalence of new charges for total, non-violent, property, and drug crimes. However, the effectiveness of the CBT program varied meaningfully depending on the participant's age. The probability of the successful reduction of drug offenses was high only for younger individuals (<26years old), while there was an impact on property offenses only for older individuals (>26years old). In general, the probability of the successful reduction of new charges was higher for the older group of people on probation. Generally, this study demonstrates that Bayesian analysis can complement the more commonplace Null Hypothesis Significance Test (NHST) analysis in experimental research by providing practically useful probability information. Additionally, the specific findings of the reestimation support the principles of risk-needs responsivity and risk-stratified community supervision and align with related findings, though important differences emerge. In this case, the Bayesian estimations suggest that the effect of the intervention may vary for different types of crime depending on the age of the participants. This is informative for the development of evidence-based correctional policy and effective community supervision programming.

The time-varying effects of physical environment for walkability on neighborhood crime

The current study conducts a spatial-temporal examination of physical environment for walkability and crime in block groups located across nine U.S. cities. We aggregated the crime data for each of the different crime types using the block group where the crime occurred and the 2-hour period of the 24-hour day when the crime occurred. We estimated models at each 2-hour time slot (for weekdays and weekends), separately, to look at the effect of the walkability measure on crime at different hours of the day. Our results show that the crime-enhancing effect of physical environment for walkability changes across different hours of the day, and such temporal patterns vary by different types of crime. Crime-enhancing effect of physical environment for walkability is relatively stronger during very early morning time (midnight-4 am), and the risk of crime as a result of neighborhood walkability peaks in the late afternoon (2–4 or 4–6 pm). The measure of physical environment for walkability has different effect on crime patterns across hours of the day as the usage and meaning of physical environment and routine activities of people visiting the area, and thus the structures of criminal opportunities and guardianship capability temporally vary at different time periods.

Changing Scenario of Crime with Special Reference to Cyber Crime

Abstract: Crime may be defined as those actions committed by human beings that cause harm to others and violate societal norms. But with the advancement in science and technology that has reshaped every aspect of human life the scenario of crime has also changed from that of traditional to technological because in ancient times the concept of crime was prevalent in the society but the nature of crime was not so sophisticated as we see in the technological era. The invention of computers and smart phones has facilitated different types of cyber crimes in the society. Almost everyone is possessing a smart phone and the usage of it depends upon the people who will use it in a positive way. The concept of digital education has emerged as an important tool in the hands of learners and educators to propagate the education. Learners of lower age group are also using these technological equipments so parents should keep a strict vigil and supervision over their children in order to avoid such types of activities that cause moral harm to the norms and values of society. The main aim of this paper is to analyse how the nature of crime has changed and the different types of crime particularly in consideration with Cyber crime

Modeling systems of sentencing in early inquisition trials: Crime, social connectivity, and punishment in the register of Peter Seila (1241–2)

Despite significant research on the techniques of repression employed by medieval inquisitors against religious dissidents, the case-level influences on the penances they meted out are understood only vaguely: the extent to which sentencing “systems” existed is unknown. To overcome this, we apply formal methods – an exploratory analysis supported by crisp-set Qualitative Comparative Analysis, and statistical modeling founded on multiple linear regression – to the large and historically significant register of Peter Seila (1241–2), captured as structured data via a statement-based approach entitled “Computer-Assisted Semantic Text Modelling” (CASTEMO). The results show that Peter systematically weighted different types of crimes and dissident interactions when sentencing; they do not suggest, however, that he was influenced by accomplicity or kinship among the sentenced.

Understanding the relationship between the spatial configuration and the crime rate of Downtown Eastside in Vancouver, Canada

As one of Vancouver's oldest neighbourhoods and the historical heart of the city, Downtown Eastside (DTES) has been home to many low-income residents and has been challenged by factors such as high crime rates, drug use, unemployment, housing issues, and loss of businesses in the community. Although a variety of studies have explored the causes and effects of DTES's social dilemma and indicated that crimes are closely related to the spatial configuration of the underlying street network, how DTES's underlying street network affects the spatial distribution of different types of crime remains unclear. Focusing on the relationship between the street network and the crime rate of DTES, the authors make three hypotheses regarding the connections between the street network of DTES and the distribution patterns of crimes, and employ the space syntax methods to analyse the street network of DTES and test the hypotheses. The results indicate that the spatial configuration of street network can be helpful in explaining where different crimes may occur, thus providing a spatial perspective to figure out approaches to addressing DTES's social issues. At last, this paper, combining spatial and social perspectives, puts forward strategies for making DTES a more liveable place, and is likely to work as a reference for academics and practitioners to further understand gentrification and urbanisation related issues.

Seasonal characteristics of crime: an empirical investigation of the temporal fluctuation of the different types of crime in London

Most types of crimes show seasonal fluctuations but the difference and similarity of the periodicity between different crimes are understudied. Interpreting the seasonality of different crime types and formulating clusters of crimes that share similar seasonal characteristics would help identify the common underlying factors and revise the patterns of patrolling and monitoring to enable sustained management of the control strategies. This study proposes a new methodological framework for measuring similarities and differences in the timing of peaks and troughs, as well as the waveforms of different crimes. The method combines a Poisson state-space model with cluster analysis and multi-dimensional scaling. A case study using twelve types of crimes in London (2013–2020) demonstrated that the amplitude of the seasonal fluctuation identified by this method explained 95.2% of the similarity in their waveforms, while the timing of the peaks covered 87.5% of the variance in their seasonal fluctuation. The high predictability of the seasonal patterns of crimes as well as the stable categorisation of crimes with similar seasonal characteristics enable sustainable and measured planning of police resource allocation and, thereby, facilitates a more efficient management of the urban environment.

Interaction of Crime Risk across Crime Types in Hotspot Areas

Repeat and near-repeat victimization are important concepts in the study of crime. The incidence of repeat offenses within a single type of crime has been confirmed. However, the study of the circumstances existing across crime types requires further investigation. This article investigates whether the phenomenon of near-repeat crime exists in different types of crime by studying the spread of crime risk within different crime types. Taking Suzhou City as the research area, a DBSCAN-based algorithm is proposed, which can detect a large number of important and stable hotspots through the multi-density self-adaptation of algorithm parameters. Pearson correlation is used to analyze the risk correlation between different types of crime. In different crime hotspots, the types of crime and the spread of crime risk among different types is also different. After a crime occurs, identifying the risk can aid crime prevention.

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.

Cybercrime Landscape and Changes During the Pandemic in Division 4 ( West North Central

Since its existence in December 2019, COVID-19 has significantly impacted different sectors of the economy in both the physical and digital worlds. In cyberspace, the Covid-19 pandemic has changed the cybercrime landscape. In this study, we focus on data collected by the Federal Bureau of Investigation (FBI) in the West North Central (Division 4) region. Data was collected for each state for different types of crimes and was divided into various age groups. Non-payment and non-delivery have the highest number of victims compared to other types of crimes. Moreover, over the past five years, the over-60 age group had the highest number of victims in Kansas, Missouri, South Dakota, and Minnesota. However, the number of elderly cybercrime victims ranked second compared to other age groups in Iowa and Nebraska and third in North Dakota.