Modeling Social Ties and Household Mobility

Authored by Sara S. Metcalf

Date Published: 2014-01-02

DOI: 10.1080/00045608.2013.846152

Sponsors: United States National Institutes of Health (NIH) United States National Science Foundation (NSF)

Platforms: AnyLogic

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: http://www.acsu.buffalo.edu/~smetcalf/resources/ModelCode.htm

Abstract

Underlying the aggregate phenomena of persistent problems such as urban sprawl and spatial socioeconomic disparity is the individual choice of where to live. This study develops an agent-based model to simulate social and economic influences on neighborhood choice. With Danville, Illinois, as an empirical context, a pattern-oriented approach is employed to examine the role of social ties in shaping intraurban household mobility. In the model, household agents decide whether and where to relocate within the community based on factors such as neighborhood attractiveness, affordability, and the density of a household's social network in the prospective block group. Social network and neighborhood choices are encoded with logit utility functions. The relative influence of factors affecting the formation of social ties in the simulated social network, such as geographic proximity, similarity of income, race, and presence of children, are adjusted using parameter variation to create alternative model settings. Simulated migration patterns resulting from different network and neighborhood choice coefficients are compared with observed migration patterns over a two-year period. Based on 1,000 simulation experiments, a regression of homeowner migration error (the difference between simulated and observed migration) relative to the parameter settings revealed components of social network choice such as income, race, and probability of local ties to be significant in matching observed migration patterns. A nonlinear effect of simulated social networks on household mobility and thus migration error was exhibited in this study.

Tags

Social networks Agent-based modeling Pattern-oriented modeling neighborhood choice