Nearest Neighbour Map on Linear Network
Compute the nearest neighbour function of a point pattern on a linear network.
## S3 method for class 'lpp'
nnfun(X, ..., k=1, value=c("index", "mark"))X |
A point pattern on a linear network
(object of class |
k |
Integer. The algorithm finds the |
value |
String (partially matched) specifying whether to return the
index of the neighbour ( |
... |
Other arguments are ignored. |
The (geodesic) nearest neighbour function of a
point pattern X on a linear network L
tells us which point of X is closest to
any given location.
If X is a point pattern on a linear network L,
the nearest neighbour function of X
is the mathematical function f defined for any
location s on the network by f(s) = i, where
X[i] is the closest point of X to the location s
measured by the shortest path. In other words the value of f(s)
is the identifier or serial number of the closest point of X.
The command nnfun.lpp is a method for the generic command
nnfun
for the class "lpp" of point patterns on a linear network.
If X is a point pattern on a linear network,
f <- nnfun(X) returns a function
in the R language, with arguments x,y, ..., that represents the
nearest neighbour 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 a vector of identifiers or serial numbers of
the data points closest to these spatial locations.
More efficiently f can take the arguments
x, y, seg, tp where seg and tp are the local
coordinates on the network.
The result of f <- nnfun(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 in the R language, 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 compute the distance to the nearest neighbour, see
distfun.lpp.
X <- runiflpp(3, simplenet) f <- nnfun(X) f plot(f) plot(nnfun(chicago, value="m"))
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