Daniel James Birks

Predictive Mapping of Crime by ProMap: Accuracy, Units of Analysis, and the Environmental Backcloth

This chapter concerns the forecasting of crime locations using burglary as an example. An overview of research concerned with when and where burglaries occur is provided, with an initial focus on patterns of risk at the individual household level. Of central importance is evidence that as well as being geographically concentrated (at a range of geographic scales), burglary clusters in space and time more than would be expected if patterns of crime were simply the result of some places being more attractive to offenders than others. One theoretical framework regarding offender spatial decision making is discussed and consideration given to how features of the urban environment which affect the accessibility of places (e.g., road networks or social barriers) might shape patterns of offending. A simple mathematical model informed by the research discussed is then presented and tested as to its accuracy in the prediction of burglary locations. The model is tested against chance expectation and popular methods of crime hot-spotting extant and found to outperform both. Consideration of the importance of different units of analysis is a recurrent theme throughout the chapter, whether this concerns the intended policy purpose of crime forecasts made, the spatial resolution of different types of data analyzed, or the attention given to the dimension of time – a unit of analysis often overlooked in this type of work. The chapter concludes with a discussion of means of developing the approach described, combining it with others, and using it, inter alia, to optimize police patrol routes.

Burglar Target Selection: A Cross-national Comparison

Objectives: This study builds on research undertaken by Bernasco and Nieuwbeerta and explores the generalizability of a theoretically derived offender target selection model in three cross-national study regions. Methods: Taking a discrete spatial choice approach, we estimate the impact of both environment- and offender-level factors on residential burglary placement in the Netherlands, the United Kingdom, and Australia. Combining cleared burglary data from all study regions in a single statistical model, we make statistical comparisons between environments. Results: In all three study regions, the likelihood an offender selects an area for burglary is positively influenced by proximity to their home, the proportion of easily accessible targets, and the total number of targets available. Furthermore, in two of the three study regions, juvenile offenders under the legal driving age are significantly more influenced by target proximity than adult offenders. Post hoc tests indicate the magnitudes of these impacts vary significantly between study regions. Conclusions: While burglary target selection strategies are consistent with opportunity-based explanations of offending, the impact of environmental context is significant. As such, the approach undertaken in combining observations from multiple study regions may aid criminology scholars in assessing the generalizability of observed findings across multiple environments.

Police Perceptions of the Long- and Short-Term Spatial Distribution of Residential Burglary

This paper seeks to explore police officer perception of the spatial distribution of residential burglary over different time periods. Using a survey of officers across three English police basic command units (BCUs), it examines the accuracy of their impressions of the locations of crime over the preceding year and the preceding two weeks. It also explores how these perceptions might affect the deployment of resources and police action. The results suggest that whilst officers have a good idea of where burglary occurred over the preceding year, they are less accurate for the recent distribution of risk because short-term hotspots are indeed significantly more unstable than long-term hotspots. The short-term predictive power of both one-year and two-week retrospective perceptions is very limited. Tactical advantages will only be afforded by the swift and routine identification of emerging short-term hotspots.

Emergent Regularities of Interpersonal Victimization: An Agent-Based Investigation

Objectives: Apply computational agent-based modeling to explore the generative sufficiency of several mechanisms derived from the field of environmental criminology in explaining commonly observed patterns of interpersonal victimization. Method: Controlled simulation experiments compared patterns of simulated interpersonal victimization to three empirically derived regularities of crime using established statistical techniques: (1) spatial clustering (nearest neighbor index), (2) repeat victimization (Gini coefficient), and (3) journeys to crime (Pearson’s coefficient of skewness). Results: Large, statistically significant increases in spatial clustering, repeat victimization, and journey to crime skewness are observed when virtual offenders operate according to mechanisms proposed by the routine activity approach, rational choice perspective, and geometry/pattern theories of crime. Conclusion: This research provides support for several propositions of environmental criminology in explaining why interpersonal victimization tends to be spatially concentrated, experienced by a small number of repeat victims, and why aggregate journey to crime curves tend to follow a distance decay relationship. By extending previous work in agent-based modeling of property victimization, it also demonstrates that the same core mechanisms are sufficient to generate plausible patterns of crime when examining fundamentally different types of offending.

Research on target: A collaboration between researchers and practitioners for a target hardening scheme

In this paper we report on a researcher–practitioner collaboration to deliver a crime reduction initiative across a borough in the West Midlands region of England. The circumstances of the collaboration and the initial analysis are explained. The crime prevention programme, which involved the situational crime prevention technique of target hardening, is described and a brief summary of the evaluation findings are included. We then consider the value of such collaborations for developing evidence-led interventions and discuss a number of issues which researchers embarking upon this type of project should consider: funding, timescales, data access, publication and evaluation. We conclude that, with due regard for these issues, such collaborations can be beneficial to all the parties involved.

STREET NETWORK STRUCTURE AND CRIME RISK: AN AGENT-BASED INVESTIGATION OF THE ENCOUNTER AND ENCLOSURE HYPOTHESES†

Street 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.

Formal Models of the Crime Event: Agent-Based Modelling in Support of Crime Prevention

In order to be effective, crime prevention interventions rely on the identification, understanding and manipulation of causal mechanisms that result in crime. This chapter explores how simulation methods, specifically computational agent-based models, can aid in the specification, testing and refinement of crime event theories that underlie prospective situational crime prevention efforts before resource intensive implementation and evaluation are undertaken. The chapter begins by summarising key components of the agent-based method, and a number of its strengths relative to other analytical approaches applied in the analysis complex social systems. Subsequently, both explanatory and predictive applications of agent-based modelling in support of crime prevention are discussed. Furthermore, drawing on several examples from the field of environmental criminology, I propose that explanatory agent-based models offer unique means to explore the relationship between individual behaviour and observed aggregate crime phenomena—arguing that such insights generate important foundational knowledge for those who seek to understand crime events, and, in turn, devise effective interventions that aim to prevent them.