Dear All,

Thank you very much for your replies to my question concerning the relationship 
between the kriging error and the sample variance. Since my data is not from 
geology it might be useful to more fully explain it.

The data is from vector ecology, estimating the density of mosquito vectors of 
West Nile Virus in a very heavily visited (more than  100,000 visitors per 
month)256 acre site. The data is from only 12 light traps, a small number but 
they are relatively close together so sampling is considered dense given 
special problems of trap placement and manpower. The furthest distance between 
traps is only 900 meters. Spatial correlation is high as measured by Moran's I 
for total mosquito's and most of the individual species that make of the total. 
Variogram range is between 300 and 500 meters and follows flight range for the 
species concerned. Data is nominally normally distributed, but in the presence 
of strong spatial correlation, sample variance can be very high during some 
weeks. 
 No trend or drift has been detected. The kriged estimates are used to 
concentrate spraying to areas of maximum density of putative vectors based on 
previous virus isolates by species. This has been much more effective than 
using just the light trap data, since modeled densities often suggest even 
higher densities away from the traps. Co-kriging with a layer created by 
combining vegetation , host availability  and previous mosquito observations by 
artificial neural networking has improved cross validation and and lowered 
kriging error. In areas where the kriging error is higher than the sample 
variance, we have assumes the sample mean. any further comments that you have 
on this approach would be greatly appreciated.

Sincerely

Russell Barbour Ph.D.
Research Associate in Applied Mathematics
Vector Ecology Laboratory
Yale School of Medicine
60 College St. Rm 600
New Haven CT. 06520
TEL: 203 785 3223
FAX 203  785 3604
email: [EMAIL PROTECTED]




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