Compute Disc of Given Radius in Linear Network
Computes the ‘disc’ of given radius and centre in a linear network.
lineardisc(L, x = locator(1), r, plotit = TRUE,
cols=c("blue", "red","green"), add=TRUE)
countends(L, x = locator(1), r, toler=NULL, internal=list())L |
Linear network (object of class |
x |
Location of centre of disc.
Either a point pattern (object of class |
r |
Radius of disc. |
plotit |
Logical. Whether to plot the disc. |
add |
Logical. If |
cols |
Colours for plotting the disc. A numeric or character vector of length 3 specifying the colours of the disc centre, disc lines and disc endpoints respectively. |
toler |
Optional. Distance threshold for |
internal |
Argument for internal use by the package. |
The ‘disc’ B(u,r) of centre x and radius r in a linear network L is the set of all points u in L such that the shortest path distance from x to u is less than or equal to r. This is a union of line segments contained in L.
The relative boundary of the disc B(u,r) is the set of points v such that the shortest path distance from x to u is equal to r.
The function lineardisc computes the
disc of radius r and its relative boundary,
optionally plots them, and returns them.
The faster function countends simply counts the number of
points in the relative boundary.
The optional threshold toler is used to suppress numerical
errors in countends.
If the distance from u to a network vertex v
is between r-toler and r+toler, the vertex
will be treated as lying on the relative boundary.
The value of lineardisc is a list with two entries:
lines |
Line segment pattern (object of class |
endpoints |
Point pattern (object of class |
The value of countends is an integer giving the number of
points in the relative boundary.
Ang Qi Wei aqw07398@hotmail.com and Adrian Baddeley Adrian.Baddeley@curtin.edu.au
Ang, Q.W. (2010) Statistical methodology for events on a network. Master's thesis, School of Mathematics and Statistics, University of Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012) Geometrically corrected second-order analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics 39, 591–617.
# letter 'A'
v <- ppp(x=(-2):2, y=3*c(0,1,2,1,0), c(-3,3), c(-1,7))
edg <- cbind(1:4, 2:5)
edg <- rbind(edg, c(2,4))
letterA <- linnet(v, edges=edg)
plot(letterA)
lineardisc(letterA, c(0,3), 1.6)
# count the endpoints
countends(letterA, c(0,3), 1.6)
# cross-check (slower)
en <- lineardisc(letterA, c(0,3), 1.6, plotit=FALSE)$endpoints
npoints(en)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.