ORDINARY COKRIGING REVISITED

This paper sets up the relations between simple cokriging and ordinary cokriging with one or several unbiased,less constraints. Differences between cokriging variants are related to differences between models a dopted for the means of primary and secondary variables. Because it is ,tot necessary for the secondary data weights to sum to zero, ordinary cokriging with a single unbiasedness constraint gives a larger weight to the secondary information while reducing the occurrence of negativ e weights. Also the weights provided by such cokriging systems written in terms of covariances or correlograms are nor related linearly, hen ce the estimates are different. The prediction performances of cokrigi ng estimators are assessed using an environmental dataset that include s concentrations of five heavy metals at 359 locations. Analysis of re estimation scores at 100 test locations shows that kriging and cokrigi ng perform equally when the primary and secondary variables are sample d at the same locations. When the secondary information is available a t the estimated location, one gains little by retaining other distant secondary data in tile estimation.