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Abstract：
Collective efficacy-the capacity of communities to exert social control toward the realization of their shared goals-is a foundational concept in the urban sociology and neighborhood effects literature. Traditionally, empirical studies of collective efficacy use large sample surveys to estimate collective efficacy of different neighborhoods within an urban setting. Such studies have demonstrated an association between collective efficacy and local variation in community violence, educational achievement, and health. Unlike traditional collective efficacy measurement strategies, the Adolescent Health and Development in Context (AHDC) Study implemented a new approach, obtaining spatially-referenced, place-based ratings of collective efficacy from a representative sample of individuals residing in Columbus, OH. In this paper we introduce a novel nonstationary spatial model for interpolation of the AHDC collective efficacy ratings across the study area, which leverages administrative data on land use. Our constructive model specification strategy involves dimension expansion of a latent spatial process and the use of a filter defined by the land-use partition of the study region to connect the latent multivariate spatial process to the observed ordinal ratings of collective efficacy. Careful consideration is given to the issues of parameter identifiability, computational efficiency of an MCMC algorithm for model fitting, and fine-scale spatial prediction of collective efficacy.
摘要：
集体效能-社区对实现其共同目标施加社会控制的能力-是城市社会学和邻里效应文献中的基本概念。传统上,集体效能的实证研究使用大样本调查来估计城市环境中不同社区的集体效能。这些研究表明，集体效能与社区暴力的局部差异之间存在关联，教育成就,和健康。与传统的集体效能测量策略不同，青少年健康与发展背景(AHDC)研究实施了一种新方法，获得空间参考，来自居住在哥伦布的个人代表性样本的基于地点的集体效能评级，Oh.在本文中，我们介绍了一种新颖的非平稳空间模型，用于对整个研究区域的AHDC集体功效等级进行插值，利用土地利用的行政数据。我们的建设性模型规范策略涉及潜在空间过程的维度扩展，以及使用由研究区域的土地利用分区定义的过滤器，以将潜在的多元空间过程与观察到的集体功效的顺序等级联系起来。仔细考虑了参数可识别性的问题，模型拟合的MCMC算法的计算效率，集体效能的精细尺度空间预测。
