Crime Incidents Research Articles

Criminal behavior in alcohol-related dementia and Wernicke-Korsakoff syndrome: a Nationwide Register Study.

Chronic heavy alcohol use may lead to permanent brain damage, cognitive impairment, and dementia. While the link between alcohol use and crime is strong, virtually no research exists on the criminal behavior of patients with the alcohol-related neurocognitive disorders of Wernicke-Korsakoff syndrome (WKS) and alcohol-related dementia (ARD). The study population included all persons diagnosed with WKS (n = 1149) or ARD (n = 2432) in Finland in 1998-2015. Data on diagnoses, mortality, and crime were obtained from Finnish nationwide registers. Crime incidences were calculated 4 years before and after diagnosis. Crime types, incidences, and mortality were compared between disorders and with the general population. Altogether 35.6% of WKS patients and 23.6% of ARD patients had committed crimes in the 4 years preceding diagnosis, most commonly property and traffic crimes, followed by violent crimes. The incidence of criminal behavior decreased significantly after diagnosis; in WKS patients, the standardized criminality ratio (SCR), the ratio of observed to expected number of crimes (95% CI), was 3.91 (3.72-4.10) in 4 years before and 2.80 (2.61-3.00) in 4 years after diagnosis. Likewise, in ARD patients, the SCRs were 2.63 (2.51-2.75) before and 0.84 (0.75-0.92) after diagnosis. No significant difference emerged in mortality between persons with and without a criminal history. Persons with alcohol-related neurocognitive disorders frequently engage in criminal behavior prior to diagnosis, especially multiple offending. In the 4 years before and after diagnosis, crime rates declined in a linear fashion, with a marked reduction after diagnosis.

Crime Reporting Patterns and Frequencies in Print Media in Post-COVID Nigeria: A Security Approach

This study systematically delves into the intricacies of crime reporting by mass media in post-COVID Nigeria, shedding light on its profound impact and intensity. Through meticulous archival methods, historical editions of The Guardian and Punch newspapers were analyzed over three years. The findings highlight a notable emphasis on crimes against individuals, such as murder and assault, compared to other categories like financial and drug-related crimes. Over 4,093 crime incidents were reported, with crimes against persons dominating in 2021 and 2022. The study underscores the need for nuanced crime reporting and advocates for substantive engagement through editorials and analyses. By fostering awareness and discourse, print media can play a pivotal role in shaping public understanding of crime dynamics and promoting societal well-being.

Hate Crimes and Black College Student Enrollment

Abstract Reported hate crimes in the United States have increased rapidly in recent years, alongside an increase in general racial animus. Scholars have shown that the larger sociopolitical environment can directly impact the campus climate and experiences of all students, particularly students of color. However, little is known about how reports of hate crime incidents relate to college enrollment levels of students of color. This lack of evidence has especially troubling implications for Black people, the most frequent targets of reported hate crimes. This paper helps to fill in that gap by exploring the association between the number of reports of hate crimes within states and Black students’ college enrollment. We examine a comprehensive dataset of institutional enrollment and characteristics, reported hate crimes, and census data on state racial demographics from 2000 to 2017 using several techniques, including institution fixed effects. We find that a 1 standard deviation increase in reports of state-level hate crimes predicts a 17 to 22 percent increase in Black first-time student enrollment at historically Black colleges and universities. As the number of reported hate crimes is almost assuredly an undercount of the actual number of incidents, we explore the implications of what these results mean.

Crime Incidents and Victim Demographics: An Examination of Reporting Behaviours

The present study used data from the National Crime Victimization Survey from 1994 to 2020 to examine reporting behaviours of respondents. The study aimed to identify if the presence of a weapon, and an offender being a stranger, impacted whether a victim reports the crime to law enforcement. Demographic variables (e.g., sex, age and income) and crime incident-related variables (e.g., relationship to the offender, distance from home, violent crime and the number of crime incidents) were used to examine the effect on reporting behaviour. Two binary logit models were run and found that the presence of a weapon, the offender being a stranger, victims who are female, a closer distance to the respondent’s home, violent crime and fewer number of crime incidents in the last year were associated with higher odds of reporting. The odds of reporting a crime were over two times larger when the offender had a weapon, providing support for weapons continuing to impact reporting behaviour. An interaction effect was run and suggests that the year of victimization does not impact the relationship between weapons and reporting behaviour.

Clustering method for criminal crime acts using K-means and principal component analysis

Criminality is an act of violating the values and norms of society that causes a lot of harm. Much of the criminal data is often just a collection of data that has no information. Analysis of crime data is key in efforts to reduce crime rates that provide an overview of the incidence of crime, patterns, levels of vulnerability, and the level of security of an area. This research proposes data analysis that provides an understanding of crime using data mining techniques, especially the K-means cluster method, both traditional and with principal component analysis (PCA) dimension reduction. Before the PCA process, the values are transformed first with Z score normalization. From the processing through the davies bouldin index (DBI) performance test with 3 clusters, it is concluded that traditional K-means produces a DBI Index value of 0.019 and K-means PCA of 0.299. Meanwhile, to see the optimal cluster, several iterations were performed and resulted in the most optimal DBI index of 4 clusters in K-means of 0.014 and K-means PCA of 0.172. From the performance test value, it means that in the context of clustering the traditional criminal K-means data is declared more optimal than K-means PCA.

Digitally improving UK police surveillance and incidence response using real-time crowd reporting app: Digipolice

Overtime, evolving technologies has made ways of monitoring and controlling crime and action in the United Kingdom (UK) become more challenging which needs optimization with the help of technologies. Also with the rate of crime across cities in the UK like mass shootings, murder and gang gun battles, the UK Police system within localities are overwhelmed or may not efficiently cope with crime alerts and incidences coming in at increasing frequencies. However looking in the area of real time crowd reporting, there is need to leverage technology to enhance public safety, improve incident response, and foster community engagement. This necessitated the aim of this study to develop a digital policing surveillance app for crowd reporting in the UK. By utilizing digital policing surveillance apps, the aim is to improve crowd management capabilities by enabling real-time reporting of incidents and observations from both the public and law enforcement personnel. This was positioned to facilitate better situational awareness, efficient resource allocation, and effective response strategies. To achieve the objectives, Python Programming Language 3.8 were deployed together with a Django Framework and a Microsoft Visual Studio Build Tools. In the DP system, the Model-View-Controller (MVC) pattern, which divides an application into three interconnected components—the model, the view, and the controller—was used for the architectural design of the system. For the database, Django's ORM makes it simple to support many databases, and in this project straightforward SQLite3 database were utilised which is ideal for small-footprint applications. Using WebSockets to establish real-time, bidirectional communication between web clients and servers, the user interface and intelligence board for the system were developed. A clean designed frontend design system with Bootstrap version 5—an open-source, reusable design framework created by Twitter—was selected for its easy user interface. The system was put through 50 concurrent load tests with actual users swarming the app since the DP app was subjected to hundreds of people complaining in real time. 2. The host computer had a Core i5 vPro CPU running at 4GHz and 4GB of RAM. The result shows that 50 users successfully generated an average of 29.7 requests per second. The system scaled up nicely to 50 users and reached its highest response time of 2600 milliseconds (2.6 seconds) in total. A dramatic rise in activity and a longer response time occurred after there were more than 50 users. The system could not operate at its peak efficiency with 100 users because response times were inconsistent and exceeded 7000 milliseconds (7 seconds) for each request. The effectiveness of the online deployment was evaluated with respectable results, and the system requirements also underwent successful testing. The appropriate investigation of ethical issues was done to meet requirements. DigiPolice has the potential to develop into a cutting-edge programme for real-time crowd surveillance and incident reporting. UK Police can employ it to as advancement to its “My Police” with as fast-track real-time and collaborative crowd reporting enhancements.

Predicting Crime rate in Punjab: An Analysis of Artificial Intelligence based Models

This study aims at measuring and evaluating the accuracy rate or Mean Square Error (MSE) of different machine learning models to predict crime count (dependent variable) on the basis of a number of independent variables such as shift, day, month, police station, district and time of the day. It is a quantitative research study where primary data is collected from rescue 15 call record. The year 2022 has been chosen for the purpose of data under the crime head of theft across the Punjab province which is divided into three eight hourly shifts each in a day during any week of a month. The crime count indicates number of incidents of theft reported during these three shifts per day per month in a district in 2022. The data containing variables is then used for prediction through an appropriate machine learning model. The MSE value of Random Forest Regression (RFR) is greater than MSE value of Multi-Layer Perceptron Regression (MLPR). It is however lower than MSE score of Linear Regression (LR) model and greater than MSE value of Support Vector Regression (SVR). This shows that MLPR model is the most suitable one with least error as compared to other models which have higher MSE values while predicting the dependent variable (theft crime count). The study findings may assist the intelligence branches of police to efficiently utilize their material and human resources while applying the appropriate model of machine learning to predict the number of crime incidents in any geographical dispensation at sub national level.

Drilling Down for Answers: Unmasking the Impact of Oil and Natural Gas Activity on Crime Rates in Texas

This article delves into the impact of oil and natural gas (ONG) production on the incidence of Part I violent crimes in Texas. Texas holds a prominent position in the energy industry, contributing to 43% of the nation's crude oil production and 26% of its natural gas production (EIA, 2021). However, alongside ONG operations come significant societal changes, including a rise in various social issues, including criminal activities. While prior research has explored the consequences of ONG-related transformations on crime rates through perceptual and economic studies, there is a scarcity of studies that investigate the intricate relationship between ONG activities and crime patterns, particularly in the Texan context. To bridge this research gap, this study employs residual change scores and multiple linear regression techniques to scrutinize county-level shifts in ONG activity and Part I violent crime incidents during the period spanning 2009 to 2019 across Texas ONG-producing counties. The findings derived from this investigation unveil a noteworthy association between six dynamic ONG measures and the alteration in specific known Part I violent crimes. This study makes a noteworthy contribution to the existing body of knowledge concerning rural crime and boomtown dynamics as it stands as the inaugural examination utilizing residual change score analysis to determine whether ONG activity indeed contributes to any variations in known Part I violent crime rates. By scrutinizing the intricate connection between energy production and crime, this research aids in fostering a better understanding of the social implications of ONG activities in resource-rich regions, particularly within Texas.

Anti-Muslim Voting and Media Coverage of Immigrant Crimes

Abstract We study how news coverage of immigrant criminality impacts voting in one of the most controversial referendums in recent years—the 2009 Swiss minaret ban. We combine a comprehensive crime detection data set with detailed information on newspaper coverage. We first document a large upward distortion in media reporting of immigrant crime during the prereferendum period. Exploiting quasi-random variations in crime incidence, we find a positive first-order effect of news coverage on support for the ban. Our quantification shows that, in absence of the media bias, the pro-ban vote would have decreased from 57.6% to 53.5% at the national level.

A state-of-the-art universal machine learning framework for decoding suspect coded messages

This proposed framework aims to mitigate global terrorist threats, suspicious activities, and legal violations by introducing an innovative system that monitors encrypted short-text messages across instant messaging programs. In the current landscape, criminals, terrorists, and clandestine entities extensively employ encrypted messages for illicit endeavours on a global scale. Despite the escalating use of encrypted messages on social media platforms, a comprehensive solution for combating online crime is notably absent.The outlined framework addresses this gap by employing a cryptographic system that integrates semantic web ontology, a Predefined Dataset, and machine learning technology for encryption, decryption, and cryptanalysis. A key feature is the framework's ability to predict potential crimes by analyzing blogs for suspicious terminology conveyed through coded information. The identified information is then promptly communicated to law enforcement agencies, thereby alleviating the burden on numerous security entities and offering an efficient strategy against global crime.What sets this proposed framework apart is its unique utilization of encrypted text, a dimension that has remained unexplored in previous studies. The cohesive integration of its components expedites the process of anticipating crime types and significantly reduces the incidence of encrypted crimes on a global scale.

Development of Pop-up Book Illustrations as Educational Media for Child Reproductive Health

The incidence of sexual crimes against children and the prevalence of health-risk behaviors resulting from a lack of reproductive health education media have been identified from an early age as problems that must be followed up immediately. Instilling conception from an early age in children regarding reproductive health education, which is still taboo in most Indonesian societies, is an indication of research achievements that are relevant to be applied in social sciences and humanity. This research aims to test the feasibility of the pop-up book with the title “Yours” (Reproductive Health Education Book) for the age group of children over 6 years. The research method applied in this study is the research and development (R&D) method with the 4D flow (define, design, development, and dissemination). The pop-up book product with the title Pop Up Yours is a product created by the author and has never been tested for feasibility before. The results showed that the Pop Up Book “Yours” can be a medium for reproductive health education in children with the results of testing on 30 children(n=30) aged 9 years, an increase in knowledge of 93.3% was observed. Based on the results of the feasibility test, it can be seen that the “Yours” Pop Up Book is suitable for use by groups of children over 6 years old with the important concern of using this pop-up book under parental guidance.
 Keywords: pop-up book, child, reproductive health

Comparative Analysis of Juvenile Crime and Protection Policies Between China and the United States and Their Implications

Youth represent the future of the nation and are the beacon of hope for people. Their actions significantly impact the country's trajectory and destiny. Juvenile delinquency has always been a focal point of societal concern. In recent years, incidents of juvenile crime have proliferated, affecting not only the development of the youth but also the progress of the nation. This study aims to compare juvenile crime protection policies between China and the United States. Through this comparison, areas where China can draw references and insights to fill the gaps in its juvenile crime protection legal framework are identified. China proposes effective measures to foster holistic development of youth, enhance the government's comprehensive governance capacity, and effectively drive societal progress and development. This research employs analogy analysis and mixed scanning theory to study the laws and policies of China and the United States. Results indicate differences between the two countries in aspects such as the establishment of juvenile courts, educational guidance, and online privacy. China can strengthen the construction of juvenile courts in underdeveloped regions, intensify community education and guidance efforts, and focus on protecting the online privacy rights of adolescents.

Bird crime and the assessment of risk areas in Slovakia

Abstract Bird crime is a serious problem in many countries around the world. This study focuses on the situation in Slovakia, summarising data on bird crime in the country between 2016 and 2022. Over the course of that period, 92 cases were recorded in 27 districts involving 33 animal species. At least 249 protected birds and 78 mammals fell victim to illegal activities, and 61 poisoned baits were discovered. Social value is the monetary value which state institutions use to quantify the damage incurred to wildlife. Slovak law determines whether an illegal act is an offence or a criminal act based on the value of the incurred damage. The total social value of all of the protected animals as a result of illegal activities was estimated at € 696,250. Intentional poisoning, typically using carbofuran, was the most common of illegal act identified in the recorded cases, followed by shooting and trapping. An analysis of the recorded cases of bird crime was also used to assess the risk in each of Slovakia’s districts in relation to landscape structure variables. The percentage share of arable land was found to be a reliable predictor of bird crime, regardless of whether the birds had been poisoned or shot. These findings can help to develop better preventive inspections to detect bird crime incidents. We also recommended several management measures to help tackle bird crime more quickly and effectively.

Studying Parole Revocation Practices: Accounting for Dependency Between Competing Events.

When individuals are released from prison, they typically enter a period of post confinement community supervision. While under community supervision, their behaviors are subject to special conditions requiring them to report to supervisors and prohibiting certain behaviors such as drug and alcohol use. Many supervisees are returned to prison because they violate those special conditions, or because they commit minor crimes that would not result in prison were they not being supervised. But others are returned to prison for serious new crimes. We distinguish the two as nuisance behaviors (the former) and pernicious behaviors (the latter). Our research applies competing events survival analysis to distinguish a structural model that accounts for nuisance behaviors from a structural model that accounts for pernicious behaviors. We demonstrate that returning offenders to prison for technical violations and minor crimes may reduce the incidence of major crimes because the occurrence of nuisance behaviors and pernicious behaviors are highly correlated. Our findings support the theory that nuisance behaviors signal the likelihood of pernicious behaviors.

Rape Crime in India: A Spatio-temporal Analysis

Analysing crime spatiotemporally involves studying the spatial and temporal patterns of criminal incidents. It helps identify high crime areas and times, aiding law enforcement in resource allocation. Using National Crime Records Bureau's (NCRB), crime in India reports, this study analysed the spatial-temporal patterns of rape crime in India to see how incidents of rape crime are distributed in the country. Arc GIS (10.4) software was employed to map these patterns. The results shows that the spatial pattern of rape crime in India displays a picture of irregular distribution.

Anti-Asian Hate's Impact on Asian American Social Workers: Implications for Professional Training and Education.

This article explores the impact of recent incidents of anti-Asian hate and violence against Asian American social workers, clients, and communities. Asian Americans represent a small but growing proportion of the U.S. population. Yet, Asians are underrepresented in the social work profession-approximately 3.6 percent of the social work workforce and 2.1 percent of licensed social workers are Asian, and data on underrepresented racial and ethnic groups in the workforce continue to omit details on Asian people. Recent social and political framing of the COVID-19 pandemic as attributable to Asian people has fueled racist rhetoric and incidents of hate and bias crimes against Asian people. Through exploratory research to understand the experiences of Asian American social workers in the proliferation of anti-Asian hate, authors identified that more should be done to support and meet the needs of Asian American social workers, clients, and communities by improving social work education and training, by addressing the social work workforce and agency practices, and by expanding upon advocacy and community building.

Location Analytics of Routine Occurrences (LARO) to Identify Locations with Regularly Occurring Events with a Case Study on Traffic Accidents

Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings.

A nationwide time-series analysis for short-term effects of ambient temperature on violent crime in South Korea

Psychological theories on heat-aggression relationship have existed for decades and recent models suggest climate change will increase violence through varying pathways. Although observational studies have examined the impact of temperature on violent crime, the evidence for associations is primarily limited to coarse temporal resolution of weather and crime (e.g., yearly/monthly) and results from a few Western communities, warranting studies based on higher temporal resolution data of modern systemic crime statistics for various regions. This observational study examined short-term temperature impacts on violent crime using national crime data for the warm months (Jun.–Sep.) across South Korea (2016–2020). Distributed lag non-linear models assessed relative risks (RRs) of daily violent crime counts at the 70th, 90th, and 99th summer temperature percentiles compared to the reference temperature (10th percentile), with adjustments for long-term trends, seasonality, weather, and air pollution. Results indicate potentially non-linear relationships between daily summer temperature (lag0–lag10) and violent crime counts. Violent crimes consistently increased from the lowest temperature and showed the highest risk at the 70th temperature (~ 28.0 °C). The RR at the 70th and 90th percentiles of daily mean temperature (lag0–lag10), compared to the reference, was 1.11 (95% CI 1.09, 1.15) and 1.04 (95% CI 1.01, 1.07), indicating significant associations. Stratified analysis showed significant increases in assault and domestic violence for increases in temperature. The lagged effects, the influences of heat on subsequent crime incidence, did not persist 21 days after the exposure, possibly due to the displacement phenomenon. We found curvilinear exposure–response relationships, which provide empirical evidence to support the psychological theories for heat and violence. Lower public safety through increased violent crime may be an additional public health harm of climate change.

Investigating and prosecuting white-collar and corporate crime: Challenges and barriers for national police agencies

Many countries have established national authorities to investigate and prosecute serious and complex white-collar and corporate crime incidents. This article reviews research literature regarding external challenges and barriers for national agencies in Norway (Økokrim), New Zealand (SFO), the United Kingdom (SFO), and the Netherlands (OSF). The policing study in this article is important as it illustrates dilemmas that governments need to address when reviewing their national fraud offices and economic crime authorities. While Økokrim in Norway seems reluctant to prosecute too complex economic crime cases, the SFO in New Zealand attempts prevention of deinstitutionalization, the SFO in the UK might have deterrence effects, while the OSF in the Netherlands is challenged by the private industry of corporate investigators. These are some of the challenges and barriers facing national authorities that are charged with the tasks of investigating and prosecuting white-collar and corporate crime at the national level. The identified challenges and barriers especially related to the convenience theory perspective should enable future research to identify relevant actions.

Associations between violent crime inside and outside, air temperature, urban heat island magnitude and urban green space

There are more incidents of violence in summer and on hot days, a trend likely to be exacerbated by climate change. Urban areas experience additional temperature modulation due to the urban form, however, to date, no studies have considered the effect of the urban heat island (UHI) or green space with respect to the temperature–violence relationship. This study modelled the relationship between the number of daily violent crime incidents that occurred inside or outside between July 2013 and June 2018, and the average surface UHI or percentage greencover (including grasses, shrubs and trees) within each local government area in Greater Sydney, Australia. Panelised negative binomial time series regression models indicated that the violent crime rate was associated with higher surface UHI for crimes committed outside (p = 0.006) but not inside (p = 0.072). Greater percentage of all vegetation was associated with significantly lower rates of violent crime committed outside (p = 0.011) but was not associated with violent crimes committed inside (p = 0.430). More socio-economic disadvantage was associated with higher rates of violent crime committed inside (p = 0.002) but not outside (p = 0.145). Greater temperature was non-linearly associated with higher rates of violent crime committed both inside and outside (p < 0.001). The findings of this study are important because both violence and heat exposure are critical health issues and will be stressed by urbanisation and climate change. The expansion of green space and/or reduction in UHI may mitigate these effects.