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The assessment of water quality across space and time is of considerableinterest for both agricultural and public health reasons. The standard methodto assess the water quality of a catchment, or a group of catchments, usuallyinvolves collecting point measurements of water quality and other additionalinformation such as the date and time of measurements, rainfall amounts, theland-use and soil-type of the catchment and the elevation. Some of thisauxiliary information will be point data, measured at the exact location,whereas other such as land-use will be areal data often in a compositionalformat. Two problems arise if analysts try to incorporate this information intoa statistical model in order to predict (for example) the influence of land-useon water quality. First is the spatial change of support problem that ariseswhen using areal data to predict outcomes at point locations. Secondly, thephysical process driving water quality is not compositional, rather it is theobservation process that provides compositional data. In this paper we presentan approach that accounts for these two issues by using a latent variable toidentify the land-use that most likely influences water quality. This latentvariable is used in a spatial mixture model to help estimate the influence ofland-use on water quality. We demonstrate the potential of this approach withdata from a water quality research study in the Mount Lofty range, in SouthAustralia.

A mixture model approach to infer land-use influence on point referenced water quality