Geostatistical Software for Non-Parametric Geostatistical Modeling of Uncertainty

用于不确定性非参数地统计建模的地统计软件

• 批准号: 10697081
• 负责人: PIERRE E GOOVAERTS
• 金额: $ 29.98万
• 依托单位: BIOMEDWARE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10697081
• 关键词: Address; Area; Back; Benchmarking; COVID-19; Censuses; Cities; Code; Computer software; Data; Data Analyses; Data Set; Development; Environment; Environmental Epidemiology; Environmental Health; European; Evaluation; Feedback; Generations; Geology; Hazardous Substances; Health; Health Sciences; Imagery; Incidence; Infrastructure; Investigation; Isometric Exercise; Lead; Location; Machine Learning; Marketing; Measurement; Methodology; Methods; Michigan; Modeling; Municipalities; Nature; Noise; Outcome; Paper; Pattern; Peer Review; Phase; Probability; Protocols documentation; Publications; ROC Curve; Records; Research; Sampling; Science; Services; Small Business Innovation Research Grant; Soil; Source; Technology; Test Result; Testing; Time; Uncertainty; United States National Institutes of Health; Validation; Visualization; Visualization software; analytical tool; cost; design; experience; geochemistry; innovation; machine learning prediction; novel; preference; prototype; remote sensing; software development; statistics; tool; usability

7. Project Summary/Abstract A key component in any investigation of association and/or cause-effect relationships between the environment and health outcomes is the availability of accurate and precise models of exposure. Because the cost of collecting field data is often prohibitive, it is critical to incorporate any source of secondary information available to supplement sparse datasets. Secondary data can take many forms (e.g., continuous or categorical measurement scale), and display different levels of reliability: hard vs soft data (e.g., interval-type data, probability distributions). Merging these different data layers while accounting for their spatial patterns, compositional nature (case of categorical attributes) and local uncertainty is thus challenging. This SBIR project is developing the first commercial software to offer tools for soft indicator coding and non- parametric geostatistical modeling of uncertainty. The research product will be a stand-alone desktop space- time (ST) analysis and visualization tool, building on the legacy core software developed by BioMedware. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, geochemistry, urban infrastructure or soil science, broadening significantly the commercial market for the end product. This project will accomplish four aims:  Develop an indicator kriging alternative to Poisson and binomial kriging for filtering noise caused by the small number problem and to disaggregate areal rate data (Area-to-Point kriging), while avoiding the generation of negative kriging estimates.  Implement simplicial indicator kriging for predicting the probability of occurrence of categorical data and, using the case of the composition of service lines (SL) in Flint Michigan, compare the accuracy of this compositional approach to: 1) traditional indicator kriging that can result in negative probabilities of occurrence and probabilities that do not sum to one, and 2) a combination of machine learning and Bayesian data analysis used by BlueConduit, a US leader in SL composition prediction.  Develop and test a prototype module that will guide non-expert through the soft indicator coding of information and variogram modeling, followed by the spatial interpolation and cross-validation based on BioMedware’s space-time visualization and analysis technology.  Conduct a usability and user experience study and identify additional methods and tools to consider in Phase II. These technologic, scientific and commercial innovations will enhance our ability to model geostatistically multivariate 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 epidemiology.

7. 项目总结/摘要 任何调查之间的关联和/或因果关系的关键组成部分 环境和健康结果是准确和精确的暴露模型的可用性。 收集现场数据的成本往往令人望而却步，纳入任何二手信息来源至关重要 可用于补充稀疏数据集。辅助数据可以采用多种形式（例如连续数据或分类数据）。 测量尺度），并显示不同级别的可靠性：硬数据与软数据（例如，区间型数据、 合并这些不同的数据层，同时考虑它们的空间模式， 因此，组合性质（分类属性的情况）和局部不确定性具有挑战性。 该 SBIR 项目正在开发第一个商业软件，为软指标编码和非指标提供工具。 研究产品将是一个独立的桌面空间。 时间 (ST) 分析和可视化工具，建立在 BioMedware 开发的传统核心软件的基础上。 这些工具将适用于健康科学以外的数据分析，例如遥感、 地球化学、城市基础设施或土壤科学，最终显着拓宽商业市场 该项目将实现四个目标： Ø 开发替代泊松和二项式克里金法的指示克里金法，用于过滤由 小数问题并分解面积率数据（面积到点克里金法），同时避免 生成负克里金估计。 Ø 实施单纯指标克里金法来预测分类数据出现的概率，并且， 以密歇根州弗林特的服务线路 (SL) 的组成为例，比较其准确性 组合方法：1) 传统的指标克里金法，可能导致负概率 发生率和概率总和不等于一，2) 机器学习和 美国 SL 成分预测领域的领导者 BlueConduit 使用贝叶斯数据分析。 Ø 开发并测试一个原型模块，该模块将指导非专家完成软指标编码 信息和变异函数建模，然后是基于空间插值和交叉验证 BioMedware的时空可视化和分析技术。 Ø 进行可用性和用户体验研究，并确定需要考虑的其他方法和工具 第二阶段。 这些技术、科学和商业创新将增强我们进行地质统计建模的能力 多元时空现象并计算估计值和相关的不确定性（例如 点位置、人口普查区级别）与环境流行病学最相关。