Spatial and spatio-temproal areal unit data are prevalent in the fields of epidemiology, geography and statistics, and the spatial autocorrelation in these data is typically modelled by a conditional autoregressive (CAR) model as part of a Bayesian hierarchical framework. These models can be implemented in the R packages CARBayes (spatial data) and CARBayesST (spatio-temporal data), and this workshop will describe the models that can be fitted and illustrate the process on real data examples. These examples will be a full spatio-temporal data analysis, including reading in and formatting data, producing exploratory maps and measures of spatial autocorrelation, model fitting and checking, and visualising the results. Univariate and multivariate spatial data models will be discussed, as will spatio-temporal data modelling.
Google Earth Engine is a planetary-scale platform for Earth science data & analysis, powered by Google's cloud infrastructure. Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.
This workshop will discuss many of the health applications of Google Earth Engine. We will walk through a short lab that will introduce you to health-specific and other relevant in the data catalog and to Earth Engine scripts to get you started.
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