Place-Based Simulation Modeling: Agent-Based Modeling and Virtual Environments

Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Power of Criminal Attractors: Modeling the Pull of Activity Nodes

The spatial distribution of crime has been a long-standing interest in the field of criminology. Research in this area has shown that activity nodes and travel paths are key components that help to define patterns of offending. Little research, however, has considered the influence of activity nodes on the spatial distribution of crimes in crime neutral areas - those where crimes are more haphazardly dispersed. Further, a review of the literature has revealed a lack of research in determining the relative strength of attraction that different types of activity nodes possess based on characteristics of criminal events in their immediate surrounds. In this paper we use offenders' home locations and the locations of their crimes to define directional and distance parameters. Using these parameters we apply mathematical structures to define rules by which different models may behave to investigate the influence of activity nodes on the spatial distribution of crimes in crime neutral areas. The findings suggest an increasing likelihood of crime as a function of geometric angle and distance from an offender's home location to the site of the criminal event. Implications of the results are discussed.Crime Attractor, Directionality of Crime, Mathematical Modeling, Computational Criminology

From individual characters to large crowds: augmenting the believability of open-world games through exploring social emotion in pedestrian groups

Crowds of non-player characters improve the game-play experiences of open-world video-games. Grouping is a common phenomenon of crowds and plays an important role in crowd behaviour. Recent crowd simulation research focuses on group modelling in pedestrian crowds and game-designers have argued that the design of non-player characters should capture and exploit the relationship between characters. The concepts of social groups and inter-character relationships are not new in social psychology, and on-going work addresses the social life of emotions and its behavioural consequences on individuals and groups alike. The aim of this paper is to provide an overview of current research in social psychology, and to use the findings as a source of inspiration to design a social network of non-player characters, with application to the problem of group modelling in simulated crowds in computer games

Crime Emergence and Simulation Modeling: Modeling Crime Space

This chapter explores several new modeling approaches and research findings, showing how they may be used to explore and enhance theory. There is a special emphasis on Target Choice Selection, focusing on Crime Pattern Theory and the Geometry of Crime (Brantingham and Brantingham, 1978a, 1984, 1991; Brantingham and Brantingham, 1981, 1993a, 2008). This exploration is described through a series of research examples and a case study of the target choice behavior of high repeat offenders. The goal is to explore the emergence of patterns better understood against the urban backcloths for high repeat offenders. Emphasis is in this case study is particularly placed on the structural backcloth but will be expanded in future studies to include other backcloth components such as the social, the cultural, the economic, and the derived vernacular architecture that combine with structural components to form neighborhoods

How Officers Create Guardianship: An Agent-based Model of Policing

Crime is a complex phenomenon, emerging from the interactions of offenders, victims, and their environment, and in particular from the presence or absence of capable guardians. Researchers have historically struggled to understand how police officers create guardianship. This presents a challenge because, in order to understand how to advise the police, researchers must have an understanding of how the current system works. The work presents an agent-based model that simulates the movement of police vehicles, using a record of real calls for service and real levels of police staffing in spatially explicit environments to emulate the demands on the police force. The GPS traces of the simulated officers are compared with real officer movement GPS data in order to assess the quality of the generated movement patterns. The model represents an improvement on existing standards of police simulation, and points the way toward more nuanced understandings of how police officers influence the criminological environment

Developing a conceptual model of marine farming in New Zealand

Survey and Geographic Information System (GIS) data analysis describes the relative influence of biophysical and human variables on site choices made by marine farmers in New Zealand. Community conflicts have grown in importance in determining farm location and different government planning strategies leave distinct signature patterns. Recent legislation empowers local governments to choose among three strategies for future regional aquaculture development. This paper suggests each strategy could result in different spatial outcomes. Simulation modelling of the type described here can provide a better understanding of farmer responses to management approaches and the range of futures that could result from planning choices made today