Dear Colleagues,
I am in the process of modeling a semivariogram for a host of chemical
contaminants that are released into the ocean via a sewage outfall in
the near shore ocean. The goal is to create a new design or "optimal"
grid for predicting contamination for future surveys. In particular I
would like to create a cost efficiency analysis with kriging error on
the y-axis and distance between grid points on the x-axis to look at the
trade offs (eg. as suggested by McBratney). I am not looking for
something complicated, but something rather straightforward. My
questions are as follows:
1. Is there a problem with first running a PCA on the chemistry and
using the 1st PC scores as a surrogate that will give me a useful
semivariogram for this purpose? In other words, rather than modeling
individual chemicals would it be better to use scores along the PCA. Am
I violating some assumption here in the presence of spatial correlation?
2. Should I include covariates like depth and grain size in the PCA or
should I use the residual fit with grain size/depth as covariates.
3. Is there a useful function in Splus for creating the cost-efficiency
curve.
4. Can you suggest any useful references.
+
+ To post a message to the list, send it to [email protected]
+ To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe
ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the
list
+ As a general service to list users, please remember to post a summary of any
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/
