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.

翻译：集体效能——即社区为实现共同目标而施加社会控制的能力——是城市社会学与邻里效应研究中的基础概念。传统上，针对集体效能的实证研究采用大样本调查，用以估算城市环境中不同社区的集体效能水平。此类研究已表明，集体效能与社区暴力、教育成就及健康状况的局部差异之间存在关联。与传统集体效能测量策略不同，“青少年健康与发展情境研究”（AHDC）采用了一种新方法，通过对俄亥俄州哥伦布市居民的代表性样本进行空间参考位置评估来获取集体效能量值。本文提出了一种新颖的非平稳空间模型，用于对研究区域内的AHDC集体效能量值进行插值，该模型利用了土地利用的行政管理数据。我们的构造性模型规范策略涉及对潜在空间过程进行维度扩展，并利用研究区域的土地利用分区所定义的过滤器，将潜在多变量空间过程与观测到的集体效能有序评级联系起来。本研究重点考虑了参数可识别性、用于模型拟合的MCMC算法的计算效率，以及集体效能的精细尺度空间预测等问题。