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.

项目摘要/摘要在过去的十年中，对时空的统计建模和分析激发了兴趣观察到不同的科学兴趣变量的数据和支持的问题不对对准问题在不同的尺度上，使它们很难被建模。这在环境中特别重要公共卫生，在攀爬时，可能基于监视数据网络的数据的暴露数据可能是基于数据的公共卫生数据通常可作为来自数值模型的栅格化输出。情况进一步复杂通过我们将这些因素与健康结果相关联的目标（例如，疾病事件，住院，死亡率等），公共卫生来源报告为区域的汇总数据，而不是点。此外，今天的公共卫生研究人员通常会遇到表现出高维的数据集空间未对准或支撑式变更，其中“维度”是指以下一个或全部：（a）空间单元的数量（例如，地理参考的坐标），（b）临时单元的数量（时间观察到变量的点，以及（c）结果和其他变量的数量 Studiod。我们提出了一项多功能的易于实施和创新的贝叶斯统计方法的集合与适当的软件一起，这将提供更全面和统计上可靠的映射以及在高维设置中未对准时空数据的分析。我们的方法和软件将帮助空间分析师建立健康成果以及与环境和气候有关的关系我们的传播努力将使我们的方法论向遥远的健康受众和环保研究人员和管理员比目前可以访问的人。