Generalized approaches to spatial sampling design

Spatial sampling design methods are commonly used to select a finite set of locations where observations provide optimal information, under a prescribed criterion, for parameter estimation and spatial inter/extrapolation on the basis of an appropriate statistical model. In some situations, environmental applications may also involve information from soft data. Moreover, the objectives may include the analysis of specific characteristics of interest such as model singularities (e.g. heterogeneity, fractal properties, etc.). The generalized random field theory provides a suitable framework for the formulation and analysis of problems related to these situations. An important case in the geostatistical context is the analysis of intrinsic random fields based on generalized increments. In this article, we formulate the entropy-based approach to spatial sampling design for random field prediction in a generalized framework, and discuss its potential application in relation to the above aspects. Copyright © 2005 John Wiley & Sons, Ltd.

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