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（通过应用程序提供）：在环境（例如空气污染，热浪）和健康结果（例如哮喘，心脏病，癌症）之间进行任何关联和/或因果关系关系的关键组成部分是在相同地理量表和临时分辨率和临时分辨率上的准确暴露模型。人类暴露的计算对于癌症来说尤其具有挑战性，因为它们可能需要数年或数十年的发展，尤其是在污染物含量低的情况下。在这种情况下 受试者；因此，需要通过时空（ST）插值填充数据差距。令人惊讶的是，目前尚无商业软件用于对时空数据的地理处理处理，包括在意外时间和位置进行插值。该SBIR项目正在开发第一个为地理插值和不确定性建模提供工具的商业软件。该研究产品将是由ESRI合作伙伴Biomedware开发的桌面时空可视化核心的独立模块。 This software package will offer a comprehensive suite for: 1) the computation and advisor-guided modeling of ST variousograms, 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通过暴露重建所附加的不确定性传播。这些工具将适用于分析健康科学以外的数据，例如遥感，核环境工程或气候变化，从而大大拓宽了最终产品的商业市场。该项目将实现四个目标：扩展第I阶段开发的统计方法：1）通过不同的采样密度和临时频率测量了多个相关属性（例如空气污染物）的情况，这将需要开发st cokrig and s cokrig tempory and Inter Inter Inter Interive Interive and cromentions（2）的临时方法，并在2）中实现了（2）的构图（2）的构图（2），并进一步构图（2）构图（2）的构图（2）构图（2）的构图（2）构图（2）的构图（2）进化（进一步）的模型（通过回归分析。构建一个功能齐全且经过测试的ST插值和仿真模块，准备商业分布。进行一项可用性研究，以根据NIH可用性协议评估原型的设计。在几项流行病学研究中应用该软件来证明该方法及其独特的好处，包括空气污染对出生结果和城市极端热量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对地列为时空现象和计算估计以及相关的不确定性对环境流行病学研究最相关的相关不确定性的能力。