‘Situating' Simulation to Model Human Spatio-Temporal Behavior Elizabeth Groff My research in spatially-aware agent-based modeling is underpinned by an on-going interest in human spatio-temporal behavior and the significant role of the urban backcloth in shaping that behavior. The importance of incorporating space and time into research on human behavior has long been recognized (Chorley & Haggett, 1967; Hagerstrand, 1973). Addressing spatio-temporal interactions among individuals and their environments is a challenging undertaking with two major hurdles. First, individual-level decision making is complex. The decisions of individuals are made based on a multitude of factors both internal and external. Those decisions are influenced by contextual factors. To add complication, human activity occurs in an ever changing environment and is influenced both by previous decisions and future expectations. Second, data describing the characteristics and behavior of individuals is required. Although there has been tremendous growth in the availability of micro-level data describing places, data regarding the daily activities of individuals (e.g. the type, location, and duration of activity) remains sparse. While these data are essential to modeling the spatio-temporal convergence and interaction of individuals, they are unlikely to become available due to privacy concerns (O'Sullivan, 2004). Many social phenomena have a spatio-temporal dimension and arise from dynamic decisions made by individuals. One example is the current obesity crisis. Individual decisions about physical activity (e.g., whether to walk or drive etc.) are influenced by opportunities available. Another example is that of crime events. The convergence of offenders and victims in space-time is the influenced by the transportation system, the distribution of opportunities for housing, employment, etc., and temporal constraints. Once convergence occurs, the decision whether or not to commit a crime is driven by both individual-level and situational factors. In order to model the dynamic, individual-level interactions across time and space that are present in social phenomena some researchers have begun to use agent-based modeling. But these models rely on artificial landscapes which are unable to adequately take into account the environment in which humans move and interact. My recent research on simulating street robbery integrates the temporal and individual-level

1 modeling capabilities of ABM with the spatial capacity of geographic information systems (GIS). Specifically, I examine how the routine activities of individuals bring them into contact with other individuals in particular settings and at particular times. To enable the exploration of the role of geography, I implement three agent-based/GIS models of street robbery. In all three models the agents move along the street network of Seattle, Washington. In order to separate out the effects of temporal and spatial constraints on crime patterns, the agents in one model have no constraints, in another they have temporal constraints, and in the final one they have both temporal and spatial constraints. Agents are assigned activity spaces which reflect the distribution of population, employment, retail, services, and recreation in Seattle. Results show that the addition of temporal and spatial constraints to activity spaces both individually and collectively change the incidence and pattern of street robbery events. In sum, existing research suggests that simulation modeling which accounts for space as well as time is a promising methodology to examine how macro level trends emerge from individual behavior. But there are significant challenges that remain. One challenge is the generation and representation of agent spatio-temporal activities. Consider an artificial world in which the agents have core places that they visit each day (e.g. their home, workplace, school etc.) and secondary places they visit often but not daily. Agents can also discover new places that may or may not become part of the agent’s activity space. In this framework, the activity spaces of agents would emerge from the simulation. Another major challenge is how to evaluate the validity of findings from spatially-aware simulation models. Dialogue with other research communities such as the one interested in the representational and computational aspects of complex dynamic systems is necessary to meet these and other challenges.

REFERENCES: Chorley, R. J., & Haggett, P. (Eds.). (1967). Models in Geography. London: Methuen & Co. Hagerstrand, T. (1973). The Domain of Human Geography. In R. J. Chorley (Ed.), Directions in Geography (pp. 67-87). London: Methuen. O'Sullivan, D. (2004). Too Much of the Wrong Kind of Data: Implications for the Practice of Micro-Scale Modeling. In M. F. Goodchild & D. G. Janelle (Eds.), Spatially Integrated Social Science (pp. 95-107). New York: Oxford University Press.

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