Hello, It is indeed correct that as for simple cokriging, the standardized OCK requires knowledge of the population means for both primary and secondary variables, and as I mentioned in my book p. 232 "Provided the data are representative of the study area, these means can be estimated from the sample means". Of course, we could also account for the uncertainty attached to those samples means.. but the same can be said regarding the uncertainty attached to the parameters of the semivariogram model...
The main reason ordinary kriging is used instead of simple kriging is its ability to accommodate changes in the mean across the study area (what I called global trend in my book) through the use of local search windows. The interesting fact for standardized OCK is that, even if a global mean is used in the standardization, local means are still re-estimated within each search window thanks to the unbiasedness constraint. The main assumption however is that after rescaling by their global means both primary and secondary variables have the same local mean, see Goovaerts (1997, 1998). For me, this might be the main weakness/limitation of the approach. As always, cross-validation is a good way to compare the prediction performances of the different estimators. Pierre Pierre Goovaerts Chief Scientist at BioMedware 516 North State Street Ann Arbor, MI 48104 Voice: (734) 913-1098 (ext. 8) Fax: (734) 913-2201 http://home.comcast.net/~goovaerts/ -----Original Message----- From: Heuvelink, Gerard [mailto:[EMAIL PROTECTED] Sent: Thu 1/5/2006 4:31 AM To: Pierre Goovaerts; Adrián Martínez Vargas; Behrang Kushavand; ai-geostats@unil.ch Cc: Subject: RE: [ai-geostats] Traditional OCK or Standardize OCK? The downside of SOCK (often not mentioned) is that as a minimum requirement one must know the difference(s) between the population means (i.e., the means of the random functions) of the primary and secondary variables. In practice, one rarely knows these and uses the differences between the sample means instead, which is incorrect, unless one takes the associated estimation errors into account. However, when the BLUE of the differences between population means is used and the associated estimation errors are taken into account, then I suspect that SOCK boils down to something very close or identical to TOCK. Along similar lines, recall that substituting the BLUE of the population mean in the simple kriging equations yields a predictor that is identical to the ordinary kriging predictor (I think it is in Cressie's book, but in fact it is not that difficult to establish this result). The main (only?) purpose of using ordinary kriging instead of simple kriging is that one often does not know the population mean and cannot simply assume that it is equal to the sample mean or some other combination of the sample data. That is why ordinary kriging is used much more often than simple kriging. It puzzles me why so many geostatisticians so easily replace TOCK by SOCK and ignore the problem above. It is not the right method to avoid large and many negative weights, there are much better ways for that (see discussion of one month ago). Gerard Gerard B.M. Heuvelink Soil Science Centre Wageningen University and Research Centre P.O. Box 47 6700 AA Wageningen The Netherlands tel +31 317 474628 / 482420 email [EMAIL PROTECTED] http://www.sil.wur.nl/UK/ -----Original Message----- From: Pierre Goovaerts [mailto:[EMAIL PROTECTED] Sent: donderdag 5 januari 2006 0:20 To: Adrián Martínez Vargas; Behrang Kushavand; ai-geostats@unil.ch Subject: RE: [ai-geostats] Traditional OCK or Standardize OCK? Hi, The main difference between SOCK and TOCK is that, in the standardized form, only one unbiasedness constraint is imposed, i.e. the sum of all primary and secondary data weights is one, while in the traditional version a separate constraint is applied for each variable, i.e. sum of primary data weights is one and the sum of secondary data weights is zero for each secondary variable. The traditional constraints lead to larger and more frequent negative weights for the secondary variables. The difference between SOCK and TOCK estimates is expected to increase as differences between the variance of primary and secondary variables increases. The different types of cokriging are described and compared in the following paper: Goovaerts, P. 1998. Ordinary cokriging revisited. Mathematical Geology, 30(1): 21-42. Cheers, Pierre Pierre Goovaerts Chief Scientist at BioMedware 516 North State Street Ann Arbor, MI 48104 Voice: (734) 913-1098 (ext. 8) Fax: (734) 913-2201 http://home.comcast.net/~goovaerts/ -----Original Message----- From: Adrián Martínez Vargas [mailto:[EMAIL PROTECTED] Sent: Wed 1/4/2006 12:53 PM To: Behrang Kushavand; ai-geostats@unil.ch Cc: Subject: Re: [ai-geostats] Traditional OCK or Standardize OCK? In the definition of the cross variogram you can see that it is not adimentional (depend of units >> Km, %, ppm, etc.), you can avoid this effect using standardize Ordinary Co-Kriging. Adrian -----Original Message----- From: "Behrang Kushavand" <[EMAIL PROTECTED]> To: <ai-geostats@unil.ch> Date: Wed, 4 Jan 2006 19:55:01 +0330 Subject: [ai-geostats] Traditional OCK or Standardize OCK? > Dear All, > > > > Is it true that estimation variance of standardize Ordinary Co-Kriging > (SOCK) is always equal or smaller than Traditional Ordinary Co-Kriging > (TOCK)? > > What is the advantage of TOCK to SOCK (I think it is about negative > weights) and are there any criteria to choice TOCK or SOCK? > > > > Thanks > > Behrang > > ____________________________________________________________________________________________ Participe en el V Congreso Internacional de Educación Superior "Universidad 2006". La Habana, Cuba, del 13 al 17 de Febrero del 2006 http://www.universidad2006.cu _____________________________________ Instituto Superior Minero Metalúrgico de Moa Dr. Antonio Núñez Jiménez http://www.ismm.edu.cu
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