Geostatistical software for health and exposure analysis

描述（由申请人提供）：SBIR项目正在开发第一个地质统计软件，提供专门用于分析健康数据的工具（例如癌症发病率），提供：描述疾病的空间模式和确定变异性的尺度，根据在不同支持下测量的经验频率估计和绘制风险图，对暴露数据进行空间插值和随机建模，以及调查和可视化暴露与健康数据之间的尺度依赖关系。该项目将实现6个目标：1。进行需求分析，以确定要纳入软件的空间方法和功能。2.开发和测试创新的地理统计技术，用于癌症发病率的空间过滤和尺度相关性分析。3.开发一种新的方法，通过当地聚类分析和生态回归来改变支持（即空间分解或侧标）、量化和传播不确定性。4.根据研究和模拟研究的结果构建和测试一套完整的功能，这些功能将被纳入Biomedware的时空可视化和分析技术。5.应用软件和方法来展示该方法及其对暴露和健康风险评估的独特益处。6.创建教学材料，包括短期课程，以促进在健康科学中采用这种方法。该项目的可行性已在一期工程中得到论证。该项目第二阶段将实现目标三至六。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，了解不同空间尺度和不同支持下癌症之间的关系，并量化风险因素。

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.