Dear AI-list,

in which context KED is mostly used? I have found examples of this methodology 
in the context of soil science and climatology:


Bourennane, H., King, D. and Couturier, A., 2000. Comparison of kriging with 
external drift and simple linear regression for predicting soil horizon 
thickness with different sample densities: Geoderma, v. 97, p. 255-271.

Bourennane, H. and King, D., 2003. Using multiple external drifts to estimate a 
soil variable: Geoderma, v. 114, p. 1-18.

Goovaerts, P., 1999. Using elevation to aid the geostatistical mapping of 
rainfall erosivity: Catena, v. 34, p. 227-242.

Hudson, G. and Wackernagel, H., 1994. Mapping temperature using kriging with 
external drift: theory and an example from Scotland: International Journal of 
Climatology, v. 14, p. 77-91.

Martinez-Cob, A. and Cuenca, R.H., 1992. Influence of elevation on regional 
evapotranspiration using multivariate geostatistics for various climatic 
regimes in Oregon. Journal of Hydrology 136, 353–380.


Are there other interesting references for this methodology in the same or 
other application fields?

Best wishes,
___________________________________________________

Els Verfaillie, PhD student
Renard Centre of Marine Geology - Ghent University 
Krijgslaan 281-S8 
B-9000 Gent - Belgium
tel: +32-9-2644573  fax: +32-9-2644967
e-mail: [EMAIL PROTECTED]
http://www.rcmg.ugent.be/
___________________________________________________
 

-----Original Message-----
From: Pierre Goovaerts [mailto:[EMAIL PROTECTED]
Sent: donderdag 30 juni 2005 16:54
To: Recep kantarci; ai-geostats@unil.ch
Subject: RE: [ai-geostats] modelling trend and kriging type


To add to the excellent comments by Edzer and Gregoire,
 
1. Universal kriging = kriging with a trend. The second terminology has been 
proposed by Andre
Journel who felt that the term "universal" was vague and misleadingly 
"ambitious".
 
2. Kriging with an external drift (KED) is mathematically the same as universal 
kriging (UK). Secondary variables
are simply replacing the spatial coordinates used in UK. 
 
3. Regression kriging denotes all the techniques where the trend is modeled 
outside the kriging algorithm.
There are various methods that can be used to model that trend, ranging from 
linear regression
to neural networks. Kriging is used to interpolate the residuals. In practice 
these techniques have more 
flexibility than universal kriging in term of modeling the trend: multiple 
variables either categorical or
continuous can be incorporated  easily and many sofwtare are available for this 
trend modeling.
The only limitation is that the trend is modeled globally (i.e. the regression 
coefficients are constant
in space) while in KED the coefficients are reestimated within each search 
window.
 
Cheers,
 
Pierre
 

Pierre Goovaerts

Chief Scientist at Biomedware

516 North State Street

Ann Arbor, MI 48104

Voice: (734) 913-1098
Fax: (734) 913-2201 

http://home.comcast.net/~goovaerts/ 

        -----Original Message----- 
        From: Recep kantarci [mailto:[EMAIL PROTECTED] 
        Sent: Thu 6/30/2005 9:38 AM 
        To: ai-geostats@unil.ch 
        Cc: 
        Subject: [ai-geostats] modelling trend and kriging type
        
        
        Dear ai-geostats members
         
        When the data used has a trend, it is needed to model trend and in this 
case there exists various types of kriging to apply (universal kriging, kriging 
with a trend, regression kriging etc).
        If this is the case, does one should use the same type of kriging or 
different depending on modeling the trend using coordinates of target variable 
or using other (namely, secondary or auxillary) variables such as elevation or 
topography ? That is , are there a dinstinction depending on the type of 
variables to model the trend while kriging?
         
        Best regards
        Recep

        
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