Collaborative Research: High-Dimensional Spatial-Temporal Modeling and Inference for Large Multi-Source Environmental Monitoring Systems

合作研究：大型多源环境监测系统的高维时空建模与推理

• 批准号: 1916395
• 负责人: Andrew Finley
• 金额: $ 8万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916395&HistoricalAwards=false
• 关键词: Collaborative; Research; Dimensional; Spatial; Temporal

Remote sensing technologies and Geographic Information Systems continue to bring about dramatic developments in scientific discovery. Scientists in a variety of disciplines today have unprecedented access to massive spatial and temporal databases comprising high resolution remote sensed measurements. Statistical modeling and analysis for such data often entail reckoning with spatial associations and variations at multiple levels while attempting to recognize underlying patterns and potentially complex relationships among the scientific variables. Traditional statistical hypothesis testing is no longer adequate for these inferential objectives and statisticians are increasingly turning to multi-level or hierarchical modeling structures for analyzing complex spatial-temporal data. However, there continue to remain substantial computational bottlenecks as scientists encounter the data deluge in remote-sensed data that demand specialized "BIG DATA" technologies. The PIs will address these problems by developing probabilistic machine learning tools for spatial-temporal BIG DATA within the context of scientific advancements in forest structure, topography, and weather-related events (e.g., storms) that can have far-reaching public health, economic, environmental, and security implications. Several innovations in statistical and computational methods and related software development are envisioned. The proposed data products will offer quantification of forest damage/change and landslide risk assessment for Puerto Rico following hurricanes Irma and Maria. Key educational components include dissemination of proposed technologies across the scientific communities including data scientists, engineers, foresters, ecologists, and climate scientists. The PIs plan to train the next generation of data scientists through dissemination efforts for undergraduate and graduate students in STEM fields. The PIs will develop a statistical framework for executing elaborate case studies and data analysis on high-dimensional remotely sensed data, where "high dimension" alludes to one or all of a massive number of (i) spatial locations; (ii) time points; and (iii) responses or outcomes. The PIs will introduce massively scalable multivariate spatial process models within a rich Bayesian hierarchical framework to obtain fully model-based inference for the underlying data generating process. Innovative statistical methodologies are proposed to implement hierarchical models at scales involving tens of millions of spatial locations, thousands of time points and possibly hundreds of remote-sensed variables. The massive scalability of these models will be achieved through sparsity-inducing spatial-temporal processes and other graphical models, matrix-variate low-rank models, conjugate Bayesian distribution theory, and meta-learning paradigms using approximations of a collection of posterior distributions. Theoretical results that enhance current methods will be explored as will be several proposed case studies at hitherto unprecedented scales. The PIs will develop a full suite of spatial models in a wide variety of experiments involving massive data sets. Since massive data sets are where complex relationships can be detected effectively, the proposed methods are well-suited for modeling complex scientific phenomena. Key substantive inference and statistical quantification will be offered for forest damage/change and landslide risk assessment for Puerto Rico following hurricanes Irma and Maria. The PIs will provide probability-based uncertainty quantification and will substantially enhance the scientific community's understanding of storm-related damage assessment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

遥感技术和地理信息系统继续给科学发现带来巨大的发展。今天，各个学科的科学家都可以前所未有地访问由高分辨率遥感测量数据组成的海量空间和时间数据库。对这些数据的统计建模和分析往往需要在多个层面上计算空间关联和变化，同时试图认识科学变量之间的潜在模式和潜在复杂关系。传统的统计假设检验不再适合这些推理目标，统计学家越来越多地转向多层次或层次建模结构来分析复杂的时空数据。然而，仍然存在大量的计算瓶颈，因为科学家遇到了大量的遥感数据，需要专门的“大数据”技术。PI将通过在森林结构、地形和天气相关事件（例如，风暴），可能会产生深远的公共卫生，经济，环境和安全影响。设想在统计和计算方法以及相关软件开发方面的若干创新。拟议的数据产品将提供飓风“厄玛”和“玛丽亚”之后波多黎各森林损害/变化和滑坡风险评估的量化。关键的教育内容包括在科学界传播拟议的技术，包括数据科学家、工程师、林业工作者、生态学家和气候科学家。PI计划通过为STEM领域的本科生和研究生开展传播工作，培养下一代数据科学家。 项目执行人将制定一个统计框架，用于对高维遥感数据进行详细的案例研究和数据分析，其中“高维”指的是大量（一）空间位置;（二）时间点;和（三）反应或结果中的一个或全部。PI将在丰富的贝叶斯分层框架内引入大规模可扩展的多变量空间过程模型，以获得底层数据生成过程的完全基于模型的推理。提出了创新的统计方法，以实施涉及数千万个空间位置、数千个时间点和可能数百个遥感变量的等级模型。这些模型的大规模可扩展性将通过稀疏诱导时空过程和其他图形模型、矩阵变量低秩模型、共轭贝叶斯分布理论和使用后验分布集合的近似的元学习范式来实现。理论结果，提高目前的方法将被探讨，因为将在前所未有的规模几个拟议的案例研究。PI将在涉及大量数据集的各种实验中开发一整套空间模型。由于海量数据集是可以有效检测复杂关系的地方，因此所提出的方法非常适合对复杂的科学现象进行建模。将提供关键的实质性推理和统计量化的森林破坏/变化和滑坡风险评估波多黎各飓风后，厄玛和玛丽亚。PI将提供基于概率的不确定性量化，并将大大提高科学界对风暴相关损害评估的理解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

spNNGP R Package for Nearest Neighbor Gaussian Process Models

• DOI: 10.18637/jss.v103.i05
• 发表时间: 2022-07-01
• 期刊: JOURNAL OF STATISTICAL SOFTWARE
• 影响因子: 5.8
• 作者: Finley, Andrew O.;Datta, Abhirup;Banerjee, Sudipto
• 通讯作者: Banerjee, Sudipto

Joint species distribution models with imperfect detection for high‐dimensional spatial data

高维空间数据不完善检测的联合物种分布模型

• DOI: 10.1002/ecy.4137
• 发表时间: 2023
• 期刊: Ecology
• 影响因子: 4.8
• 作者: Doser, Jeffrey W.;Finley, Andrew O.;Banerjee, Sudipto
• 通讯作者: Banerjee, Sudipto

Trends in bird abundance differ among protected forests but not bird guilds

受保护森林之间的鸟类丰度趋势有所不同，但鸟类协会之间没有差异

• DOI: 10.1002/eap.2377
• 发表时间: 2021
• 期刊: Ecological Applications
• 影响因子: 5
• 作者: Doser, Jeffrey W.;Weed, Aaron S.;Zipkin, Elise F.;Miller, Kathryn M.;Finley, Andrew O.
• 通讯作者: Finley, Andrew O.

Conjugate sparse plus low rank models for efficient Bayesian interpolation of large spatial data

• DOI: 10.1002/env.2748
• 发表时间: 2022-08
• 期刊: Environmetrics
• 影响因子: 1.7
• 作者: Shinichiro Shirota;A. Finley;B. Cook;Sudipto Banerjee

Bayesian spatially varying coefficient models in the spBayes R package

spBayes R 包中的贝叶斯空间变化系数模型

• DOI: 10.1016/j.envsoft.2019.104608
• 发表时间: 2020
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: Finley, Andrew O.;Banerjee, Sudipto
• 通讯作者: Banerjee, Sudipto