Hi, I am working myself with pollution data in soils and i have very high values very close to very low values, and highly skewed distribution. I am more and more concerned with doing kriging on transformed data. This simply means we believe the data came from only one population. But what if it comes from 2 different populations representing 2 different polluting processes? Much more if we do believe there are no gross error measurements. The fact that high values are very close to low values would tell me that the spatial autocorrelation is violated locally. I would try first to see if the outliers (local and global) represent a different population, if these values cluster or not, how significant is the association high- low values, and if the global Moran's I increases if i eliminate the "outliers". Maybe the majority of the data which have a higher spatial autocorrelation belong to a "better expressed" diffusive process, (maybe an older one) while the rest of the data which were identified as outliers before, represent a more patch-y or point source pollution process which didn't have time to diffuse over the entire study area (a younger process, maybe?).
Of course if you have proof that the data came from only one population then .... it is a different story. I will really appreciate to hear other opinions about these thoughts. Thanks, Monica -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
