Latent-Gaussian Spatio-temporal models for complex problems

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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.**