Previous studies of ethnic residential segregation within urban environments have suggested several factors as potential determinants of the phenomenon. Physical characteristics of the urban environment, demographic and socioeconomic characteristics, preferences for neighborhood composition, and discrimination have all been identified as playing a major role in determining the spatial distribution of ethnic groups within modern cities. However, few studies have attempted to explicitly identify the manner in which residential patterns emerge from the interaction of these factors in field settings. Recently, important strides have been made in this direction by researchers using agent-based and cellular automata models; unfortunately, these efforts have been hampered by a lack of inferential tools to connect theoretical models with extant data.
Using a recently developed statistical framework based on discrete exponential family models that bridges this "inferential gap," this project addresses the following questions: 1. What are, in general, the conditions under which residential systems become segregated? 2. Given information on the current configuration of a residential system, how can we plausibly identify the mechanisms that could have led to that particular configuration? 3. How do metropolitan areas across the United States differ in terms of the mechanisms that drive residential settlement?
I first explore the circumstances in which settlement processes lead to segregation through simulations of an artificial residential system with two racial/ethnic groups, focusing in particular on in-group preference (homophily) and outgroup avoidance (xenophobia). I then turn to a sample of metropolitan areas and seek to identify the types of processes that could have led to their present residential system configuration, as captured by the 2000 U.S. census. I discuss the contribution of the project to our understanding of residential settlement and segregation and suggest some directions for future work.
