PAUSED for thought? Using verbal protocol analysis to understand the situational and temporal cues in the decision-making of residential burglars

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

An interactive human centered data science approach towards crime pattern analysis

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis

Application of Spatiotemporal Fuzzy C-Means Clustering for Crime Spot Detection

The various sources generate large volume of spatiotemporal data of different types including crime events. In order to detect crime spot and predict future events, their analysis is important. Crime events are spatiotemporal in nature; therefore a distance function is defined for spatiotemporal events and is used in Fuzzy C-Means algorithm for crime analysis. This distance function takes care of both spatial and temporal components of spatiotemporal data. We adopt sum of squared error (SSE) approach and Dunn index to measure the quality of clusters. We also perform the experimentation on real world crime data to identify spatiotemporal crime clusters.

A Statistical Approach to Crime Linkage

The object of this paper is to develop a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation. Bayes factors are used to describe the strength of evidence that two crimes are linked. Using concepts from agglomerative hierarchical clustering, the Bayes factors for crime pairs are combined to provide similarity measures for comparing two crime series. This facilitates crime series clustering, crime series identification, and suspect prioritization. The ability of our models to make correct linkages and predictions is demonstrated under a variety of real-world scenarios with a large number of solved and unsolved breaking and entering crimes. For example, a na\"ive Bayes model for pairwise case linkage can identify 82\% of actual linkages with a 5\% false positive rate. For crime series identification, 77\%-89\% of the additional crimes in a crime series can be identified from a ranked list of 50 incidents

What do we really know about police patrol? A systematic review of routine police patrol research

Purpose: Research on routine police patrol has experienced little attention in criminology for the past four decades. Despite the fact that little is known about this mode of policing, a consensus seems to prevail regarding its ineffectiveness for crime deterrence and crime prevention. To emphasize this gap of research, this study systematically reviews existing literature on routine police patrol. Methods: A systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines of scientific studies (n=4) was conducted. Evidence was synthesized quantitatively (e.g., tabular) and qualitatively (e.g., narrative argumentation). Results: The synthesized results provide no ground for the diagnosed ineffectiveness of routine police patrol, that seems to be believed throughout criminological studies. Despite the outdated character of the majority of reviewed studies, results show inconsistencies and fail to clearly establish positive or negative quantitative crime deterrent effects. Conclusion: Contemporary research does not adequately understand the effects of routine police patrol and builds leading police research on a limited number of methodically flawed studies from the mid 1970’s. Future research should establish the effectiveness of this mode of policing and optimal spatial allocation of police officers following a sound methodological framework

Behaviour Tracking: Using geospatial and behaviour sequence analysis to map crime

Crime is a complex phenomenon. To understand the commission of crime, researchers must map both the temporal and the spatial processes involved. The current research combines a temporal method of analysis, Behaviour Sequence Analysis, with geospatial mapping, to outline a new method of integrating temporal and spatial movements of criminals. To show how the new method can be applied, a burglary scenario was used, and the movements and behaviours of a criminal tracked around the property. Results showed that combining temporal and spatial analyses allows for a clearer account of the process of a crime scene. The current method has application to a large range of other crimes and terrorist movements, for instance between cities and movements within each city. Therefore, the current research provides the foundation framework for a novel method of spatio-temporal analyses of crime

Criminal Victimisation in Taiwan: an opportunity perspective

Environmental criminology concerns the role of opportunities (both people and objects) existing in the environment that make crimes more likely to occur. Research consistently shows that opportunity perspectives (particularly with regard to individuals’ lifestyles and routines) help in explaining the prevalence and concentration of crimes. However, there is a paucity of studies investigating crime patterns from an opportunity perspective both outside western countries and in relation to cybercrimes. Hence, it is not clear whether non-Western and online contexts exhibit similar patterns of crime as would be predicted by an opportunity perspective. This thesis is concerned with criminal victimisation in Taiwan – a less researched setting in the field of environmental criminology. It covers both offline victimisation (with a focus on burglary) and online victimisation from the aforementioned opportunity perspective. The goal of this thesis is to identify individual- and area-level characteristics that affect the patterns of victimisation in Taiwan. To achieve this, the thesis draws on a range of secondary datasets, including police recorded crime statistics, the Taiwan Area Victimisation Survey, and the Digital Opportunity Survey for Individuals and Households. With the application of quantitative modelling, the thesis suggests that the generalisability the lifestyle-routine activity approach in explaining crime patterns in Taiwan should be taken with caution. The findings provide partial support for its applicability in relation to burglary and cybercrime in Taiwan. Furthermore, the findings reported here in relation to patterns of repeat and near repeat victimisation depart from those observed in the western literature. The thesis concludes by discussing the implications of the findings for academic research and practice in crime prevention

Ergebnisse einer wissenschaftlichen Befassung mit Predictive Policing

Der für Deutschland neue Polizeiansatz Predictive Policing stellt die Polizeien sowie polizeiexterne Fachleute, vor die Herausforderung, sich angesichts der Entwicklung der Einbruchskriminalität, dem Aufkommen kommerzieller Prognose-Software sowie der fortschreitenden Digitalisierung der polizeilichen Arbeit ausgiebig mit diesem als Innovation gehandelten Trend zu befassen. Die Kriminologische Forschungsstelle des LKA Hamburg widmete sich daher seit 2016 den Voraussetzungen und Potenzialen von raumbezogenem Predictive Policing. Fragen der Kosten-Nutzen-Bilanz, der sozialen Folgen und der polizeilichen Datenbasis waren bis dato nicht beantwortet. Der Fokus des Forschungsprojekts lag auf dem polizeilichen Wissens- und Informationsmanagement, also dem Entstehungsprozess von Daten, am Beispiel der Einbruchssachbearbeitung. Es erfolgte eine ergebnisoffene Auseinandersetzung mit den Grundlagen von Predictive Policing und der gesamten Einbruchsphänomenologie sowie eine Bilanzierung der digitalen Informationsverarbeitung für die Polizei Hamburg. Der nun vorliegende Forschungsbericht ist ein Beitrag zur Grundlagenforschung rund um den Polizeiansatz Predictive Policing und stellt für die Polizei Hamburg die Weichen zur Ausrichtung einer zukunftsweisenden Strategie in den Bereichen Datenanalyse und -auswertung. Aus den Erkenntnissen des Forschungsprojekts resultiert das Erfordernis, digitales Informationsmanagement in der Ermittlungsarbeit zu optimieren und zukunftssicher zu gestalten. Vorangetrieben werden soll dies über die Entwicklung von Auswertungstools, die Daten von Massendelikten vorstrukturieren, um die Serienerkennung softwaregestützt zu unterstützen. Problem- und raumbezogene Kriminalitätsauswertung durch ausgebildete Kriminalitätsanalytiker könnten ein Alternativmodell zu algorithmenbasierter Kriminalitätsauswertung und -prognose sein. Die Potenziale der ‚digitalen Spur‘ sind bis heute nicht ausgeschöpft. Die facettenreiche Gliederung des Forschungsberichts verdeutlicht den breiten Forschungsansatz. Der Bericht liefert somit eine umfassende Wissensbasis für die weitere Befassung mit dem Prädiktionspotenzial der schweren Einbruchskriminalität