Geostatistical software for health and exposure analysis

描述(由申请人提供)：该SBIR项目正在开发第一个地统计学软件，提供专门为分析健康数据(例如癌症发病率)而设计的工具，提供：疾病空间模式的描述和变异性尺度的确定，根据不同支持测量的经验频率估计和绘制风险图，暴露数据的空间内插和随机建模，以及暴露和健康数据之间尺度依赖关系的调查和可视化。该项目将实现6个目标：1。进行需求分析，以确定要整合到软件中的空间方法和功能。2.开发和测试创新的地统计学技术，用于癌症发病率的空间过滤和尺度相关性分析。3.提出了一种通过局部聚类分析和生态回归来改变支持度(即空间分解或侧向尺度)、量化和传播不确定性的新方法。4.基于研究和模拟研究的结果，构建和测试一整套功能，这些功能将被纳入Biomedware的时空可视化和分析技术。5.应用软件和方法演示该方法及其在暴露和健康风险评估方面的独特益处。6.编写教学材料，包括一个短期课程，以促进在卫生科学中采用这一方法。该项目的可行性在一期工程中得到了论证。二期工程将实现目标三至六。这些技术、科学和商业创新将彻底改变我们解释癌症死亡率地理差异的能力，理解癌症在不同空间尺度和不同支持下的关系，以及量化风险因素。

DESCRIPTION (provided by applicant): This SBIR project is developing the first geostatistical software to offer tools that are specifically designed for the analysis of health data (e.g. cancer rates), providing: description of spatial patterns of disease and identification of scales of variability, estimation and mapping of risk from empirical frequencies measured over different supports, spatial interpolation and stochastic modeling of exposure data, and the investigation and visualization of scale-dependent relationships between exposure and health data. This project will accomplish 6 aims: 1. Conduct a requirements analysis to identify the spatial methods and functionality to incorporate into the software. 2. Develop and test innovative geostatistical techniques for spatial filtering of cancer rates and analysis of scale-dependent correlations. 3. Develop a novel approach for change of support (i.e. spatial disaggregation or side-scaling), quantification and propagation of uncertainty through local cluster analysis and ecological regression. 4. Build and test a complete set of functionalities based on results of research and simulation studies, which will be incorporated into Biomedware's space-time visualization and analysis technology. 5. Apply the software and methods to demonstrate the approach and its unique benefits for exposure and health risk assessment. 6. Create instructional materials, including a short course, to foster the adoption of this approach in health science. Feasibility of this project was demonstrated in the Phase I. This Phase II project will accomplish aims three through six. These technologic, scientific and commercial innovations will revolutionize our ability to interpret geographic variation in cancer mortality, understand relationships between cancers at different spatial scales and over different supports, and to quantify risk factors.