Geostatistical software for merging multivariate data with various spatial supports

用于将多元数据与各种空间支持合并的地统计软件

• 批准号: 10323718
• 负责人: PIERRE E GOOVAERTS
• 金额: $ 82.19万
• 依托单位: BIOMEDWARE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10323718
• 关键词: Accounting; Address; Air; Air Pollution; American; Area; Cardiovascular Diseases; Categories; Censuses; Chronic Disease; Cities; Communities; Computer software; Data; Data Analyses; Data Set; Development; Diabetes Mellitus; Environment; Environmental Epidemiology; Evaluation; Geography; Geology; Health; Health Sciences; Health Status; Imagery; Individual; Investigation; Lead; Lead levels; Linear Models; Literature; Location; Malignant Neoplasms; Measurement; Measures; Methods; Modeling; Outcome; Performance; Phase; Protocols documentation; Radon; Recommendation; Records; Research; Sampling; Science; Services; Small Business Innovation Research Grant; Soil; Source; Space Models; Techniques; Test Result; Testing; Time; Uncertainty; United States National Institutes of Health; Variant; Visualization; Visualization software; Water; base; cost; density; design; geochemistry; improved; innovation; land use; neoplasm registry; prototype; reconstruction; remote sensing; simulation; software development; sound; theories; tool; usability; user-friendly; working group

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 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), display various sampling densities (e.g., data available everywhere or at specific locations), and be recorded over different spatial supports (e.g., point observations, census tracts, rasters). Surprisingly, there is currently no commercial software for the geostatistical treatment of multivariate space- time data, including the merging of data layers measured on different spatial supports. This SBIR project is developing the first commercial software to offer tools for geostatistical multivariate space- time (ST) interpolation and modeling of uncertainty. The research product will be a stand-alone desktop ST tool, building on the legacy core software developed by BioMedware, an Esri partner. These tools will be suited for the analysis of data outside health sciences, such as in remote sensing, geochemistry or soil science, broadening significantly the commercial market for the end product. This project will accomplish three aims:  Review of the main spatial coregionalization models available in the geostatistical literature (i.e., traditional vs extended, intrinsic) and the comparison of their performances (i.e., prediction accuracy) and user- friendliness (i.e., ease of inference) for multivariate spatial interpolation. This will be followed by an extension to the space-time framework.  Develop a fully functional and tested multivariate ST interpolation, simulation and visualization module ready for commercial distribution.  Conduct a formal usability study to evaluate the design of the prototype based on usability protocols developed by the NIH involving (i) expert evaluation by the firm Tec-Ed and (ii) usability testing by representative users. 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 环境和健康结果取决于准确的暴露模型的可用性。 收集现场数据通常是令人望而却步的，整合任何可用的二手信息来源至关重要 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），显示各种采样密度（例如，随处可用或在特定处可用的数据） 位置），并通过不同的空间支持（例如点观测、人口普查区、栅格）进行记录。 令人惊讶的是，目前还没有用于多元空间地统计处理的商业软件 时间数据，包括在不同空间支持上测量的数据层的合并。 该 SBIR 项目正在开发第一个商业软件，为地统计多元空间提供工具 时间 (ST) 插值和不确定性建模 该研究产品将是一个独立的桌面 ST。 工具，以 Esri 合作伙伴 BioMedware 开发的传统核心软件为基础，这些工具将适用。 用于分析健康科学以外的数据，例如遥感、地球化学或土壤科学， 扩大最终产品的重要商业市场该项目将实现三个目标： Ø 回顾地统计文献中可用的主要空间共区化模型（即传统的空间共区化模型） 与扩展的、内在的）以及它们的性能（即预测准确性）和用户的比较 多元空间插值的友好性（即易于推理）。 扩展到时空框架。 Ø 开发功能齐全且经过测试的多元ST插值、仿真和可视化模块 准备商业发行。 Ø 进行正式的可用性研究，根据可用性协议评估原型的设计 由 NIH 开发，涉及 (i) Tec-Ed 公司的专家评估和 (ii) 由 代表用户。 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。