Data Fusion Approaches for Analysing Many Spatial Outcomes Jointly - Semiparametric Mixture Methods for Correlated Counting Processes and Joint Outcome Analyses

用于联合分析许多空间结果的数据融合方法 - 用于相关计数过程和联合结果分析的半参数混合方法

• 批准号: RGPIN-2014-06187
• 负责人: Dean, Charmaine
• 金额: $ 3.28万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635953
• 关键词: Data; Fusion; Approaches; Analysing; Many

This work develops new statistical tools to address serious issues in data analysis and brings these to bear in important applications related to water, surveillance and forestry. Statistical methods for using related sources to build information are in critical demand for advancing science; indeed this gap is a major obstacle in environmetrics. The focus of this proposal is to address this gap in several related projects as described below.The proposed tools will serve to jointly map several diseases or mortality rates in order to identify the commonalities in the spatial structure across the maps. Visualizing rates is important because this may provide cues to suggest factors which may be linked to mortality or disease, for further detailed study. As well, maps provide an overall description of mortality or disease rates that can identify disease inequities or areas of resistance to disease. The strength of the methods developed is their capacity to take into account spatial aspects of the data, and any connections which exist because of spatial configurations. By so doing, the methods are able to build better estimates for any specific region, because they use all the information in the map, and across maps, in a combined analysis, as appropriate. More importantly, the spatial connections across the map are themselves of interest in order to understand the disease process. We will propose new ways for analyzing natural processes which result in data that have many zero values, which is common in ecological studies. This research fills the gap in analytical methods for handling this sort of disparate data. It will help to address situations where there is a spatial connection between the zeros and between the non-zero counts, as well as across these two compartments of the data, with this spatial connection changing over time. In some contexts, the data contain both an abundance of zeros and high extreme values, such as in studies of river flow in intermittent streams, where floods occur. The difficulty of dealing with extremes in both ends of the data has long frustrated hydrologists. New statistical tools will be brought to bear to aid in models which are based on hydrological principles to solve these challenges. Importantly, the research considers the development of tools to automatically isolate specific kinds of information from satellite images. This will raise the efficiency and effectiveness of earth surveillance. New methods will be developed to model mixed spatial data sources, such as from satellite data and ground observations, and improve surveillance of forest fires using satellite images. We will also study novel ways of extracting information from satellite data to estimate soil moisture for agricultural purposes.The proposed research will serve to increase expertise in Canada in such complex data analysis. Students trained will gain significant experience, increase development of new methods as well as the marketability of their knowledge and skill sets within academia, industry and at government agencies.

这项工作开发了新的统计工具，以解决数据分析中的严重问题，并将这些工具用于与水、监测和林业有关的重要应用。使用相关来源来构建信息的统计方法是推进科学的关键需求;事实上，这一差距是计量学中的一个主要障碍。本提案的重点是在下文所述的几个相关项目中解决这一差距，拟议的工具将有助于联合绘制几种疾病或死亡率的地图，以确定地图空间结构的共同点。可视化比率很重要，因为这可能为进一步详细研究提供线索，提示可能与死亡率或疾病有关的因素。此外，地图还提供了死亡率或发病率的总体描述，可以确定疾病不平等或抗病领域。所开发的方法的优势在于它们能够考虑到数据的空间方面，以及由于空间配置而存在的任何联系。通过这样做，这些方法能够为任何特定区域建立更好的估计，因为它们酌情在综合分析中使用地图中的所有信息和跨地图的所有信息。更重要的是，地图上的空间联系本身就是理解疾病过程的兴趣所在。我们将提出分析自然过程的新方法，这些方法会导致数据具有许多零值，这在生态研究中很常见。这项研究填补了处理这类不同数据的分析方法的差距。它将有助于解决零之间和非零计数之间以及数据的这两个部分之间存在空间联系的情况，这种空间联系随着时间的推移而变化。在某些情况下，数据包含大量的零和高极值，例如在间歇性河流中的河流流量研究中，洪水发生。长期以来，处理数据两端的极端情况的困难一直困扰着水文学家。新的统计工具将被用来帮助建立基于水文学原理的模型，以解决这些挑战。重要的是，该研究考虑开发工具，以自动从卫星图像中分离特定类型的信息。这将提高地球监视的效率和有效性。将开发新的方法，对卫星数据和地面观测等混合空间数据源进行建模，并利用卫星图像改进对森林火灾的监测。我们还将研究从卫星数据中提取信息的新方法，以便为农业目的估计土壤湿度，拟议的研究将有助于提高加拿大在此类复杂数据分析方面的专门知识。接受培训的学生将获得重要的经验，增加新方法的开发以及他们的知识和技能在学术界，工业界和政府机构的市场化。