Latent-Gaussian Spatio-temporal models for complex problems

“大数据”时代的一个特点是，越来越多的空间信息被例行地收集并存储在行政数据库中。这使得研究人员能够在比以前更精细的空间尺度上回答问题，例如检查城市内癌症风险的差异，而不是城市之间的差异。这些新的数据来源和随之而来的研究问题要求在空间统计领域取得进展，因为适用于50个健康区域模型的方法在应用于10,000个人口普查区域的数据时往往效果不佳。*本研究计划将以最近的研究为基础，推进与以下方面有关的统计方法：*--以高空间分辨率(人口普查区域、邮政区域)预测健康结果的病例；*--以混合空间分辨率(即点位置、邮政编码、人口普查区域)将统计模型与空间数据相适应；*-使用行政健康数据解决目前需要临床记录的问题；以及*-简化统计软件，以适应时空模型。*将开发新的统计方法，以解决每个领域的重要悬而未决的问题。**

One feature of the `big data' era is that increasingly large amounts of spatial information are routinely collected and stored in administrative databases. This has enabled researchers to answer questions at a finer spatial scale than was previously possible, for instance examining variation in cancer risk within a city as opposed to between cities. These new data sources and the research questions which accompany them have required advancements to be made in the area of Spatial Statistics, as methods which were well suited to models for 50 health regions often work poorly when applied to data from 10,000 census regions.***This research plan will build on recent research to advance statistical methodology related to:***- forecasting cases of a health outcome at a high spatial resolution (census tracts, postal regions);***- fitting statistical models to spatial data at mixtures of spatial resolutions (i.e. point locations, postal codes, census regions);***- using administrative health data to address questions currently requiring clinical records; and***- simplifying statistical software for fitting spatio-temporal models.***New statistical methodologies will be developed to address important outstanding issues in each of these areas.**