Search Results (7)

Most policies to combat gang criminal behavior are rooted in deterrence and punitive strategies. This is fueled by moral panic, a get tough on crime rhetoric, and a lack of understanding for the psychological factors that may influence this behavior. Further, the extant literature has consistently observed that gang membership is associated with increased criminal behavior. In an effort to promote and shift away from punitive approaches in response to gang delinquency, the current study investigates the role psychopathic traits have in violent and property offending, longitudinally, in a sample of gang-involved youth. The study implemented count mixed effect models to investigate the topic longitudinally in waves 3, 5, 7, 8, 9, 10, and 11, while controlling for other variables with violent and property offending frequency. The current study found that some psychopathic traits are associated with offending behavior, longitudinally, in gang members and youth with a history of gang involvement. The findings suggest that gang intervention strategies should include empirically supported programs for treating psychopathic traits in gang identified youth to reduce involvement in delinquent behavior. Further, practitioners, researchers, and policymakers should collaborate to develop more empirically supported strategies to reduce and prevent gang delinquent behavior from an empathetic lens. Full article

In recent years, the application of biocompatible and non-toxic nanomaterials for the detection of fingerprints has become the major interest in the forensic sector and crime investigation. In this study, waste chickpea seeds, as a natural resource, were bioprocessed and utilized for the synthesis of non-toxic graphene nano-sheets (GNSs) with high fluorescence. The graphene GNS were synthesized via pyrolysis at high temperatures and were characterized by TEM, XPS, fluorescence and UV-Vis spectroscopy, and FTIR analysis. The GNS exhibited excitation-independent emission at about 620 nm with a quantum yield of over 10% and showed more distinct blue light under a UV lamp. Biocompatibility of the synthesized GNS in terms of cell viability (88.28% and 74.19%) was observed even at high concentrations (50 and 100 mg/mL), respectively. In addition, the antimicrobial properties of the synthesized GNS-based coatings were tested with the pathogenic strain of Bacillus cereus via live/dead cell counts and a plate counting method confirming their biocompatible and antimicrobial nature for their potential use in safe fingerprint detection. The developed chickpea-originated fluorescent GNS-based spray coatings were tested on different surfaces, including plastic, glass, silicon, steel, and soft plastic for the detection of crime scene fingerprints. Results confirmed that GNS can be used for the detection of latent fingerprints on multiple non-porous surfaces and were easy to detect under a UV lamp at 395 nm. These findings reinforce the suggestion that the developed fluorescent GNS spray coating has a high potential to increase sensitive and stable crime traces for forensic latent fingerprint detection on nonporous surface material. Capitalizing on their color-tunable behavior, the developed chickpea-originated fluorescent GNS-based spray coating is ideal for the visual enhancement of latent fingerprints. Full article

The measures in the fight against COVID-19 have reshaped the functions of urban facilities, which might cause the associated crimes to vary with the occurrence of the pandemic. This paper aimed to study this phenomenon by conducting quantitative research. By treating the area under the jurisdiction of the police station (AJPS) as spatial units, the residential burglary and non-motor vehicle theft that occurred during the first-level response to the public health emergencies (pandemic) period in 2020 and the corresponding temporal window (pre-pandemic) in 2019 were collected and a practical study to Beijing was made. The impact of urban facilities on crimes during both periods was analyzed independently by using negative binomial regression (NBR) and geographical weight regression (GWR). The findings demonstrated that during the pandemic period, a reduction in the count and spatial concentration of both property crimes were observed, and the impact of facilities on crime changed. Some facilities lost their impact on crime during the pandemic period, while other facilities played a significant role in generating crime. Additionally, the variables that always kept a stable significant impact on crime during the pre- and pandemic periods demonstrated a heterogeneous impact in space and experienced some variations across the periods. The study proved that the strategies in the fight against COVID-19 changed the impact of urban facilities on crime occurrence, which deeply reshaped the crime patterns. Full article

The article aims to propose a new way of estimating the ambient and immobile urban population using geotagged tweets and age structure, and to test how they are related to urban crime patterns. Using geotagged tweets and age structure data in 37 neighborhoods of Szczecin, Poland, we analyzed the following crime types that occurred during 2015–2017: burglary in commercial buildings, drug crime, fight and battery, property damage, and theft. Using negative binomial regression models, we found a positive correlation between the size of the ambient population and all investigated crime types. Additionally, neighborhoods with more immobile populations (younger than 16 or older than 65) tend to experience more commercial burglaries, but not other crime types. This may be related to the urban structure of Szczecin, Poland. Neighborhoods with higher rates of poverty and unemployment tend to experience more commercial burglaries, drug problems, property damage, and thefts. Additionally, the count of liquor stores is positively related to drug crime, fight-battery, and theft. This article suggests that the age structure of the population has an influence on the distribution of crime, thus it is necessary to tailor crime prevention strategies for different areas of the city. Full article

The occurrence of street crime is affected by socioeconomic and demographic characteristics and is also influenced by streetscape conditions. Understanding how the spatial distribution of street crime is associated with different streetscape features is significant for establishing crime prevention and city management strategies. Conventional data sources that quantify people on the street and streetscape characteristics, such as questionnaires, field surveys, or manual audits, are labor-intensive, time-consuming, and unable to cover a large area with a sufficient spatial resolution. Emerging cell phone and social media data have been used to measure ambient population, but they cannot distinguish between the street and indoor populations. This study addresses these limitations by combining Baidu Street View (BSV) images, deep learning algorithms, and spatial statistical regression models to examine the influences of people on the street and in the streetscape physical environment on street crime in a large Chinese city. First, we collected fine-grained street view images from the Baidu Map website. Then, we constructed a Faster R-CNN network to detect discrete elements with distinct outlines (such as persons) in each image. From this, we counted the number of people on the street in every BSV image and finally obtained the community-level total amounts. Additionally, the PSPNet network was developed for pixel-wise semantic segmentation to determine the proportions of other streetscape features such as buildings in each BSV image, based on which we obtained their community-level averages. The quantitative measurement of people on the street and a set of streetscape features that had potential influences on crime were finally derived by combining the outputs of two deep learning networks. To account for the spatial autocorrelation effect and distributional characteristics of crime data, we constructed a set of spatial lag negative binomial regression models to investigate how three types of street crime (i.e., total crime, property crime, and violent crime) were affected by the number of people on the street and the streetscape-built conditions. The models also controlled the effect of socioeconomic and demographic factors, land use features, the formal surveillance level, and transportation facilities. The models with people on the street and streetscape environment features had noticeable performance improvements, demonstrating the necessity for accounting for the effect of these factors when understanding street crime. Specifically, the number of people on the street had significantly positive impacts on the total street crime and street property crime. However, no statistically significant impact was found on street violent crime. The average proportions of the paths, buildings, and trees were associated with significantly lower street crime among physical streetscape features. Additionally, the statistical significances of most control variables conformed to previous research findings. This study is the first to combine Street View images and deep learning algorithms to retrieve the number of people on the street and the features of the visual streetscape environment to understand street crime. Full article

While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases that tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73% and 77% when predicting property crimes and violent crimes, respectively. Full article

Do larger cities still suffer from higher crime rates? The scaling relationship between the number of crimes and the population size for the maximum of 758 cities with more than 50,000 inhabitants in the United States from 1999 to 2014 was analyzed. For the total group of cities, the relationship is superlinear for both violent and property crimes. However, for the subgroups of the top 12, top 24, and top 50 largest cities, the relationship changes to sublinear for both violent and property crimes. Results from the panel data analysis are in support of these findings. Along with population size, income per capita and population density also influence the outcome of crime counts. Implications from these findings will be discussed. Full article