Geostatistical software for space-time interpolation and uncertainty modeling

用于时空插值和不确定性建模的地统计软件

• 批准号: 9138888
• 负责人: PIERRE E GOOVAERTS
• 金额: $ 42.87万
• 依托单位: BIOMEDWARE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9138888
• 关键词: Air; Air Pollutants; Air Pollution; Algorithms; Anisotropy; Asthma; Back; Birth; Cardiovascular system; Censuses; Child; Cities; Communities; Computer software; Data; Data Analyses; Data Set; Development; Environment; Environmental Engineering technology; Environmental Exposure; Environmental Health; Evaluation; Feedback; Frequencies; Geography; Health; Health Sciences; Heart Diseases; Heat Waves; Imagery; Individual; Investigation; Location; Malignant Neoplasms; Measurement; Measures; Methodology; Methods; Mexico; Michigan; Modeling; Nuclear; Outcome; Patients; Pattern; Performance; Phase; Pilot Projects; Process; Protocols documentation; Public Health; Regression Analysis; Research; Research Personnel; Sampling; Small Business Innovation Research Grant; Space Models; Test Result; Testing; Time; Time trend; Uncertainty; United States National Institutes of Health; Universities; Visit; base; climate change; density; design; epidemiology study; exposed human population; extreme heat; improved; innovation; land use; mortality; multithreading; pollutant; prototype; public health relevance; reconstruction; remote sensing; respiratory; simulation; sound; temporal measurement; time interval; tool; usability; user-friendly

 DESCRIPTION (provided by applicant): A key component in any investigation of association and/or cause-effect relationships between the environment (e.g. air pollution, heat waves) and health outcomes (e.g. asthma, heart disease, cancer) is the availability of accurate models of exposure at the same geographical scale and temporal resolution as the health outcomes. The computation of human exposure is particularly challenging for cancers since they may take years or decades to develop, especially in presence of low level of contaminants. In this situation pollutant levels are rarely available for every location and time interval visited by the subjects; therefore data gaps need to be filled-in through space-time (ST) interpolation. Surprisingly, there is currently no commercial software for the geostatistical treatment of space-time data, including the interpolation at unmonitored times and locations. This SBIR project is developing the first commercial software to offer tools for geostatistical ST interpolation and modeling of uncertainty. The research product will be a stand-alone module into the desktop space-time visualization core developed by BioMedware, an Esri partner. This software package will offer a comprehensive suite for: 1) the computation and advisor-guided modeling of ST variograms, 2) the ST prediction and stochastic modeling of exposure data at the same scale as health outcome (i.e. aggregated or individual-level) and using any secondary information available (e.g. remote sensing, land-use regression model, air dispersion model, other air pollutants), and 3) the quantification and Monte-Carlo based propagation of uncertainty attached to estimates through exposure reconstruction. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, nuclear environmental engineering or climate change, broadening significantly the commercial market for the end product. This project will accomplish four aims:  Expand the statistical methodology developed in Phase I to tackle: 1) the case where multiple correlated attributes (e.g. air pollutants) were measured with different sampling densities and temporal frequencies, which will require developing ST cokriging and testing its performance over the kriging approach implemented in Phase I, and 2) stochastic modeling and propagation of exposure uncertainty (exposure measurement errors) through regression analysis. Build a fully functional and tested ST interpolation and simulation module ready for commercial distribution. Conduct a usability study to evaluate the design of the prototype based on NIH usability protocols. Apply the software to demonstrate the approach and its unique benefits in several epidemiological studies, including impact of air pollution on birth outcomes and urban extreme heat on cardiovascular mortality. These technologic, scientific and commercial innovations will revolutionize our ability to model geostatistically space-time phenomena and compute estimates and the associated uncertainty at the scale (e.g. point location, census-tract level) the most relevant for environmental epidemiological studies.

 描述（由申请人提供）：环境（例如空气污染、热浪）与健康结果（例如哮喘、心脏病、癌症）之间的关联和/或因果关系的任何调查的关键组成部分是提供准确的数据与健康结果相同的地理尺度和时间分辨率的暴露模型对于癌症来说，计算人类暴露尤其具有挑战性，因为它们可能需要数年或数十年的时间才能发展，特别是在污染物水平较低的情况下。水平很少适用于访问者访问的每个位置和时间间隔 令人惊讶的是，目前还没有用于时空数据的地统计处理的商业软件，包括在不受监控的时间和地点进行插值。正在开发第一个商业软件，提供地统计 ST 插值和不确定性建模工具。该研究产品将成为 Esri 合作伙伴 BioMedware 开发的桌面时空可视化核心的独立模块。该软件包将提供一套全面的套件，用于：1) ST 变异函数的计算和顾问指导建模，2) 与健康结果相同规模的暴露数据的 ST 预测和随机建模（即聚合或个体水平）以及使用任何可用的二手信息（例如遥感、土地利用回归模型、空气扩散模型、其他空气污染物），以及 3）通过量化和基于蒙特卡罗的估计来传播不确定性这些工具将适用于健康科学以外的数据分析，例如遥感、核环境工程或气候变化，从而显着拓宽最终产品的商业市场。该项目将实现四个目标：第一阶段开发的统计方法用于解决：1）用不同的采样密度和时间频率测量多个相关属性（例如空气污染物）的情况，这将需要开发 ST 协同克里金法并测试其相对于第一阶段实施的克里金方法的性能, 2) 通过回归分析构建功能齐全且经过测试的 ST 插值和模拟模块，以进行可用性研究，以评估基于 NIH 可用性的原型设计。应用该软件在多项流行病学研究中展示该方法及其独特的优势，包括空气污染对出生结果的影响和城市极端高温对心血管死亡率的影响。这些技术、科学和商业创新将彻底改变我们的建模能力。地统计时空现象并计算与环境流行病学研究最相关的规模（例如点位置、人口普查区域水平）的估计值和相关不确定性。