Distance Map on Linear Network
Compute the distance function of a point pattern on a linear network.
## S3 method for class 'lpp' distfun(X, ..., k=1)
X |
A point pattern on a linear network
(object of class |
k |
An integer. The distance to the |
... |
Extra arguments are ignored. |
On a linear network L, the “geodesic distance function”
of a set of points A in L is the
mathematical function f such that, for any
location s on L,
the function value f(s)
is the shortest-path distance from s to A.
The command distfun.lpp is a method for the generic command
distfun
for the class "lpp" of point patterns on a linear network.
If X is a point pattern on a linear network,
f <- distfun(X) returns a function
in the R language that represents the
distance function of X. Evaluating the function f
in the form v <- f(x,y), where x and y
are any numeric vectors of equal length containing coordinates of
spatial locations, yields the values of the distance function at these
locations. More efficiently f can be called in the form
v <- f(x, y, seg, tp) where seg and tp are the local
coordinates on the network. It can also be called as
v <- f(x) where x is a point pattern on the same linear
network.
The function f obtained from f <- distfun(X)
also belongs to the class "linfun".
It can be printed and plotted immediately as shown in the Examples.
It can be
converted to a pixel image using as.linim.
A function with arguments x,y and optional
arguments seg,tp.
It also belongs to the class "linfun" which has methods
for plot, print etc.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
To identify which point is the nearest neighbour, see
nnfun.lpp.
X <- runiflpp(3, simplenet) f <- distfun(X) f plot(f) # using a distfun as a covariate in a point process model: Y <- runiflpp(4, simplenet) fit <- lppm(Y ~D, covariates=list(D=f)) f(Y)
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