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）：在对环境（例如空气污染、热浪）和健康结果（例如哮喘、心脏病、癌症）之间的关联和/或因果关系进行任何调查时，一个关键组成部分是在与健康结果相同的地理尺度和时间分辨率下获得准确的暴露模型。计算人类接触量对癌症来说尤其具有挑战性，因为癌症可能需要数年或数十年的时间才能发展，特别是在低水平污染物的情况下。在这种情况下，污染物水平很少可用于每个地点和时间间隔访问的 因此，需要通过时空插值填补数据空白。令人惊讶的是，目前还没有用于时空数据地质统计处理的商业软件，包括在未监测的时间和地点进行插值。该SBIR项目正在开发第一个商业软件，为地质统计ST插值和不确定性建模提供工具。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。该软件包将提供一个全面的套件，用于：1）ST变差函数的计算和预测器引导的建模，2）与健康结果相同尺度的暴露数据的ST预测和随机建模（即总体或个人层面），并使用任何可用的辅助信息（如遥感、土地利用回归模型、空气扩散模型、其他空气污染物）; 3）通过暴露重建对估计值所附的不确定性进行量化和基于蒙特-卡罗的传播。这些工具将适用于分析健康科学以外的数据，如遥感、核环境工程或气候变化，从而大大拓宽最终产品的商业市场。该项目将实现四个目标：1）多个相关属性的情况（例如空气污染物），这将需要开发ST协同克里金法，并测试其在第一阶段实施的克里金法上的性能，以及2）通过回归分析对曝光不确定性（曝光测量误差）进行随机建模和传播。构建一个功能齐全且经过测试的ST插值和仿真模块，可用于商业分发。根据NIH可用性方案进行可用性研究，以评估原型的设计。应用该软件在几项流行病学研究中展示该方法及其独特的益处，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对地理统计学时空现象进行建模的能力，并在与环境流行病学研究最相关的尺度（例如点位置、普查区一级）上计算估计值和相关的不确定性。