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

项目摘要/摘要 在过去的十年里，人们对时空的统计建模和分析产生了爆炸性的兴趣 未对齐的数据和支持度变化问题，其中观察到不同的科学fi感兴趣的变量 在不同的规模，使他们很难被一致地建模fi邪教。这一点在环境方面尤其相关 公共卫生，其中暴露数据可能基于来自监测数据网络的数据，而气候 数据通常以数值模型的栅格化输出形式提供。情况进一步复杂化 通过我们将这些因素与健康结果相关联的目标(例如，发病率、住院率、 死亡率等)，这些数据由公共卫生来源报告为各地区的汇总数据，而不是 积分。此外，公共卫生研究人员今天经常遇到显示高维数据的数据集 空间错位或支承改变，其中“维度”是指以下一项或全部：(A) 空间单位数(例如，地理参考坐标)，(B)时间单位数(时间 点)观察到变量，以及(C)结果数和其他变量 学习。我们提出了一组易于实现和创新的贝叶斯统计方法 与适当的软件相结合，将提供更全面和统计可靠的地图 以及对高维环境中未对准的时空数据的分析。我们的方法和软件将 帮助空间分析人员建立健康结果与环境和气候相关的关系 预测者。我们的传播努力将把我们的方法传递给更广泛的健康和 环境研究人员和管理人员，而不是目前可以访问的。

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.