Search Results (89)

American citizens are significantly more likely to experience property crime victimization than violent crime victimization. During a staffing crisis, police prioritize limited resources in combating serious crime; however, property crimes remain impactful to the community. Therefore, agencies need to consider innovative ways to control property crime, such as “super-cocooning” strategies that alert residents to recent offenses. These strategies intend to empower the community to implement guardianship and crime prevention measures. For these strategies to be effective, they require public buy-in and support. The present study implements a preregistered information provision survey experiment (N = 2412), similar to the strategy of super-cocooning, to assess whether the public is more likely to support such strategies to combat property crime. Although the sample held overall high support of this strategy, exposure to a super-cocooning door hanger prime produced no significant changes in perceived effectiveness. However, there was observed racial heterogeneity in the treatments: non-White respondents assigned to the treatment relative to White respondents experienced significantly increased support of super-cocooning strategies. Implications for light-footprint crime control strategies, particularly during a staffing crisis, are discussed. Full article

Integrated sensing and communication (ISAC) has attracted extensive attention as a key technology to improve spectrum utilization and system performance for future wireless sensor networks. At the same time, active surveillance, as a legitimate means of surveillance, can improve the success rate of surveillance by sending interference signals to suspicious receivers, which is important for crime prevention and public safety. In this paper, we investigate the joint optimization of performance of both ISAC and active surveillance. Specifically, we formulate a bilevel optimization problem where the upper-level objective aims to maximize the probability of successful eavesdropping while the lower-level objective aims to optimize the localization performance of the radar on suspicious transmitters. By employing the Rayleigh quotient, introducing a decoupling strategy, and adding penalty terms, we propose an algorithm to solve the bilevel problem where the lower-level objective is convex. With the help of the proposed algorithm, we obtain the optimal solution of the analog transmit beamforming matrix and the digital beamforming vector. Performance analysis and discussion of key insights, such as the trade-off between eavesdropping success probability and radar localization accuracy, are also provided. Finally, comprehensive simulation results validate the effectiveness of our proposed algorithm in enhancing both the eavesdropping success probability and the accuracy of radar localization. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

This article explores the rise of organized crime and violence within the Palestinian community in Israel, focusing on the past two decades. It examines the internal fragmentation of the community, Israeli policies that exacerbated these divisions, and the impact of these factors on the surge in criminal activity. The article further analyzes community responses, highlighting faith-based initiatives like the Committee for Spreading Peace (CSP), led by Sheikh Raed Salah. This initiative, although limited in resources, seeks to address the cycle of violence through prevention, mediation, and collaboration with local authorities in Israel. However, the CSP faces significant challenges, including distrust in Israeli government efforts and the deep-rooted involvement of criminal organizations in local politics. The article concludes that while initiatives like CSP offer hope, a more comprehensive and collaborative approach is needed to effectively combat organized crime and restore community cohesion. Such approaches will also have implications for the Israeli–Palestinian conflict and possible future community-based initiatives. Full article

Recreational hunting is a highly regulated activity, in part because it can give rise to a variety of deleterious social, environmental, and economic harms. It provides an interesting area for those interested in community safety because of the way in which both formal (e.g., enforcement officers, proscribed areas and times for hunting, licensing, etc.) and informal (e.g., community awareness and education, conservation) methods of crime prevention are applied. And yet, the criminological literature on effective regulation is not only limited but diverse in terms of scope, types of behavior considered (e.g., poaching, wildlife trading, recreation, etc.), and the context that is considered (e.g., geographical, cultural, etc.). In this paper, we present how a crime prevention and compliance response can be used to understand the nature of the issue and the individual and socio-political processes that result in non-compliance with hunting regulations. We present an overview of the status of recreational hunting in an Australian jurisdiction and locate the regulatory issues that arise within the research literature that explores the various motivations that are known to drive illegal hunting. These are then considered in relation to how community-oriented and non-coercive measures might be employed to improve prevent criminal behavior at the primary, secondary, and tertiary levels. Full article

In a country where the majority of crimes remain unreported, uninvestigated, and unpunished, law enforcement faces considerable challenges in obtaining high-quality data that are consistent, reliable, and timely to effectively plan and deploy their strategies. By leveraging publicly available data, this paper identifies high-vulnerability areas for commercial burglary within the Metropolitan Area of Monterrey, utilizing a variable that incorporates the key dimensions of routine activity theory in criminology. This is accomplished by constructing an index through principal component analysis, followed by spatially grouping the resulting variable using the global indicator of spatial association (LISA). The results allow us to focus strategies to combat commercial burglary on 16.82% of the studied territory and establish an order of priorities to address the most vulnerable areas one by one. Also, the results allow us to implement prevention actions in broader zones by generating clusters around areas that share similar attributes. Full article

In the rapidly evolving landscape of biometric technologies, integrating artificial intelligence (AI) and predictive analytics offers promising opportunities and significant challenges for law enforcement and violence prevention. This paper examines the current state of biometric surveillance systems, emphasizing the application of new sensor technologies and machine learning algorithms and their impact on crime prevention strategies. While advancements in facial recognition and predictive policing models have shown varying degrees of accuracy in determining violence, their efficiency and ethical concerns regarding privacy, bias, and civil liberties remain critically important. By analyzing the effectiveness of these technologies within public safety contexts, this study aims to highlight the potential of biometric systems to improve identification processes while addressing the urgent need for strong frameworks that ensure improvements in violent crime prevention while providing moral accountability and equitable implementation in diverse communities. Ultimately, this research contributes to ongoing discussions about the future of biometric sensing technologies and their role in creating safer communities. Full article

This study assesses public trust in Bahrain regarding the potential of artificial intelligence (AI) to mitigate the use of cryptocurrencies in financial fraud and terrorist financing. The increasing risks associated with illicit financial activities have been exacerbated by the rapid expansion of e-commerce linked to cryptocurrencies, leading to vulnerabilities in financial technology systems. AI presents a viable solution for detecting, analyzing, and assessing the risks associated with cryptocurrency transactions, strengthening confidence in financial institutions’ regulatory measures. Evaluating public trust is crucial to understanding societal awareness of AI’s role in monitoring and regulating virtual financial transactions to prevent fraud. This research employs a quantitative approach to examine the key factors that enhance confidence in AI-driven auditing and oversight of cryptocurrency transfers. The findings indicate that, while AI offers significant advantages in combating financial crime, certain challenges remain. These include technological complexities, difficulties in accurately identifying users, and weaknesses in electronic financial and legal regulatory frameworks. Such challenges may undermine public trust in AI’s effectiveness in financial oversight. Addressing these concerns is essential to ensuring the successful integration of AI in financial regulation and reinforcing its role in enhancing security and transparency in cryptocurrency transactions. Full article

Rising global temperatures and increasing extreme weather events pose challenges to social stability and public security. This study examines the relationship between extreme weather and crime in China using fixed-effects quasi-Poisson and negative binomial regression models, along with a generalized additive model to explore nonlinear effects. The results show that extreme heat significantly increases crime, following an “S” shaped pattern. This intense heat heightens emotional instability and impulsivity, leading to a crime surge. While moderate heat reduces crime, extreme cold and heavy rainfall have no significant effects. These findings highlight the need for stratified policy interventions. Based on empirical evidence, this study proposes three key recommendations: (1) developing a weather warning and public security risk coordination system, (2) promoting community-based crime prevention through mutual assistance networks and infrastructure improvements, and (3) enhancing psychological interventions to mitigate mental health challenges linked to extreme weather. Integrating meteorological data, law enforcement, and interventions to help potential perpetrators can strengthen urban resilience and public safety against climate-induced crime risks. Full article

Sexual violence among school-aged girls is a global health problem. Research has shown that school-aged girls experience high rates of sexual violence that often go unreported. In Kenya, one in three girls experiences sexual violence before the age of 18. Sexual violence against girls can prevent them from safely attending school and cause health issues that affect school performance. This qualitative study explored community and environmental factors associated with sexual violence against school-aged girls in Kitui County, Kenya. A total of 25 in-depth interviews were conducted with key stakeholders from Kitui South Sub County. The stakeholders were from six sectors, including the police, health, education, community, religious, and criminal justice sectors. The data were analyzed using conventional content analysis to gain an understanding of the stakeholder’s perspectives and knowledge relating to sexual violence against school-aged girls. Stakeholders frequently identified the criminal justice system, culture and traditional beliefs, and threats to well-being as barriers to reporting sexual violence offenses. Girls who experience sexual violence often contend with shame from the community, and the effects of stigma include loss of resources, additional violence, poorer marriage prospects, unplanned pregnancies, school dropouts, or abandonment. Perpetrators often threatened or bribed the families of girls who experienced sexual violence with gifts or monetary incentives to prevent them from reporting the crime to local authorities, while the criminal justice system itself presents numerous challenges for victims. The reporting of sexual violence could be increased by focusing on intervention strategies that challenge attitudes, norms, and behaviors rooted in gender inequality. By addressing the underlying causes of stigma and inequality, we can create a safer environment for school-aged girls to report sexual violence and seek justice. Full article

The issue of personal safety for crime prevention has become a significant societal concern. Existing software on smartwatches developed for personal protection might provide GPS location tracking and emergency reporting, but this is limited to proactively detecting and responding to actual at-risk situations. This paper presents a real-time motion detection algorithm for smartwatches that utilizes an accelerometer to identify at-risk movements when a wearer is under threat. Daily activities, including walking, running, desk work, and being threatened, are distinguished by a machine learning-based alarm application. A total of 5534 data points across four classes were collected from experiments. The proposed 1D-CNN model exhibited the highest performance in comparison with SVM, k-NN, random forest, SGD. Additionally, our comparative analysis of using time-domain versus frequency-domain data in machine learning revealed that frequency-domain features offer advantages in both accuracy and real-time performance. Finally, the proposed inference model was implemented as a smartwatch application that can detect at-risk situations in real time. The application was tested in real-world scenarios, showcasing the effectiveness of personal safety. Full article

We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. The latter have been shown to be promising in violence detection. The aim of our architecture is to harness the combined strengths of the two known methods for improved accuracy. First, we leverage the pre-trained weights of MobileNetV2 and ResNet50V2 to initialize the network. Next, we fine-tune the network by training it on a dataset of labeled surveillance videos, with a focus on optimizing the fusion process between the two architectures. Experimental results demonstrate a significant improvement in accuracy compared with individual models. MobileNetV2 achieves an accuracy of 90%, while ResNet50V2 achieves a 94% accuracy in violence detection tasks. SynthSecureNet achieves an accuracy of 99.22%, surpassing the performance of individual models. The integration of MobileNetV2 and ResNet50V2 in SynthSecureNet offers a comprehensive solution that addresses the limitations of the existing architectures, paving the way for more effective surveillance and crime prevention strategies. Full article

While urban greening is an effective adaptation strategy for building resilient cities, socioeconomic factors and individual perceptions of urban parks play a significant role in enhancing their safety and inclusiveness. Crime Prevention through Environmental Design (CPTED) is a widely recognized approach for enhancing safety in urban public spaces. However, existing research has largely overlooked the impact of socioeconomic factors and interpersonal needs on shaping perceptions of safety. Baitashan Park is Located in Lanzhou City, Gansu Province, China. It is an iconic urban park with significant cultural and recreational value. Despite the park’s popularity, it faces challenges such as uneven accessibility, maintenance discrepancies, and perceived safety concerns, especially among users from the lower socioeconomic status (SES) group. This study examines how SES and interpersonal needs affect the relationship between CPTED principles and perceived safety. Our findings reveal that interpersonal needs significantly mediate the impact of CPTED on perceived safety, with SES playing a moderating role in both the direct and indirect effects. Specifically, the influence of CPTED on perceived safety through interpersonal needs is more pronounced for individuals with higher SES, while the direct effect of CPTED on perceived safety is also stronger for users with higher SES. These results suggest that the effectiveness of CPTED principles can be enhanced by considering the interplay between socioeconomic status and interpersonal dynamics. This study underscores the importance of adopting a holistic approach to urban park design, integrating environmental, social, and economic factors to promote safety, inclusivity, and well-being for all park users. Full article

This paper proposes a method enhancing the accuracy of object detectors by utilizing background images for stationary camera systems. Object detection with stationary cameras is highly valuable across various applications, such as traffic control, crime prevention, and abnormal behavior detection. Deep learning-based object detectors, which are mainly used in such cases, are developed for general purposes and do not take advantage of stationary cameras at all. Previously, cascade-based object detection methods utilizing background have been studied for stationary camera systems. These methods typically consist of two stages: background subtraction followed by object classification. However, their object detection performance is highly dependent on the accuracy of the background subtraction results, and numerous parameters must be adjusted during background subtraction to adapt to varying conditions. This paper proposes an end-to-end object detection method named BGI-YOLO, which uses a background image simply by combining it with an input image before feeding it into the object detection network. In our experiments, the following five methods are compared: three candidate methods of combining input and background images, baseline YOLOv7, and a traditional cascade method. BGI-YOLO, which combines input and background images at image level, showed a detection performance (mAP) improvement compared to baseline YOLOv7, with an increase of 5.6%p on the WITHROBOT S1 dataset and 2.5%p on the LLVIP dataset. In terms of computational cost (GFLOPs), the proposed method showed a slight increase of 0.19% compared to baseline YOLOv7. The experimental results demonstrated that the proposed method is highly effective for improving detection accuracy without increasing computational cost. Full article

Understanding the relationship between urban crime and the built environment is crucial for developing effective crime prevention strategies, particularly in the context of rapid urban development and city planning. As cities grow, urbanization leads to environments that either promote or inhibit criminal activity, making it essential to explore the interactions between urban design and crime. This study investigates the impact of micro built environment (MBE) elements and place perceptions on crime occurrences in Toronto using street view imagery (SVI) data and machine learning models. We used logistic regression models and an XGBoost (Version 1.7.5) classifier to assess the significance of MBE and perception variables in classifying crime and non-crime intersections. Our findings reveal that intersections with criminal activity tend to be related to more mobility-related features, such as roads and vehicles, and fewer natural elements, such as vegetation. The “beautiful” and “depressing” perceptions emerged as the most significant variables in explaining crime events, surpassing the commonly studied “safety” perception. The XGBoost model achieved 86% accuracy, indicating that MBE and perception variables are strong predictors of crime risk. These findings suggest that enhancing vegetation and improving street aesthetics could serve as effective crime prevention measures in urban environments. However, limitations include the general nature of the perception model and the reliance on aggregated crime data. Future research should incorporate local perceptions and fine-scale crime data to provide more tailored insights for urban planning and crime prevention Full article