Hi Julian, and list,
the variogram calculates the average squared difference between z-values
separated by particular lags. Calculating differences with nominal data, and
certainly IDs, is wrong. You will get results, but garbage in, garbage out
applies here.
That said, you can calculate indicator variograms on nominal data. See a recent
paper by Ola Ahqvist and myself, or other papers, mostly in soil science. (What
fun to self-cite!):
Spatial and semantic dimensions of landscape heterogeneity, Landscape Ecology
(2010).
I don't think the gstat variogram functionality for nominal data extends to
more than two binary classes, but is not hard to implement more than that. I
have R script on my machine at work if you would be interested.
Hope this helps,
Ashton Shortridge
Quoting Julien Beguin <julien.begui...@ulaval.ca>:
> Dear list,
>
> I would like to evaluate spatial dependence for about 10000 spatial
> data points. However, the variable associated with those points is
> not continuous as in the data "meuse" but is a land cover type (e.g.
> WATER). What should I consider as variable Z in the variogram()
> function with gstat? is it right to take the ID of each point as in
> the code below?
> -----------------------------------------------------------------------------
> Z <- data.frame[ which(data.frame$landcover=='WATER'),]$ID
> X <- frame$X
> Y <- frame$Y
> mydata1 <- data.frame(Z, X, Y);
> coordinates(mydata1) <- c("X", "Y");
> vario01 <- variogram(Z~1, mydata1, cutoff=50000, alpha=c(0,45,90,135))
> ------------------------------------------------------------------------------
>
> Any advice would be very appreciated. Thank you for your time,
>
> Julien Beguin
> ------------------
> Ph.D. student
> Laval University
> Québec, Canada
>
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