Thanks for all suggestions! I was thinking that in the end a plasible approach would be to sample points, say if I have 100,000 and sample 1,000 then I can say that each point represents 100 people. Juta
On Wed, Apr 27, 2016 at 11:13 PM, rubenfcasal <rubenfcasal at gmail.com> wrote:
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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