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Understanding the spatial distribution of crime patterns or poor safety perceptions equips us with valuable insights to guide resource allocation. In this special issue, we present examples of articles in environmental criminology, focusing on the use of spatial and temporal-based methods for crime and safety analysis. The contributions are from Sweden, Canada, New Zealand, and Pakistan, showcasing a diverse array of data and methods. Almost all articles are from sessions presented in the International Symposium in "Environmental Criminology and Crime Analysis" ECCA 2023, organized by the guest editor's university, and in "the Stockholm Criminology Symposium" that took place in Stockholm in June 2023. This introduction finishes with a summary of the articles of this collection. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the impact of Crime Prevention Through Environmental Design (CPTED) principles on burglary intentions in high-density low-rise residential areas in South Korea using a virtual reality (VR) experiment. An ordered logistic regression model was developed based on data from a VR-aided survey involving 100 young adults. The findings revealed that the installation of security features, such as barred windows and signs indicating a security system, significantly reduced the intention to commit burglary in single-family houses and walk-up apartments. In contrast to studies conducted in Western contexts, the results indicated that higher fences were associated with a lower intention to burgle in these high-density low-rise residential areas. Territoriality was found to have no significant effect on burglary intentions. These results suggest that CPTED strategies should be tailored to local environmental contexts and community atmospheres. Plain language summary: How crime prevention design affects burglary intentions in South Korea ' s high-density low-rise areas: A virtual reality study In this study, we wanted to find out if certain changes in the way homes are designed and secured can make a difference in whether someone might think about breaking into them. We did this by using a special kind of computer simulation that feels like you're in a different world, called virtual reality. We asked 100 young adults about their thoughts on burglarizing houses in neighborhoods in South Korea using this virtual reality setup. Our research showed that adding things like strong windows and signs that show a security system in houses and walk-up apartments can make people less likely to consider breaking in. Surprisingly, in South Korea, having taller fences around these types of homes also seemed to discourage the idea of burglary, which is different from what studies in Western countries have found. However, the idea of who "owns" or takes care of a space, which we call territoriality, didn't seem to make much of a difference in whether someone would think about breaking into a home. So, what we can learn from all of this is that when it comes to making neighborhoods safer from burglaries, we should think about the local environment and how people in the community feel, rather than just using the same ideas that work in other places. [ABSTRACT FROM AUTHOR]

Do informal and formal mechanisms of guardianship work together to reduce residential burglary? In this article we argue that informal guardianship moderates the relationship between formal mechanisms of guardianship and residential burglary. Formal guardianship requires some level of social cohesion and trust to be effective against residential burglary. We test this argument with the use of robust panel quantile methods controlling for time effects, spatial effects, and alternative explanations. Using Mexico City neighborhood crime and census data, we show evidence of a moderating weakening effect of informal guardianship on the previous relationship, particularly in deprived neighborhoods and only in the upper quantiles of the residential burglary distribution. In addition, the moderation effects seem to have weakened over time. In sum, the combination of guardianship mechanisms seems to have been more effective in high burglary risk deprived neighborhoods, although their combination seems to have become less relevant. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine the applicability of methods that use spatial interaction modelling to predict the most probable area for an offender's residence. Tokyo, which is the capital of Japan and is divided into 1507 square‐kilometre zones, was selected as the study area. We analysed 4316 criminal trips to commit residential burglaries by 1089 offenders who lived in Tokyo. The following neighbourhood‐level factors, and a distance‐decay effect between zones, were incorporated into the proposed model: the size of the residential population aged 15–59 years in origin zones, the numbers of households living in detached houses, newcomers, the number of police facilities in destination zones, and the spatial structures of the criminal trips. Search areas calculated by the proposed model were smaller than previous models, suggesting that neighbourhood‐level factors are important for predicting the location of an offender's residence. [ABSTRACT FROM AUTHOR]

Data on the decision-making process of residential burglars at the micro-level are scarce. Furthermore, to the best of our knowledge, none of the related studies have investigated multiple relationships between the design features of a target, a burglar's assessment of effort and risk involved in the crime, and the final decision on target selection. This study aimed to test the hypothesis that a burglar's judgment of how difficult (ease of intrusion) and how risky (risk of detection) it would be to break into a certain target may mediate the relationship between the target's design features and target selection. The experiment using virtual reality was conducted to obtain more credible data by maximizing the immersion of participants, and the collected data were analyzed using path analysis. The results showed that the assessment of ease of intrusion and risk of detection for a burglary target served as mediators between the design features of the target and the decision on target selection. This study also found that the ease of intrusion and risk of detection were not evaluated independently but instead had influential relationships. These results suggest that when developing design strategies for burglary prevention, it is important to check the overall level of ease and risk of the possible intrusion routes of a target and their correlation by considering various environmental factors around the intrusion routes. [ABSTRACT FROM AUTHOR]

Urban morphology plays a significant role in shaping the spatial distribution of crime. This study takes an environmental criminology perspective on crime and examines how residential burglary is related to two typical morphology features—housing density and composition, which were rarely concerned by previous research. Wuhan, the largest city in central China, was selected as the case study. We first applied a new urban morphology approach to identify the morphology category of each neighborhood based on its housing density and composition. Negative binomial regression models were adopted to evaluate the impacts of morphology factors on the residential burglary at the neighborhood level while controlling for socio-demographic features, transport facilities, housing price and age. Results suggest that both housing composition and density are significantly associated with residential burglary. In particular, one unit increase in Floor Space Index, an indicator of housing density and Ground Space Index, an indicator of housing composition could lead to an 11.9% and 9.1% increase in the incident rate of residential burglary. The 'block' and 'strip' composition exert more substantial impacts than 'point' composition; neighborhoods with 'high' and 'medium' residences tend to be more dangerous than neighborhoods with 'low' residences. Results of this study reveal that communities must be designed with the relationship between risk levels of residential burglary and the ways by which communities are designed in mind. Implications regarding burglary prevention and neighborhood planning practices are discussed. [ABSTRACT FROM AUTHOR]

Previous empirical studies on the correlation between residential burglary and concentrated disadvantage (CD) in Latin America commonly omit the spatial elements of the relationship. Using Mexico City (CDMX) residential burglary data for the period 2016 to 2018, we examine the predictive capacity of concentrated disadvantage in relation to residential burglary patterns, using a Geographically Weighted Regression approach to check whether their correlation varies across CDMX police quadrants. Controlling for relevant structural variables associated with residential burglary in previous studies, we find that the relationship between CD and residential burglary is positive in 844 out of 846 police quadrants (99.7%) and significantly much steeper in some quadrants than others –up to four times the median local slope. Thus, one key implication is that as this relationship is affected by spatial heterogeneity, traditional regression-to-the-mean analyses may misinform evidence-based crime prevention policies. [ABSTRACT FROM AUTHOR]

The near-repeat phenomenon usually occurs with any crime. Hence, to implement preventive measures, it is of great interest to figure out at which spatio-temporal scale crimes are more likely to be repeated by offenders. The Knox test is the most used statistical tool for evaluating the presence of the near-repeat phenomenon given a dataset of crimes that are located in space and time. The classic version of this test assumes that crime risk is homogeneous in both space and time, although this assumption rarely holds in reality. Therefore, the main goal of this article is to highlight the necessity of adjusting the standard version of the Knox test, including spatial and temporal effects that allow for the consideration of crime risk heterogeneity. In this regard, a methodology that has already been proposed for addressing this issue is described and adapted. This methodology is then put into practice through a dataset of burglaries recorded in the city of Valencia (Spain) in 2016 and 2017. The results yielded by both versions (standard and adjusted) of the Knox test confirm that the near-repeat phenomenon took place for the burglaries that occurred in Valencia during the period under investigation. However, using the adjusted version of the Knox test leads to a reduction in the number of spatio-temporal intervals that are declared as statistically significant. This fact should be born in mind before making decisions on preventive measures. [ABSTRACT FROM AUTHOR]

This study used spatial interaction modeling to examine whether origin-specific and destination-specific factors, distance decay effects, and spatial structures explain the criminal trips of residential burglars. In total, 4041 criminal trips committed by 892 individual offenders who lived and committed residential burglary in Tokyo were analyzed. Each criminal trip was allocated to an origin–destination pair created from the combination of potential departure and arrival zones. The following explanatory variables were created from an external dataset and used: residential population, density of residential burglaries, and mobility patterns of the general population. The origin-specific factors served as indices of not only the production of criminal trips, but also the opportunity to commit crimes in the origin zones. Moreover, the criminal trips were related to the mobility patterns of the general population representing daily leisure (noncriminal) trips, and relatively large origin- and destination-based spatial spillover effects were estimated. It was shown that considering not only destination-specific but also origin-specific factors, spatial structures are important for investigating the criminal trips of residential burglars. The current findings could be applicable to future research on geographical profiling by incorporating neighborhood-level factors into existing models. [ABSTRACT FROM AUTHOR]

Co-offending is a common phenomenon in criminology. In previous years, countless studies have contributed to describing the co-offending cooperator's selection process. It has been established that personal characteristics such as age, sex, social/economic status, social/spatial distance, and so on play important roles in co-offender group formation; however, the geographic background of members of the co-offending groups has not received much attention. This study analyzes the assumption that the offender's hometown's homophily enhances co-offending generation and that the size of the offender population of the same hometown (OPSH) would positively impact co-offending formation. To test this, two types of co-offending connections, cross-hometown co-offending (CHCO) and homophily-hometown co-offending (HHCO), are defined respectively, and residential burglary reporting data from Beijing Police from 2005 to 2014 are utilized to investigate the relationship between co-offenders from their hometown backgrounds. The results indicate that HHCO cooperation is more likely to be generated for those who come from large OPSHs, while conversely a large proportion of CHCO cooperation is generated for smaller OPSHs. Additionally, large OPSHs have diverse co-offending connections and attract more partners from the smaller OPSHs, suggesting their more centralized role in co-offending networks. [ABSTRACT FROM AUTHOR]

Objectives: We investigate the spatio-temporal variation of monthly residential burglary frequencies across neighborhoods as a function of crime generators, street network features and temporally and spatially lagged burglary frequencies. In addition, we evaluate the performance of the model as a forecasting tool. Methods: We analyze 48 months of police-recorded residential burglaries across 20 neighborhoods in Amsterdam, the Netherlands, in combination with data on the locations of urban facilities (crime generators), frequencies of other crime types, and street network data. We apply the Integrated Laplace Approximation method, a Bayesian forecasting framework that is less computationally demanding than prior frameworks. Results: The local number of retail stores, the number of street robberies perpetrated and the closeness of the local street network are positively related to residential burglary. Inclusion of a general spatio-temporal interaction component significantly improves forecasting performance, but inclusion of spatial proximity or temporal recency components does not. Discussion: Our findings on crime generators and street network characteristics support evidence in the literature on environmental correlates of burglary. The significance of spatio-temporal interaction indicates that residential burglary is spatio-temporally concentrated. Our finding that recency and proximity of prior burglaries do not contribute to the performance of the forecast, probably indicates that relevant spatio-temporal interaction is limited to fine-grained spatial and temporal units of analysis, such as days and street blocks. [ABSTRACT FROM AUTHOR]

Based on semi-structured interviews with 23 incarcerated burglars, this paper details findings from a qualitative examination into how the principles of Optimal Forager Theory (to minimise time and effort, minimise risk of detection, and maximise reward) apply to the behavioural methods utilised by offenders. Findings included the use of 'serial targets' (to minimise time and effort), as well as offenders' ability to 'blend in' to their surroundings (to minimise risk of detection). To maximise reward, offenders used brands of consumables (evident from packaging found in residents' rubbish) as a proxy for wealth, as well as personal details gathered through residents' discarded mail to establish their ethnicity (for the targeting of Asian gold). The findings support the notion of 'dysfunctional expertise', and demonstrate how efforts to maximise time and effort, minimise reward, and maximise risk of detection for offenders can be used to develop crime prevention policy to reduce future burglaries. [ABSTRACT FROM AUTHOR]

It is widely recognized that crimes are clustered in space. Features of the built environment are among various factors associated with crime clustering patterns. There are two competing perspectives on this association: the "encounter" and "enclosure" hypotheses. This study examines the effect of the built environment on the spatial distribution of residential burglary in Wuhan, China, aiming to test these two competing perspectives. First, we review existing studies analyzing the influence of the built environment on residential burglary, especially those based on the two hypotheses. We then conduct an exploratory data analysis to observe the spatial and temporal distribution patterns of residential burglaries. Next, we use a multivariate spatial regression model to evaluate the influence of various built environment characteristics on residential burglaries, after controlling for socio-demographic variables, guardianship, and the time-of-day effect. The results suggest a double peak in the hourly distribution of burglaries: 6:00 a.m.–8:59 a.m. and 5:00 p.m.–7:59 p.m. Burglaries are positively and significantly autocorrelated in space and mainly concentrated in the north-west and central areas of Wuhan. Two transport facilities features (intersection density and the presence of subway stations), three building features (multiple stories, high housing prices, and older houses), and land-use mix are each positively correlated with burglary. However, the burglary count is negatively correlated with the ratio of the residential land. Overall, neighborhoods with high permeability are vulnerable to residential burglaries. Thus, the principle of access control promoted by the enclosure hypothesis appears effective in preventing residential crime. The results' implications for crime prevention and urban design practice are also discussed. [ABSTRACT FROM AUTHOR]

This research aims to empirically test a newly developed "place in neighborhood (PIN)" theory, proposed by Wilcox and Tillyer recently. Using secondary data, the authors generate measures of crime and opportunity factors in street blocks nested in neighborhood clusters in Chicago. Hierarchical non-linear models are used to assess the impact of factors in street block level, neighborhood level, and their cross-level interactions on residential burglary. The results show that all four street block level variables are associated with levels of burglary, and two of three neighborhood level variables (social control and delinquency rate) are significantly associated with lower and higher levels of residential burglary, respectively. Five of the twelve cross-level interaction terms are significantly associated with residential burglary rates. Two of the main effects and one of the cross-level interaction terms show unexpected opposite directions. The findings lend partial support to PIN theory. [ABSTRACT FROM AUTHOR]

The space-time risk window associated with near-repeat burglary patterns would seem to present a natural opportunity for burglary prevention efforts. However, constraints associated with the reporting of, police response to, and space-time patterning of burglaries can reduce the crime prevention potential of such efforts. To better estimate the crime prevention potential of focusing on near-repeat burglaries, we studied burglary patterns in 10 U.S. cities. Descriptive aspatial and spatial statistics were used to answer the research questions. Significant space-time clustering does not necessarily indicate an actionable near-repeat problem. Police analysts and researchers should also consider the crime prevention potential of focusing on near repeats—in other words, the proportion of burglaries that are preventable. The results of this test provide new information to guide the implementation and evaluation of crime prevention efforts focused on near-repeat events. [ABSTRACT FROM AUTHOR]

The aim of the current study was to examine the repeat residential burglars’ target selection behaviors. The data consisted of variables relating to target selection behaviors and decision making of 104 serial residential burglars, who had arrest records for residential burglaries in the past. The results of multiple correspondence analysis revealed that residential burglars’ target selection behaviors could be differentiated into three styles: “burgling in the absence of residents”, “burgling in the presence of residents while they are sleeping at night”, and “burgling of mixed styles”. Among 14 variables relating to burglars’ decision making while committing crimes, three factors (risk/reward-oriented, traceless entry-oriented, low effort area-oriented) were extracted by exploratory factor analysis. The level of burgling skill evaluated by the suspect interviewers positively correlated with a factor score of the “risk/reward-oriented” factor (ρ = .20, p < .05) but negatively correlated with a factor score of the “low effort area-oriented” factor (ρ = -.24, p <.05). These results suggest that repeat residential burglars are rational decision makers, but the way they are rational varies. [ABSTRACT FROM AUTHOR]

Objectives: Test the efficacy of swift resident notification for preventing subsequent burglaries within near-repeat high-risk zones (NR-HRZ). Methods: The experiment was conducted in Baltimore County, Maryland and Redlands, California. As residential burglaries came to the attention of the police, a trickle randomization process was used to assign each micro-level NR-HRZ (measured 800 ft, 244 m from the burglary location) and associated buffer (400 ft, 122 m) to treatment or control. Treatment was performed by uniform agency volunteers and consisted of swift notification to residents in the area of increased risk of burglary victimization. Treatment and control zones were compared for differences in the mean count of residential burglary using independent samples t tests. Two surveys were administered to gauge the impact of the program: one targeted residents and one targeted at the treatment providers. Results: There was limited evidence that the treatment reduced follow-on burglaries. The effectiveness of the intervention varied depending on the post-intervention time period being considered. The results of the community survey suggested that: (1) the most frequent crime prevention actions taken by residents were relatively low-cost and low-effort and (2) notification did not increase resident fear of burglary. The treatment provider survey found that the program was effective at increasing the level of engagement between volunteers and the agency and had positive impacts on community perception. Conclusions: This research demonstrated that law enforcement volunteers can be used to undertake programs that have positive impacts on police-community relations. Limitations included low near-repeat counts, delays in discovering/reporting burglary, and staffing constraints. [ABSTRACT FROM AUTHOR]

In October 2015 the 'Pilot Project Predictive Policing' (P4) was started in the German federal state of Baden Württemberg. A predictive policing strategy was applied in the context of residential burglary. An evaluation study of the first six months of the pilot was carried out by the Max Planck Institute for Foreign and International Criminal Law. The article describes how the strategy was applied and summarizes the main findings of the evaluation study. Despite some positive findings the impact remains unclear and the expectable crime reducing effects appear to be moderate. Within the police force the acceptance of predictive policing is a divisive issue. Future research is recommended. [ABSTRACT FROM AUTHOR]

“Strike Hard” is an enhanced law-enforcement strategy in China that aims to suppress crime, but measurement of the crime-reducing effect and potential changes in the spatiotemporal concentration of crime associated with “Strike Hard” remain unknown. This paper seeks to examine the impact, if any, of “Strike Hard” on the spatiotemporal clustering of burglary incidents. Two and half years of residential burglary incidents from Chaoyang, Beijing are used to examine repeat and near-repeat burglary incidents before, during, and after the “Strike Hard” intervention and a new technique that enables the comparison of repeat and near repeat patterns across different temporal periods is introduced to achieve this. The results demonstrate the intervention disrupted the repeat pattern during the “Strike Hard” period reducing the observed ratio of single-day repeat burglaries by 155%; however, these same single-day repeat burglary events increased by 41% after the cessation of the intervention. Findings with respect to near repeats are less remarkable with nominal evidence to support that the intervention produced a significant decrease, but coupled with other results, suggest that spatiotemporal displacement may have been an undesired by-product of “Strike Hard”. This study from a non-Western setting provides further evidence of the generalizability of findings related to repeat and near repeat patterns of burglary and further highlights the limited preventative effect that the “Strike Hard” enhanced law enforcement campaign had on burglary.

Research on the spatial dimension of crime has developed significantly over the past few decades. An important aspect of this research is the visualization of this dimension and its underlying risk across space. However, most methods of such visualization, and subsequent analyses, only consider crime data or, perhaps, a population at risk in a crime rate. Risk terrain modeling (RTM) provides an alternative to such methods and can incorporate the entire environmental backcloth, data permitting. To date, the RTM literature has dominantly focused on violent crime in the United States. In this paper, we apply RTM to property crime victimization (residential burglary) in Vancouver, Canada. We are able to show that not only does RTM have applicability in a Canadian context but provides insight into nonviolent victimization. [ABSTRACT FROM AUTHOR]

Risk terrain modelling (RTM) is emerging as an effective approach for predicting how and where crimes concentrate within cities and regions. However, in its previous applications there is a tendency to overestimate the influence of external environmental risks and preventive factors. Most studies applying RTM have investigated factors associated with the characteristics of the urban setting, whilst only a limited number have focused on identifying the risks associated with the availability and the characteristics of potential targets for criminals. This study uses RTM to identify the spatial risk and protective factors related to residential burglaries in the city of Milan, Italy. Factors considered are the neighbourhood- and target-related contextual factors, the exposure to crime and potential mitigating strategies. The results show that when the place and target of the offence are intrinsically related a target-oriented approach to select factors is useful for increasing the understanding of why some locations are most likely to experience future crimes. Indeed, the peculiarities of the target itself are integral to understan both the decision-making of criminals and the overall level of crime risk. Related policy implications are discussed. [ABSTRACT FROM AUTHOR]

Abstract Predictive hotspot mapping plays a critical role in hotspot policing. Existing methods such as the popular kernel density estimation (KDE) do not consider the temporal dimension of crime. Building upon recent works in related fields, this article proposes a spatio-temporal framework for predictive hotspot mapping and evaluation. Comparing to existing work in this scope, the proposed framework has four major features: (1) a spatio-temporal kernel density estimation (STKDE) method is applied to include the temporal component in predictive hotspot mapping, (2) a data-driven optimization technique, the likelihood cross-validation, is used to select the most appropriate bandwidths, (3) a statistical significance test is designed to filter out false positives in the density estimates, and (4) a new metric, the predictive accuracy index (PAI) curve, is proposed to evaluate predictive hotspots at multiple areal scales. The framework is illustrated in a case study of residential burglaries in Baton Rouge, Louisiana in 2011, and the results validate its utility. Highlights • Predictive hotspot mapping plays a critical role in hotspot policing. • A spatio-temporal kernel density estimation (STKDE) method is developed. • The likelihood cross-validation is used to select the best bandwidths in STKDE. • A statistical significance test filters out false hotspots. • The predictive accuracy index (PAI) curve evaluates predictive hotspots. • A case study examines residential burglaries in Baton Rouge, Louisiana in 2011. [ABSTRACT FROM AUTHOR]

Abstract: This study examines the effects of neighbourhood attractiveness on the residential burglar's crime location choice process using a discrete choice model. We show that past crime data are an important index of a neighbourhood's attractiveness and can be combined with other attractiveness indices adapted from previous studies. We used data from 369 solved cases committed by 70 offenders and related these data to 1,134 areas (500 m grid cells) in Sendai City, Japan. The results showed that residential burglars were attracted to the following potential locations for crimes: (a) areas in proximity to his or her own residence; (b) areas having many or at least a higher proportion of residential burglaries in the past; (c) areas having many residential units; and (d) areas having a higher proportion of single‐family dwellings. The results confirm the validity of past crime data as an index of a neighbourhood's attractiveness for residential burglary. [ABSTRACT FROM AUTHOR]

Near repeat analysis has been increasingly used to measure the spatiotemporal clustering of crime in contemporary criminology. Despite its predictive capacity, the typically short time frame of near repeat crime patterns can negatively affect the crime prevention utility of near repeat analysis. Thus, recent research has argued for a greater understanding of the types of places that are most likely to generate near repeat crime patterns. The current study contributes to the literature through a spatiotemporal analysis of residential burglary and motor vehicle theft in Indianapolis, IN. Near Repeat analyses were followed by multinomial logistic regression models to identify covariates related to the occurrence of initiator (the first event in a near repeat chain) and near repeat (the subsequent event in a near repeat chain) events. The overall findings provide additional support for the argument that neighborhood context can influence the formation and context of spatiotemporal crime patterns. [ABSTRACT FROM AUTHOR]

Criminological research has long utilized visual representations of environments in seeking to explore perceptions of crime and personal safety and to investigate the relevance of specific design and security features. Much of this research has been in the field of environmental psychology and crime prevention through environmental design (CPTED). This paper traces the development of visual representations and the use of environmental stimuli in criminological research. We briefly review the contributions made by various visual media, including maps and drawings, photographs, models, video, virtual reality and gaming engines. These developments in visualization, building design and management technology have drastically improved the realism of representations of environments, and the potential for more standardized, widespread exploration and application of CPTED principles to reduce crime. To scope and evaluate the future potential of such emergent technologies, we critically review the literature concerned with virtual reality and building information technology (BIM), outlining uses in practice and new opportunities for criminological research. We frame the discussion with specific focus on analysing proposed residential dwellings to reduce vulnerability to burglary. A layered exploration for how BIM technologies may assist in implementing increasingly sophisticated assessments of crime vulnerability for proposed residential building designs brings the paper to a close. These discussions provide both a comprehensive overview for interested practitioners and chart specific opportunities for further research. [ABSTRACT FROM AUTHOR]

Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling.

Using verbal protocol analysis (VPA) alongside semi-structured interviews, this research aimed to explicate the situational dynamics that inform the decision-making and target selection of residential burglars. Focusing on the VPA method, novel to criminological research, the paper considers the contribution of this empirical approach for studying the decision-making of offenders in situ. The findings reveal a series of cues, encapsulated in the 'PAUSED' model, that are drawn upon by residential burglars to assess the suitability of a target; determining whether it is profitable, accessible, uninterruptible, surveillable, escapable and/or dishonourable. The PAUSED model is unpacked to articulate a collection of visual stimuli that serve to disrupt and suspend the otherwise rapid flow of target appraisal. Discussion of the strengths and limitations of the VPA method, and how it can compliment other approaches to understanding the decision-making of residential burglars, is provided. [ABSTRACT FROM AUTHOR]

OBJECTIVES: The present study aims to extend current research on how offenders' modus operandi (MO) can be used in crime linkage, by investigating the possibility to automatically estimate offenders' risk exposure and level of pre-crime preparation for residential burglaries. Such estimations can assist law enforcement agencies when linking crimes into series and thus provide a more comprehensive understanding of offenders and targets, based on the combined knowledge and evidence collected from different crime scenes. METHODS: Two criminal profilers manually rated offenders' risk exposure and level of pre-crime preparation for 50 burglaries each. In an experiment we then analyzed to what extent 16 machine-learning algorithms could generalize both offenders' risk exposure and preparation scores from the criminal profilers' ratings onto 15,598 residential burglaries. All included burglaries contain structured and feature-rich crime descriptions which learning algorithms can use to generalize offenders' risk and preparation scores from. RESULTS: Two models created by Naïve Bayes-based algorithms showed best performance with an AUC of 0.79 and 0.77 for estimating offenders' risk and preparation scores respectively. These algorithms were significantly better than most, but not all, algorithms. Both scores showed promising distinctiveness between linked series, as well as consistency for crimes within series compared to randomly sampled crimes. CONCLUSIONS: Estimating offenders' risk exposure and pre-crime preparation can complement traditional MO characteristics in the crime linkage process. The estimations are also indicative to function for cross-category crimes that otherwise lack comparable MO. Future work could focus on increasing the number of manually rated offenses as well as fine-tuning the Naïve Bayes algorithm to increase its estimation performance. [ABSTRACT FROM AUTHOR]

Street networks shape day-to-day activities in complex ways, dictating where, when, and in what contexts potential victims, offenders, and crime preventers interact with one another. Identifying generalizable principles of such influence offers considerable utility to theorists, policy makers, and practitioners. Unfortunately, key difficulties associated with the observation of these interactions, and control of the settings within which they take place, limit traditional empirical approaches that aim to uncover mechanisms linking street network structure with crime risk. By drawing on parallel advances in the formal analyses of street networks and the computational modeling of crime events interactions, we present a theoretically informed and empirically validated agent-based model of residential burglary that permits investigation of the relationship between street network structure and crime commission and prevention through guardianship. Through the use of this model, we explore the validity of competing theoretical accounts of street network permeability and crime risk-the encounter (eyes on the street) and enclosure (defensible space) hypotheses. The results of our analyses provide support for both hypotheses, but in doing so, they reveal that the relationship between street network permeability and crime is likely nonlinear. We discuss the ramifications of these findings for both criminological theory and crime prevention practice. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, the demand for labor in the coastal cities continues to grow. Due to restrictions imposed by China's household registration system, a large number of floating populations have subsequently appeared. The relationship between floating populations and crime, however, is not well understood. This paper investigates the impact of a floating population on residential burglary on a fine spatial scale. The floating population was divided into the floating population from other provinces (FPFOP) and the floating population from the same province as ZG city (FPFSP), because of the high heterogeneity. Univariate spatial patterns in residential burglary and the floating population in ZG were explored using Moran's I and LISA (local indicators of spatial association) models. Furthermore, a geographically weighted Poisson regression model, which addressed the spatial effects in the data, was employed to explore the relationship between the floating population and residential burglary. The results revealed that the impact of the floating population on residential burglary is complex. The floating population from the same province did not have a significant impact on residential burglary in most parts of the city, while the floating population from other provinces had a significantly positive impact on residential burglary in most of the study areas and the magnitude of this impact varied across the study area. [ABSTRACT FROM AUTHOR]

Understanding of the potential ‘rewards’ that residential burglars gain through their offences has largely been confined to monetary return, thus overlooking the possibility that offenders obtain other ‘goods’ from their criminal activities. By illustrating how burglars could be attending to various lifestyle deficits through crime, the article reveals indicators for case management, rehabilitation and desistance. The Good Lives Model (GLM) is employed as a framework to explore the ‘primary human goods’ attained through residential burglary. The article concludes with suggestions to encourage desistance by challenging the authenticity of the goods burglars seek to fulfil or remedy through criminal behaviour. [ABSTRACT FROM AUTHOR]

The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15% and the R2d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R2d. The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries. [ABSTRACT FROM AUTHOR]

Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by.

Current methods for predicting residential burglaries mostly rely on analyses of crime patterns based on a real information. While this model is valid on an urban scale, it fails to consider street-scale environmental factors as well as offender behaviors in response to those factors. To improve the predictability of crime-simulation studies, this study investigated two influential factors in the occurrence of residential burglary: the physical properties of building elements and offender behaviors in response to those properties. First, a prediction algorithm was designed based on analyses of the factors. Next, a prediction method was established by modeling a virtual 3-D environment and a virtual offender using the algorithm. Lastly, the probability of residential burglary was analyzed via a simulation using the prediction method. A comparison of the simulation results with actual residential burglary data confirmed that the proposed method has statistically significant predictability. [ABSTRACT FROM AUTHOR]

Criminologists often claim that neighborhoods with a higher proportion of commercial land uses are likely to be more vulnerable to crime because commercial facilities attract outsiders to the neighborhood, thereby increasing its exposure to potential offenders. Advocates of mixed-use neighborhoods, on the other hand, suggest that combining commercial and residential uses can reduce crime by increasing opportunities for surveillance, encouraging social interaction, and promoting a sense of community and social control among neighborhood members. This theoretical contradiction of the effect of land-use mix on crime makes it difficult to reach a conclusive agreement and thus warrants further investigation. This study attempts to fill this gap by examining the association of residential burglary with five commercial land-use types (i.e., offices, retail stores, shopping centers, restaurants, and grocery stores) most commonly found in residential neighborhoods in Seattle. The findings show that grocery stores, restaurants, and offices have a positive role in improving neighborhood safety, whereas neighborhoods with more shopping center areas have experienced higher levels of residential burglary. These results indicate that the effects of commercial land-use mix on residential burglary vary depending on the type of commercial facility, suggesting that not all commercial uses increase crime. This study claims that the possible positive effects of increased surveillance can outweigh the opportunity effect (i.e., attracting offenders to possible targets) when commercial uses encouraging legitimate activities are integrated within the neighborhood. The implications for commercial establishments in relation to neighborhood safety are discussed. [ABSTRACT FROM AUTHOR]

Objectives: This study explores preference variation in location choice strategies of residential burglars. Applying a model of offender target selection that is grounded in assertions of the routine activity approach, rational choice perspective, crime pattern and social disorganization theories, it seeks to address the as yet untested assumption that crime location choice preferences are the same for all offenders. Methods: Analyzing detected residential burglaries from Brisbane, Australia, we apply a random effects variant of the discrete spatial choice model to estimate preference variation between offenders across six location choice characteristics. Furthermore, in attempting to understand the causes of this variation we estimate how offenders' spatial target preferences might be affected by where they live and by their age. Results: Findings of this analysis demonstrate that while in the aggregate the characteristics of location choice are consistent with the findings from previous studies, considerable preference variation is found between offenders. Conclusions: This research highlights that current understanding of choice outcomes is relatively poor and that existing applications of the discrete spatial choice approach may underestimate preference variation between offenders. [ABSTRACT FROM AUTHOR]