Search Results (146)

As the demand for more accurate crime prediction and risk assessment grows, researchers have been developing smarter models that blend statistical methods with machine learning. This study compares a hybrid ARIMA-ANN model with traditional classification techniques to see which best forecast monthly crime risk levels in Galați County, Romania. The analysis is based on a newly compiled dataset of 132 monthly observations from January 2014 to December 2024, which combines a broad array of social, economic, and environmental data points. The main variable, ‘Crime risk’, is based on normalized counts of offenses per capita and divided into five balanced levels: very low, low, moderate, high, and very high. The hybrid ARIMA-ANN model merges the strengths of statistical time series analysis with the flexible learning ability of artificial neural networks. Performance is evaluated against multinomial logistic regression, decision trees, random forests, and support vector machines. Overall, the results show that an ARIMA-ANN model consistently outperforms traditional methods, especially in recognizing patterns over time, seasonal trends, and complex nonlinear relationships in crime data. This study not only sets a new benchmark for crime analytics in Romania but also offers a flexible, scalable framework for classifying crime risk levels across different regions. 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

Poverty is a multifaceted phenomenon impacting millions globally, defined by a deficiency in both material and immaterial resources, which consequently restricts access to satisfactory living conditions. Comprehensive poverty analysis can be accomplished through the application of mathematical and modeling techniques, which are useful in understanding and predicting poverty trends. These models, which often incorporate principles from economics, stochastic processes, and dynamic systems, enable the assessment of the factors influencing poverty and the effectiveness of public policies in alleviating it. This paper introduces a mathematical compartmental model to investigate poverty within a population ($\psi \left(t\right)$), considering the effects of immigration, crime, and incarceration. The model aims to elucidate the interconnections between these factors and their combined impact on poverty levels. We begin the study by ensuring the mathematical validity of the model by demonstrating the uniqueness of a positive solution. Next, it is shown that under specific conditions, the probability of poverty persistence approaches certainty. Conversely, conditions leading to an exponential reduction in poverty are identified. Additionally, the semigroup associated with our model is proven to possess the Feller property, and its distribution has a unique invariant measure. To confirm and validate these theoretical results, interesting numerical simulations are performed. Full article

Street crime is a critical public concern, attracting wide social and research attention. Conventional solutions to reduce street crimes are dispatching more police force in patrol and installing more cameras for street surveillance, which, however, suffer from huge manpower and financial consumption and limited performance. Inspired by the wide application of Unmanned Aerial Vehicles (UAVs) in policing and other related missions such as street surveillance, we investigate the use of UAVs in patrolling along high-risk streets to deter street crimes. UAVs significantly outperform police officers and street cameras in terms of cost reduction and deterring performance improvement. Technically, this paper proposes a data-driven framework to schedule the patrol UAVs, including an online patrol path planning module and an offline UAV station siting module. In the first module, the street-level deterring effect of the UAVs is estimated using a prediction-enhanced method, which guides the UAVs to patrol the high-risk streets more efficiently. Evolved from the path planning algorithm, the second module utilizes a data-driven method to estimate the deterring effect of the candidate UAV stations with different numbers of UAVs. Then both the location of the UAV stations and the UAVs at each station are determined. The proposed framework is comprehensively evaluated using a 6-year crime dataset of the Denver city. The results show that the proposed framework improves the deterring effect by 58.49% on average, and up to 157.32% in extreme cases compared to baselines. Full article

In the United States, courts say a jury must determine whether a defendant is an Indian subject to federal jurisdiction; however, jurors are provided little guidance regarding what to consider in their Indian status determinations. Given the jurisdictional and legal defense implications Indian status decisions have, we tested whether jurors consider two easily accessible potential indicators of Indian race: appearance and name. We examined whether mock jurors’ (N = 825) stereotypes of Indians influenced their determinations of whether a defendant is an Indian and whether that defendant is guilty of the crime alleged using a fully crossed 3 (defendant photo Indian stereotypicality: high, low, and none) × 3 (defendant name Indian stereotypicality: high, low, and none) between-participants design, controlling for participants’ feelings toward Indians as a group and internal and external motivations to respond without prejudice. In general, neither the defendant’s name nor photo stereotypicality predicted Indian status determinations, but jurors who thought the defendant was an Indian were more likely to find the defendant guilty. Thus, mock jurors consider factors other than the defendant’s name and appearance when deciding whether the defendant is Indian, but if the defendant is considered Indian, mock jurors are more likely to find the defendant guilty. Full article

Objective: Low birth weight is a serious public health problem even in developed countries. The objective of this study was to assess the ability of machine learning to predict low birth weight rates in big cities in the USA on an ecological/population level. Study design: The study was based on publicly available data from the Big Cities Health Inventory Data Platform. The collected data related to the 35 largest, most urban cities in the United States from 2010 to 2022. The model-agnostic approach was used to assess and visualize the magnitude and direction of the most influential predictors. Results: The models showed excellent performance with R-squared values of 0.82, 0.81, 0.81, and 0.79, and residual root mean squared error values of 1.06, 0.87, 1.03, 0.99 for KNN, Best subset, Lasso, and XGBoost, respectively. It is noteworthy that the Best subset selection approach had a high RSq and the lowest residual root mean squared error, with only a four-predictor subset. Influential predictors that appeared in three/four models were rate of chlamydia infection, racial segregation, prenatal care, percentage of single-parent families, and poverty. Other important predictors were the rate of violent crimes, life expectancy, mental distress, income inequality, hazardous air quality, prevalence of hypertension, percent of foreign-born citizens, and smoking. This study was limited by the unavailability of data on gestational age. Conclusions: The machine learning algorithms showed excellent performance for the prediction of low birth weight rate in big cities. The identification of influential predictors can help local and state authorities and health policy decision makers to more effectively tackle this important health problem. Full article

An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL. 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

Despite the increase in the study of women and crimes committed by them, investigations continue to be scarce. Self-control and psychopathy have been widely studied in incarcerated populations, though more frequently in males than females. This study examines these psychological variables related to substance use history and violent crime in a sample of 94 incarcerated women in Portugal. Participants completed a sociodemographic questionnaire, the Self-Control Scale, and Levenson’s Self-Report Psychopathy Scale—VP. We found average self-control levels, with lower scores among participants with substance use. Significant differences in self-control emerged between women who committed homicide and those who did not. Psychopathy scores were above average, with significant differences in Factor 2 (impulsivity) between those who committed homicide and those who did not. Self-control and psychopathy were negatively associated, and psychopathy predicted self-control. These findings, which are not entirely consistent with the literature, challenge common assumptions about self-control, psychopathy, and crime, particularly in incarcerated women, and suggest that different mechanisms may drive violent and non-violent crimes in women. These results reinforce the need to consider gender-specific pathways to crime, highlighting the urgency of continuing to investigate the manifestation, in women, of widely studied variables in male samples. Full article

Background: The COVID-19 pandemic significantly disrupted forensic science, exposing vulnerabilities and introducing unprecedented challenges. Five years later, its impact persists, necessitating ongoing adaptations in forensic practice. This study examines key transformations, persistent issues, and emerging challenges in forensic science post-pandemic. Methods: A critical analysis of forensic science’s response to the pandemic was conducted, focusing on operational disruptions, methodological advancements, educational shifts, and technological integration. Results: Forensic operations faced delays due to case backlogs, restricted in-person work, and postponed court proceedings. Forensic pathology evolved with increased reliance on molecular autopsy techniques to clarify COVID-19-related deaths. Educational methods shifted toward virtual learning, prompting discussions on standardized digital training. Additionally, artificial intelligence and automation gained prominence in forensic investigations, enhancing crime scene analysis and predictive modeling. Discussion: While forensic science demonstrated adaptability, challenges remain in international collaboration, resource distribution, and professional training. The pandemic accelerated technological integration but also raised ethical and procedural concerns, particularly regarding AI applications in legal contexts. Virtual learning innovations necessitate further development to ensure competency in forensic training. Conclusions: Forensic science continues to evolve in response to post-pandemic realities. Addressing gaps in cooperation, technology implementation, and training will be crucial to strengthening the field. By assessing these changes, this study underscores forensic science’s resilience and adaptability, offering insights into its future trajectory amid ongoing challenges. Full article

Although there is a growing body of research focused on the personality characteristics of victims and offenders, only a few studies have investigated both groups through robust and comprehensive measures of personality. The present study aimed to compare the PAI and MMPI-2-RF profiles between victims and offenders and investigate the influence of adverse childhood experiences (ACEs) on their results. Samples of 107 female victims (age: M = 42.71; SD = 11.25) and 154 male inmates (age: M = 36.51; SD = 12.72) were compared, and statistically significant differences were found on several PAI and MMPI-2-RF scales. While the victims tended to score higher on scales such as Anxiety, Stress, Somatic Complaints and Thought Dysfunction, the inmates scored higher on scales related to Antisocial Traits, Drug Problems, and Aggressiveness-Revised, among others. Both groups reported a large number of ACEs, and linear regression analyses revealed that ACEs predicted PAI and MMPI-2-RF scores. A discriminant analysis also found that specific ACEs accurately discriminate psychological characteristics between victim and offender groups. In conclusion, the PAI and the MMPI-2-RF provided valuable information on the characteristics of victims and inmates, contributing to a better understanding of the nature of victimization and crime perpetration. Full article

This study advances crime analysis methodologies in Maryland by leveraging sophisticated machine learning (ML) techniques designed to cater to the state’s varied urban, suburban, and rural contexts. Our research utilized an enhanced combination of machine learning models, including random forest, gradient boosting, XGBoost, extra trees, and advanced ensemble methods like stacking regressors. These models have been meticulously optimized to address the unique dynamics and demographic variations across Maryland, enhancing our capability to capture localized crime trends with high precision. Through the integration of a comprehensive dataset comprising five years of detailed police reports and multiple crime databases, we executed a rigorous spatial and temporal analysis to identify crime hotspots. The novelty of our methodology lies in its technical sophistication and contextual sensitivity, ensuring that the models are not only accurate but also highly adaptable to local variations. Our models’ performance was extensively validated across various train–test split ratios, utilizing R-squared and RMSE metrics to confirm their efficacy and reliability for practical applications. The findings from this study contribute significantly to the field by offering new insights into localized crime patterns and demonstrating how tailored, data-driven strategies can effectively enhance public safety. This research importantly bridges the gap between general analytical techniques and the bespoke solutions required for detailed crime pattern analysis, providing a crucial resource for policymakers and law enforcement agencies dedicated to developing precise, adaptive public safety strategies. Full article

With the rapid growth in maritime traffic, navigational safety has become a pressing concern. Some vessels deliberately manipulate their type information to evade regulatory oversight, either to circumvent legal sanctions or engage in illicit activities. Such practices not only undermine the accuracy of maritime supervision but also pose significant risks to maritime traffic management and safety. Therefore, accurately identifying vessel types is essential for effective maritime traffic regulation, combating maritime crimes, and ensuring safe maritime transportation. However, the existing methods fail to fully exploit the long-term sequential dependencies and intricate mobility patterns embedded in vessel trajectory data, leading to suboptimal identification accuracy and reliability. To address these limitations, we propose MESTR, a Multi-Task Enhanced Ship-Type Recognition model based on Automatic Identification System (AIS) data. MESTR leverages a Transformer-based deep learning framework with a motion-pattern-aware trajectory segment masking strategy. By jointly optimizing two learning tasks—trajectory segment masking prediction and ship-type prediction—MESTR effectively captures deep spatiotemporal features of various vessel types. This approach enables the accurate classification of six common vessel categories: tug, sailing, fishing, passenger, tanker, and cargo. Experimental evaluations on real-world maritime datasets demonstrate the effectiveness of MESTR, achieving an average accuracy improvement of 12.04% over the existing methods. Full article

Weakly supervised video anomaly detection (WS-VAD) is often addressed as a multi-instance learning problem in which a few fixed number of video segments are selected for classifier training. However, this kind of selection strategy usually leads to a biased classifier. To solve this problem, we propose a novel self-paced multiple-instance learning (SP-MIL) framework for WS-VAD. Given a pre-trained baseline model, the proposed SP-MIL can enhance its performance by adaptively selecting video segments (from easy to hard) and persistently updating the classifier. In particular, for each training epoch, the baseline classifier is firstly used to predict the anomaly score of each segment, and their pseudo-labels are generated. Then, for all segments in each video, their age parameter is estimated based on their loss values. Based on the age parameter, we can determine the self-paced learning weight (hard weight with values of 0 or 1) of each segment, which is used to select the subset of segments. Finally, the selected segments, along with their pseudo-labels, are used to update the classifier. Extensive experiments conducted on the UCF-Crime, ShanghaiTech, and XD-Violence datasets demonstrate the effectiveness of the proposed framework, outperforming state-of-the-art methods. Full article

Recent advancements in crime prediction have increasingly focused on street networks, which offer finer granularity and a closer reflection of real-world urban dynamics. However, existing studies on street-level graph representation learning often overlook the variability in node features when aggregating information from neighboring nodes. This limitation reduces the model’s capacity to fully capture the diverse street attributes and their influence on crime patterns. To address this issue, we introduce an end-to-end deep spatio-temporal learning model that employs a graph attention mechanism (GAT) to analyze the spatio-temporal features of 110 call incidents. Experimental results show that our proposed model outperforms existing methods across multiple prediction metrics. Additionally, ablation studies confirm that the GAT’s capacity to capture spatial dependencies within the street network significantly enhances the model’s overall predictive performance. Full article