Hello Sarah
thanks a lot for your advice.
I followed your suggestions unitl the creation of "result"
The allocation of the values of result$distance to the matrix result.m,
however ,does not seem to work: it produces a matrix with identical columns
corresponding to the last values of result$distance. Maybe my description
of the dataset was not clear enough.
I produced the final matrix with a loop, that I report below (it takes
about 1 hour on my macbook pro),
set_i = -1 # create a variable to store the i values already examined
for(i in unique(result$id)){
set_i=c(set_i,i) # store the value of the i
set_neigh = result$id_neigh[result$id==i & !result$id_neigh %in% set_i] #
identify the locations connected to i. Exclude those
for(j in set_neigh){
if(i!=j){
spat_dist[i,j] = result$distance[result$id==i & result$id_neigh==j]
spat_dist[j,i] = spat_dist[i,j]
}
else{
spat_dist[i,j]=0
}
}
}
It not the most elegant and efficient solution in the world, that's for sure
On Thu, May 12, 2016 at 2:51 PM, Sarah Goslee <sarah.gos...@gmail.com>
wrote:
> I don't see any reason why a loop is out of the question, and
> answering would have been much easier if you'd included the requested
> reproducible data, but what about this?
>
> This solution is robust to pairs from idcell being absent in censDist,
> and to the difference from A to B being different than the distance
> from B to A, but not to A-B appearing twice. If that's possible,
> you'll need to figure out how to manage it.
>
> # create some fake data
>
> idcell <- data.frame(
> id = seq_len(5),
> fcell = sample(1:100, 5))
>
> censDist <- expand.grid(fcell=seq_len(100), cellneigh=seq_len(100))
> censDist$distance <- runif(nrow(censDist))
>
> # assemble the non-symmetric distance matrix
> result <- subset(censDist, fcell %in% idcell$fcell & cellneigh %in%
> idcell$fcell)
> result.m <- matrix(NA, nrow=nrow(idcell), ncol=nrow(idcell))
> result.m[factor(result$fcell), factor(result$cellneigh)] <- result$distance
>
> Sarah
>
> On Thu, May 12, 2016 at 5:26 AM, A M Lavezzi <mario.lave...@unipa.it>
> wrote:
> > Hello,
> >
> > I have a sample of 1327 locations, each one idetified by an id and a
> > numerical code.
> >
> > I need to build a spatial matrix, say, M, i.e. a 1327x1327 matrix
> > collecting distances among the locations.
> >
> > M(i,i) should be 0, M(i,j) should contain the distance among location i
> and
> > j
> >
> > I shoud use data organized in the following way:
> >
> > 1) id_cell contains the identifier (id) of each location (1...1327) and
> the
> > numerical code of the location (f_cell) (see head of id_cell below)
> >
> >> head(id_cell)
> > id f_cell
> > 1 1 2120
> > 12 2 204
> > 22 3 2546
> > 24 4 1327
> > 34 5 1729
> > 43 6 2293
> >
> > 2) censDist contains, for each location identified by its numerical code,
> > the distance to other locations (censDist has 1.5 million rows). The
> > head(consist) below, for example, reads like this:
> >
> > location 2924 has a distance to 2732 of 1309.7525
> > location 2924 has a distance to 2875 of 696.2891,
> > etc.
> >
> >> head(censDist)
> > f_cell f _cell_neigh distance
> > 1 2924 2732 1309.7525
> > 2 2924 2875 696.2891
> > 3 2924 2351 1346.0561
> > 4 2924 2350 1296.9804
> > 5 2924 2725 1278.1877
> > 6 2924 2721 1346.9126
> >
> >
> > Basically, for every location in id_cell I should pick up the distance
> to
> > other locations in id_cell from censDist, and allocate it in M
> >
> > I have not come up with a satisfactory vectorizion of this problem and
> > using a loop is out of question.
> >
> > Thanks for your help
> > Mario
> >
> >
>
--
Andrea Mario Lavezzi
DiGi,Sezione Diritto e Società
Università di Palermo
Piazza Bologni 8
90134 Palermo, Italy
tel. ++39 091 23892208
fax ++39 091 6111268
skype: lavezzimario
email: mario.lavezzi (at) unipa.it
web: http://www.unipa.it/~mario.lavezzi
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