[R-sig-Geo] how to choose best parameters of variogram model in gstat

[Jessie Zhang]

Dear all,

I’m very confused about kriging interpolation using package “gstat”. As I
know, there are two steps to do kriging interpolation. Firstly, define the
variogram structure of the sample data and choose the best parameters (Sill,
nugget and range). Secondly, predict the values of the unknown locations. I
want to interpolate the daily concentration of PM10 using Kriging with 27
observed stations for three years (1109 days). 

My questions as follow:
1.  How to choose the best parameters of model, by visual fit or statistical
fit? I attached my program and results for visual fit. Where was I wrong?
For statistical fit, what should I do?

##visual fit##
data<-read.csv("pm.csv",header=T)
http://r-sig-geo.2731867.n2.nabble.com/file/n7394459/pm.csv pm.csv 
coordinates(data)<-~x+y
d<-variogram(pm10~1,data)
plot(d)##Figure 1##
http://r-sig-geo.2731867.n2.nabble.com/file/n7394459/Figure_1.png 

## as display in the figure, I choose the parameters like b##
b<-vgm(2500,"Sph",0.2,100)
plot(d,model=b)##Figure 2
http://r-sig-geo.2731867.n2.nabble.com/file/n7394459/Figure_2.png 
 
2.  As the variogram structure is very different for each day, I think it’s
better to define the parameters day by day. I need to do it for 1109 times.
Is it possible to do it automatically?

Thanks a lot for everyone.

Jessie zhang


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