Yes, but the problem with averaging the data in the cell is that the average
has a different standard deviation, depending on the layout of the sampling
within each cell.
So, if you decluster by averaging each cell you can end up with a set of
cells which all come from different distributions -- same mean but different
variance. Not stationary at all! Better to select one sample from each cell.
Isobel
http://www.kriging.com
Digby Millikan <[EMAIL PROTECTED]> wrote:
v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);}
w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);}
You have to uncluster the data e.g. in resource exploration programs
often more sampling takes place
in the higher grade zones, so this has to be compensated for by using an
equal amount of sample
data from each area. If two samples are taken at one location it makes sense
to average them, and
if the data is normally distributed and stationariay the data within each
cell is normally distributed
so the average of that cell, is the mean of the data within it?
---------------------------------
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Bill Thayer
Sent: Saturday, 17 February 2007 7:29 AM
To: ai-geostats@jrc.it
Subject: AI-GEOSTATS: spatial weights
In Isaaks and Srivastavas Applied Geostatistics (1989), the use of
de-clustering weights are described as a method for computing estimates of
the mean and variance with data that are clustered geographically.
I would appreciate feedback regarding the theoretical basis for using spatial
weights to compute estimates of the mean and (population) variance, and for
making inferences regarding population parameters. Through simulation tests, I
have some evidence that this method performs fairly well with weights derived
from Thiessen polygons for populations with varying degrees of spatial
autocorrelation and skewness. However, I am not aware of any theoretical
basis/justification for the weights. Intuitively, the use of spatial weights
to account for geographic location of the observations (and possibly spatial
autocorrelation among the observations) seems analogous to the common practice
in survey statistics of adjusting sample weights to correct for non-response,
etc, where the objective is to adjust the weights to account for observed
differences between some attribute of the observations (e.g., socioeconomic
status) and the target population. In the spatial weighting case,
the adjustment is to correct for observed geographical clustering. One
notable difference is that in many cases, the data that I work with was not
collected using random sampling methods.
Your feedback would be appreciated.
Best regards,
Bill
