Geostatistical software for merging multivariate data with various spatial supports

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， 环境和健康成果的关键是提供准确的接触模型。因为 收集现场数据通常是禁止的，关键是要纳入任何可用的辅助信息源 来补充稀疏数据集。辅助数据可以采取多种形式（例如，连续或分类 测量标度），显示各种采样密度（例如，数据可在任何地方或特定 位置），并且可以在不同的空间支持物上记录（例如，点观测、人口普查区域、栅格）。 令人惊讶的是，目前还没有用于多变量空间的地质统计处理的商业软件- 时间数据，包括合并在不同空间支持上测量的数据层。 该SBIR项目正在开发第一个商业软件，为地质统计多变量空间提供工具- 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 该工具基于Esri合作伙伴BioMedware开发的传统核心软件。这些工具将适用于 用于分析健康科学以外的数据，如遥感、地球化学或土壤科学， 大大拓宽了最终产品的商业市场。该项目将实现三个目标： * 审查地质统计文献中现有的主要空间协同区域化模型（即，传统 与扩展的、本征的）以及它们的性能的比较（即，预测精度）和用户- 友好性（即，易于推断）用于多变量空间插值。这将是继一个 时空框架的延伸。 开发功能齐全且经过测试的多元ST插值、模拟和可视化模块 准备进行商业分销。 进行正式的可用性研究，以评估基于可用性协议的原型设计 由NIH开发，涉及（i）Tec-Ed公司的专家评估和（ii）可用性测试， 用户代表。 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。

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.