Residential Burglary Research Articles

Examining the immediate effects of COVID-19 on residential and commercial burglaries in Michigan: An interrupted time-series analysis

PurposeResearch assessing the relationship between COVID-19 lockdown restrictions and crime is growing. Attention to the nuances of burglary and intra-state variation in lockdown restrictions has received less attention. The current study contributes to this ongoing body of research by investigating the relationship between COVID-19 lockdown restrictions and burglary across four cities in Michigan. MethodWe use an interrupted time-series design to analyze weekly counts of residential and non-residential burglary from 2018 to 2020 in four Michigan cities: Detroit, Grand Rapids, Kalamazoo, and Lansing. ResultsFindings demonstrate that the relationship between the lockdown and weekly counts of burglary vary by the type of burglary and the city being analyzed. Residential burglaries in half of the sampled cities declined during the Michigan lockdown order. Commercial burglaries increased during the lockdown order in only one of the four cities examined. ConclusionsCity-wide crime trends following lockdown orders during the pandemic are not shared equally and vary according to the type of burglary under examination. In studying the effects of COVID-19 on crime, researchers should account for crime- and city-specific dynamics which may influence crime trends. Further, practitioners and policymakers should consider the impact of similar lockdown orders on burglary trends during public health emergencies.

Cyber-enabled burglary of smart homes

Over the last few years, there has been a steady increase in smart home technology's pervasiveness, to the degree where consumer IoT is part of many homes. As our homes become complex cyber-physical spaces, the risk to our physical security from attacks originating in cyberspace becomes much more significant. Within the literature, there is much discussion about the technical vulnerabilities within the smart home. However, this is often not linked to a rich understanding of how an attacker could exploit them. In this paper, we focus on residential burglary and develop a rich understanding of the process by which residential burglary is committed and the effect of the smart home on this process. By combining two areas of the academic literature, residential burglary and smart-home security, this paper provides an academically grounded discussion that places the nascent vulnerabilities associated with the smart-home into the context of the process by which burglary is committed. The commission of residential burglary is a complex decision-making process, which the public often simplifies into planned or unplanned crimes; this is a dangerous oversimplification. The analysis identifies some increased risk during the target selection stage phase. However, in the short term, residential burglars are unlikely to exploit smart home technology routinely.

The effectiveness of forensic evidence in the investigation of volume crime scenes

This study investigates the effectiveness of forensic evidence in UK volume crime investigations. The main aim was to identify characteristics of forensic evidence that influence its effectiveness in converting detections into criminal charges, as well as to critically consider the effectiveness of a recent service level agreement (SLA) implemented by Wiltshire Police, which aimed at reducing CSI attendance. The sample consisted of 445 police recorded cases received from Wiltshire Police. Presence or absence and location-related characteristics of fingerprint, DNA, and footwear evidence were evaluated on the effectiveness of forensic evidence and examined within the contexts of different volume crimes. Results showed a high level of correlation in converting detections into criminal charges where the presence of DNA, footwear, and multiple evidence types was recorded; and a positive correlation between forensic evidence ineffectiveness and presence of fingerprints, particularly in residential burglaries. Differences between individual offence types were expressed. The most prominent feature influencing the effectiveness of forensic evidence was found to be related to the movability of the exhibit associated with the recovered evidence, with DNA recovered from non-movable items presenting the strongest effectiveness. Cases processed after the implementation of the SLA did not show significant differences in forensic evidence effectiveness as compared to cases processed prior to the SLA, however, they demonstrated a lack in effectiveness of DNA evidence. The findings of the current research provide a better understanding of the contextual influences on the potential of forensic evidence and can support improvement of crime scene screening and CSI resource deployment.

Testing a multilevel opportunity theory of crime (place in neighborhood theory)

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.

Improving Victimization Risk Estimation: A Geographically Weighted Regression Approach

Standardized crime rates (e.g., “homicides per 100,000 people”) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment.

Towards Automating Crime Prevention through Environmental Design (CPTED) Analysis to Predict Burglary

The design of the built environment (such as housing developments, street networks) can increase the opportunity for crime and disorder to occur. For example, a housing development with poor surveillance can provide an opportunity for offenders to commit residential burglary and avoid detection. Crime Prevention through Environmental Design (CPTED) aims to reduce crime and disorder through the design and manipulation of the built environment. The police typically play an important role in the delivery and application of CPTED by assessing planning applications, identifying design features that may provide an opportunity for crime and offering remedial advice. In England and Wales, it is common practice for police specialists – Designing out Crime Officers (DOCOs) – to review architectural site plans during the planning process. However, owing to significant cuts to policing budgets, the number of DOCOs in post is reducing whilst the demand for new housing is on the increase. In this novel work, it is demonstrated that key knowledge about the opportunities for crime and disorder within the built environment can be elicited from a purposive sample of 28 experienced DOCOs, encoded in a domain model and utilised by Automated Planning techniques to automatically assess architectural site plans for future crime risk.

A Case Study of Non-Violent Property Crime Victimisation in a South African Urban Residential Neighbourhood: Exploring the Excessive Use of Force and Destruction Caused by Burglars to Gain Entry to Victims’ Properties

Commentators frequently report on the high prevalence of violent crime in South Africa and often label the country as one of the most violent in the world, with a subculture of violence and criminality. This paper focuses on a different perspective, reporting on the excessive use of force and destruction caused by offenders in South Africa to gain entry to victims’ properties in the execution of non-violent property crimes, in a particular residential burglary. Literature on property crimes has been considering the aggravating circumstances of violent property crimes. However, the use of excessive force and destruction caused by burglars to gain access to victims’ properties in the execution of residential burglary remains relatively untested in the literature. In this light, the purpose of this study is to describe the unprecedented levels of force used and destruction caused by burglars to gain access to victims’ properties during residential burglary victimisation in an urban residential neighbourhood in Johannesburg, South Africa. A qualitative research approach is followed. A case study design was used to select an urban residential neighbourhood in Johannesburg as a case study. A data set of (n = 1 431) crimes were purposively selected by means of non-probability sampling. Qualitative and quantitative content analysis was used to analyse the data. This paper offers valuable insight into the forceful and destructive conduct of burglars in the selected neighbourhood and contributes to the body of knowledge by providing an improved understanding of target hardening as a preventive measure against residential burglary victimisation as well as on methods of entry used by burglars in incidents of residential burglary. The results of reported non-violent property crime victimisation incidences by this community’s neighbourhood watch scheme suggest that residential burglars in the selected neighbourhood are uncharacteristically forceful and ravage in their actions since they frequently revert to extreme use of force and destruction, disproportionate to the crime perpetrated. It is concluded that this radical degree of force used and destruction caused by residential burglars to gain entry to victims’ properties in the execution of non-violent property crimes is not typically associated with residential burglary as compared to countries internationally.

Breaking the Chain: How Arrests Reduce the Probability of Near Repeat Crimes

Objectives: Near repeat patterns have been identified for a host of different crimes, but effective strategies to reduce near repeats have had more variable results. This study identifies near repeat crime patterns in Dallas, TX, and examines the effects of an arrest on reducing the probability of future crime. Method: Using open-source crime data from the Dallas Police Department from July 2014 through June 2018, we identified near repeat patterns for shootings, interpersonal robberies, residential burglaries, and thefts from motor vehicles. Logistic regression models were used to test the effect of an arrest on reducing near repeat crimes; controls for geographic, demographic, and temporal factors were included in each model. Results: Near repeat calculations suggest violent crime clustered closely in time and space, with property crime dispersed over larger spatial and temporal dimensions. Across all four crime types, findings suggest arrests resulted in 20%–40% reductions in a near repeat follow-up crime. Conclusions: In line with past research on shootings, arrests reduced the likelihood of subsequent crimes. This suggests policing strategies to increase arrests may be a fruitful way to reduce near repeat crime patterns.

Exploring regional variability in the short-term impact of COVID-19 on property crime in Queensland, Australia

Confronted by rapidly growing infection rates, hospitalizations and deaths, governments around the world have introduced stringent containment measures to help reduce the spread of COVID-19. This public health response has had an unprecedented impact on people’s daily lives which, unsurprisingly, has also had widely observed implications in terms of crime and public safety. Drawing upon theories from environmental criminology, this study examines officially recorded property crime rates between March and June 2020 as reported for the state of Queensland, Australia. We use ARIMA modeling techniques to compute 6-month-ahead forecasts of property damage, shop theft, residential burglary, fraud, and motor vehicle theft rates and then compare these forecasts (and their 95% confidence intervals) with the observed data for March through to June. We conclude that, with the exception of fraud, all property offence categories declined significantly. For some offence types (shop stealing, other theft offences, and residential burglary), the decrease commenced as early as March. For other offence types, the decline was lagged and did not occur until April or May. Non-residential burglary was the only offence type to significantly increase, which it did in March, only to then decline significantly thereafter. These trends, while broadly consistent across the state’s 77 local government areas still varied in meaningful ways and we discuss possible explanations and implications.

Forty years of declining burglary in the United States: Explanation and evidence relating to the security hypothesis

Residential burglary in the United States has declined by over 80% across the last four decades, representing a major social phenomenon that remains largely unexplained. International research indicates a need for investigation of the security hypothesis. Here, 50 years of studies are examined chronologically. A consistent narrative emerges which indicates that household security, largely absent in the 1970s, improved gradually over time. Improvement occurred via several mechanisms: the increased prevalence, quality, coverage, and routine use of security fixtures and fittings. In addition, crime displacement declined over time as fewer households offered easy crime opportunities, and the average age of burglars increased as juveniles found burglary increasingly difficult. Hence the study concludes that gradual household security improvements played a central role in the decline in residential burglary. While the findings suggest a considerable revison is needed to our understanding of burglary and burglars, the likelihood that 50 years of diverse burglary research points in the same direction by chance, and without significant contrary evidence, seems remote. Further implications for theory, policy, and research are identified.

Crime linkage practice in New Zealand

PurposeThe purpose of this paper is to understand (i) how crime linkage is currently performed with residential burglaries in New Zealand, (ii) the factors that promote/hinder accurate crime linkage and (iii)whether computerised decision-support tools might assist crime linkage practice.Design/methodology/approachA total of 39 New Zealand Police staff completed a questionnaire/interview/focus group relating to the process, challenges, products and uses of crime linkage with residential burglary in New Zealand. These data (alongside four redacted crime linkage reports) were subjected to thematic analysis.FindingsThe data clearly indicated wide variation in crime linkage process, methods and products (Theme 1). Furthermore, a number of factors were identified that impacted on crime linkage practice (Theme 2).Research limitations/implicationsFuture research should develop computerised crime linkage decision-support tools and evaluate their ability to enhance crime linkage practice. Also, researchers should explore the use of crime linkage in court proceedings.Practical implicationsTo overcome the barriers identified in the current study, greater training in and understanding of crime linkage is needed. Moreover, efforts to enhance the quality of crime data recorded by the police will only serve to enhance crime linkage practice.Social implicationsBy enhancing crime linkage practice, opportunities to reduce crime, protect the public and deliver justice for victims will be maximised.Originality/valueThe practice of crime linkage is under-researched, which makes it difficult to determine if/how existing empirical research can be used to support ongoing police investigations. The current project fills that gap by providing a national overview of crime linkage practice in New Zealand, a country where crime linkage is regularly conducted by the police, but no published linkage research exists.

COVID and crime: An early empirical look

Data from 25 large U.S. cities is assembled to estimate the impact of the onset of the COVID-19 pandemic on crime. There is a widespread immediate drop in both criminal incidents and arrests most heavily pronounced among drug crimes, theft, residential burglaries, and most violent crimes. The decline appears to precede stay-at-home orders, and arrests follow a similar pattern as reports. There is no decline in homicides and shootings, and an increase in non-residential burglary and car theft in most cities, suggesting that criminal activity was displaced to locations with fewer people. Pittsburgh, New York City, San Francisco, Philadelphia, Washington DC and Chicago each saw overall crime drops of at least 35%. Evidence from police-initiated reports and geographic variation in crime change suggests that most of the observed changes are not due to changes in crime reporting.

A multiscale stochastic criminal behavior model under a hybrid scheme

<p style='text-indent:20px;'>Crime in urban environment is a major social problem nowadays. As such, many efforts have been made to develop mathematical models for this type of crime. The pioneering work [M. B. Short, M. R. D'Orsogna, V. B. Pasour, G. E. Tita, P. J. Brantingham, A. L. Bertozzi and L. B. Chayes, Math. Models Methods Appl. Sci., 18, (2008), pp. 1249-1267] establishes an agent-based human-environment interaction model of criminal behavior for residential burglary, where aggregate pattern formation of "hotspots" is quantitatively studied for the first time. Potential offenders are assumed to interact with environment according to well-known criminology and sociology notions. However long-term simulations for the coupled dynamics are computationally costly due to all components evolving on slow time scales. In this paper, we introduce a new-generation criminal behavior model with separated spatio-temporal scales for the agent actions and the environment parameter reactions. The computational cost is reduced significantly, while the essential stochastic features of the pioneering model are preserved. Moreover, the separation of scales brings the model into the theoretical framework of piecewise deterministic Markov processes (PDMP). A martingale approach is applicable which will be useful to analyze both stochastic and statistical features of the model in subsequent studies.</p>

Testing Routine Activity Theory in Mexico

Abstract Using a comprehensive victimization survey data set, we develop the first test of routine activity (RA) theory in a Latin American country. Estimating a complementary log–log multilevel model of the theory in several stages, we investigate what factors in the theory predict residential burglary risk. We find that measures associated with the exposure to potential offenders, target attractiveness, target accessibility, personal guardianship, social guardianship, and natural guardianship predict residential burglary in our sample of Mexican cities. As such, residential burglary is a complex crime with multiple factors at play, for which RA theory is a suitable theory given the conciseness of its propositions and operational definitions. We foresee RA theory successfully influencing future studies of crime in the Latin American region.

Burglars blocked by barriers? The impact of physical and social barriers on residential burglars' target location choices in China

Based on an offender spatial decision-making perspective, this burglary target location choice study aims to understand how physical and social barriers affect why residential burglars commit their crimes at particular locations in a major Chinese city. Using data on 3860 residential burglaries committed by 3772 burglars between January 2012 and June 2016 in ZG city, China, conditional logit (discrete choice) models were estimated to assess residential burglars' target location choice preferences. Three types of physical barriers were distinguished: major roads with access control, major roads without access control, and major rivers. Social barriers were constructed based on the Hukou system to reflect how local and nonlocal residents live segregated lives. Results show that residential burglars are less likely to target areas for which they have to cross a physical barrier and even less likely to do so if they have to cross multiple rivers. Local burglars are more likely to target communities with a majority of local residents than communities with a majority nonlocal population or a mixed community. Such a social barrier was less pronounced for nonlocal burglars. These findings add new insight that physical and social barriers affect, to various degrees, where residential burglars in China commit their crimes.

Spatially Varying Unemployment and Crime Effects in the Long Run and Short Run

The Cantor and Land model of unemployment and crime separates the effects of long- and short-run unemployment. In the long run, increases in unemployment are expected to increase crime, whereas the same increases are expected to decrease crime in the short run. This model has been tested for decades, generally supporting these predictions. In this article, we investigate spatial variations in these relationships using geographically weighted regression. Using crime data from Vancouver, Canada (commercial burglary, residential burglary, mischief, theft, theft from vehicle, theft of vehicle, and aggregate property), we find global models do not exhibit statistically significant unemployment–crime relationships, but they do emerge in local (geographically weighted) regression. These results have important implications for theoretical development, policy formation, and policy evaluation.

Let’s go throwing stones and stealing cars: offender adaptability and the security hypothesis

The security hypothesis posits that the international crime drop can be attributed to, in large part, improvements in security technology. However, in recent years, certain types of property crime are increasing. In this paper, we test the hypothesis that offenders are adapting to increased security in vehicles through burglarizing residences to steal keys. We analyze residential burglaries, thefts of vehicles, and their co-occurrences in Brisbane Australia (2009–2018) to identify changes in their temporal patterns. Residential burglaries declined while thefts of vehicle remained relatively constant. Co-occurrences of residential burglaries and thefts of vehicle doubled while the rate almost tripled. Data signatures indicate a shift in modus operandi showing significant increases in the use of residential burglaries to facilitate thefts of vehicle.

The impact of city block type on residential burglary: Mexico City as case study

Using block level data for Mexico City as case study, this article provides evidence that the type of city block type correlates with the likelihood of residential burglary. We employed five multilevel random intercept models to relate burglary incidents to city block types. We nested the 64,282 city blocks of Mexico City within their corresponding 846 local police quadrants. Our results show that Container-type city block configurations are associated with residential burglary to a greater degree than other physical and social environmental variables. Also, we find that close proximity to mass transit locations is not associated with residential burglary activity. The overall findings of this study describe fundamental dynamics between urban form and burglary.

Crimes in the Time of COVID-19

Due to the current pandemic, governments all over the world have given stay at home orders and have advised people to follow precautions like social distancing. These measures have a significant impact on our social, economic, and political setting which ultimately affects the volume and distribution of crimes worldwide. To understand the impact of social distancing policies on crime, we have compared the crime statistics during the pandemic (2020) with that of the previous year (2019) for the two metropolitan cities New Delhi, India, and New York, USA. We observe the trends of different types of crime and conclude that while crimes like residential burglaries have reduced, crimes like domestic violence have increased significantly. A rise in commercial burglaries1 was observed in New York whereas in New Delhi a decline was observed because of the presence of the police on roads. Furthermore, A decline in crimes like robbery, burglary, larceny, etc. was observed whereas a sharp increase was seen in the cases of domestic violence in both the cities.

Unsupervised identification of crime problems from police free-text data

We present a novel exploratory application of unsupervised machine-learning methods to identify clusters of specific crime problems from unstructured modus operandi free-text data within a single administrative crime classification. To illustrate our proposed approach, we analyse police recorded free-text narrative descriptions of residential burglaries occurring over a two-year period in a major metropolitan area of the UK. Results of our analyses demonstrate that topic modelling algorithms are capable of clustering substantively different burglary problems without prior knowledge of such groupings. Subsequently, we describe a prototype dashboard that allows replication of our analytical workflow and could be applied to support operational decision making in the identification of specific crime problems. This approach to grouping distinct types of offences within existing offence categories, we argue, has the potential to support crime analysts in proactively analysing large volumes of modus operandi free-text data—with the ultimate aims of developing a greater understanding of crime problems and supporting the design of tailored crime reduction interventions.