Giulia,

>  What is the best statistics to quantify this difference?

I think you already have it (the map of differences). You could add the
means, or perhaps quantiles and/or boxplot or scatterplot and slope of
regression line. Depending on what you see and why you care, that may be as
much as you should do. Perhaps regression and correlation can help, but I do
not think statistical tests designed to compare two samples are very
relevant here. You have data on the entire population and whether certain
types of differences are relevant will likely depend on the intended use the
data.

Robert

library(raster)
b <- a <- raster(nc=10, nr=10)
a[] <- rnorm(ncell(a), 0,1)
b[] <- rnorm(ncell(b), 1, 1)

x <- a-b

quantile(x)
boxplot(x)

s <- stack(a, b)
boxplot(s)

v <- na.omit(values(s))
colnames(v) <- c('a', 'b')
m <- lm(b~a, data=data.frame(v))
m
plot(a,b)
abline(m)


On Thu, Sep 1, 2011 at 5:41 AM, Conchedda, Giulia (AGAL) <
Giulia.Conchedda@fao.org> wrote:

> Hi to All,
>
> My question concerns the best practice to compare cell-by-cell two grids
> that are representing the same phenomenon (distribution of rural poor). The
> grids have the same spatial extent and cell size and I would like to
> quantify and provide statistics on how pixel values in the two grids differ.
> I first subtracted one from the other and in this way I can already say and
> spot where differences are more frequent. What is the best statistics to
> quantify this difference?
>
> Thanks for advising,
>
> Giulia
>
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