I don't think this is a productive avenue, as geostatistics is mostly
about prediction, but SAR/CAR are mostly about mitigating
mis-specification problems in inference from covariates. This means that
prediction say from SAR/CAR fitted models is less well understood, for SAR
see for example Michel Goulard, Thibault Laurent & Christine Thomas-Agnan,
2017 \emph{About predictions in spatial autoregressive models: optimal and
almost optimal strategies}, Spatial Economic Analysis Volume 12, Issue
2--3, 304--325 https://doi.org/10.1080/17421772.2017.1300679.
However, INLA (and others) use GMRF in building prediction models, so the
lattice (mesh) may play a role. See for example:
https://becarioprecario.bitbucket.io/spde-gitbook/
http://www.r-inla.org/spde-book
Hope this helps,
Roger
On Mon, 21 Jan 2019, Bedilu Ejigu wrote:
Could it be reasonable to analyze a geostatistical data using spatial
autoregreesive models (i.e. SAR and CAR) by treating the geostatistical
data as it was observed on a discrete indexing set instead of continuous
indexing set?
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Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
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https://orcid.org/0000-0003-2392-6140
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