Search Results (12)

Safety and crime prevention are significant concerns in both urban and rural areas. Crime Prevention Through Environmental Design (CPTED) guidelines provide an architectural strategy to deter criminal activities by implementing strategic design plans, particularly through effective lighting schemes in urban settings. These measures aim to reduce the fear of crime and enhance the overall quality of life. Enhanced street lighting plays a crucial role in environmental crime prevention by lowering both actual crime rates and the perceived risk of criminal activity in built environments. Current recommendations emphasize installing lighting in poorly lit areas for safety; however, assessing road surface luminance solely based on existing streetlights is insufficient. The research underscores that well-illuminated streets with uniform lighting and higher illuminance levels enhance pedestrian safety and comfort. In addition, this study proposes standardized illumination levels specifically for outdoor facial recognition to aid in identifying potential offenders. It outlines the critical vertical illuminance range and Color Rendering Index (CRI) values necessary for this purpose. Furthermore, metrics like the Brightness Index and Safety Index were developed to enhance night-time security and illustrate their correlation with crime rates. Ultimately, this research introduces quantitative lighting standards to enhance the effectiveness of CPTED guidelines, contributing to efforts to reduce crime incidence. Full article

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. Full article

A smarter city should be a safer city. Nighttime safety in metropolitan areas has long been a global concern, particularly for large cities with diverse demographics and intricate urban forms, whose citizens are often threatened by higher street-level crime rates. However, due to the lack of night-time urban appearance data, prior studies based on street view imagery (SVI) rarely addressed the perceived night-time safety issue, which can generate important implications for crime prevention. This study hypothesizes that night-time SVI can be effectively generated from widely existing daytime SVIs using generative AI (GenAI). To test the hypothesis, this study first collects pairwise day-and-night SVIs across four cities diverged in urban landscapes to construct a comprehensive day-and-night SVI dataset. It then trains and validates a day-to-night (D2N) model with fine-tuned brightness adjustment, effectively transforming daytime SVIs to nighttime ones for distinct urban forms tailored for urban scene perception studies. Our findings indicate that: (1) the performance of D2N transformation varies significantly by urban-scape variations related to urban density; (2) the proportion of building and sky views are important determinants of transformation accuracy; (3) within prevailed models, CycleGAN maintains the consistency of D2N scene conversion, but requires abundant data. Pix2Pix achieves considerable accuracy when pairwise day–and–night-night SVIs are available and are sensitive to data quality. StableDiffusion yields high-quality images with expensive training costs. Therefore, CycleGAN is most effective in balancing the accuracy, data requirement, and cost. This study contributes to urban scene studies by constructing a first-of-its-kind D2N dataset consisting of pairwise day-and-night SVIs across various urban forms. The D2N generator will provide a cornerstone for future urban studies that heavily utilize SVIs to audit urban environments. Full article

Several cities have been greatly affected by economic crisis, unregulated gentrification, and the pandemic, resulting in increased vacancy rates. Abandoned buildings have various negative implications on their neighborhoods, including an increased chance of fire and crime and a drastic reduction in their monetary value. This paper focuses on the use of satellite data and machine learning to provide insights for businesses and policymakers within Greece and beyond. Our objective is two-fold: to provide a comprehensive literature review on recent results concerning the opportunities offered by satellite images for business intelligence and to design and implement an open-source software system for the detection of abandoned or disused buildings based on nighttime lights and built-up area indices. Our preliminary experimentation provides promising results that can be used for location intelligence and beyond. Full article

Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible modality is not suitable for low-light environments or at night, when crime is frequent. In contrast, infrared images can reflect the nighttime environment, and most surveillance systems are equipped with dual-mode cameras that can automatically switch between visible and infrared modalities based on light conditions. In contrast to visible-light cameras, infrared (IR) cameras can still capture enough information from the scene in those dark environments. Therefore, the problem of visible-infrared cross-modality person re-identification (VI-ReID) is proposed. To improve the identification rate of cross-modality person re-identification, a cross-modality person re-identification method based on a two-branch network is proposed. Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. The effectiveness of the proposed method is verified by extensive experimental analysis, and the method achieves good recognition accuracy on the visible-infrared image person re-identification standard dataset SYSU-MM01. Full article

With Chinese people’s increasing willingness to participate in night activities, local governments have begun regarding the nighttime economy as an important means to stimulate urban vitality and increase social employment. This study uses changes in urban nighttime light brightness as a measure of environmental factors to examine the social effects of nighttime activities. Based on panel data for 227 prefecture-level cities in China from 2000 to 2013, this study empirically investigates the effect and mechanism of nighttime light brightness on the urban crime rate. Empirical results show that (1) a 1% increase in nighttime light brightness increases criminal arrest rate and prosecution rate by 1.474% and 2.371%, respectively; (2) the effects are larger in developed areas with higher levels of lighting and economic development, or in urban areas (compared with rural areas), and (3) the mechanism test shows that such effects are more pronounced in cities with more nighttime business, confirming the existence of a crime opportunity effect. Full article

The primary purpose of this paper is to test the applicability of environmental criminology in South Korea. Moreover, it explores effective strategies from a spatial planning perspective by taking control of diverse spatial planning factors. The study area is South Korea, and the base year is 2016. A spatial econometric model is built to analyze the relationship between the built environment and three crimes (theft, violence, and sexual assault). As a result, the best spatial regression models for violent crime rate and sexual assault rate are a spatial error model (SEM) and a spatial autoregressive model (SAC), respectively. The most prominent finding is that the regression results in the three crimes are slightly different. The broken windows effect was negligible for significant crimes in South Korea. The influence of regional disorders on the incidence of crimes was marginal. In the three crime types, mixed land use affected rising crime rates, which aligns with some previous studies that mixed land use increases the likelihood of crime incidences. In contrast with a series of relevant works, brighter nighttime light has not effectively decreased crimes in South Korea. In South Korea, closed-circuit television (CCTV) did not play a role in deterring crimes. Lastly, socio-economic characteristics were closely connected with crime rates in South Korea. The theft rate, violent crime rate, and sexual assault rate confirm the reliability of environmental criminology. Although this study has examined the likelihood of applying environmental criminology, further research and discussions are followed for concrete plans. Full article

Safe spaces are increasingly utilized to reduce alcohol-related harm, violence, crime and improve public safety in nightlife settings. This study aimed to determine the impact and return on investment of the Take Kare Safe Space (TKSS) program—a harm reduction program implemented to address alcohol-related violence and disorder in three locations in Sydney’s night-time economy between 2014 and 2019. TKSS ambassadors provided support at static safe spaces and patrolled designated nightlife precincts to provide practical assistance to vulnerable and intoxicated people. Ambassadors recorded information relating to these interactions including ‘client’ age, gender, perceived level of intoxication, time and length of engagement with the program. Costs of program implementation and benefits of major incidents averted were obtained to allow calculation of return on investment. From December 2014 to April 2019, 66,455 people were supported by TKSS ambassadors. Most users were male (62%) and aged 18–25 years (66%). Of 3633 interventions by ambassadors, serious risk of harm was averted in 735 cases (20%). The program’s return on investment is estimated at 2.67, suggesting that a $1 investment results in $2.67 in benefits. Safe Spaces are extensively utilized, particularly by young males with high levels of intoxication, and represent a positive return on investment. Despite the growth of such services, there remains a notable absence of rigorous, independent evaluation regarding the outcomes and/or social benefit of safe space programs. From a policy perspective, there is a need for more high-quality economic evaluations to better inform decisions about competing uses of limited resources. Full article

This cross-sectional study investigated regional differences in outdoor playtime and screen time among preschool-age children in Japan, and their associations with environmental factors, in order to identify the modifiable determinants of the differences between urban and rural areas. Data were obtained from the “Eat, be active, and sleep well” study. Children from three (n = 872) and five (n = 744) kindergartens in urban and rural regions of Japan, respectively, who completed a questionnaire survey, were sampled. Finally, data from 1128 participants (age: 3–5 years) were analyzed. To assess the environmental factors, the International Physical Activity Questionnaire was used for neighborhood environments; the Global Physical Activity Questionnaire was used for physical home environments; and a three-item questionnaire was used for sociocultural environments. Outdoor activity was significantly associated with items related to the mother’s sedentary time and the number of siblings/friends in both urban and rural regions. Screen time was associated with the mother’s screen time, television in the room, and esthetic qualities, which were consistent between urban and rural regions, while screen time was associated with night-time crime rates and traffic in the urban region. Our study further bolsters evidence suggesting that outdoor playtime and screen time are strongly associated with various environmental factors among Japanese preschool-age children. Full article

Since the target of burglars is generally the property of the inhabitant, it is crucial to accurately measure potential victims when analyzing burglaries, especially in small areas. Previous studies on burglary are mostly based on large units such as census tracts or communities. One of the difficulties is the measurement of the potential victims of burglary at the mesoscale. We compare the measuring effects of census population, census households, nighttime mobile phone users, and nighttime social media, such as the Tencent regional heatmap (TRH), on potential victims of burglary on 150 m × 150 m grids. Based on the rational choice theory, and controlling for the potentially confounding effects of risks and cost, we show that the TRH performed best, followed by census households and census population, and phone users performed poorly. The best-performing time period for TRH data was 3:00–5:00 am on weekends. These findings could lead to an improved measurement of potential victims of burglary at the mesoscale, and could provide scientific insight for crime prevention. Full article

In the spatial analysis of crime, the residential population has been a conventional measure of the population at risk. Recent studies suggest that the ambient population is a useful alternative measure of the population at risk that can better capture the activity patterns of a population. However, current studies are limited by the availability of high precision demographic characteristics, such as social activities and the origins of residents. In this research, we use spatially referenced mobile phone data to measure the size and activity patterns of various types of ambient population, and further investigate the link between urban larceny-theft and population with multiple demographic and activity characteristics. A series of crime attractors, generators, and detractors are also considered in the analysis to account for the spatial variation of crime opportunities. The major findings based on a negative binomial model are three-fold. (1) The size of the non-local population and people’s social regularity calculated from mobile phone big data significantly correlate with the spatial variation of larceny-theft. (2) Crime attractors, generators, and detractors, measured by five types of Points of Interest (POIs), significantly depict the criminality of places and impact opportunities for crime. (3) Higher levels of nighttime light are associated with increased levels of larceny-theft. The results have practical implications for linking the ambient population to crime, and the insights are informative for several theories of crime and crime prevention efforts. Full article

Public space lighting (PSL) is indispensable after the natural dark. However, little is known about how much PSL people actually need to feel sufficiently safe in different real-world urban settings. The present study attempts to answer this question by employing a novel real-time interactive approach, according to which, observers use a specially-designed mobile phone application to assess and report the perceived attributes of street lighting and the feeling of safety (FoS) it generates. To validate the proposed approach, a systematic survey was conducted in three cities in Israel—Tel Aviv-Yafo and Haifa, which lie on the Mediterranean coast, and Be’er Sheba, which lies inland. Additionally, instrumental PSL measurements were performed at the same locations. As the study reveals, the necessary level of illumination required by urban residents to feel safe differs by city and is significantly higher in Be’er Sheba, other factors held equal, in compare to Haifa and Tel Aviv-Yafo. This difference may be attributed to stronger daylight that the residents of the desert city of Be’er Sheba are accustomed to, and, therefore, may prefer stronger nighttime illumination. The difference could also be related to the relatively low socio-economic status and somewhat higher crime rates in the latter city. Findings also show a significant and positive association between FoS and instrumentally measured PSL levels, although this association exhibits diminishing returns. To the best of our knowledge, the present study is the first to use an interactive location- and time-based mobile phone technology, which can potentially provide more accurate and reliable assessments, compared to traditional “pen and paper” survey techniques. Full article