Cell Declustering
Stefano, sorry for my rudeness to forward your private reply to r-sig-geo. A quick and dirty answer would be to work with grids, and e.g. retain a single value in each grid cell. Does anyone more familiar with the point pattern theory know of a more elegant way that does not need all kind of ad hoc grid choices? I believe the problem is thinning of a data set in areas where data are redundant. -- Edzer
Stefano Pegoretti wrote:
Dear Edzer,
many thanks for your very quick answer! Here is my "problem": I
have a large dataset of georeferenced samples, and I want to get a
smaller subset that must reproduce the same spatial pattern of the
biggest one; say it in another way, I want to "through away" randomly
some data, but honouring the spatial distribution of the starting
dataset. I don't know it's clear enough... :-P
Have a nice evening!
stefano
Edzer J. Pebesma ha scritto:
Stefano, I thought cell declustering meant finding the size of the region of influence for an observation and using that as weight in further analysis. An approach would be using voronoi diagrams (package deldir, and read the list archives), another using the number of nearest cells based on a regular discretization of the study area; for the latter you could misuse package gstat, interpolate record number with nmax=1 and compute a table of the resulting "prediction" grid. Ask me if you need an example. The description you give sounds like spatial stratfied sampling, and can be accomplished by method spsample in package sp, type = "stratified". -- Edzer Stefano Pegoretti wrote:
Dear List,
I have to perform a "cell spatial declustering" on my radon data,
i.e. divide the study domain into a defined number of cell and for each
of them randomly extracts a defined number of samples: does anybody
know
if there are function or packages in R to quickly to this?
Thanks, and have a good day!
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