Geospatial Uncertainties: A Focus on Intervals and Spatial Models Based on Inverse Distance Weighting

Priscillia Labourg, Sébastien Destercke, Romain Guillaume, Jérémy Rohmer, Benjamin Quost, Stéphane Belbèze

Abstract: Processing geospatial data requires to manage many sources of uncertainties; some appear in classical inference problems, some others are specific to this setting. The goal of this paper is to study the management of these uncertainties via standard intervals and sets when the inference model considered relies on inverse distance weighting. We provide a general discussion with examples, together with a study of the associated optimisation problems induced by different sources of uncertainty. We conclude the paper by an illustration on a semi-synthetic use case, generated according to data recorded via real studies.