Hi all, I am employing colocated cokriging to develop realizations of elevation for a study area. I have scattered reference elevations and exhaustive, but definitely inaccurate, gridded elevations serving as the secondary data. I'm using GSLIB's sgsim module to do this. To conform with the Gaussian framework the data (hard and soft) were transformed to normal deviates using GSLIB's nscore prior to the simulation.
The question arises on back-transforming the realizations. GSLIB's backtr program requires min and max values for the tail extrapolation. I adopted the following approach to estimate these values: 1) Develop a linear model using OLS: reference = B0 + B1 * secondary elevation 2) Identify the minimum and maximum elevations in the exhaustive secondary dataset 3) Use the model coefficients to predict the reference values for those min and max elevations; plug those predictions into the parameter file for backtr. This is admittedly a pretty back-of-the-envelope approach, and I'd welcome the list's thoughts or suggestions. Thanks, Ashton -- Ashton Shortridge Associate Professor [EMAIL PROTECTED] Dept of Geography http://www.msu.edu/~ashton 235 Geography Building ph (517) 432-3561 Michigan State University fx (517) 432-1671 + + To post a message to the list, send it to [email protected] + To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
