Residential Burglary Research Articles

Exploring the impact of cyber and traditional crime victimization: Impact comparisons and explanatory factors

As cybercrime victimization rates continue to rise, our understanding of their impact, especially compared to traditional crime, remains limited. To address this knowledge gap, we conducted surveys among 910 crime victims, measuring the impact of cyber and traditional variants of property, person-centered, and sexual crime. We compared the impact of: (1) residential burglary and hacking of online bank accounts; (2) doorstep deception and bank helpdesk fraud; (3) in-person threats and online threats; and (4) sexual assault and image-based sexual abuse. Impact was categorized into peritraumatic stress, internalizing problems, externalizing problems, financial impact, and damaged self-image. Our findings reveal that cybercrime impact mostly equals or exceeds that of traditional crimes. Disparities especially emerge in terms of higher peritraumatic stress experienced by victims of cyber property crime, and more damage to self-image suffered by victims of both cyber property and cyber sexual crimes. Furthermore, we identified significant determinants of victim impact among crime characteristics, and social, personal, and demographic factors, some varying between cyber and traditional crime. Our study emphasizes the implications of the technological nature of cybercrime and the relatively active role played by cybercrime victims, while also considering the issue of victim blaming. Furthermore, we underscore the need for targeted victim support and prioritization of cybercrime.

Examining the Boost Account for Repeat and Near Repeat Burglary in Canada

Research suggests that previously burglarized targets, and targets located near such locations, are at an increased risk of being victimized. However, this elevated risk is only temporary and appears to subside over time. The boost account is one theory that attempts to describe the occurrence of repeat, and near repeat, burglaries. The boost account suggests that these burglaries are the result of the same offender returning to burglarize a dwelling that they have successfully burglarized in the past, or one near the previously victimized target. In the current study, we first determined the repeat and near repeat space-time clustering of solved residential burglaries committed in Edmonton, Alberta, Canada, from 2007 to 2008. The results indicate that solved Edmonton burglaries do cluster together in time and space (i.e., residences within 700 m of a previous burgled target are at an increased risk for a period of 7 days). We also investigated whether repeat and near repeat burglaries in the dataset were more likely than distant burglaries to be committed by the same offender. It was found that serial offending by the same offender offers a viable rationale for much of the repeat and near repeat burglaries committed in Edmonton from 2007 to 2008. The practical implications of these results, as well as some limitations and directions for future research, are discussed.

Learning the rational choice perspective: A reinforcement learning approach to simulating offender behaviours in criminological agent-based models

Over the past 15 years, environmental criminologists have explored the application of agent-based models (ABMs) of crime events and various theoretical frameworks applied to understand them. Models have supported criminological theorising and, in some cases, been applied to make predictions about the impact of interventions devised to reduce crime. However, decision-making frameworks utilised in criminological ABMs have typically been implemented through traditional techniques such as condition-action rules. While these models have provided significant insights, they neglect a crucial component of theoretical accounts of offending, the notion that offenders are learning agents whose behavioural dynamics change over time and space. In response, this article presents an ABM of residential burglary in which offender agents utilise reinforcement learning (RL) to learn behaviours. This solution enables offender agents to learn from individual-level perceptions of the environment and, given these perceptions, develop behavioural responses that benefit themselves. The model includes conceptualisations of the Routine Activity Theory (RAT), Crime Pattern Theory (CPT) and a utility function, Target Attractiveness, which acts as a behavioural mould to nudge offender agents to learn behaviours in keeping with the Rational Choice Perspective (RCP). Trained behaviours are then tested by introducing crime prevention interventions into the model and examining the reactions of offender agents. In keeping with empirical studies of offending, experimental results demonstrate that offender agents utilising RL learn to offend at targets where rewards outweigh risks and effort, offend close to home, frequently victimise high-rewarding targets, and conversely learn to avoid offending in areas associated with high levels of risk and effort.

Familiar Locations and Similar Activities: Examining the Contributions of Reliable and Relevant Knowledge in Offenders’ Crime Location Choices

This paper examines the recently theorized roles of the reliability and relevance of offenders’ knowledge of locations in their crime location choices. Using discrete choice models, we analyzed offenders’ pre-offense activity locations from police data (home addresses, family members’ home addresses, work, school, prior offenses, victimizations, non-crime incidents, and other police contacts) and 17,054 residential burglaries, 10,353 non-residential burglaries, 1,977 commercial robberies, 4,315 personal robberies, and 4,421 extra-familial sex offenses, in New Zealand. Offenders were most likely to offend where their prior activity locations indicated they had highly reliable and highly relevant knowledge—where they were both highly familiar with the area and had conducted similar activities—and less likely where offenders had less familiarity or less similar activities. The results support a recent extension of crime pattern theory and highlight the importance of including both reliability and relevance factors when modeling or predicting offenders’ crime location choices.

CORRELATE OF SOCIO-ECONOMIC CHARACTERISTICS AND USE OF ANTI-BURGLARY DEVICES IN RESIDENTIAL AREA OF OGBOMOSO, NIGERIA

This study examines relationship between the residents’ Socio-economic Status (SeS) and choices in the adoption of anti-burglary devices across the residential zones of Ogbomoso City. It surveys households’ houses and their environments across the Core, Intermediate and Suburb residential zones of the study area. The variables used to examine the analysis of installed anti-burglary devices include the adoption of iron / steel bar, electronic devices, wire mesh, ethno-medical, and others. The examination of the aforementioned devices in relation to the socio-economic status of residents using Spearman’s rho Correlation analysis revealed that there is positive correlation between monthly income of residents and all the housing anti-burglary devices adopted with the p-values of 0.000, 0.010, 0.000, and 0.014 for Iron/steel bar, electronic devices, wire mesh and ethno-medical devices respectively. It further revealed that education has a significant correlation but negative with ethno-medical devices due to the p-value of 0.051and r-value of -0.070. This implies that increase in education reduces the use of ethno-medical devices in the study area. The study concluded that Socio-economics Status of residents have a great influence on the type and choice of anti-burglary devices adopted in the fight against residential burglary and home invasion across the City.

Predicting and analysing initiator crime environments based on machine learning for improving urban safety

PurposeInitiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.Design/methodology/approachWe compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.FindingsBy analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.Originality/valueOur findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.

The pandemic as a criminological experiment: Crime in the Netherlands during 12 months of COVID-19 measures

COVID-19 caused serious personal and societal damage. It also created, however, a unique natural experiment. How did the pandemic affect crime levels? In this article, we analyse unique empirical data for the Netherlands, covering January 2016–March 2021, to assess if and how crime patterns changed during the first 12 months of COVID-19-related measures. Domestic violence, which was expected to rise, did not increase, nor did distraction burglary. Residential burglary, bicycle theft and pickpocketing, however, decreased considerably, especially during (some of the) weeks when lockdown measures were into force. Online crime rates on the other hand increased. An opportunity theoretical approach regarding crime offers a good explanation for these changes.

Using remote sensing data to derive built-form indexes to analyze the geography of residential burglary and street thefts

ABSTRACT By deploying remotely sensed data together with spatial statistical modeling, we use regression modeling to investigate the relationship between the density of the built environment and two types of crime. We show how the Global Human Settlement Layer (GHSL) data set, which is a measure of building density generated from Sentinel 2A satellite imagery, can be used to create different indexes to describe the built environment for the purpose of analyzing crime patterns for indoor crimes (residential burglary) and open space crimes (street theft). Analysis is at neighborhood level for Stockholm, Sweden. Modeling is then extended to incorporate six planning areas which represent different neighborhood types within the city. Modeling is further extended by adding selected social, economic, demographic and land use variables that have been found to be significant in explaining spatial variation in the two crime categories in Stockholm. Significant associations between the GHSL-based indexes and the two crime rates are observed but results indicate that allowance for differences in neighborhood type should be recognized. Average income and transport hubs were also significant variables in the investigated crime categories. The article provides a practical demonstration and assessment of the use of high-resolution satellite data to examine the association between urban density and two common types of crime and offers reflections about the use of satellite image data in crime analysis.

A Bayesian Aoristic Logistic Regression to Model Spatio-Temporal Crime Risk Under the Presence of Interval-Censored Event Times

PurposeCrime data analysis has gained significant interest due to its peculiarities. One key characteristic of property crimes is the uncertainty surrounding their exact temporal location, often limited to a time window.MethodsThis study introduces a spatio-temporal logistic regression model that addresses the challenges posed by temporal uncertainty in crime data analysis. Inspired by the aoristic method, our Bayesian approach allows for the inclusion of temporal uncertainty in the model.ResultsTo demonstrate the effectiveness of our proposed model, we apply it to both simulated datasets and a dataset of residential burglaries recorded in Valencia, Spain. We compare our proposal with a complete cases model, which excludes temporally-uncertain events, and also with alternative models that rely on imputation procedures. Our model exhibits superior performance in terms of recovering the true underlying crime risk.ConclusionsThe proposed modeling framework effectively handles interval-censored temporal observations while incorporating covariate and space–time effects. This flexible model can be implemented to analyze crime data with uncertainty in temporal locations, providing valuable insights for crime prevention and law enforcement strategies.

Nudging down residential burglaries: A randomized control trial

Abstract Although the use of ‘nudges’ to influence decision-making and behaviour continues to be widely adopted by social policymakers, such as encouraging increases in organ donations, it is yet to get a similar foothold in the thinking and practice of reducing crime and policing. This paper seeks to assess the utility of the Nudge approach for achieving a reduction in residential burglary in Durham, England, by testing its efficacy using a Randomised Control Trial.

Effects of Internal Forced Displacement on Crime: Evidence from Colombia

Internal forced displacement, a consequence of territorial control strategies by armed groups, has resulted in profound socioeconomic challenges for receiving areas. Approximately eight million people have been affected in Colombia, where internal conflict and criminal networks have prevailed for over five decades. This paper explores the relationship between internal forced migration and crime rates in Colombian municipalities from 2003 to 2016, focusing on spatial dynamics. The study utilizes a spatial panel model and considers five crime categories: homicides, kidnapping, personal injuries, automobile theft, and residential burglary. The results reveal significant associations between forced displacement and certain types of crime, highlighting the importance of spatial analysis in understanding the interaction between migration and crime. The findings offer valuable insights for policymakers and researchers, facilitating informed policy interventions and community initiatives to address the repercussions of internal migration on crime dynamics.

A Temporal Assessment of Burglary at Residential Premises in the Newlands East Policing Precint

Knowing when crimes occur most predominantly in a specific area, such as which hour/s of the day, which day/s of the week, or which month/s of the year, is fundamental for the formulation of crime prevention strategies. This information facilitates operational as well as tactical resource deployment in areas at times when they are needed the most. This article aimed to contribute to this knowledge by exploring when most residential burglaries occurred in the Newlands East policing precinct in the Durban area. This was deemed necessary as residential burglary had been reported as the highest property-related crime in the study area for the 5-year period (2015–2019) preceding the study. To ensure that the aim of this study was achieved, a qualitative research design was utilized which assisted the researcher to focus on the temporal assessment of burglary at residential premises in the Newlands East policing precinct. Data collection was achieved by means of Focus Group Discussions and semi-structured interviews that involved a total of 37 participants comprising of South African Police Service members, Community Policing Forum representatives, local Ward councillors, and ordinary community members. The overall results suggest that the Newlands area experiences fluctuations in the frequency of residential burglaries throughout the year by hour, day, week, month, and season. This information can inform policymakers and law enforcement agents on when to implement crime prevention strategies.

Spatial Distribution of Burglary at Residential Premises in the Newlands East Policing Precint

It is in the best interest of community members and policy makers to comprehend not only which crime prevention methods are most effective, but also where they are appropriate to apply. This study aimed at exploring the spatial distribution of burglary at residential premises in the Newlands East policing precinct. This was deemed necessary as residential burglary had been reported as the highest property-related crime in the study area for the five-year period (2015-2019) preceding the study. To ensure that the aim of this study was achieved, a qualitative research approach was utilised which assisted the researcher to focus on the spatial assessment of burglary at residential premises in the Newlands East policing precinct. Data collection was achieved by means of Focus Group Discussions and semi-structured interviews that involved a total of thirty-seven participants comprising of South African Police Service members, Community Policing Forum representatives, local Ward councillors, and ordinary community members. A set of pre-determined questions was used that enabled the researcher to explore themes and elicit responses that focused on achieving the objectives of the study. The findings of the study indicated that burglaries in the study area occurred spatially. This means that burglaries are highly concentrated at specific residential premises, particularly in the Newlands West area. The findings also indicated that various factors at local community level were instrumental in instigating this crime, and it is thus argued that SAPS and CPF members need to work collaboratively to develop strategies to curb the spatial distribution of burglaries in the study area. These findings suggest that a more concerted effort is required to forge collaboration among the government, the SAPS, and the community to effectively curb burglaries at residential premises. New evidence also highlights the role of construction/development companies as role-players in the fight against the scourge of residential burglaries.

Evaluating current strategies in Response to burglary at residential premises

It is in the best interest of the South African Police Services (SAPS) to assess the effectiveness of the current strategies which they are employing to respond to burglaries at residential premises in South Africa, this knowledge would assist in addressing burglaries more effectively in the future. This study aimed to contribute to this knowledge by evaluating the current strategies which the SAPS in the Newlands East policing precinct are utilizing to respond to burglaries in the study area. This was deemed necessary as residential burglary had been reported as the highest property-related crime in the study area for the five years (2015-2019) preceding the study. In ensuring that the aim of this study was achieved, the qualitative research design was used, which assisted the researcher to focus on exploring the views of the SAPS members on the current strategies they are employing to respond to burglaries. Data collection for this study was based on semi-structured interviews conducted on an individual basis with selected participants. It combined a set of pre-determined questions that enabled the interviewer to explore further particular themes and responses. The findings of this study revealed that the South African Police Service as well as the Community Policing Forum in the study area have implemented some viable strategies to combat burglary at residential premises. However, these strategies are not entirely effective due to limited manpower and limited vehicles allocated to the police station. These findings suggest that there is an urgent need for the government to intervene by increasing the SAPS budget so that more vehicles and manpower may be deployed to local police stations. This would assist the SAPS to effectively respond to burglaries at residential premises in the study area.

Measuring the impact of the state of emergency on crime trends in Japan: a panel data analysis

Purpose/BackgroundCity-specific temporal analysis has been commonly used to investigate the impact of COVID-19-related behavioural regulation policies on crime. However, these previous studies fail to consider differences in the intensity of intervention among cities and the impact of these behavioural regulation policies on crime trends nationwide. This study performs panel data analyses to examine how the declaration of a state of emergency (SoE) affected ambient population and crime in Japan, taking advantage of the fact that the SoE was implemented at different times in different prefectures.MethodsThe current study uses two sets of panel data of 47 prefectures for 22 weeks from February to July 2020: (1) the data on ambient population in five types of locations provided by the Google Mobility Reports, and (2) official crime data of six types of crime: residential burglary, commercial burglary, theft of/from vehicle, bicycle theft, sexual assault, and violence and injury. Firstly, an ordinary least squares regression analysis was performed to examine the impact of the SoE on the ambient population. Then a negative binomial model with fixed effects was adopted to examine the effect of the ambient population on the crime trends.FindingsThe SoE declaration was found to increase the ambient population in ‘residential’, and decrease that in other settings including ‘workplaces’, ‘transit stations’, and ‘retail and recreation’ in targeted prefectures. Spill-over effects of the SoE were observed on the ambient population of non-SoE prefectures. The ambient population have impacted five out of the six types of crime examined, except for sexual assault. After controlling for the ambient population, we observed an increase in commercial burglary and theft of/from the vehicle in all prefectures during the SoE weeks, compared to the weeks when the SoE was not declared.ConclusionsThe declaration of the SoE during the COVID-19 pandemic changed the ambient population in the SoE-prefectures, resulting the changes in crime levels as well. In addition, the implementation of the SoE in specific prefectures was found to have a contextual impact on national-level crime trends. Furthermore, the implementation of the SoE caused changes in some crime types that could not be explained by the changes in the ambient population, suggesting that the implementation of the SoE affected offenders’ decision-making. It is also worth noting that the changes in ambient population and crime trends during the pandemic were observed in Japan where the behavioural regulation policy without law enforcement was introduced.

Sustainable urban form: socio-demographic and permeability factors as determinants of crime spots in cities, case of Akure, Nigeria

ABSTRACT Urbanization comes with the price of the negative complexity of crime. It is a reflection of the socio-demographic and permeability factors. This paper aims to integrate socio-demographic crime factors with street permeability to find the association with residential burglary spots. We first find the residential burglary hotspots area related to socio-demographic factors of the study area, then identify potential residential burglary risk areas based on the factors of street permeability and find the association of residential burglary with socio-demographics and street permeability factors in Akure. The methodology employed includes the Inverse Distance Weight factor analysis, space syntax, and the Poisson regression analysis. The findings showed hotspots of burglary within neighbourhoods, confirming the relationship between the factors. Issues identified herein denote some logical starting points for criminological engagement with the Sustainable Development Goals. In the conclusion, we discussed the implications for the statistical output.

A quantile panel examination of the moderation effects of guardianship on residential burglary.

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.

Geographical profiling incorporating neighbourhood‐level factors using spatial interaction modelling

AbstractThe 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.

The Impact of Urban Facilities on Crime during the Pre- and Pandemic Periods: A Practical Study in Beijing.

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

Impact of COVID-19 on crime in Russia

The government of the Russian Federation, like most countries, has taken a number of measures to counter the spread of the coronavirus (COVID-19) pandemic. We analyzed the crime rate in the Russian Federation before the introduction of the COVID-19 containment measures and during their implementation to assess the effectiveness of measures to combat crime during the COVID-19 pandemic. We describe the tendencies in the development of the main types of crimes: murder and attempted murder, intentional infliction of grievous bodily harm, robberies, and residential burglaries. Our analysis used econometric research methods: we built correlation regression models of the number of registered crimes and calculated seasonal indices of changes in the crime rate, the results of which made it possible to calculate the predicted crime level in Russia. We determined that before the introduction of the COVID-19 containment policy, the number of current registered crimes in the Russian Federation was characterized by a high dependence on the dynamics of previous years; the number of investigated types of crimes was steadily decreasing, but in the months with official holidays(January, March, May) the number of murders and crimes related to deliberate harm to health increased. Over the 9 months after the introduction of COVID-19 in Russia, the number of murders, crimes related to the intentional infliction of grievous bodily harm, and the number of reported robberies was higher than the expected values, while the number of residential burglaries was reduced.