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为科学发现带来巨大发展。如今，各个学科的科学家可以前所未有地访问包含高分辨率遥感测量结果的海量空间和时间数据库。此类数据的统计建模和分析通常需要考虑多个层面的空间关联和变化，同时尝试识别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足以满足这些推理目标，统计学家越来越多地转向多层次或分层建模结构来分析复杂的时空数据。然而，随着科学家们遇到需要专门的“大数据”技术的遥感数据中的数据洪流，仍然存在巨大的计算瓶颈。 PI 将在森林结构、地形和天气相关事件（例如风暴）科学进步的背景下，通过开发用于时空大数据的概率机器学习工具来解决这些问题，这些事件可能对公共卫生、经济产生深远影响。 、环境和安全影响。预计统计和计算方法以及相关软件开发方面的几项创新。拟议的数据产品将为波多黎各飓风艾尔玛和玛丽亚之后的森林破坏/变化和山体滑坡风险评估提供量化。关键的教育内容包括在科学界（包括数据科学家、工程师、林务员、生态学家和气候科学家）传播拟议技术。 PI 计划通过针对 STEM 领域的本科生和研究生的传播工作来培训下一代数据科学家。 PI 将开发一个统计框架，用于对高维遥感数据执行详尽的案例研究和数据分析，其中“高维”指的是大量 (i) 空间位置中的一个或全部； (ii) 时间点； (iii) 回应或结果。 PI 将在丰富的贝叶斯分层框架内引入大规模可扩展的多元空间过程模型，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来实现涉及数千万个空间位置、数千个时间点以及可能数百个遥感变量的规模的分层模型。这些模型的大规模可扩展性将通过稀疏性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以及使用后验分布集合近似的元学习范例来实现。将探索增强当前方法的理论结果，以及迄今为止规模空前的几个拟议案例研究。 PI 将在涉及海量数据集的各种实验中开发一整套空间模型。由于海量数据集可以有效地检测复杂的关系，因此所提出的方法非常适合对复杂的科学现象进行建模。将为波多黎各飓风艾尔玛和玛丽亚之后的森林破坏/变化和山体滑坡风险评估提供关键的实质性推论和统计量化。 PI 将提供基于概率的不确定性量化，并将大大增强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，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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: 2021-09
• 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.

spNNGP R Package for Nearest Neighbor Gaussian Process Models

用于最近邻高斯过程模型的 spNNGP R 包

• DOI: 10.18637/jss.v103.i05
• 发表时间: 2022-01
• 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

Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains

通过分区域上的网格高斯过程进行高度可扩展的贝叶斯地统计建模

• DOI: 10.1080/01621459.2020.1833889
• 发表时间: 2020-03-25
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: M. Peruzzi;Sudipto Banerjee;A. Finley
• 通讯作者: A. Finley

Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes

贝叶斯最近邻高斯过程的高效算法

• DOI: 10.1080/10618600.2018.1537924
• 发表时间: 2017-02-01
• 期刊: Journal of Computational and Graphical Statistics
• 影响因子: 2.4
• 作者: A. Finley;A. Datta;B. Cook;D. Morton;H. Andersen;Sudipto Banerjee
• 通讯作者: Sudipto Banerjee

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: 2023-07
• 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