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。 该SBIR项目正在开发第一个提供地统计多元ST工具的商业软件 不确定性的内插和建模。研究产品将是桌面上的一个独立模块 时空可视化核心由ESRI的合作伙伴BioMedware开发。这些工具将适用于 对健康科学以外的数据的分析，如遥感、地球化学或土壤科学，拓宽了 值得注意的是，终端产品的商业市场。该项目将实现三个目标： 回顾了地统计学文献中可用的主要空间协同区域化模型(即传统VS 扩展的、内在的)，并比较它们的性能(即，预测精度)和用户友好性(即， 易推论)通过4个数据集的交叉验证分析进行多变量空间内插 处理水铅水平、氡、气象和地球化学数据的测绘。比较 将包括各种协同克里金法类型(即一个或多个无偏约束)和 环境流行病学家，例如最近的监测者、反距离或纯粹的空间克里格法。 开发并测试了一个原型模块，该模块将指导非专家用户通过拟合 协同区域化(LMC)和选择适当的多变量内插方法(例如，协同克里格法， 带有外部漂移的克里格法、回归克里格法)，然后基于 BioMedware的时空可视化和分析技术。 进行可用性研究，并确定要在第二阶段考虑的其他方法和工具。 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 ST interpolation and modeling of uncertainty. The research product will be a stand-alone module into the desktop space-time visualization core 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 the main spatial coregionalization models available in the geostatistical literature (i.e., traditional vs extended, intrinsic) and compare their performances (i.e., prediction accuracy) and user-friendliness (i.e., ease of inference) for multivariate spatial interpolation through the cross-validation analysis of 4 datasets dealing with mapping of water lead levels, radon, meteorological and geochemical data. The comparison will include various cokriging types (i.e., one or several unbiasedness constraints) and other tools used by environmental epidemiologists, such as nearest monitors, inverse distance or purely spatial kriging.  Develop and test a prototype module that will guide non-expert users through the fitting of a linear model of coregionalization (LMC) and selection of an appropriate multivariate interpolation method (e.g., cokriging, kriging with an external drift, regression kriging), followed by the spatial interpolation based on BioMedware’s space-time visualization and analysis technology.  Conduct a usability 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.