Plausible reasoning from spatial observations

This article deals with plausible reasoning from incomplete knowledge about large-scale spatial properties. The available information, consisting of a set of pointwise observations, is extrapolated to neighbour points. We use belief functions to represent the influence of the knowledge at a given point to another point; the quantitative strength of this influence decreases when the distance between both points increases. These influences are aggregated using a variant of Dempster's rule of combination taking into account the relative dependence between observations.