Search Results (13)

Urban safety is a critical concern for sustainable city development, with crime patterns often linked to localized environmental factors. Understanding the spatial dynamics of safety is critical for informed design and planning of urban environments. This study employs a Geographically Weighted Regression (GWR) approach to investigate how crime in Austin, Texas, correlates with Points of Interest (POIs) such as bars, transit stations, financial businesses, and public spaces, while accounting for localized socio-economic factors. Building on theoretical frameworks like Routine Activity Theory and Crime Pattern Theory, the analysis integrates crime data from the Austin Police Department (APD), POI datasets, and census variables to explore spatially varying relationships often overlooked by traditional global models (e.g., OLS). A novel adaptive geo-grid method refines spatial units by clustering high-density downtown areas into smaller zones and retaining larger grids in suburban regions, ensuring precision without over-fragmentation. Analysis of crime incidents and POI data reveals significant spatial non-stationarity in crime–environment associations. Transportation-related facilities demonstrate strong spatial correlation with crime citywide, particularly forming persistent crime hotspots around transit hubs in areas like Rundberg Lane, South Congress, and East Riverside. Alcohol-related establishments show a strong positive correlation with crime in entertainment districts (coefficient up to 13.5, p < 0.001) but a negligible association in suburban residential areas (coefficient close to 0, p > 0.05). The GWR model significantly outperforms traditional OLS regression, capturing critical local variations obscured by global models. Downtown Austin emerges as a complex hotspot for urban safety where multiple high-risk POI types overlap. This research advances urban design and planning knowledge by providing empirical evidence that environmental factors’ influence on safety is spatially conditional rather than universally consistent, aligning with Crime Pattern Theory and Routine Activity Theory. The findings support place-specific crime prevention strategies, offering policymakers data-driven insights for developing targeted design strategies for urban zones. Full article

With rapid urbanization, the spatial layout and functional characteristics of urban facilities have a strong correlation with the spatial distribution of criminal activities. Using Changsha City as a case study, this research analyzes 2023 urban crime data, Point of Interest (POI) data, and socioeconomic data. The Multi-scale Geographically Weighted Regression (MGWR) model and clustering analysis are applied to examine how different types of urban facilities influence the spatial heterogeneity of crimes and propose tailored urban planning recommendations and crime prevention strategies. The findings reveal the following: (1) The spatial distribution of crimes in Changsha’s central urban area demonstrates significant spatial heterogeneity. Property crimes dominate in frequency and spatial distribution, primarily clustering around commercial hubs and transport nodes, while violent crimes are more common in scenic areas and open spaces with high pedestrian flow. (2) The impact of built facilities on crime exhibits spatial variability. Facilities such as Financial Services Facilities (FSF) and Shopping facilities (SHF) significantly contribute to property crime in core urban areas, while Scientific, educational, and cultural facilities (SEC) suppress crime in university towns. Scenic spots and facilities (SPF) are associated with violent crimes near scenic site entrances and transport hubs. (3) Facility resource allocation and preventive strategies should be optimized based on dominant factors in different areas to enhance security management efficiency through precise and differentiated planning, fostering sustainable urban safety systems. This study provides insights into the spatial patterns of crime distribution and its dominant factors from the perspective of urban facilities, offering a scientific basis for improving urban crime management and facility planning. 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

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

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

In all countries, cities and their suburbs are the most densely populated areas. They are also the places visited by the largest number of tourists and one-day visitors, who inevitably run the risk of becoming victims of crime. It is, therefore, important, not only at national but also at the international level, to know the structure of urban crime and identify urban areas that differ in terms of their criminogenic situation. This requires a geographical approach and regionalisation based on the quantitative data that can offer it. This paper presents the results of a study using big data regarding violent crime, property crime and infringements against public order registered by the police in 2020 in the territories of three major Lithuanian cities and their suburbs (n = 149,239). Events in open spaces were separately addressed. A series of experiments were carried out using several spatial clustering methods. The automatic zoning procedure method that gave the best statistical results was then tested with different combinations of parameters. In each city, seven types of areas of urban crime were identified. Maps of crime areas (regions) were created for each city. The results of the regionalisation have been interpreted from a socio-geographical point of view and conform with previous sociological urban studies. Seven types of areas of crime have been identified, which are present in all the cities studied and, according to a preliminary assessment, roughly correspond to the socio-demographic and urban zones of each city. The maps of crime areas can be applied for crime prevention planning and communication, real estate valuation, strategic urban development planning and other purposes. Full article

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates. Full article

Currently, we observe an increasing number of terrorist attacks and violent crime, resulting in a noticeable increase in nervousness and tension in society. Not only on the territory of Europe, but also throughout the world, terrorist attacks and violent crime are mainly concentrated near objects that are accessible to the public, characterized by a relatively low level of security and a high concentration of people, so-called soft targets. The tendency to control and influence human behaviors by reshaping the environment dates back to ancient times, with the crime prevention through environmental design strategy being a later product that arose as a result of the impact of urbanization and industrialization on the rise of crime in Chicago. The aim of the article is the contribution of new knowledge to society, which is presented through graphic designs of technical measures on the sustainability of the development of public spaces and communities, as well as the reduction of crime. The acquired knowledge is based on a holistic and integrated theory of crime prevention called the concept of CPTED (crime prevention through environmental design), which is dedicated to the identification of the conditions of the physical and social environment that allow the possibility of committing crimes and their subsequent modification intended to limit it. At the end of the article, a use case is presented on the object of the railway station, which is a public space and at the same time falls into the category of soft targets. Human choices shape the built environment, as well as tools that can be used to create sustainable urban and village development, which are also dependent on an acceptable crime rate. We are not testing environmental design as a way of thinking and a field of practices, but we are testing a particular method of environmental design (CPTED concept), which is focused on crime prevention, for a particular set of targets. Full article

Environmental criminological research on rape series is an understudied field due largely to deficiencies in official and publicly available data. Additionally, little is known about the spatial patterns of rapists with a large number of stranger rapes. With a unique integration and application of spatial, temporal, behavioral, forensic, investigative, and personal history data, we explore the geography of rape of a prolific, mobile serial stranger rapist identified through initiatives to address thousands of previously untested rape kits in two U.S. urban, neighboring jurisdictions. Rape kit data provide the opportunity for a more complete and comprehensive understanding of stranger rape series by linking crimes that likely never would have been linked if not for the DNA evidence. This study fills a knowledge gap by exploring the spatial offending patterns of extremely prolific serial stranger rapists. Through the lens of routine activities theory, we explore the motivated offender, the lack of capable guardianship (e.g., built environment), and the targeted victims. The findings have important implications for gaining practical and useful insight into rapists’ use of space and behavioral decision-making processes, effective public health interventions and prevention approaches, and urban planning strategies in communities subjected to repeat targeting by violent offenders. Full article

Whether newly implemented public transit stations influence the nearby crime pattern has been debated for years. In ZG City, China, 2 new subway lines and 20 new stations were implemented in 2017. This intervention allows us to test the plausible relationship between new public transit stations and thefts in the surrounding areas. We use the difference-in-differences (DID) model to assess the theft in the treatment and control areas before and after the implementation of the new stations, with necessary socioeconomic and land-use variables and time from the addition of the station being controlled. We also explicitly examine the impacts of the proximity of the stations and the Spring Festival on theft. The results suggest the following: (1) theft around the new subway stations significantly increases after the stations’ implementation, while the control area does not see much change in thefts; (2) proximity between the neighboring stations’ increases thefts; and (3) theft near the new stations significantly decreases during the month of the Spring Festival. This study contributes to the literature on the relationship between the subway system and crime, especially from a Chinese perspective. The finding of the research can bring insights to urban transit planning, allocation of the police force, and crime prevention. Full article

This research aims to identify spatial and time patterns of theft in Manhattan, NY, to reveal urban factors that contribute to thefts from motor vehicles and to build a prediction model for thefts. Methods include time series and hot spot analysis, linear regression, elastic-net, Support vector machines SVM with radial and linear kernels, decision tree, bagged CART, random forest, and stochastic gradient boosting. Machine learning methods reveal that linear models perform better on our data (linear regression, elastic-net), specifying that a higher number of subway entrances, graffiti, and restaurants on streets contribute to higher theft rates from motor vehicles. Although the prediction model for thefts meets almost all assumptions (five of six), its accuracy is 77%, suggesting that there are other undiscovered factors making a contribution to the generation of thefts. As an output demonstrating final results, the application prototype for searching safer parking in Manhattan, NY based on the prediction model, has been developed. Full article

Crime prevention through environmental design (CPTED) has become a popular urban planning approach to preventing crime and mitigating fear of crime through the improvement of physical neighborhood environments. CPTED is widely used to improve deteriorated neighborhoods that suffer from crime. However, few studies have empirically examined the complex relationships among CPTED, fear of crime, and active living. Our study, therefore, investigated the effects of CPTED measures on walking frequency and fear of crime, analyzing behavioral data of residents living in participatory neighborhood regeneration areas and matched neighborhoods. We analyzed survey data from 12 neighborhoods that implemented CPTED approaches and 12 matched neighborhoods in Seoul, Korea, using structural equation modeling, which could consistently estimate complex direct and indirect relationships between a latent variable (fear of crime) and observable variables (CPTED measures and walking frequency). We designed the survey instrument as a smartphone app. Participants were recruited from 102 locations within the 24 selected neighborhoods; in total, 623 individuals returned surveys. The results revealed that sufficient closed-circuit television, street lighting, and maintenance played a significant role in mitigating fear of crime. This study has implications for planning and policy issues related to CPTED, mental health, and active living. Full article

Game theoretic approaches have recently been used to model the deterrence effect of patrol officers’ assignments on opportunistic crimes in urban areas. One major challenge in this domain is modeling the behavior of opportunistic criminals. Compared to strategic attackers (such as terrorists) who execute a well-laid out plan, opportunistic criminals are less strategic in planning attacks and more flexible in executing well-laid plans based on their knowledge of patrol officers’ assignments. In this paper, we aim to design an optimal police patrolling strategy against opportunistic criminals in urban areas. Our approach is comprised by two major parts: learning a model of the opportunistic criminal (and how he or she responds to patrols) and then planning optimal patrols against this learned model. The planning part, by using information about how criminals responds to patrols, takes into account the strategic game interaction between the police and criminals. In more detail, first, we propose two categories of models for modeling opportunistic crimes. The first category of models learns the relationship between defender strategy and crime distribution as a Markov chain. The second category of models represents the interaction of criminals and patrol officers as a Dynamic Bayesian Network (DBN) with the number of criminals as the unobserved hidden states. To this end, we: (i) apply standard algorithms, such as Expectation Maximization (EM), to learn the parameters of the DBN; (ii) modify the DBN representation that allows for a compact representation of the model, resulting in better learning accuracy and the increased speed of learning of the EM algorithm when used for the modified DBN. These modifications exploit the structure of the problem and use independence assumptions to factorize the large joint probability distributions. Next, we propose an iterative learning and planning mechanism that periodically updates the adversary model. We demonstrate the efficiency of our learning algorithms by applying them to a real dataset of criminal activity obtained from the police department of the University of Southern California (USC) situated in Los Angeles, CA, USA. We project a significant reduction in crime rate using our planning strategy as compared to the actual strategy deployed by the police department. We also demonstrate the improvement in crime prevention in simulation when we use our iterative planning and learning mechanism when compared to just learning once and planning. Finally, we introduce a web-based software for recommending patrol strategies, which is currently deployed at USC. In the near future, our learning and planning algorithm is planned to be integrated with this software. This work was done in collaboration with the police department of USC. Full article