The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis

Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low-educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed.

Automated summary

The study examines the spatial structure of neighborhood socioeconomic disadvantage in Toronto, Canada. It shows that the prevalence of low-income households has the strongest positive association with disadvantage and spatial clustering is more important than spatial heterogeneity in explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and understanding the interactions amongst multiple dimensions of disadvantage are discussed.