spatialpoints: each dot represents 100 individuals?

Alternatively, you might also consider data binning (implemented in 
several packages: KernSmooth, ks, sm, npsp ,...). This technique is 
commonly used in nonparametric statistics to reduce the computational 
time (see e.g. Wand, M. P. (1994), Fast Computation of Multivariate 
Kernel Estimators, Journal of Computational and Graphical Statistics, 3, 
433-445).

For instance, using the npsp package (maintained by me...), you could do 
something like this:

library(npsp)

bin <- binning(earthquakes[, c("lon", "lat")], nbin = c(50,50))

# ?bin$binw? will contain the binning weights (aggregations) at 
locations ?coords(bin)?

simage(bin)

Additionally, you could estimate (nonparametrically) the spatial density:

h <- h.cv(bin, ncv = 2)$h

den <- np.den(bin, h = h)

plot(den, log = FALSE, main = 'Estimated density')

Best regards,

Ruben.


El 25/04/2016 a las 13:35, Juta Kawalerowicz escribi?:

Hi,

I have a dataset with couple of million of points (individuals) and
would like to do some mapping (I have the coordinates of each point)
but given the number of observation I think it may be usuful to plot
dots which represent 100 individuals (of a given group). Does anyone
know a good way to aggregate up spatialpoints? Any suggestions would
be much appreciated!

Best wishes,
Juta

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