5
$\begingroup$

I am applying diferent methods to interpolate continuous spatial surfaces (kriging, splines, glm,etc). Most of the studies that have enough detail for me to follow usually focus on one specific method.

Is there any study that I might have missed where deterministic and statistical models are compared?

I understand that the selection of the best model is made by the quantification of error and statistical techniques, what I am looking for are studies/examples that for a certain type of dataset(static, time-series, climate, topographic) show that one model is more adapted than other and why.

$\endgroup$
5
$\begingroup$

An early paper to this effect is Evan Englund's A Variance of Geostatisticians (Math. Geo. 1990) in which irregularly sampled elevation data from the Walker Lake area of Nevada were transformed and given to a dozen statisticians for interpolation using whatever methods they preferred. The results varied widely and the best ones were not obtained by kriging, leading Englund to suggest (if I recall correctly) that various methods may be more suitable for particular kinds of datasets.

A version of this paper is available online.

More of this literature can be found by chasing the citation trail. There are a huge number of papers in which two or more spatial interpolators are compared for a particular dataset.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.