Hi, I am not sure i understood correctly your question. Fist of all, do the interpolated data have come from your sparse data interpolation? What method of interpolation did you use in this case?
After Burrough and McDonnel, 2000, you need at least 50 points to have reliable results through kriging. Certainly you can do it on less data, but until now i never saw a study considering this problem in depth (maybe there is literature out there, and if it does and anybody knows about it - i would like to know it also ;-)) Secondly, if you know the outlier is not an error, but you interpret it as representing a different combination of properties than the rest of your data - i am not very sure it is wise to use it together with your rest of the data in any interpolation exercise. The outlier may represent a different population and in this case i cannot see any "physical" reason to treat all your data together if parts of the data represent different things. At least this is my opinion. Besides, if your data is not only sparse (5 or 6 data points .... it is really very sparse i think) but also far away in space, they can be at distances grater than the spatial correlation range, and in this case i really don't think you can use kriging .... you will have either a pure nugget effect or a very high nugget value and not a too high spatial correlation. 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
