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This study observes the spatial distribution of robbery using a multi-level framework to further understand the unique contribution of both micro-places and neighborhoods to the spatial variability of crime patterns. Mixed-effects regression models were estimated to determine if the influence of spatial interdependence measured with a spatial lag variable at street segments had a random effect between Chicago's community areas. The variance parameters guided an exploratory spatial data analysis which revealed clusters of robbery patterns which were larger than crime hot spots and often nested within neighborhoods. We found there is an important distinction between places in Chicago where the spatial transmissibility of robberies is most acutely experienced across entire neighborhoods or within the immediate proximity of certain micro-places. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of commercial sex activities (CSA) in urban environments is important in addressing the harms against sex workers and identifying sex trafficking operations. Guided by crime pattern theory, using Census data and New York Police Department (NYPD) prostitution-related arrests data (N = 29,075) from 2010 to 2019, this study examines the "hot spots" of CSA in New York City. Local Spatial Autocorrelation Analysis (LISA) identified distinctive spatial clusters of CSA, whereas logistic regression explained their significant congregation in immigrant and racial-ethnic enclaves. The heterogeneity of clusters by boroughs portrays the convergence of activity space of sex workers, patrons, and sex business opportunities reflecting the high-demand locations of CSA and human trafficking in NYC. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractBuilding upon prior work, we propose an alternative way to look at the pattern of spatial crime concentration and temporal stability of it. We first identify a high-crime cluster using the sample block groups in New York City by employing a k-means clustering method. We then examine the temporal stability of the high-crime cluster over time. We also longitudinally assess how our high-crime cluster classification is associated with the actual amount of crime while accounting for the measures of social and physical environments. We observed that about 6–12% of total areas are identified to be in the high-crime cluster. We also found that block groups identified to be high-crime cluster in one year are more likely to be that way in the next year. We hope future research may consider using data-driven approaches to expand understanding of spatial and temporal crime patterns. [ABSTRACT FROM AUTHOR]

With the rise of economic inequality in recent decades, dollar stores have flourished in the United States. Although the media has increasingly portrayed dollar stores as violent crime magnets, limited academic research has examined their impact on nearby crime. This study employed a difference-in-differences approach to investigate the impact of dollar stores on localized violent crime patterns in Chicago. Results showed a significant increase in violent crimes, especially robberies, in nearby areas relative to the control areas, following the opening of dollar stores. There were no signs of spatial crime displacement. Moreover, after the closing of dollar stores, the levels of violent crime in nearby areas relative to the control areas returned to those of the prestore opening period, regardless of the type of crime. This study also explored how the impact of dollar store openings on nearby crime rates evolves over time and varies according to the surrounding context of the store's location. [ABSTRACT FROM AUTHOR]

Despite a pervasive anti-money laundering regime worldwide and growing media attention, the scant academic attention on the threat side of money laundering has led to an insufficient understanding of the actors, processes, and behaviors involved. Existing empirical studies show that money launderers are not a set of unitary and homogenous actors. However, criminological literature still lacks a systematic analysis that links distinct types of offenders with specific practices for money laundering. To address this knowledge gap, the present study analyzes 348 money laundering investigations conducted by Italian law enforcement agencies over the period 2016–2022 by the means of a multiple correspondence analysis. Italian mafias tend to launder illicit proceeds by integrating them into the legitimate economy, whereas foreign organized crime groups often favor schemes that operate independently of the financial system. Concurrently, other organized crime groups operating in Italy, as well as non-organized crime offenders, exhibit a broader spectrum of laundering behaviors. The study concludes by discussing implications for both research and policy. [ABSTRACT FROM AUTHOR]

Criminal behavior may be different on weekends and holidays compared to business days. Understanding the distinctive crime patterns on non-business days is useful for crime research and crime control. This study contributes to the literature by explicitly investigating the small-area spatiotemporal variation in five types of major crimes between business days and non-business days using a Bayesian modeling approach in Old Toronto, Canada. The results show that criminal activity varies between business days and non-business days, influenced by the types of crimes, geographic locations, and local neighborhood characteristics. Compared to business days, on non-business days, southern areas with high business and entertainment activity exhibit increased assault and robbery levels, while northern residential areas experience reduced activity of break and enter, auto theft, and theft over $5,000. Nonetheless, spatial crime hot spots generally remain consistent between the two date categories, with some hot spots presenting an exacerbation of criminal activity during non-business days. A few sociodemographic variables and built environment features are associated with the spatiotemporal variation in crime. These findings demonstrate the spatiotemporal variation in criminal behavior and crime patterns between business days and non-business days and highlight the need for customized crime control measures at the small area level. [ABSTRACT FROM AUTHOR]

Urban crime prevention remains a critical challenge intertwined with socioeconomic disparities. This study investigates the spatial nexus between urban deprivation and crime decline in Chicago from 2015 to 2022, addressing the question: How do changes in neighborhood deprivation relate to crime reduction patterns? Using comprehensive crime incident and census tract Area Deprivation Index (ADI) data, this study conducted exploratory spatial analysis, regression modeling, and local bivariate relationship analysis. The findings reveal persistent hotspots of concentrated deprivation on Chicago's south and west sides, alongside a general citywide crime decline. However, the current research uncovered significant spatial heterogeneity in both deprivation patterns and crime reduction, challenging the achievement of equitable public safety outcomes. Surprisingly, while crime incidents in 2015 and 2022 significantly predicted crime change, ADI scores were not statistically significant predictors in our regression model. Local bivariate analysis exposed diverse relationships between ADI changes and crime rate changes across neighborhoods, with 72.2% of census tracts showing no statistically significant relationship. This underscores the complexity of urban crime dynamics and the importance of local context in understanding these patterns. Our research contributes a nuanced understanding of the intricate relationship between urban deprivation and crime patterns, providing valuable insights for policymakers, law enforcement agencies, and urban planners. These findings highlight the need for integrated, long-term strategies that address both crime prevention and socioeconomic disparities, ultimately fostering safer, more equitable urban environments and informing evidence-based interventions tailored to specific neighborhood contexts. [ABSTRACT FROM AUTHOR]

Although studies have been made of different subtypes of individuals who committed sexual homicides, the research into nonstranger and stranger sexual homicides remains limited. This study therefore aimed to examine whether those who sexually murder nonstrangers differ from those who kill strangers. Data derived from police records, court documents, and published case reports spanning a 31 year period (1988–2018) in mainland China were used to examine the modus operandi of 127 males who committed sexual homicides (45 nonstranger and 82 stranger cases). Relative to nonstranger sexual homicides, stranger sexual homicides were more likely to have been committed by individuals with a previous sexual offense conviction and the victims were more likely to have been single and employed at the time of the offense. Furthering the analysis, a logistic regression found that individuals who targeted strangers were significantly more likely to have committed their homicide at an outdoor location, to have been sexually motivated, and to have used murder weapons that required more physical strength than those who killed nonstranger victims. These findings can be informative to law enforcement agents and security professionals in their investigative processes. [ABSTRACT FROM AUTHOR]

The article is devoted to one of the ways of committing a criminal offense under Article 206 of the Criminal Code of Ukraine "Counteraction to legitimate economic activity". In particular, the author examines the concept of threats of violence against the victim or persons close to the victim in the course of counteracting legitimate economic activity. It is determined that the method of committing a criminal offense in the form of a threat of violence against the victim or persons close to him/her is present both in Article 206 of the Criminal Code of Ukraine "Counteraction to Legitimate Economic Activity" and in some other corpus delicti of criminal offenses. The author establishes that in the Criminal Code of Ukraine "threat of violence" appears either as a separate criminal offense or as a method of committing a criminal offense. The study reveals the content of the terms "threat" and "threat of violence" used in the disposition of Article 206 of the Criminal Code of Ukraine. Based on the results of the analysis, the author suggests that a threat of violence should be understood as actions of a person aimed at intimidation without physical contact with the victim, which are intended to influence the psycho-emotional state of the victim with a view to achieving the desired criminal result. For the purpose of recognizing a threat as a method of counteracting legitimate business activity, it does not matter whether the perpetrator actually intended and was actually able to carry out the threat. From the moment the demand combined with the threat is made, this criminal offense is complete. It has been determined that, within the meaning of Article 206 of the Criminal Code of Ukraine, close persons are understood to mean close relatives - parents (foster parents), children (foster children), grandparents and grandchildren. Family members are parents (foster parents), children (foster children), brothers, sisters and their spouses who live together, as well as a spouse who lives together but is not married, and the spouses' parents. The closeness of persons who are family members is based primarily on marriage (husband, wife) and consanguinity (children, parents, other relatives), and may also arise on other grounds (friendship, love, etc.). [ABSTRACT FROM AUTHOR]

Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. [ABSTRACT FROM AUTHOR]

The comparative examination of different groups of sexual homicide offenders (SHOs) is currently limited. To expand our understanding of Chinese sexual homicides, this study aimed to distinguish between the modus operandi (MO) characteristics of repeat (i.e., with previous arrest and/or conviction) and nonrepeat (i.e., without previous arrest and/or conviction) offenders. Data were gathered from police arrest records, court documents, and published case reports in mainland China, covering a 31‐year period (1988–2018). A sample of 86 male SHOs (31 repeat and 55 nonrepeat offenders) was identified and the offenders' MO characteristics were examined. Compared with nonrepeat offenders, repeat offenders were significantly more likely to commit sexual murder in outdoor locations, approach their victims initially using a non‐surprise approach, engage in nonvaginal penetration of their victims, use a personal weapon to kill their victims, and move their victims' bodies away from the crime scene. However, repeat offenders were less likely to be arrested immediately after committing the murder. The findings have practical implications for police investigative strategies, such as suspect prioritization. [ABSTRACT FROM AUTHOR]

The current study examines the relationship between the features of physical and social environments and neighborhood crime in a large Korean city. We utilized the 112 Crime Calls Data from May 1, 2018 to April 30, 2019, aggregated at the ¼-mile egohood level. We estimated a series of negative binomial regression models to test the effects of social and physical environmental features on crime rates. Furthermore, we examine potential moderating effects between the measures of physical and social environments. The results indicate that incorporating the physical and social environmental features based on the theoretical framework of criminal opportunities and social disorganization can be useful for understanding the spatial patterns of crime in Korean context. [ABSTRACT FROM AUTHOR]

COVID-19 caused serious personal and societal damage. It also created, however, a unique natural experiment. How did the pandemic affect crime levels? In this article, we analyse unique empirical data for the Netherlands, covering January 2016–March 2021, to assess if and how crime patterns changed during the first 12 months of COVID-19-related measures. Domestic violence, which was expected to rise, did not increase, nor did distraction burglary. Residential burglary, bicycle theft and pickpocketing, however, decreased considerably, especially during (some of the) weeks when lockdown measures were into force. Online crime rates on the other hand increased. An opportunity theoretical approach regarding crime offers a good explanation for these changes. [ABSTRACT FROM AUTHOR]

Numerous cities are experimenting with pre-booking diversion programs that allow police officers to divert community members to supportive services in instances that would otherwise result in arrest and prosecution. These programs aim to decrease harmful involvement with the criminal justice system while reducing crime and public disorder. Although previous research has explored the experiences of people receiving diversion referrals and of police officers initiating them, none have examined the perspectives of community members who can offer crucial insights into planning, evaluation, and implementation barriers. We administered a survey to 293 people living in four police districts where the Philadelphia Police Department operates a pre-booking diversion program. The survey explored residents' perspectives on the program as well as their broader views on associated issues like decriminalization of substance use, sex work, and minor theft. Bivariate χ2 tests and multivariate logistic regression examined differences in responses between subgroups. Perspectives were diverse and varied based on demographic attributes of the respondents as well as on district-level attributes, like crime patterns. Most surveyed community members perceived pre-booking diversion to be a valuable tool for improving the experiences and outcomes of policing. However, residents living in areas with more crime and with more public disorder held significantly less positive perspectives. Their skepticism may reflect the possibility that pre-booking diversion and similar reforms are necessary but not sufficient to transforming individual health and public safety in some areas. [ABSTRACT FROM AUTHOR]

Background: A child is a nation’s supreme asset and future. India homes 444 million children, aged between 0 and 18 years, contributing to 19% of the world’s children. Crime against children is detrimental to their mental and physical health and affects their growth and development. The National Crime Record Bureau recently reported that a crime targeting children happens every 4 minutes. There is a paucity of literature regarding the burden of crime against children. To understand the magnitude and spatial distribution of crime against children, a retrospective surveillance study was conducted in the state of Tamil Nadu, India, from 2017 to 2021. Materials and Methods: This is a cross‑sectional analytical type of study conducted in KIMSRC, Chengalpattu, Tamil Nadu. The data from the yearly crime review bulletin of Tamil Nadu from 2017 to 2021 were cleaned, transformed, and analyzed using Python v3.8 and subjected to geospatial auto‑correlation and hotspot analysis using the Getis‑Ord Gi* in ArcGIS Pro v3.1. The endemicity pattern was studied through cluster analysis with Hierarchical Density Based Scanning in Python and visualization in ArcGIS pro v3.1 in the study area. Results: In Tamil Nadu, only one hotspot district in 2017 [Tiruppattur (95% confidence, P < 0.05)] and one hotspot in 2020 [Villupuram (90% confidence, P < 0.1)] were identified, with others being insignificant. The districts which show very high prevalence of crimes against children are Chennai, Ranipet, Chengalpattu, Viluppuram, Tiruvannamalai, Vellore, Tiruppattur, Krishnagiri, Dharmapuri, Salem, Cuddalore, Thanjavur, Tiruchirappalli, Karur, Tiruppur, Coimbatore, Dindigul, Pudukkottai, Sivaganga, Tenkasi, Thoothukkudi, Tirunelveli, and Kanniyakumari. Conclusion: This study identifies key areas within the state of Tamil Nadu which have a high prevalence of crimes against children and also areas that are hotspots for such crimes. Greater resources and measures can now be targeted toward these areas by stakeholders, which can help in the reduction of crimes against children. [ABSTRACT FROM AUTHOR]

The increasing pervasiveness of ICT technologies and sensor infrastructures is enabling police departments to gather and store increasing volumes of spatio-temporal crime data. This offers the opportunity to apply data analytics methodologies to extract useful crime predictive models, which can effectively detect spatial and temporal patterns of crime events, and can support police departments in implementing more effective strategies for crime prevention. The detection of crime hotspots from geo-referenced data is a crucial aspect of discovering effective predictive models and implementing efficient crime prevention decisions. In particular, since metropolitan cities are heavily characterized by variable spatial densities of crime events, multi-density clustering seems to be more effective than classic techniques for discovering crime hotspots. This paper presents the design and implementation of MD-CrimePredictor (Multi- Density Crime Predictor), an approach based on multi-density crime hotspots and regressive models to automatically detect high-risk crime areas in urban environments, and to reliably forecast crime trends in each area. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of multi-density crime hotspots, their densities and a set of associated crime predictors, each one representing a predictive model to forecast the number of crimes that are estimated to happen in its specific hotspot. The experimental evaluation of the proposed approach has been performed by analyzing a large area of Chicago, involving more than two million crime events (over a period of 19 years). This evaluation shows that the proposed approach, based on multi-density clustering and regressive models, achieves good accuracy in spatial and temporal crime forecasting over rolling prediction horizons. It also presents a comparative analysis between SARIMA and LSTM models, showing higher accuracy of the first method with respect to the second one. [ABSTRACT FROM AUTHOR]

The exponential growth of crime, violence, and wrongdoing is linked to dysfunction in restraining aggressive impulses, leading to neglect and disrespect of others' feelings, rights, and needs. Two basic forms of communication, verbal and nonverbal, serve individuals to interact and exchange ideas and attitudes with other people in everyday life situations, including within the framework of criminal investigations and criminal justice. The purpose of this study was to draw an analogy between verbal communication (oral speech, written speech) and nonverbal communication (body language, gestures, and silence), with the tendency to explore different components of nonverbal communication and place them in the context of criminal profiling as a method for detecting lies in nonverbal communication. The aim of this study was to emphasize the importance of nonverbal communication as an equal part of the communication process, and often a predominant one in this domain, which helps us demystify suspects, perpetrators, and criminal acts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When reconstructing a shooting incident with a shotgun, the muzzle‐to‐target distance can be determined by relating the size of a dispersion pattern found on a crime scene to that of test shots. Ideally, the test shots are performed with the weapon and ammunition that were used in the incident. But sometimes examiners will have to resort to alternatives, such as using cartridges of the same brand and type but with another pellet size. For this reason, the relationship between pellet size and shotgun dispersion patterns was studied with both lead and steel shotgun pellets. Cartridges were loaded with identical cartridge cases, powder charges, and wads but with different pellet sizes, below size B. The cartridges were fired, and the dispersion patterns at 5 m in front of the muzzle were measured and compared. The results provide strong support for the proposition that shotgun dispersion patterns with both lead and steel shot increase with decreasing pellet size if all other relevant parameters are kept equal. The results also provide an indicative measure of the magnitude of the effect. Pattern sizes were approximately 1.7 times larger with #9 than with #0 lead shot and 1.4 times larger with #9 than with #1 steel shot. The differences between consecutive shot sizes were generally smaller. This means that cartridges of equal brand and type but with the next nearest shot number can be used for a muzzle‐to‐target distance determination, keeping the information of the current study in mind in the final interpretation of the results. [ABSTRACT FROM AUTHOR]

Punishment involves the intentional infliction of harm and suffering. Both of the most prominent families of justifications of punishment – retributivism and consequentialism – face several moral concerns that are hard to overcome. Moreover, the effectiveness of current criminal punishment methods in ensuring society’s safety is seriously undermined by empirical research. Thus, it appears to be a moral imperative for a modern and humane society to seek alternative means of administering justice. The special issue of Diametros “The Abolition of Punishment: Is a Non-Punitive Criminal Justice System Ethically Justified?” was brought into life precisely to give the authors a platform for such progressive inquiries. And it is now safe to say that this platform has been put to excellent use, since Valerij Zisman, Alexander Stachurski, Giorgia Brucato, Perica Jovchevski, Sofia M. I. Jeppsson, Stephen G. Morris, Benjamin Vilhauer, John Lemos, Saul Smilansky, Elizabeth Shaw, Mirko Farina, Andrea Lavazza and Sergei Levin have presented such thought-provoking texts that they are bound to set the stage for debate in the years to come. This article is an introduction to this special issue and to the authors’ papers. [ABSTRACT FROM AUTHOR]

Copyright of International Annals of Criminology is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wildlife crime has emerged as one of the most crucial threats to biodiversity conservation and is particularly severe in south and southeast Asia. Addressing the ever-increasing challenges of wildlife crime in Nepal requires strategies informed by rigorous analysis of spatiotemporal patterns of wildlife crime. However, little do we know about the nature and trends of wildlife crimes in Nepal. Retrieving the information on the registered wild-life crime cases of Annapurna Conservation Area (ACA) from the Annapurna Conservation Area Liaison Office (ACALO) and Kaski District Court, this study assessed the temporal trend and spatial pattern of wildlife crime in the ACA from 1994 to 2019. Additionally, this study assessed the sociodemographic characteristics of the people convicted in wildlife crime cases. A total of 48 cases of crimes were registered over the last 25 years among which the majority of the cases were of poaching and wildlife trophies transportation (89.6%) mainly from Kaski district (60%). For those cases, a total of 132 people were convicted (65% from local villages and 35% from outside the ACA). About 68% of the convicted perpetrators were from Janajati ethnicity with poor economic conditions. These inferences emphasize the necessity of understanding the severity and pattern of the crime to prevent it by synthesizing and implementing conservation programs such as educating targeted groups and providing alternative sources of income. [ABSTRACT FROM AUTHOR]

Crime and criminality are global concerns that pose significant risks to societal well-being and quality of life. The urbanization process and the land use of cities profoundly influence the occurrence, types, and intensity of crimes. The design and management of cities play a pivotal role in ensuring overall safety and security. Although Kolkata is considered a safe city, recent reports from the National Crime Records Bureau highlight an increase in crimes against women, placing Kolkata as the second-highest in West Bengal. The crime rate in the city has witnessed a substantial 63.46% increase over the past three years, categorized as "high" on the rating scale. Little is known about the spatial and temporal patterns of crimes in Kolkata. This study aims to analyze the spatiotemporal patterns of murder, theft, snatching, drugging, pickpocketing, and crimes against women, using regression models. Using geoinformatics and geostatistics, the research also seeks to identify the spatial clustering, significant crime hotspots, and their shifting between 2015 and 2020 and also attempts to analyze the impact of urban land use on crime rates. The study reveals a significant temporal change in crime patterns over 30 years, with bare land consistently impacting crime across all categories. While thefts, snatching, pickpocketing, drugging, and crimes against women exhibit relatively stable hotspots, murder hotspots have increased and become more dispersed. The unique land use patterns of port areas influence murder, drugging, and crimes against women, while dense built-up spaces contribute to higher rates of pickpocketing. [ABSTRACT FROM AUTHOR]

Crime scene photography plays a fundamental role in forensic investigations. Its primary purpose is the recording of the entire crime scene, both the context and specific details, for later recollection, analysis and presentation. Bloodstain pattern analysis (BPA) relies extensively on the recording of context and detail. This study evaluated the use of panoramic imaging for the recording of impact patterns at crime scenes to support the off-site determination of area of origin (AO). This evaluation used a commercially available hardware system that consisted of a robotized, tripod-mounted platform, interfaced with a digital camera, to provide an automated panoramic image capture process. Software was subsequently used to construct a digital panorama. Bloodstain pattern analysis software was then used for the off-site determination of AO from the panoramic image. The results of this research demonstrated that the developed method can be employed to effectively capture a panoramic image of an impact pattern with sufficient detail for accurate AO determination. The approach can enable crime scene officers to record impact patterns without extensive training on BPA or the recording of blood spatter. The approach also enables BPA experts to digitally analyse spatter information from a single image as opposed to evaluating multiple photographs. [ABSTRACT FROM AUTHOR]

Soviet medical science went through a deep and systematic transformation. Initiated in the very early days of the Communist regime, a moment of disrupting and isolating impact uprooted it from its former organizational and epistemic foundations and re-adjusted the expert output and modus operandi to state needs. Professional identities, paradigmatic approaches and everyday practices resulting from these were transformed and forcibly aligned with the expectations of the new powerholders, aligned with the newly formulated party-bound loyalties and secured by the broad array of severe sanctions. This article systematically approaches the dark side of the Soviet interwar transformation in medicine with a special focus on a sample of psychiatric experts. The paper analyses the entanglement between domestic pressures exercised upon scholars by the emerging totalitarianism, forcing them to detach and considerably re-adjust their international ties, while nonetheless trying to remain a part of this cross-border epistemic community. While international contacts did not cease entirely, their expanse, intensity and the very mode of cooperation had to change dramatically so as to fit the new regime´s vision upon medicine. International networks were re-appropriated for the sake of promoting the success of the Communist state, with scholars functioning as involuntary ambassadors of the regime. Though perfectly compatible with the practices and outputs of their fellow experts elsewhere across the globe while exposed to the permanent pressure of political terror at home, Soviet public health and its experts became deeply transformed and detached in manifold ways from their peers and their medical science. Largely overlooked, the early fractures of the interwar period carry on into the Cold War-influenced second half of the twentieth century. [ABSTRACT FROM AUTHOR]

Previous studies of violent crime have been focused on identifying hot spots areas, ignoring crime movement and distribution, particularly violent-related land use on four different time frames. Studies showed that they have used police station boundaries or sectors to detect crime hotspots without considering land use type and the distribution and movement of crime. Understanding the relationship between violent crime and land use allows stakeholders to make informed decisions about land use, urban planning, and community development projects that can help reduce crime and improve overall community safety and quality of life. The aims of this study to investigate the relationship between violent crimes, time and land use in Selangor State, Kuala Lumpur Federal Territory (KLFT) and Putrajaya Federal Territory (PFT) from 2015 to 2020 using Mean Center and Standard Deviational Ellipses (SDE). These methodologies can assist authorities such as the Royal Malaysia Police (RMP) and Local Authority (LA) to identify potential variables leading to the high prevalence of violent crime in specific locations, which can then be used by police and local authorities to collaborate, establish crime prevention initiatives, and more effectively allocate resources. This study considers four-time frames, namely midnight (12:00-6:59 am), morning (7:00-11:59 am), evening (12:00-6:59 pm) and night (7:00-11:59 pm). The main concern in this article is that the SDE and Mean Center are still being investigated for use in studies of violent crime that are related to land use and spatial temporal. The overall Direction Mean Center and SDE at evening time, has the highest area of 128,146,621.17m². All SDEs are within the boundaries of Kuala Lumpur Contingent Police Headquarters (KLCPH) and Selangor Contingent Police Headquarters (SCPH) has been justify in a shape of Figure and Table in this article. However, only District Police Headquarters (DPH) Putrajaya, namely the police station in Presint 11 and 7 are not included in the analysis. Overall, police and local authorities may find it useful to employ SDE and the Mean Center technique to investigate violent crime using land use and spatial-temporal data their efforts to reduce violent crime, enhance public safety, and implement preventative measures in high-crime areas. [ABSTRACT FROM AUTHOR]

The objective of this work was to analyze the urban crime complexity, taking as a case study the border city of Mexicali, capital of the state of Baja California, which, according to the Peace Index in Mexico, for four consecutive years has been placed as the least pacific. For this purpose, three indicators from economic analysis were used: location quotient, ubiquity and diversity, using the records of crimes that occurred in the period 2015-2019. The results show the existence of spatial patterns of crime diversity and stability in some districts of the city characterized by highly ubiquitous crimes, which overlap each other, generating areas with criminal diversity that remain stable over time. In this way, public insecurity covers the entire urban space, as a result of an environment in which high levels of impunity and the multiplier effect of organized crime prevail. [ABSTRACT FROM AUTHOR]

Online grooming has increased along with the development of new technologies. To prevent it, we need to know the characteristics of offenders and victims, and the modus operandi. To do so, we analysed 20 Spanish court sentences (2017-2020 period). The sample is made up of 20 offenders (men, mean age = 35 years old) and 65 victims (46% girls, mean age = 14 years old). Although there was no homogeneous profile, in line with previous investigations, offenders were mostly unknown young men with no criminal records with a higher average age. The persuasion strategies that were found were similar with those of the literature but different by sex (deception and coercion girls, corruption boys). An offline meeting was suggested in 90% of the cases, being more accepted if the offender was known. Disability in the victims worked as a risk factor. In the discussion section we debunk myths, hoping to contribute with evidence-based material for further prevention programs. [ABSTRACT FROM AUTHOR]

On the basis that the literature shows that most of the serial murders are committed by men and with sexual motivation, the research is a preliminary approach which aims to approach the cultural influence on the sexual motivation of serial killers. Eighty-six cases of serial murders with male perpetrators from different countries have been analyzed, committed between 1900 and 2020. The variables evaluated are related to the serial offender, victimology, modus operandi, sexual motivation typology and the actions related to such classification. In indulgence countries, pre mortem sexual acts (44.20%), interference with the victim's body (36.0%) and mutilation (27.90%) are the most common; the same occurs in restrictive countries, although in a lower percentage, with mutilation 7.0% being the only statistically significant variable (X² = 0.034). Direct-sadistic motivation is the most present, being 52.30% in indulgence countries and 18.60% in restrictive countries, there being a significant relationship (X² = 0.026). There is a partial influence of culture on modus operandi and sexual motivation according to the higher or lesser presence of social norms and how they influence the individual's decision making. [ABSTRACT FROM AUTHOR]

Resisting the aggression of the Azerbaijani Armed Forces and pursuing the enemy fleeing from the battlefield, the Artsakh Self-Defence Forces in 1991-1994 liberated the territories of the historical Syunik, Artsakh and Utik provinces, which had been illegally annexed to Soviet Azerbaijan. The latter, continuing the Turkish policy of Armenocide in Transcaucasia, depatriated the autochthonous Armenian population by criminal methods of massacres, ethnic cleansing and forced religious conversion, mercilessly destroyed or usurped in the occupied settlements the centuries-old heritage of Armenian culture, which for centuries had served to preserve the national identity of the autochthonous Armenian population of Artsakh. The purpose of reinterpreting the cultural heritage of the Soviet period is to emphasise the urgency of liberating historical Artsakh based on Armenia's defence, security and military-political priorities and the need for the immediate return of the Armenian population in one of the most important territories of Armenian culture. [ABSTRACT FROM AUTHOR]

In this world, the rate of crimes is increasing as well as challenging the capabilities of people who are investigating crimes. Proper crime analysis and clustering has to be done in those cases. Crime analysis is the analysis of crime patterns and trends. It also assists in the research and planning necessary for the functioning of tactical forces and administrative services. Crime data grouping and clustering is very important to analyse the crime patterns and trends. By identifying patterns of crime committed in the past and the most common types of crime, crimes can be prevented from recurring in an area. Machine learning plays a key role in efficiently clustering today’s crime data. [ABSTRACT FROM AUTHOR]

In recent decades, the analysis of different geographic scales for studying the spatial patterning of crime has profoundly deepened our theoretical grasp of crime dynamics. However, a similar investigation is lacking when it comes to the patterning of offender residences, despite there being clear theoretical and empirical reasons for doing so, among them, the close relationship between where offenders live and where their corresponding crimes are committed. This paper delves into the concentration and variance of offender residences across different levels of spatial aggregation. The data used contains the locations of residence for known offenders in Birmingham between the years 2006 and 2016. Resident locations are aggregated to Output Areas (OA), nested within Lower Super Output Areas (LSOA), further nested within Middle Super Output Areas (MSOA). Descriptive and model-based statistics are deployed to quantify concentration and variation at each spatial scale. Results suggest that most variance (48%) in offender residence concentrations is attributable to the largest spatial scale (MSOA level). Output Areas capture approximately 38% of the variance. Findings open up discussions on the role of urban development in determining the appropriateness of spatial scale. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the relationships between different groups of explanatory variables (i.e., dynamic human activity variables, static variables of social disorganisation and crime generators, and combinations of both sets of variables) and property crime patterns across neighbourhood areas of London during the pandemic (from 2020 to 2021). Using the dynamic human activity variables sensed from mobile phone GPS big data sets, three types of 'Least Absolute Shrinkage and Selection Operator' (LASSO) regression models (i.e., static, dynamic, and static and dynamic) differentiated into explanatory variable groups were developed for seven types of property crime. Then, the geographically weighted regression (GWR) model was used to reveal the spatial associations between distinct explanatory variables and the specific type of crime. The findings demonstrated that human activity dynamics impose a substantially stronger influence on specific types of property crimes than other static variables. In terms of crime type, theft obtained particularly high relationships with dynamic human activity compared to other property crimes. Further analysis revealed important nuances in the spatial associations between property crimes and human activity across different contexts during the pandemic. The result provides support for crime risk prediction that considers the impact of dynamic human activity variables and their varying influences in distinct situations. [ABSTRACT FROM AUTHOR]

In post-pandemic, climate-changing societies, the presence of urban greenspace assumes paramount functions, at the same time that socio-economic crises and shocks augment vulnerabilities and insecurities. The recent literature on environmental criminology argues that the geography of crime is not random, and that the presence of greenery, due to its impact on well-being and the environment, can have positive associations with feeling safe; although the opposite effect can occur if spaces are not properly designed or maintained. In this paper, the case study of Porto, Portugal, is presented; one of the municipalities with higher crime rates, that also pledged to double the available greenspace in the near future. As a way to support decision-making, the aim of this study was to present an overall exploratory diagnosis of how street crime patterns, of different typologies, spatially co-exist with greenspaces. Using a 10-year street crime dataset at the segment level, descriptive quantitative methods with the support of GIS have been applied to plot crime's spatial distribution over time, as well as the walking accessibility to greenspaces. The results confirm crime's geographical non-randomness, with distinct categories occupying specific locations, even though there was a consistently proportional distribution in the different distance bands. On the contrary, the cumulative effect of the proximity to greenspaces was variable. Almost half of the city's street crimes (46%) were within a 5 min walking distance of greenspaces, but they were much closer to smaller inner-city urban gardens, with higher densities of street crimes (hot spots), than to larger municipal parks, where lower densities (cold spots) were seen. [ABSTRACT FROM AUTHOR]

Objectives: Examine the degree of crime concentration at micro-places across six large cities, the spatial clustering of high and low crime micro-places within cities, the presence of outliers within those clusters, and extent to which there is stability and change in micro-place classification over time. Methods: Using crime incident data gathered from six U.S. municipal police departments (Chicago, Los Angeles, New York City, Philadelphia, San Antonio, and Seattle) and aggregated to the street segment, Local Moran's I is calculated to identify statistically significant high and low crime clusters across each city and outliers within those clusters that differ significantly from their local spatial neighbors. Results: Within cities, the proportion of segments that are like their neighbors and fall within a statistically significant high or low crime cluster are relatively stable over time. For all cities, the largest proportion of street segments fell into the same classification over time (47.5% to 69.3%); changing segments were less common (4.7% to 20.5%). Changing clusters (i.e., segments that fell into both low and high clusters during the study) were rare. Outliers in each city reveal statistically significant street-to-street variability. Conclusions: The findings revealed similarities across cities, including considerable stability over time in segment classification. There were also cross-city differences that warrant further investigation, such as varying levels of spatial clustering. Understanding stable and changing clusters and outliers offers an opportunity for future research to explore the mechanisms that shape a city's spatiotemporal crime patterns to inform strategic resource allocation at smaller spatial scales. [ABSTRACT FROM AUTHOR]

The statistical analysis of crime data has gained attention in the last decade. In particular, the availability of spatio‐temporal crime data at the event level allows us to model the incidence of crime with high precision. Point process models are the natural tool to study crime patterns. As it is well‐known that crime events often spread as a contagion process, mechanistic self‐exciting models are usually considered in this context. In this paper, we propose a mechanistic bivariate spatio‐temporal model for the first‐order intensity function of the point processes associated with the intensity of two crime types. Specifically, the model includes separate estimates of the overall temporal and spatial intensities of crime and a spatio‐temporal interaction term for each of the crime types under analysis. Regarding the spatio‐temporal term, we model how the occurrence of previous crime events (from any of the two types) influences the intensity of each type of crime under study. We consider a dataset of crime events recorded in Valencia (Spain) during the year 2017 and focus on two crime types for the analysis: property crime and robbery. The results show that there is an association between the recent occurrence of either property crimes or robberies and the intensity of both crime types. Several spatio‐temporal monitoring tools are described and discussed as well. [ABSTRACT FROM AUTHOR]

Despite the changes they have undergone over time, acts of terrorism and, in particular, the ways in which they are committed have remained virtually the same, in fact, as the psychology of those who commit such acts. All acts of terrorism share a number of common features: first of all, they are characterized by the transnational, international nature of the attack and a pronounced physical and psychological aggression. Acts of terrorism are also usually characterized by their complex nature. The process of learning about these acts does not always have a corresponding finality. Prosecuting authorities often gather incomplete information and draw unjustified conclusions, incorrectly determine the direction of the investigation and make errors in the process of drafting their statements. However, knowledge of the criminal event must be obtained on the basis of the provisions determining the scope and limits of evidence in criminal cases and the forensic nature of the criminal act. In relation to the work of investigating acts of terrorism, success is guaranteed by the creative use of scientific knowledge in practical work, including forensic knowledge. Only under such conditions will the decisions taken be legal and well-founded, ensuring that the results correspond to the knowledge of objective reality. In connection with this, a proper examination of the forensic nature of the crime of terrorism and how it was committed is necessary. [ABSTRACT FROM AUTHOR]

In terms of reducing the fear of crime in the community, AI has the potential to be a useful tool. For example, AI can be used to analyze crime patterns and predict where crimes are likely to occur, allowing law enforcement to focus their resources on those areas. AI can also be used to monitor public spaces and detect suspicious behavior, helping to prevent crimes before they happen. However, it is important to ensure that the use of AI in law enforcement is done in a way that protects people's privacy and civil liberties. For example, there are concerns about the use of facial recognition technology, which can be used to identify individuals in public spaces without their consent. There are also concerns about AI's potential to perpetuate bias and discrimination, especially if the data used to train the AI is biased. Overall, the role of AI in reducing community fear of crime is a complex issue that requires careful consideration of potential benefits and risks. It is important to approach this issue with a critical and ethical perspective to ensure that the use of AI is beneficial for all. [ABSTRACT FROM AUTHOR]

Copyright of International Journal on Criminology is the property of Policy Studies Organization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes a novel contextualized colocation analysis to examine spatial crime patterns within their social contexts. The sample includes all reported MCI crime incidents (i.e., assault, break and enter, robbery, auto theft, and theft over incidents) in the city of Toronto between 2014 and 2019 (n = 178,892). Following a stepwise clustering feature selection, we begin our analysis by regionalizing the city based on the relevant social context indicators through a ward-like hierarchical spatial clustering algorithm. Then, we use a modified colocation miner algorithm with a novel Validity Score (VS) to select significant citywide and regional crime colocation patterns. The results indicate that eating establishments, commercial parking lots, and retail food stores are the most frequent urban facilities in citywide and regional crime colocation patterns. We also note several peculiar crime colocation patterns across disadvantaged neighborhoods. Additionally, the proposed analysis selects the patterns that explain an average of 11% more crime events through the use of VS. Our study offers an alternative method for colocation analysis by effectively identifying crime-specific citywide and regional crime colocation patterns. It also prioritizes the identified colocation patterns by ranking them based on their significance. [ABSTRACT FROM AUTHOR]

Urban crimes are not homogeneously distributed but exhibit spatial heterogeneity across a range of spatial scales. Meanwhile, while geographic space shapes human activities, it is also closely related to multiscale characteristics. Previous studies have explored the influence of underlying geographic space on crime occurrence from the mechanistic perspective, treating geographic space as a collection of points or lines, neglecting the multiscale nature of the spatial heterogeneity of crime and underlying geographic space. Therefore, inspired by the recent concept of "living structure" in geographic information science, this study applied a multiscale analysis method to explore the association between underlying geographic space and crime distribution. Firstly, the multiscale heterogeneity is described while simultaneously considering both the statistical and geometrical characteristics. Then, the spatial association rule mining approach is adopted to quantitatively measure the association between crime occurrence and geographic space at multiple scales. Finally, the effectiveness of the proposed methods is evaluated by crime incidents in the city of Philadelphia. Experimental results show that crime heterogeneity is indeed closely related with the spatial scales. It is also proven that the influence of underlying geographic space on crime heterogeneity varies with the spatial scales. This study may enrich the methodology in crime pattern and crime explanation analysis, and it provides useful insights for effective crime prevention. [ABSTRACT FROM AUTHOR]

Focusing on two of the most common deceptive strategies employed by fraudsters, we assess which type of cue—politeness or urgency—is more likely to result in an email fraud attempt. We also examine whether these cues are mutually exclusive and consistent throughout the progression of a fraud attempt. To answer our research questions, we posted "for-sale" advertisements on classified-advertisement websites and interacted with fraudsters who responded to our advertisements. Findings reveal that fraud attempts are more likely to follow probe emails that include cues of urgency than cues of politeness. Moreover, although the majority of fraudsters' probe emails include deceptive cues of either politeness or urgency, the majority of subsequent emails include deceptive cues of both politeness and urgency. [ABSTRACT FROM AUTHOR]

In the current study, we examine the effects of the number of subway stations as well as magnitude of subway ridership on crime in 1/4-mile egohoods in New York City, while accounting for structural characteristics and land uses. Longitudinal analyses are conducted using a set of negative binomial regression fixed effects models. We found that the number of subway stations have crime-enhancing effects for robbery, aggravated assault, burglary, and larceny, but crime-reducing for motor vehicle theft. We found mixed findings for subway ridership across different crime types. We also found that the effects of subway stations are moderated by the proportion of retail land use in place. The findings provide evidence that subway stations and ridership have important consequences for the spatial patterns of crime. [ABSTRACT FROM AUTHOR]

Copyright of Foro Internacional is the property of El Colegio de Mexico AC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, telecommunications fraud crimes are on the rise, posing a serious threat to the safety of people's property. In order to optimize anti-fraud strategies, objectively and accurately analyze the trends and characteristics of different types of telecommunications fraud cases, and determine the most influential criminal methods, a ChatGPT based telecommunications fraud case type impact assessment method is proposed. By utilizing a knowledge graph, the content of the case text is structured, and the methods of telecommunications fraud are quantified by taking the time of the incident, the amount involved, and the number of individuals involved as factors to evaluate the impact of the case. Firstly, ChatGPT is used to preprocess and extract knowledge from the text corpus of telecommunications fraud cases through multiple rounds of Q&A, in order to quickly and timely construct a case knowledge graph in the field of telecommunications fraud with low resources. Based on the knowledge graph, various factors such as incident time, amount involved, and the number of involved parties are statistically analyzed, and the impact of different types of cases is abstracted into influencing factors. The influencing factors are used to depict the trend and characteristics of incidents, to conduct comprehensive analysis and judgment. This paper analyzes existing case data and calculates the impact factors of case types, obtaining the changes in impact factors of different case types, verifying the scientific and effective calculation methods of impact factors, and providing a new method for the evaluation of telecommunications fraud types. Combining the advantages of ChatGPT and knowledge graph helps to timely grasp the trend of case development and changes, provides strong support and guidance to combat telecommunications fraud, and is of great significance for protecting public property safety and social stability. [ABSTRACT FROM AUTHOR]

This brief commentary recalls the origins of North-South cooperation in probation work and, in particular, references the evolution of the Irish Probation Journal (IPJ) since its launch in 2004, acknowledging the contribution from various editors, editorial committees, advisory panels and contributors, as well as support from ministers and departments of Justice. Reflecting on significant trends in the justice world internationally, with a particular focus on probation, the paper sets out the contribution of IPJ to probation policy and practice on this island. This includes remembering where the two services, the Probation Service and the Probation Board for Northern Ireland, have led the way - in relation to rehabilitation and reintegration, restorative justice, problem-solving justice, community service/unpaid work, addressing victim issues, service-user involvement, community engagement and their shared commitment to social work. The authors acknowledge the impact of significant political developments, including Brexit, devolution of justice in Northern Ireland (NI) and the changing patterns of crime in both jurisdictions. Finally, the paper anticipates some of the challenges and opportunities for both services over the next twenty years and the authors' thoughts on how the Irish Probation Journal can play a role in shaping that future. The authors approach subjects covered and offer reflections and opinions from our own respective and unique positions as inaugural editors of IPJ and experience in the fields discussed. [ABSTRACT FROM AUTHOR]