Characterizing Location Preferences in an Exurban Population: Implications for Agent-Based Modeling

Abstract

Powerful computational tools are becoming available to represent the behavior of complex systems. Agent-based modeling, in particular, facilitates an examination of the system-level outcomes of the heterogeneous actions of a set of heterogeneous agents: for example, patterns of land-use and land-cover change, such as urban sprawl as a result of residential location decisions. These new tools create new demands for data, and empirical studies of the selection behavior of residents. Using resident responses from the 2001 Detroit Area Study survey, we compared two alternative approaches to characterizing the heterogeneous preferences of agents; both based on a factor analysis of resident responses to questions about their reasons for moving to their current location. We used cluster analysis to identify how many and what types of residents there are, grouped by similar preferences. We also evaluated the relationships between socioeconomic and demographic characteristics and location preferences using regression trees, and evaluated the fit of the relationship to determine the degree to which socioeconomic characteristics predict preferences. The results showed that the preferences of resident exurbans of single-family homes in the Detroit metropolitan area were heterogeneous and that distinct preference groups do exist in resident populations, but are not well characterized on the basis of simple socioeconomic and demographic variables. We conclude that, given the heterogeneous nature of preferences and a relatively limited number of preference groupings observed in the survey respondents, agent-based models simulating resident behavior should reflect this diversity in the population and incorporate distinct agent classes of empirically derived preference distributions.