Combining Areal and Point Data in Geostatistical Interpolation: Applications to Soil Science and Medical Geography

Merging Areal and Point Data in Medical Geography and Soil Mapping

A parallel computing approach to fast geostatistical areal interpolation

Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpo...

Geostatistical Alternatives for Incorporating Covariates in Areal Interpolation

Geostatistical modelling of uncertainty in soil science

This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž . attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž . locations multiple-point or spatial uncertainty . Two approaches are presented: kriging-based and Ž . simulation-based techniques that can be implemented within a parametric e.g. multi-Gaussian o...

A Geostatistical Framework for Area-to-Point Spatial Interpolation

The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data...