Nick Malleson

Crime reduction through simulation: An agent-based model of burglary

Traditionally, researchers have employed statistical methods to model crime. However, these approaches are limited by being unable to model individual actions and behaviour. Brantingham and Brantingham (1993) described that in their opinion a useful and productive model for simulating crime would have the ability to model the occurrence of crime and the motivations behind it both temporally and spatially. This paper presents the construction and application of an agent-based model (ABM) for simulating occurrences of residential burglary at an individual level. It presents a novel framework that allows both human and environmental factors to be simulated. Although other agent-based models of crime do exist, this research represents the first working example of integrating a behavioural framework into an ABM for the simulation of crime. An artificial city, loosely based on the real city of Leeds, UK, and an artificial population were constructed, and experiments were run to explore the potential of the model to realistically simulate the main processes and drivers within this system. The results are highly promising, demonstrating the potential of this approach for both understanding processes behind crime and improving policies and developing effective crime prevention strategies.

The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns

Crime rate is a statistic used to summarize the risk of criminal events. However, research has shown that choosing the appropriate denominator is non-trivial. Different crime types exhibit different spatial opportunities and so does the population at risk. The residential population is the most commonly used population at risk, but is unlikely to be suitable for crimes that involve mobile populations. In this article, we use “crowd-sourced” data in Leeds, England, to measure the population at risk, considering violent crime. These new data sources have the potential to represent mobile populations at higher spatial and temporal resolutions than other available data. Through the use of two local spatial statistics (Getis-Ord GI* and the Geographical Analysis Machine) and visualization, we show that when the volume of social media messages, as opposed to the residential population, is used as a proxy for the population at risk, criminal event hot spots shift spatially. Specifically, the results indicate a significant shift in the city center, eliminating its hot spot. Consequently, if crime reduction/prevention efforts are based on resident population based crime rates, such efforts may not only be ineffective in reducing criminal event risk, but be a waste of public resources.

Implementing comprehensive offender behaviour in a realistic agent-based model of burglary

Explaining and modelling crime patterns is an exercise that has taxed policy-makers, criminologists, social reformers and the police ever since the first crime patterns were recorded. Crime is a particularly difficult phenomenon to model because of its inherent complexity; crime patterns are built up from a multitude of human-human and human-environment micro-interactions that ultimately lead to individual crime events. Commonly used modelling techniques, such as regression, struggle to fully account for the dynamics of the crime system. They work at aggregate scales thereby disregarding important individual-level variation and also struggle to account for the effects of different types of human behaviour. Furthermore, important concepts from environmental criminology – such as individual offender awareness spaces or heterogeneity in offender decision-making – cannot be included directly when working at a resolution above that of the individual. This research addresses the drawbacks associated with traditional mathematical crime models by building an agent-based simulation with a unique offender behavioural model. Through use of the PECS framework for modelling human behaviour, agents are endowed with needs and motives that drive their behaviour and ultimately lead to the commission of crime. As the model uses real-world environmental data, it can be used to make predictions in existing cities. The paper demonstrates that use of this framework, in combination with an agent-based model, can replicate patterns and trends that are supported by the current theoretical understanding of offending behaviour.

Analysis of crime patterns through the integration of an agent-based model and a population microsimulation

In recent years, criminologists have become interested in understanding crime variations at progressively finer spatial scales, right down to individual streets or even houses. To model at these fine spatial scales, and to better account for the dynamics of the crime system, agent-based models of crime are emerging. Generally, these have been more successful in representing the behaviour of criminals than their victims. In this paper it is suggested that individual representations of criminal behaviour can be enhanced by combining them with models of the criminal environment which are specified at a similar scale. In the case of burglary this means the identification of individual households as targets. We will show how this can be achieved using the complementary technique of microsimulation. The work is significant because it allows agent-based models of crime to be refined geographically (to allow, for example, individual households with varying wealth or occupancy measures) and leads to the identification of the characteristics of individual victims.

An Agent-Based Model of Burglary

Occurrences of crime are complex phenomena. They are the result of a large number of interrelated elements which can include environmental factors as well as complex human behaviours. Traditionally, crime occurrences have been modelled using statistical techniques, and although such approaches are useful, they face difficulties in providing predictive analyses and with the integration of behavioural information. Also, it is particularly difficult to account for the strongly influential effect of local urban form. Agent-based modelling is a relatively new modelling paradigm that has generated a considerable amount of interest. An agent is an independent component of a system which interacts with other agents and its environment to achieve goals. In this manner, large systems of agents can be created to mimic real scenarios. Most importantly, the agents can incorporate behavioural information to determine how they should achieve their goals, and models can include a highly detailed environment. This paper presents an agent-based model used to predict burglary rates, which, despite its simplicity, yields interesting results. We apply the model to the city of Leeds, UK. The model indicates that the urban configuration in Leeds is a major element in determining the level of crime across the city. It also demonstrates that agent-based modelling is an excellent tool for these types of analyses with much potential.

Towards victim-oriented crime modelling in a social science e-infrastructure

The National e-Infrastructure for Social Simulation (NeISS) is a multi-disciplinary collaboration between computation and social science within the UK Digital Social Research programme. The project aims to develop new tools and services for social scientists and planners to assist in performing ‘what-if’ scenario predictions in a variety of policy contexts. A key part of the NeISS remit is to explore real-world scenarios and evaluate real policy applications. Research into the processes and drivers behind crime is an important application area that has major implications for both improving crime-related policy and developing effective crime prevention strategies. This paper will discuss how the current e-infrastructure and available microsimulation tools can be used to improve an existing agent-based burglary simulation (BurgdSIM) by including a more realistic representation of the victims of crime. Results show that the model produces different spatial patterns when individual-level victim data are used and a risk profile of the synthetic victims suggests which types of people have the largest burglary risk.

How Places Influence Crime: The Impact of Surrounding Areas on Neighbourhood Burglary Rates in a British City

Burglary prevalence within neighbourhoods is well understood but the risk from bordering areas is under-theorised and under-researched. If it were possible to fix a neighbourhood’s location but substitute its surrounding areas, one might expect to see some influence on its crime rate. However, by treating surrounding areas as independent observations, ecological studies assume that identical neighbourhoods with markedly different surroundings are equivalent. If not, knowing the impact of different peripheries would have significance for crime prevention, land use planning and other policy domains. This paper tests whether knowledge of the demographic make-up of surrounding areas can improve on the prediction of a neighbourhood’s burglary rate based solely on its internal socio-demographics. Results identify significant between-area effects with certain types of periphery exerting stronger influences than others. The advantages and drawbacks of the spatial error and predictor lag model used in the analysis are discussed and areas for further research defined.

Calibration of a spatial simulation model with volunteered geographical information

For many scientific disciplines, the continued progression of information technology has increased the availability of data, computation and analytical methodologies including simulation and visualisation. Geographical information science is no exception. In this article, we investigate the possibilities for deployment of e-infrastructures to inform spatial planning, analysis and policy-making. We describe an existing architecture that feeds both static and dynamic simulation models from a variety of sources, including not only administrative datasets but also attitudes and behaviours which are harvested online from crowds. This infrastructure also supports visualisation and computationally intensive processing. The main aim of this article is to illustrate how spatial simulation models can be calibrated with crowd-sourced data. We introduce an example in which popular attitudes to congestion charging in a major UK city (Manchester) were collected, with promotional support from a high-profile media organisation (the BBC). These data are used to estimate the parameters of a transport simulation model, using a hungry estimation procedure which is deployed within a high-performance computational grid. We indicate how the resulting model might be used to evaluate the impact of alternative policy options for regulating the traffic in Manchester. Whilst the procedure is novel in itself, we argue that greater credibility could be added by the incorporation of open-source simulation models and by the use of social networking mechanisms to share policy evaluations much more widely.

Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars

Agent-based methods are one approach for modelling complex social systems but one issue with these models is the large number of parameters that require estimation. This chapter examines the effect of using a genetic algorithm (GA) for the parameter estimation of an agent-based model (ABM) of burglary. One of the main issues encountered in the implementation was the computation time required to run the algorithm. Nevertheless a set of preliminary results were obtained, which indicated that visibility is the most important parameter in the decision of whether to burgle a house while accessibility was the least important. Such tools may eventually provide the means to gain a greater understanding of the factors that determine criminological behaviour.

Agent-Based Modeling

Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.