Hi,

assuming a spatial dataframe consisting of 4 points with
each a X and Y coordinate:

n <- c("A","B","C","D")
x <- c(1,2,10,10)
y <- c(1,1,3,4)
df <- data.frame(n,x,y)

I would like to clump now the points into clusters based on a
neighborhood criteria. Id' like to cluster all points, to end up
with a minimum spatial distance (e.g. 5) between each cluster.
Thus, the number of clusters is not fixed appriori but depends on
the distances of the points to each other.
Or are there other useful methods to spatially cluster points,
and so to reduce and homogenize point densities to achieve
more regular point patterns?

Maybe someone can provide an example either based
on the small dataframe above or based on another minimal working
example. Thank you very much!!

best regards,
Johannes

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