Thank you very much! This is a good trick...!
I was not able to use your function, hclust (I don't know why, my R on
Windows go in crash), but I analyze my final dist object "dspatiotemporal"
with DBSCAN and the results are much better than before.

sismi_spatiotemporal <- dbscan(dspatiotemporal, 3, MinPts = 5,
countmode=NULL, method="dist")

Thanks again to all,
Riccardo


2011/9/9 <marcelino.delacruz@upm.es>

> Maybe I didn't undestand your question on my first answer.
>
> If you want to cluster your points you may use standard functions in R.
> For example:
>
> #compute a spatial distance between points:
> dspatial <- dist (cbind(utm_x,utm_y))
>
> # compute a temporal distance between points:
>
> dtemporal <- wathever function to compute temporal distances (I don't
> work with temporal data so I don't know yet!)
>
> # truncate spatial distance matrix to work with distnaces less than 3 Km
>
> dspatial.l.3 <-dspatial
> dspatial.l.3[dspatial.l.3 > 3] <- 99999 # or wathever other big number
> plot(hclust(dspatial.l.3, method = "single"))
>
> # these are the clusters of points within 3 km from each other:
> cutree(hclust(dspatial.l.3, method = "single"),h=3)
>
> # truncate temporal distance matrix to work with distances less than 14
> days
>
> dtemporal.l.14 <- dtemporal
> dtemporal.l.14[dtemporal.l.14 >14] <- 99999
> dtemporal.l.14[dtemporal.l.14 <=14] <- 0
>
>
> # sum spatial and teporal distances:
> dspatiotemporal <- dspatial.l.3 + dtemporal.l.14
>
> # compute clusters that include points within 3 Km and which had
> earthquakes within 14 days
> plot(hclust(dspatiotemporal,"single"))
> cutree(hclust(dspatiotemporal,"single"),h=3)
>
>
>
> HTH,
>
> MArcelino
>
>
>
>
>
> Con fecha 8/9/2011, "Riccardo Gaeta" <riccardog79@gmail.com> escribió:
>
> >I've a look at spatstat, I've found the new object "ppx" but no idea to
> how
> >manipulate it. Also in the guide on CRAN they don't speak about cluster or
> >grouping data in 3D, there is just a function that calculate the distance
> in
> >3D, but I have as result a matrix like a "dist" object, and the problem
> >remains the same.
> >
> >Thanks anyway!
> >
> >
> >
> >2011/9/8 Marcelino de la Cruz <marcelino.delacruz@upm.es>
> >
> >>  Hi,
> >>
> >> you may consider using package spatstat, that has some functions to deal
> >> with  multi-dimensional space-time point pattern objects
> >>
> >>
> >> Cheers,
> >>
> >> Marcelino
> >>
> >>
> >> At 15:15 08/09/2011, Riccardo Gaeta wrote:
> >>
> >> Hi all,
> >> it's my first time I write on this list.
> >> I expose my problem quickly and (hope) clearly.
> >>
> >> 1) FIRST APPROACH:
> >> I have some spatial points that are related to each other by some
> >> spatial&temporal conditions. I have a dataframe that contains the couple
> of
> >> points in relation. For example:
> >>
> >> col1 ; col2
> >> A       B
> >> B       C
> >> B       D
> >> E       D
> >>
> >> I've obtained this list of couples by creating some "dist" object and
> >> searching correspondence between them.
> >> This list of couples means that: A is relate to B, B is relate to C and
> to
> >> D, E is relate to D.
> >> I would like to write a script that create a vector in which I have: A B
> C
> >> D
> >> E recognized as a unique group.
> >> I've tried some "for loop" but I'm wondering if maybe there is a more
> >> conventional function.
> >>
> >> 2) SECOND APPROACH:
> >> In particularly I'm working on seismological data, and I'm studying a
> way
> >> to
> >> recognize seismic sequences, so to elaborate space-time cluster on my
> data.
> >> I have 2D spatial points with indication of time of occurrence of the
> >> seism,
> >> and I would like to clusterize them with concern about these "3
> >> dimensions":
> >> coordinates x-y and time. I've successful used DBSCAN for
> space-clustering,
> >> but it supports (I suppose) only 2 dimensions.
> >>
> >> I give you at the end of the mail more details in case you would like to
> >> help me.
> >>
> >>
> >> Thanks very much for any help,
> >> Riccardo
> >>
> >>
> >> ********************
> >>
> >> My "3D" spatial-temporal points:
> >>
> >>     utm_x  utm_y  date
> >> 1  100000 4200 <callto:000 4200>000 "2008-06-17 04:12:27 CEST"
> >> 2  155000 4250 <callto:000 4250>000 "2008-10-15 09:06:43 CEST"
> >> 3  277000 4750 <callto:000 4750>000 "2010-10-25 22:50:42 CEST"
> >>
> >> ...(5600 points)
> >>
> >> My criterion to build cluster:
> >> a- points within 3km of distance from each other
> >> b- earthquakes happened within 14 days (2 weeks) from each other
> >>
> >> **
> >>         ** [[alternative HTML version deleted]]
> >>
> >> _______________________________________________
> >> R-sig-Geo mailing list
> >> R-sig-Geo@r-project.org
> >>  https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >>
> >> **
> >>
> >> ** ____________________________________
> >>
> >> Marcelino de la Cruz Rot
> >> Depto. Biologia Vegetal
> >> EUIT Agricola
> >> Universidad Politecnica de Madrid
> >>
> >> tel: 34 + 913365435
> >> ____________________________________
> >>
>

	[[alternative HTML version deleted]]

