Area-to-point Kriging with inequality-type data

In practical applications of area-to-point spatial interpolation, inequality constraints, such as non-negativity, or more general constraints on the maximum and/or minimum allowable value of the resulting predictions, should be taken into account. The geostatistical framework proposed in this paper deals with area-to-point interpolation problems under such constraints, while: (i) explicitly accounting for support differences between sample data and unknown values, (ii) guaranteeing coherent predictions, and (iii) providing a measure of reliability for the resulting predictions. The analogy between the dual form of area-to-point interpolation and a spline allows to solve constrained area-to-point interpolation problems via a constrained quadratic minimization algorithm, after accounting for the following three issues: (i) equality and inequality constraints could be applied to different supports, and such support differences should be considered explicitly in the problem formulation, (...

• Type: Journal
• Year: 2006
• Where: JGS
• Authors: E.-H. Yoo, Phaedon C. Kyriakidis
• Keywords: Area-to-point Interpolation, Inequality Constraints, Interpolation, JGS 2006