[GRASSLIST:732] Spatial Clustering or Network Problem?

[Trevor Wiens]

I have an interesting problem and after spending some time looking
through the clustering documentation in R, I'm not sure that is the
right approach.

I have a region for which a series of bird surveys were conducted
within a vector defined grid. The coverage is not complete and I want to
assess how best to clump the grid squares based on a survey
completeness. Thus some grid squares will have a had no survey and
others will be partial and others will be complete. I want to group them
irregularly so that I end up with groups of squares that have had n
number of completed surveys conducted. 

When I first thought of this problem I thought of R and using some
clustering mechanism, but it would appear to me that these are all
based on the idea of grouping data based on similarity of value and
specifying at the outset how many groups you will end up with at the
end. My problem is the opposite in that I want to value for each group
to be as close to each other as possible, but I don't know how many
groups I will end up with.

Then I started wondering about turning the centroids of these squares
into a network using v.delaunay and using a network approach
to the problem, although I'm not sure how I might do this.

Any suggestions on how to approach this problem would be appreciated.

Thanks

T
-- 
Trevor Wiens 
twiens at interbaun.com

The significant problems that we face cannot be solved at the same 
level of thinking we were at when we created them. 
(Albert Einstein)