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

环境健康应用中大型时空错位数据的分层建模和分析

基本信息

批准号：
10094059
负责人：
Sudipto Banerjee
金额：
$ 33.93万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-05-01 至 2023-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10094059
关键词：
Accounting Administrator Algorithms Area Bayesian Method Bayesian Modeling Case Study Climate Collection Complex Computer software Computing Methodologies Data Data Aggregation Data Set Databases Decision Making Development Dimensions Disease Effectiveness Environment Environmental Exposure Environmental Health Exhibits Explosion Funding Geographic Information Systems Geography Health Healthcare Systems Hospitalization Human Incidence Joints Link Markov Chains Markov chain Monte Carlo methodology Measures Methodology Methods Modeling Monitor Outcome Output Patients Policy Maker Process Public Health Qualitative Methods Reporting Research Personnel Resolution Societies Source Statistical Data Interpretation Statistical Methods Statistical Models Stochastic Processes Technology Time Touch sensation Uncertainty Weather base climate data computerized design experimental study flexibility health application high dimensionality improved indexing innovation interest mortality pollutant public health research research and development scale up simulation sociodemographics socioeconomics spatiotemporal user friendly software user-friendly

项目摘要

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.

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，以及（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环境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。