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 在任何调查的关联和/或因果关系之间的一个关键组成部分， 环境和健康成果的一个重要方面是提供准确和精确的接触模型。因为 收集现场数据的成本通常过高，因此，将任何辅助信息源纳入其中至关重要 可用于补充稀疏数据集。辅助数据可以采取多种形式（例如，连续或分类 测量尺度），并显示不同的可靠性水平：硬数据与软数据（例如，间隔型数据， 概率分布）。合并这些不同的数据层，同时考虑其空间模式， 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。 这个SBIR项目正在开发第一个商业软件，为软指标编码和非指标编码提供工具。 不确定性的参数地质统计建模该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上。 这些工具将适用于分析卫生科学以外的数据，如遥感， 地球化学，城市基础设施或土壤科学，大大拓宽了最终的商业市场 产品该项目将实现四个目标： 开发一种替代泊松和二项式克里金的指示克里金法，用于过滤 小数字问题和分解面速率数据（面到点克里金法），同时避免 生成负克里金估计值。 实现简单指示克里金法，用于预测分类数据的发生概率， 使用密歇根州弗林特的服务线（SL）组成的情况下，比较这一准确性， 组合方法：1）传统的指标克里金法，可能导致 发生率和概率之和不为1，以及2）机器学习和 BlueConduit使用的贝叶斯数据分析，BlueConduit是美国SL成分预测的领导者。 * 开发和测试一个原型模块，指导非专家完成以下软指标编码： 信息和变差函数建模，然后进行空间插值和交叉验证， BioMedware的时空可视化和分析技术。 ·进行可用性和用户体验研究，并确定其他方法和工具， 第二阶段。 这些技术、科学和商业上的创新将增强我们建立地质统计模型的能力 多变量时空现象和计算估计和相关的不确定性在尺度（例如， 点的位置，普查区的水平）最相关的环境流行病学。