Hierarchical Modeling and Analysis for Large Spatially and Temporally Misaligned Data in Environmental Health Applications

项目总结/摘要 在过去的十年里，人们对时空统计建模和分析的兴趣激增。 不一致的数据和支持的变化问题，其中观察到不同的科学兴趣变量 在不同的尺度上，使它们难以连贯地建模。这一点在环境领域尤其重要。 公共卫生，其中接触数据可能基于监测数据网络的数据，而气候 数据通常作为来自数值模型的光栅化输出而可获得。局势进一步复杂化 我们的目标是将这些因素与健康结果（如疾病发生率，住院， 死亡率等），这些数据由公共卫生来源报告为各区域的汇总数据，而不是 点此外，公共卫生研究人员今天经常遇到表现出高维数据集， 空间错位或支撑变化，其中“尺寸”是指以下一项或全部： 空间单元的数量（例如，地理参考坐标），（B）时间单位（时间 点）的变量已被观察到，（c）的结果和其他变量的数量， 研究了我们提出了一个通用的集合，易于实施和创新的贝叶斯统计方法 该系统与适当软件配合使用，将提供更全面和统计上更可靠的绘图 以及分析高维设置中的未对准时空数据。我们的方法和软件将 帮助空间分析师建立健康结果与金属和气候相关之间的关系 预测器我们的传播工作将把我们的方法提供给更广泛的健康和 环境研究人员和管理人员比目前访问。

Project Summary/Abstract The last decade has seen an explosion of interest in statistical modeling and analysis of spatiotemporally misaligned data and change-of-support problems, where different variables of scientific interest are observed at disparate scales making them difficult to be coherently modeled. This is especially relevant in environmental public health, where exposure data may be based upon data from monitoring data networks, while climate data are usually available as rasterized outputs from numerical models. The situation is further compounded by our objective of associating these factors with health outcomes (e.g. disease incidence, hospitalizations, mortality and so on), which are reported by public health sources as aggregated data over regions rather than at points. Furthermore, public health researchers today routinely encounter datasets exhibiting high-dimensional spatial misalignment or change-of-support, where “dimension” refers to one or all of the following: (a) the number of spatial units (e.g., geographically referenced coordinates), (b) the number of temporal units (time points) at which the variables have been observed, and (c) the number of outcomes and other variables being studied. We propose a versatile collection of easily implementable and innovative Bayesian statistical methods that, in conjunction with appropriate software, will offer more comprehensive and statistically reliable mapping and analysis for misaligned spatiotemporal data in high-dimensional settings. Our methods and software will help spatial analysts to establish relationships among health outcomes and environmetal and climate-related predictors. Our dissemination efforts will deliver our methodology to a far broader audience of health and environmental researchers and administrators than is currently accessible.