Geostatistical software for merging multivariate data with various spatial supports

7.项目摘要/摘要 在对两国之间的关联和/或因果关系进行任何调查中的关键组成部分 环境和健康结果取决于是否有准确的暴露模型。因为这样做的成本 收集外业数据通常是令人望而却步的，纳入任何可用的次要信息来源至关重要 以补充稀疏数据集。辅助数据可以采用多种形式(例如，连续的或分类的 测量比例)，显示各种采样密度(例如，随处可用或特定位置的数据 位置)，并通过不同的空间支持(例如，点观测、人口普查区域、栅格)记录。 令人惊讶的是，目前还没有用于多变量空间地质统计学处理的商业软件。 时间数据，包括在不同空间支撑上测量的数据层的合并。 这个SBIR项目正在开发第一个商业软件，提供用于地质统计多变量空间的工具- 不确定性的时间(ST)内插和建模。研究产品将是一款独立的台式ST 工具，基于由Esri合作伙伴BioMedware开发的遗留核心软件。这些工具将非常适合 对于健康科学以外的数据的分析，如遥感、地球化学或土壤科学， 大大拓宽了终端产品的商业市场。该项目将实现三个目标： 回顾地统计学文献中可用的主要空间协同区域化模型(即传统的 VS扩展的、固有的)，并比较它们的性能(即预测精度)和用户- 多变量空间内插的友好性(即，易于推理)。这之后将是一个 时空框架的延伸。 开发了一个功能齐全且经过测试的多元ST段内插、模拟和可视化模块 为商业发行做好准备。 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)由Tec-Ed公司进行的专家评估和(Ii)由 代表性用户。 这些技术、科学和商业创新将增强我们对地统计学建模的能力 多变量时空现象和计算估计以及尺度上的相关不确定性(例如 地点位置、人口普查区域级别)与环境流行病学最相关。

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.