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：集体效能是城市社会学和邻里效应文献中的基础概念，指社区施展社会控制实现共同目标的能力。传统上，集体效能的实证研究使用大规模问卷调查来估计城市中不同社区的集体效能。这些研究已经证明，集体效能和社区暴力、教育成就和健康的本地差异之间存在关联。与传统的集体效能测量策略不同，青少年健康和发展在环境中 (AHDC) 研究采用了一种新方法，从生活在俄亥俄州哥伦布的代表性个体中获取具有空间参考的基于地点的集体效能评级。在本文中，我们介绍了一种新颖的非平稳空间模型，用于插值 AHDC 中的集体效能评级跨研究区域，该模型利用土地使用的行政数据。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和由研究区域的土地使用分区定义的过滤器的使用，以将潜在的多元空间过程连接到观察到的集体效能的序数评分。对参数可识别性、MCMC 算法的计算效率和集体效能的细粒度空间预测等问题进行了认真考虑。