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We investigate whether the type of crime committed early in an individual's career has a bearing on patterns of subsequent offending (the so-called debut hypothesis) in an Australian setting. Police-recorded crime data from 2008 to 2020 were used and partitioned into cohorts based on the year of the first offence. We computed, for each cohort, the rate at which individuals progressed to established criminal careers conditioned by their first recorded offence. Individuals who commit burglary, vehicle theft, or robbery as their first recorded offence were observed to become chronic offenders at higher rates than those who commit other types of first offences. We also demonstrated that this relationship exists for other dimensions of the criminal career and is time stable. The policy implications of our findings suggest that a combination of opportunity reduction and diversionary policies has the potential to substantially reduce the prospects of chronic criminal careers and their societal impact. [ABSTRACT FROM AUTHOR]

This study identifies subtypes of citizen behavioral characteristics within police use of force events and assesses ecological influences on these subtypes. Data comprised police use of force reports, recorded crime data, and census data. Latent class analysis was applied to 19,900 police use of force events to identify latent classes of citizen behavioral characteristics. A five class-solution demonstrated best fit and comprised the following classes: violent, unstable, and drug or alcohol affected; apparent mental disorder and possessing a weapon; alcohol-related violence; suspicious and fleeing; and violent behavior and threatening a weapon. Chi-square analysis and multilevel logistic regression showed each citizen presentation class was associated with distinct patterns of offence types and contexts for police use of force. Ecological influences were notable for three classes of citizen presentations. The clustering of citizen behaviors within incidents provides insights into the nature and distribution of types of police–citizen encounters where force is used. [ABSTRACT FROM AUTHOR]

With the advent of the Internet and the emergence of cybercrimes (e.g., cyber stalking, cyber harassment), criminologists have begun to explore the empirical utility of lifestyle exposure and routine activity theories (RATs) to account for personal victimization as a consequence of cyber abuse. Available cyber abuse studies have produced inconsistent empirical support for both models, which has reignited the debate about whether terrestrial theories, such as RAT, will ever be able to adequately explain cybercrimes due to the spatial and temporal disconnect between the theories and the cyber environment. This article reviews existing cyber abuse scholarship, explores potential reasons for the weak empirical support for routine activity and lifestyle exposure theories in cyberspace, and proposes several directions for future research. We suggest that to further our understanding of cyber abuse processes, scholars need to carefully define and operationalize the key theoretical concepts in the light of latest developments in RAT (i.e., addition of new controllers—handlers and place managers, and super controllers), and conduct in-depth qualitative studies, as well as quantitative studies, that employ robust methodological designs and multi-level statistical analyses. [ABSTRACT FROM AUTHOR]

The article presents the study on the common patterns of interpersonal victimization. It discusses how the study was conducted which involved controlled simulation experiments to assess three regularities of crime by various statistical techniques including repeat victimization and spatial clustering. The results reportedly supports beliefs of environmental criminology on why interpersonal victimization is usually spatially concentrated.

Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation. [ABSTRACT FROM AUTHOR]

This paper ascertains the feasibility of using a data mining supervised learning algorithm to predict which crime scenes potentially offer the best opportunity of recovering forensic samples such as finger prints or DNA. UK police forces have a finite number of Crime Scene Investigators and an ever-increasing demand to attend crime scenes. Most forces have a documented attendance criterion but crimes are not evenly distributed throughout the working day. This, at times, results in more scenes to examine than there are investigators, thereby posing the question, Which scenes should be attended first in order to retrieve forensics?. The Insightful Miner data mining workbench tool has been used to retrieve, combine, clean, manipulate and model the data. The results from Northamptonshire Police have demonstrated that the modelling process is able to predict crime scene attendance to an accuracy of 68 per cent which is significantly better than human experts. The methodology was used in a second police force (Gwent) which records data into different computer systems and the results were comparable to those achieved in Northamptonshire Police. This demonstrates that the methodology is portable between forces. [ABSTRACT FROM AUTHOR]

The September 2005 review of policing structure in England and Wales by Her Majesty's Inspectorate of Constabulary (HMIC) is considered in terms of its impact on forces' ability to detect volume crime (crime types that occur in relatively large numbers). We demonstrate that there is no relationship between force size and detection rates, however there is a difference with respect to the means of detecting volume crime. Smaller forces tend to record a greater proportion of their volume crime detections through forensic means. We explore one possibility of this observation — greater forensic deployment due to lower demand or better management — and find some evidence to support better management of forensic resources in smaller forces. [ABSTRACT FROM AUTHOR]