[R-sig-Geo] clustering spatial point data

[marcelino.delacruz at upm.es]

My less of two cents:

I  think Tal Avgar is asking for some kind of spatial-constrained
clustering tool as, e.g.,  in Legendre and Legendre (1999 Numerical
Ecology: 756-760).

Maybe function chclust  in the rioja package could  help with this
problem. It it would not, Legendre and Legendre (1999) describe very
clearly an algorithm to perfom this task that, surely, may be easily
implemented in R.

HTH,
Marcelino

Con fecha 3/6/2011, "Rolf Turner" <r.turner at auckland.ac.nz> escribió:

>On 03/06/11 04:49, Tal Avgar wrote:
>> I am looking for a code/function/algorithm for clustering spatial point data
>> into two distinct groups, based on spatial coordinates and a measure of a
>> continuous response variable at these locations. The requirement is for
>> group members to be as similar as possible in their affiliated response
>> values but also group members must be clustered in space so that there are
>> no events belonging to one group within the space affiliated with the other.
>> Any ideas?
>> Thanks,
>> Tal.
>
>I haven't yet seen any replies to your post, so I'll chip in with my
>two cents (or less!) worth.
>
>I think you need to be more explicit/specific as to how you wish
>to form the clusters.  Clustering is usually based on some sort
>of distance measure between the points.  Your distance measure
>will need to be based both on the spatial distance between the
>points and the difference in the values of ``the continuous response
>variable''  (such a value is referred to in the trade as a numeric
>*mark*) corresponding to a given point.
>
>Once you've defined the distance measure you should be able
>to create a ``distance matrix'' and then apply standard clustering
>techniques (readily available in R) to that matrix.
>
>One very naive approach would be just to use the Euclidean distance
>between the triples (x_i, y_i, z_i) where x_i and y_i specify the point
>locations and z_i is the numeric mark of the point in question.
>
>Using Euclidean distance is almost surely *not* the right thing to do.
>However you could try it, since it would be very easy to implement,
>and see what it tells you.  You might thereby get some insight into
>how to define the distance measure properly.
>
>     cheers,
>
>         Rolf Turner
>
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