Reference number : GC-2017-Q1-03518, amendment number : , agreement type : G, recipient legal name : Brown, Patrick (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : One feature of the `big data&rsquo; 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., program purpose french : One feature of the `big data&rsquo; 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., agreement title english : Latent-Gaussian Spatio-temporal models for complex problems, agreement title french : Latent-Gaussian Spatio-temporal models for complex problems, agreement number : RGPIN, agreement value : 120000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada