Yong Li wrote:
Hi ALL,
I have been with the attached dataset and R code to use OK to
interpolate soil OLSEN P spatial distribution for a couple of weeks. So
far I have not found a satisfactory solution using OK or local OK or
block OK, compared to IDW method. However theoretically OK is always
better than IDW as also a journal editor advised me in my submitted
manuscript.
"always better" is quite a strong statement. It does have a minimum
variance property, but only under a number of assumptions that need to
hold. IDW has not a naturally quantified variance, but has e.g. the nice
property that the interpolated values do stay within the data range,
which is not true for OK.
Normally if we do not find a significant spatial structure for a soil
variable, we may choose IDW or other methods. How is your guys' opinion
on this or may you help me to find a better solution using OK with my
dataset?
Why would IDW be useful when no significant spatial structure is
present? Why not use the global mean as predictor?
Other questions are whether variables exist out there that have no
spatial structure, and also what significance means in your comment.
Should we conclude that spatial correlation is zero when it is not
significant? I would say no.
I appreciate any help.
And I'm looking for more opinions--anything!
Regards
Yong Li
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Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de/
http://www.springer.com/978-0-387-78170-9 e.pebe...@wwu.de
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