Bayesian Methods for Socio-Spatial Point Patterns and Networks

该项目旨在为空间统计、网络分析、贝叶斯参数和非参数建模等领域的统计科学做出贡献。研究方法方面的基础是一个特定的应用领域，背景效应和暴露分析研究。将发展统计方法，以提高利用最先进的大规模抽样调查数据描述人类活动模式和社会网络的能力。具体贡献包括基于相关非参数潜在强度函数的多元空间点模式的新型贝叶斯随机模型的发展。还将为空间参照的社会网络开发统计模型；这些模型描述了基于地理空间随机变形中的接近性的关系关系。这两类模型都将从理论和实证的角度进行深入研究。这个项目的统计研究重点深深植根于社会、地理和健康科学之间的一个应用领域。背景效应研究的目的是了解社会和环境暴露对个体结果的影响。由于现有数据源固有的复杂性，以及目前正在使用最先进的基于全球定位系统的技术收集的数据，量化个人所面临的多种环境影响的努力是有限的。该项目旨在通过引入新颖的统计方法来克服这些数据挑战，并有望对统计领域以及激励应用领域做出普遍贡献。该项目的教育和培训部分将反映这种多学科研究环境。

This project seeks to make contributions to statistical science in the areas of spatial statistics, network analysis, and Bayesian parametric and nonparametric modeling. Underlying the methodological aspects of the research is a particular area of application, contextual effects and exposure analysis research. Statistical methodology will be developed to improve the ability to characterize human activity patterns and social networks using state-of-the-art large-scale sample survey data. Specific contributions include the development of novel Bayesian stochastic models for multivariate spatial point patterns based on dependent nonparametric latent intensity functions. Statistical models will also be developed for spatially-referenced social networks; these models describe relational ties based on proximity in a random deformation of geographic space. Both classes of models will be thoroughly studied from both a theoretical and empirical perspective. The statistical research focus of this project is deeply rooted in an area of application bridging the social, geographic, and health sciences. Research on contextual effects aims to understand the consequences of social and environmental exposures on individual outcomes. Efforts to quantify the influence of the multiple contexts to which individuals are exposed are limited due the inherent complexities of existing data sources, as well as those currently being collected using state-of-the-art GPS-based technologies. This project aims to overcome these data challenges by introducing novel statistical methods and is expected to contribute generally to the field of statistics, as well as the motivating area of application. Educational and training components of the project will reflect this multidisciplinary research setting.