I am working in building some geochemical map of soils using point data and geological maps as covariates. The geological covariate is a simplified geological map that groups geological units into 5 clusters. I have first used regression kriging by fitting a model between the geological variable and my target variable and further kriging the residuals. I obtained some decent rme but I was not too pleased with the visualization. I initially assumed that cokriging was not appropriate in my case because of the non gaussian distribution of the geological covariate. However, I tried to apply simple cokriging just to see the results. When using my geological map as a covariate and my target variable observations, i got really good results in term of map visualization and model performance. I did not apply any transformation and only tuned the variogram. The simple cokriging model gave a much better rmse than the regression kriging. That was counter intuitivr to me as i had read that regressiin krigging does better at handling categorical covariates. Surprisingly, when applying an ordinary cokriging and tuning the variogram using exactly the same input data I could not obtain anything close in term of performance or visualization as the simple cokriging. The ordinary cokriging using exactly the same input data gave completely different results and did not handle the categorical geological covariate well.
So here is my question: Does the simple cokriging algorithm in arcgis has a built in function to recognize categorical covariates? It seems to me that when I applied the simple cokriging, it worked as if I applied a kriging with barriers and fitted variograms independently for each geological clusters. Can a esri specialist on kriging check on the simple cokriging algorithm? I would like to be able to justify this map and why simple cokriging does so well.
Thank you for your help.