MODELING UNCERTAINTY AND SPATIAL DEPENDENCE - STOCHASTIC IMAGING

The most vibrant area of research in geostatistics is stochastic imagi ng, that is, the modelling of spatial uncertainty through alternative, equiprobable, numerical representations (maps) of spatially distribut ed phenomena. These stochastic images are conditioned to a variety of data accounting for their specific measurement scale and reliability. Any geostatistical prediction is built on a prior model of spatial cor relation that ties data to unsampled values and, equally importantly, unsampled values at different locations together. Since a major goal i n the exercise of mapping is to display organization in space, spatial correlation is a necessity. As for uncertainty it is so pervasive tha t it is imperative to account for it.