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Hi Eric, Ok. Thank you all for your replies and your time. As you said it must be something weird about this particular dataset. The category (or geological clusters) are coded as integers (1 to 5). The codes were assigned randomly (i.e. there is no linear correlation between my target variable and the cluster codes). I could try to change the geological covariate coding and see if it changes the results. It might have something to do with that. Thanks for the idea on the coding. Even though that would be the coding of the category, I still cannot explain why the cokriging using OK is so different from the cokiriging using SK. This is just very strange. Best, Clement
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07-10-2017
12:28 PM
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Hi, 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. Clement
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07-05-2017
02:05 PM
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Hi, I have been using USGS grids (e.g. http://mrdata.usgs.gov/radiometric/). You can download a geosoft plu in for arcGIS to read this binary raster format. The problem I have is that those grids have a very peculiar projection (user defined datum and ellipsoid): Here is the metadata: Map_Projection: Map_Projection_Name: Transverse Mercator Transverse_Mercator: Scale_Factor_at_Central_Meridian: 0.926000 Longitude_of_Central_Meridian: -100.000000 Latitude_of_Projection_Origin: 0.000000 False_Easting: 0.000000 False_Northing: 0.000000 Planar_Coordinate_Information: Planar_Coordinate_Encoding_Method: coordinate pair Coordinate_Representation: Abscissa_Resolution: 0.000100 Ordinate_Resolution: 0.000100 Planar_Distance_Units: meters Geodetic_Model: Horizontal_Datum_Name: D_User_Defined Ellipsoid_Name: User_Defined_Spheroid Semi-major_Axis: 6371204.000000 Denominator_of_Flattening_Ratio: infinity Vertical_Coordinate_System_Definition: Altitude_System_Definition: Altitude_Resolution: 0.000100 Altitude_Encoding_Method: I tried to define this projection for my grid but ArcGIS10 does not want to define it. It comes back to undefined everytime I try to apply it. Second problem I will run into if I manage to define the projection for this grid is that I will not be able to re-project such a weird projection system in a more common projection? There are no transformation existing in ArcGIS. This is a pretty commonly used projection in USGS, they actually use it for many of their geological data (Geological Map of North America, radiometric, gamma ray...etc...) but those datasets are all useless if you can not work with them in an other projection. If somebody know what to do with this that would be great. Clement
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