AbstractWe recently rejected the hypothesis that increases in cybercrime may have caused the international crime drop. Critics subsequently argued that offenders switched from physical crime to cybercrime in recent years, and that lifestyle changes due to 'leisure IT' may have caused the international crime drop. Here we explain how the critics misrepresented our argument and do not appear to introduce anything new.
AbstractStreet 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.
AbstractIn recent years, internet connectivity and the ubiquitous use of digital devices have afforded a landscape of expanding opportunity for the proliferation of scams involving attempts to deceive individuals into giving away money or personal information. The impacts of these schemes on victims have shown to encompass social, psychological, emotional and economic harms. Consequently, there is a strong rationale to enhance our understanding of scams in order to devise ways in which they can be disrupted. One way to do so is through crime scripting, an analytical approach which seeks to characterise processes underpinning crime events. In this paper, we explore how Natural Language Processing (NLP) methods might be applied to support crime script analyses, in particular to extract insights into crime event sequences from large quantities of unstructured textual data in a scalable and efficient manner. To illustrate this, we apply NLP methods to a public dataset of victims' stories of scams perpetrated in Singapore. We first explore approaches to automatically isolate scams with similar modus operandi using two distinct similarity measures. Subsequently, we use Term Frequency-Inverse Document Frequency (TF-IDF) to extract key terms in scam stories, which are then used to identify a temporal ordering of actions in ways that seek to characterise how a particular scam operates. Finally, by means of a case study, we demonstrate how the proposed methods are capable of leveraging the collective wisdom of multiple similar reports to identify a consensus in terms of likely crime event sequences, illustrating how NLP may in the future enable crime preventers to better harness unstructured free text data to better understand crime problems.
AbstractWe 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.
This study demonstrates that computational modeling and, in particular, agent‐based modeling (ABM) offers a viable compatriot to traditional experimental methodologies for criminology scholars. ABM can be used as a means to operationalize and test hypothetical mechanisms that offer a potential explanation for commonly observed criminological phenomena. This study tests whether the hypothesized mechanisms of environmental criminology are sufficient to produce several commonly observed characteristics of crime. We present an ABM of residential burglary, simulating a world inhabited by potential targets and offenders who behave according to the theoretical propositions of environmental criminology. A series of simulated experiments examining the impact of these mechanisms on patterns of offending are performed. The outputs of these simulations then are compared with several well‐established findings derived from empirical studies of residential burglary, including the spatial concentration of crime, repeat victimization, and the journey to crime curve. The results from this research demonstrate that the propositions of the routine activity approach, rational choice perspective, and crime pattern theory provide a viable generative explanation for several independent characteristics of crime.
Introduction & BackgroundThe types of challenges police and ambulance services deal with often overlap, for instance supporting those who suffer from mental ill-health. Research has shown that emergency service problems often concentrate, but also that some individuals who come to the attention of one service may not be as visible to another despite their overlap in roles.
Objectives & ApproachThis study explored how routinely collected 999 data may reveal insights into how these services support potentially vulnerable populations. We argue that better understanding the nature and distribution of vulnerability-related calls may help to inform future preventative or harm reduction-based interventions. We analysed administrative data provided by Yorkshire Ambulance Service for the Bradford region through the Connected Bradford research database, posing the following questions: (1) can 999 call data provide insights into vulnerability-related incidents attended by ambulances?; (2) where and when are these incidents most prevalent?; and (3) what are the spatial patterns of calls and patient home locations associated with them?
Relevance to Digital FootprintsWe first select calls associated with nine callout reasons indicative of vulnerability. Patients can choose to share their data with each healthcare service they use, so we harnessed this digital footprint to analyse the spatial distribution of call locations (at postcode sector level) and patient home location (at MSOA level).
ResultsResults indicate substantial concentrations of vulnerability-related calls in multiple postcode sectors including the City Centre (where we estimate 18% of calls may be vulnerability-related) and several other areas which are associated with deprivation. Exploring flows of people from their home location to incident location we also see substantial spatial variation in the locations in which patients involved in these types of incidents reside.
Conclusions & ImplicationsThese analyses represent initial efforts to better understand how vulnerable groups are supported by public services, and have the potential to inform future resource allocation and targeting of upstream interventions.
Abstract Independent analysis of police, fire, and ambulance calls for service demonstrates common patterns in emergency service activity. Targeted, place-focused interventions have been demonstrated to prevent future problems for emergency services. This research builds on these findings to examine the spatial and temporal intersection of police, fire, and ambulance incidents to explore the potential utility of enhanced collaboration between emergency-first responders. Using police and fire data from Surrey, BC, Canada, from 2011 to 2013, spatial and temporal patterns of police-, fire-, and ambulance-related incidents were examined. Initial analyses demonstrate that 36% of the City's area experienced 72% of incidents responded to over this 3-year study period. Focusing on this high-volume area, the spatial and temporal intersection of these incident types was explored. Spatially, lattices of varying cell sizes (250 m, 500 m, and 1,000 m) were placed over the study area. Temporally, incident volume was examined across the entire 3-year study period, and at yearly and monthly intervals. Incidents were placed within these spatial and temporal frameworks and visual inspection was utilized to assess the convergence of service demand. Regardless of the cell grid size, police, fire, and ambulance incidents were spatially and temporally concentrated, with the top 10% of cells accounting for approximately 50% of all incidents across all services. Furthermore, there was considerable spatio-temporal convergence in cells which account for the top decile of call volume for all incident types. A 2 × 2 typology is proposed to classify locations (in this case grid cells) based on (1) the frequency at which they generate high demand for services (sporadic versus persistent), and (2) the combination of agencies required to respond to high demand problems (single versus convergent). The spatial and temporal convergence of emergency service problems observed in this study suggests that an interagency approach to problem identification will enhance problem analysis processes. Working in conjunction with established problem-focused intervention strategies (such as problem-oriented policing), the volume-service typology provides a framework that can contribute to the development of appropriate problem-responses. This, we hope, will support emerging efforts to increase the extent to which emergency-first responder agencies collaborate to maximize efficiency and effectiveness, and reduce harm.
