[GRASS-user] Re: proximity mapping / site clustering

[Dylan Beaudette]

Indeed. The examples posted were using standard numerical clustering,
which just happened to incorporate spatial coordinates. the packages
mentioned up thread are probably better suited for spatial studies.
Also, see the most recent GRASS Newsletter for a great article by R.
Bivand on getting raster / point data into R.

Good luck,

Dylan

On 7/22/06, Jarek Jasiewicz <jarekj at amu.edu.pl> wrote:
> Dylan Beaudette napisał(a):
> > This sounds like a fun job for GRASS-R coupling!
> >
> > sample case with the spearfish dataset and the vector 'bugsites'
> >
> > #GRASS6.1
> > v.out.ascii bugsites > bugsites.xy
> >
> > start R:
> > -----------------------------------------------------------------
> > x <- read.table('bugsites.xy', sep="|")
> > names(x) <- c('easting', 'northing', 'cat')
> >
> > y <- data.frame(x[,1:2])
> >
> > library(cluster)
> >
> > y.pam <- pam(y, 5)
> > plot(y, col=y.pam$clustering, main="Bugsites Spatial Clustering, 5 classes",
> > cex=0.5, pch=16)
> >
> > #the centroids of the clustering
> > points(y.pam$medoids, pch=15, col=1:5)
> >
> > y$cluster <- y.pam$clustering
> > y$orig_cat <- as.numeric(row.names(y))
> >
> > write.table(y, file='bugsites.clust', row.names=FALSE)
> > -----------------------------------------------------------------
> >
> > #back in GRASS6.1
> > v.in.ascii in=bugsites.clust out=bclust fs=" " columns='x double, y double,
> > cluster integer, orig_cat integer' skip=1
> >
> > #display the clustered data with convex hulls:
> > d.rast elevation.10m
> >
> > # there are 5 clusters
> > for x in `seq 1 5`
> > do v.extract --o in=bclust where="cluster=$x" out=bclust_$x
> > v.hull in=bclust_$x out=bclust_hull_$x; g.remove vect=bclust_$x
> > d.vect bclust_hull_$x
> > done
> >
> > #display the clustered points
> > d.vect bclust icon=basic/box fcol=blue col=blue size=5
> >
> > #note that in the attribute table there exists the original CAT and spatial
> > cluster number:
> >  v.db.select bclust
> >
> > cat|x|y|cluster|orig_cat
> > 1|590232|4915039|1|1
> > 2|590430|4915204|1|2
> > 3|590529|4914625|1|3
> > 4|590546|4915353|1|4
> > 5|590612|4915320|1|5
> > 6|590744|4915535|1|6
> >
> >
> > just for fun, here is a link to the image.
> > http://169.237.35.250/~dylan/temp/spatial_clustering_idea1.png
> >
> >
> > Cheers,
> >
> > Dylan
> >
> >
> > On Friday 21 July 2006 14:33, M S wrote:
> >
> >> followup/correction.
> >>
> >> The "hubbing" does not have to occur along the optimal route (steiner
> >> tree).  this is "line of sight" orientated (cell towers as example).
> >>
> >> On 7/21/06, M S <mseibel at gmail.com> wrote:
> >>
> >>>  So far the network engineer/planner (who happens to be my Dad) I have
> >>> been working with on using GRASS for WAN Network engineering and design
> >>> has been working out awesome.  After a successful steiner tree, i have
> >>> come to another problem to solve with GRASS.
> >>>
> >>> i have all these sites on the minimal connection network.  what i need to
> >>> do now on a simplistic level, is cluster the sites or aggregate them.
> >>> Clustering them by proximity is the first step, even though traffic
> >>> generation is another factor that will have to be added.
> >>>
> >>> I have looked at v.net.iso and v.net.alloc, but none seem to produce the
> >>> results sought after.  is there a function or combination of functions
> >>> that might cluster sites on the network based upon their proximity to
> >>> each other?
> >>>
> >>>
> >>> i used v.net.iso with roughly a mile iso band.  i was able to highlight
> >>> on the optimal network all sites within a mile of each other but those
> >>> were not the desired results.
> >>>
> >>> Any ideas?
> >>>
> >>> thanks in advance
> >>>
> >
> >
> Yes, you shoud use R: with following packages:
> DCluster, splances, spdep and spatstat. In that packages are lots
> clustering mtohods
> Best regards
> jarek Jasiewicz
>