Geostatistical software for space-time interpolation and uncertainty modeling

 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提供准确的暴露模型。人体接触剂量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时间才能发展，特别是在存在低水平污染物的情况下。在这种情况下，很少能获得访问的每个地点和时间间隔的污染物水平 因此，需要通过空间-时间(ST)内插来填补数据缺口。令人惊讶的是，目前还没有商业软件用于时空数据的地质统计处理，包括在未监测的时间和地点进行内插。这个SBIR项目正在开发第一个商业软件，提供用于地质统计学ST内插和不确定性建模的工具。该研究产品将是由Esri的合作伙伴BioMedware开发的桌面时空可视化核心中的一个独立模块。该软件包将提供一套全面的软件，用于：1)ST变异函数的计算和由顾问指导的建模；2)在与健康结果相同的尺度(即聚合或个体水平)并使用任何可用二次信息(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)对暴露数据进行ST预测和随机建模；以及3)通过暴露重建来量化和基于蒙特卡洛的不确定性传播。这些工具将适用于分析健康科学以外的数据，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：扩展第一阶段开发的统计方法，以处理：1)以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协同克里格法并测试其相对于第一阶段实施的克里格法的性能；以及2)通过回归分析随机建模和传播暴露不确定性(暴露测量误差)。构建一个功能齐全且经过测试的ST内插和模拟模块，为商业分发做好准备。进行可用性研究，以评估基于NIH可用性协议的原型设计。应用该软件在几项流行病学研究中演示该方法及其独特的好处，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模拟地质统计学时空现象和计算估计值的能力，以及与环境流行病学研究最相关的尺度(如地点位置、人口普查-区域水平)的相关不确定性。

 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.