Dear List,
I have a (maybe) strange question about interpolation methods.
Just to make an example, let's suppose I have a SpatialPointsDataFrame of
100 meteorological station. Each point is a station with, for example,
coordinates, mean annual temperature, elevation, distance from sea, slope
and aspect data. Temperatures are normally distributed and I have the
Digital Elevation a Model of my study area.
I know differences between ordinary, universal and regression Kriging and
to choose the best method I'm use to do a regression model to study
relationships between temperatures (in my example) and elevation, latitude,
longitude, etc... But how should I decide if to use Kriging or cokriging?
Which statistical way must be chosen to have strong evidences to support a
decision? Covariance matrix for example?

I hope to have been clear enough,
many thanks,
Maurizio


-- 
Maurizio Marchi, Ph.D. student
ID skype: maurizioxyz
*Ubuntu 12.04 LTS*
*"Il bello dell'open-source è che le domande possono essere poste alla
fonte"*
*utente linux 552.742*

	[[alternative HTML version deleted]]

