ABSTRACT 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.

中文翻译：

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社会经济劣势的空间结构：贝叶斯多元空间因素分析

摘要 邻里社会经济劣势是衡量社会空间不平等的一种指标，已被证明与各种社会、经济和健康结果有关。探索劣势的局部模式的现有研究通常构建综合指数，总结社会地位的多个维度之间的相互作用，但没有考虑劣势是否以及如何表现出空间结构。本研究应用贝叶斯多元因子分析建模方法来检验加拿大多伦多社会经济劣势的空间结构。社会经济劣势被建模为基于区域的综合指数，与衡量低收入、低教育程度和低职业地位的三个变量相关，并比较了一系列对劣势空间结构具有不同假设的模型。最佳拟合模型表明，低收入家庭的盛行率与劣势的正相关最强，在解释劣势的空间结构时，空间聚类的重要性是空间异质性的三倍。讨论了这项研究对分析多元空间数据和理解劣势的多个维度之间的相互作用的影响。