Hi, I do not know whether you received any answers off-list, so here goes.
The "spherical" model of geostatistics was so-named by Matheron and is sometimes also known as the Matheron model. His idea was that a sample has a 'sphere of influence' around it. Potential (or actual) samples within this sphere have values which are 'related' to the value at the central point. Imagine, now, a second such point with its own sphere of influence. If the spheres do not touch, there is no relationship between the values at the two central points. If the spheres overlap, there will be a relationship. The more the spheres overlap, the stronger the relationship.
The spherical semi-variogram is the simple geometric calculation for the volume of NON-overlap of the two spheres, given the distance between their centres.
There is no real reason why it should work in so many cases --
any more than there is for the Normal (Gaussian) distribution being found so often in nature. In fact, there is often a possibility to fit several of the semi-variogram models in practice. You could decide which is most appropriate using something like Cressie's goodness of fit test (analagous to a sort of chi-squared statistic).
Isobel
Dear list,
I have small query. Why almost all kind of spatial
data set (ore grade data, sea surface temperature
data, soil thickness, etc.) is fitted to the spherical
(variogram) model? May anyone explain the origin of
this spherical model?
Thank you for your help.
Regards,
M. Nur Heriawan
---------------
Graduate School of Science and Technology
Kumamoto University
Kurokami 2-39-1, Kumamoto 860-8555, JAPAN
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