as.linnet.psp: Convert Line Segment Pattern to Linear Network

View source: R/linnet.R

as.linnet.pspR Documentation

Convert Line Segment Pattern to Linear Network


Converts a line segment pattern to a linear network.


 ## S3 method for class 'psp'
as.linnet(X, ..., eps, sparse=FALSE)



Line segment pattern (object of class "psp").




Optional. Distance threshold. If two segment endpoints are closer than eps units apart, they will be treated as the same point, and will become a single vertex in the linear network.


Logical value indicating whether to use a sparse matrix representation, as explained in linnet.


This command converts any collection of line segments into a linear network by guessing the connectivity of the network, using the distance threshold eps.

If any segments in X cross over each other, they are first cut into pieces using selfcut.psp.

Then any pair of segment endpoints lying closer than eps units apart, is treated as a single vertex. The linear network is then constructed using linnet.

It would be wise to check the result by plotting the degree of each vertex, as shown in the Examples.

If X has marks, then these are stored in the resulting linear network Y <- as.linnet(X), and can be extracted as marks(as.psp(Y)) or marks(Y$lines).


A linear network (object of class "linnet").

The result also has an attribute "camefrom" indicating the provenance of each line in the resulting network. For example camefrom[3]=2 means that the third line segment in the result is a piece of the second segment of X.




See Also

linnet, selfcut.psp, methods.linnet.


  # make some data
  A <- psp(0.09, 0.55, 0.79, 0.80, window=owin())
  B <- superimpose(A, as.psp(simplenet))

  # convert to a linear network
  L <- as.linnet(B)

  # check validity
  text(vertices(L), labels=vertexdegree(L))

  # show the pieces that came from original segment number 1
  S <- as.psp(L)
  (camefrom <- attr(L, "camefrom"))
  parts <- which(camefrom == 1)
  plot(S[parts], add=TRUE, col="green", lwd=2)

spatstat.linnet documentation built on March 18, 2022, 6:40 p.m.