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

Abstract

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.

An agent‐based model of police corruption

“…Scholars use agent‐based models to study a wide range of phenomena, including flows and evacuations (Helbing, Farkas & Vicsek, 2000), market dynamics (Palmer et al., 1994), organisations (Bonabeau, 2000, 2002; Prietula, Gasser & Carley, 1998). Scholars in criminal justice and criminology also have used agent‐based models to study drug market dynamics (Dray et al., 2008), street robbery (Groff, 2007, 2008; Groff & Mazerolle, 2008; Liu et al., 2005; Wang, Liu & Eck, 2008), burglary (Birks, Donkin & Wellsmith, 2008; Birks, Townsley & Stewart, 2012; Hayslett‐McCall et al., 2008; Johnson, 2008; Malleson, Heppenstall & Crooks, 2018), victimisation risk (Park et al., 2011), cybercrime (Gunderson & Brown, 2000), organised crime (Nardin, Székely & Andrighetto, 2017), the efficacy of law enforcement strategies (Dray et al., 2008; Groff & Birks, 2008; Zhang & Brown, 2013), and race gap in incarceration rates (Hawdon et al., 2017; Lum et al., 2014).…”

“stppSim”: A Novel Analytical Tool for Creating Synthetic Spatio-Temporal Point Data

“…In particular, the RAT describes the conditions that have to be met while the offender moves and interacts with the environment. It states that a crime occurs when three elements, namely; motivated offenders, suitable targets, and the absence of capable guardians, converge in space and time: The use of RAT and other related theories for the simulation studies of crime can be found in many existing literature [38] [39] [40].…”

Guest editorial for spatial agent-based models: current practices and future trends

“…This rise in popularity has been aided by technological advances such as the emergence of accessible development platforms, greater processing power and storage capacity [6]. However, it could also be argued that the proliferation and availability of new and novel forms of micro-level data have equally contributed to its uptake [7].…”