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。项目摘要/摘要 协会投资和/或因果关系的关键组成部分 环境和健康结果是可用的准确和精确的暴露模型。因为 通常禁止收集现场数据的成本，至关重要的是要合并任何辅助信息来源 可用于补充稀疏数据集。辅助数据可以采用多种形式（例如，连续或分类 测量量表），并显示不同级别的可靠性：硬数据与软数据（例如，间隔类型数据， 概率分布）。在考虑其空间模式时合并这些不同的数据层， 综合性质（分类属性的情况）和局部不确定性因此具有挑战性。 该SBIR项目正在开发第一个提供用于软指标编码和非 - 非指示器编码工具的商业软件 不确定性的参数地位模型。研究产品将是独立的桌面空间 - Time（ST）分析和可视化工具，基于生物膜开发的旧式核心软件。 这些工具将适合分析健康科学以外的数据，例如在遥感中， 地球化学，城市基础设施或土壤科学，最终拓宽了商业市场 产品。该项目将实现四个目标： 开发一种指标Kriging替代泊松和二项式kriging，以滤波由 小数问题并分解面积率数据（面积到点Kriging），同时避免 产生负面的估计值。 实施简单指标Kriging，以预测出现分类数据的可能性以及 使用密歇根州弗林特的服务线组成（SL）的情况，比较此的准确性 组成方法：1）传统指标kriging可能导致负面可能性 不总结的情况和可能性，以及2）机器学习和 SL组成预测的美国领导者BlueConduit使用的贝叶斯数据分析。 开发和测试一个原型模块，该模块将通过软指标编码指导非专家 信息和变化函数建模，然后基于空间插值和交叉验证 生物制品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对地列为地列为建模的能力 多变量时空现象和计算估计以及量表处的相关不确定性（例如 点位置，人口普查水平）与环境流行病学最相关。