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: 8523583
• 负责人: PIERRE E GOOVAERTS
• 金额: $ 18.35万
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8523583
• 关键词: Air; Air Pollution; Asthma; Censuses; Code; Communities; Computer software; Data; Data Analyses; Data Set; Databases; Development; Environment; Environmental Engineering technology; Environmental Exposure; Environmental Health; Epidemiologic Studies; Evaluation; Feedback; Health; Health Sciences; Heart Diseases; Heat Waves; Imagery; Individual; Investigation; Letters; Location; Malignant Neoplasms; Marketing; Measurement; Methods; Modeling; Nuclear; Outcome; Output; Patients; Performance; Phase; Process; Protocols documentation; Public Health; Research; Resolution; Small Business Innovation Research Grant; Space Models; Techniques; Technology; Test Result; Testing; Time; Uncertainty; United States National Institutes of Health; Variant; Visit; Weight; base; climate change; data modeling; design; exposed human population; human data; improved; innovation; land use; pollutant; prototype; public health relevance; reconstruction; remote sensing; response; simulation; theories; time interval; tool; usability

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 provide a comprehensive suite for: 1) the computation and advisor-guided modeling of space-time covariance functions, 2) the ST interpolation and stochastic modeling of exposure data at the same scale as health outcomes (i.e. individual-level or aggregated) and using any secondary information available (e.g. remote sensing, land-use regression model, air dispersion model), 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 three aims: ¿ Compare the performance (i.e. prediction accuracy, impact on exposure-response assessment) and user- friendliness (i.e. ease of inference, potential for automatic implementation in software) of two classes of ST covariance models that encompass the main hypotheses of stationarity, full symmetry, separability and supported compactness. ¿ Develop and test a prototype module that will guide non-expert users through the selection and optimal fitting of space-time covariance models, followed by the interpolation of space-time data based on BioMedware's space-time visualization and analysis technology. ¿ Conduct a usability study and identify additional methods and tools to consider in Phase II. 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)时空协方差函数的计算和顾问指导下的建模；2)在与健康结果相同的尺度上(即，个人水平或聚集的)和使用任何可用的次要信息(例如，遥感、土地利用回归模型、空气扩散模型)对暴露数据进行ST内插和随机建模；以及3)通过暴露重建，量化和基于蒙特卡洛的不确定度传播。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。本项目将实现三个目标：比较两类ST协方差模型的性能(即预测精度、对暴露-反应评估的影响)和用户友好性(即易于推断、可能在软件中自动实施)，这两类模型包括平稳性、完全对称性、可分离性和支持的紧凑性等主要假设。？开发和测试一个原型模块，该模块将指导非专家用户选择和优化时空协方差模型，然后基于BioMedware的时空可视化和分析技术对时空数据进行内插。？进行可用性研究并确定要在第二阶段考虑的其他方法和工具。这些技术、科学和商业创新将彻底改变我们对地质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(如点位置、人口普查区域水平)计算估计和相关不确定性。