I am sure this can be written in a much more elegant and faster code.
One way I can think of, is to modify cell2nb code and create a new
function that creates the neighbour lists of only cells that are not
0. These are best directed to R-sig-Geo list.
However, the following might work.
library(spdep)
library(igraph)
x=matrix(c(0,1,0,0,0,
0,1,1,0,0,
0,0,0,0,0,
0,0,0,1,0,
0,0,0,1,0),nrow=5)
a <- nb2mat(cell2nb(nrow(x),ncol(x)), style="B", torus="TRUE")
g <- delete.vertices(graph.adjacency(a), which(x!=1)-1)
plot(g)
clusters(g)
Nikhil
---
Nikhil Kaza
Asst. Professor,
City and Regional Planning
University of North Carolina
nikhil.l...@gmail.com
On Jun 20, 2010, at 7:17 PM, Daniel Malter wrote:
Hi all, I am sorry if this is a very basic quesion, but I have no
experience
with analyzing spatial data and could not find the right function/
package
quickly. Any hints would be much appreciated. I have a matrix of
spatial
point patterns like the one below and want to find the number of
independent
components (if that's the right term) in that matrix (or in that
image).
x=matrix(c(0,1,0,0,0,
0,1,1,0,0,
0,0,0,0,0,
0,0,0,1,0,
0,0,0,1,0),nrow=5)
image(x)
I can find the number of populated points easily
table(x) #or more generally
sum(x!=0)
But I want to find the number of independent components. The answer
in this
example should be 2. There are three criteria to the function I am
seeking:
1. Points that have a neighboring nonzero point should be counted as
one
contiguous component.
2. The function should respect that the matrix is projected on a
torso. That
is, points in the leftmost column border points in the rightmost
column and
points in the top row border points in the bottom row (if they are
contiguous when you wrap the image around a cylinder).
3. The function should be fast/efficient since I need to run this over
thousands of images/matrices.
Is anyone aware of an implementation of such a function?
Thanks much for your help,
Daniel
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