Create a tessellation on a linear network.
lintess(L, df, marks=NULL)An object of class "lintess".
There are methods for print, plot and
summary for this object.
Linear network (object of class "linnet").
Data frame of local coordinates for the pieces that make up the tiles of the tessellation. See Details.
Vector or data frame of marks associated with the tiles of the tessellation.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Greg McSwiggan.
A tessellation on a linear network L is a partition of the
network into non-overlapping pieces (tiles). Each tile consists of one
or more line segments which are subsets of the line segments making up
the network. A tile can consist of several disjoint pieces.
The data frame df should have columns named
seg, t0, t1 and tile.
Any additional columns will be ignored.
Each row of the data frame specifies one sub-segment of the network and allocates it to a particular tile.
The seg column specifies which line segment of the network
contains the sub-segment. Values of seg are integer indices
for the segments in as.psp(L).
The t0 and t1 columns specify the start and end points
of the sub-segment. They should be numeric values between 0 and 1
inclusive, where the values 0 and 1 representing the network vertices
that are joined by this network segment.
The tile column specifies which tile of the tessellation
includes this sub-segment. It will be coerced to a factor and its
levels will be the names of the tiles.
If df is missing or NULL, the result is a tessellation
with only one tile, consisting of the entire network L.
Additional data called marks may be associated with
each tile of the tessellation. The argument marks should be
a vector with one entry for each tile (that is, one entry for each
level of df$tile) or a data frame with one row for each tile.
In general df and marks will have different numbers of rows.
linnet for linear networks.
plot.lintess for plotting.
divide.linnet to make a tessellation demarcated by
given points.
chop.linnet to make a tessellation demarcated by
infinite lines.
lineardirichlet to create the Dirichlet-Voronoi
tessellation from a point pattern on a linear network.
as.linfun.lintess, as.linnet.lintess and
as.linim to convert to other classes.
tile.lengths to compute the length of each tile
in the tessellation.
The undocumented methods Window.lintess and
as.owin.lintess extract the spatial window.
# tessellation consisting of one tile for each existing segment
ns <- nsegments(simplenet)
df <- data.frame(seg=1:ns, t0=0, t1=1, tile=letters[1:ns])
u <- lintess(simplenet, df)
u
plot(u)
S <- as.psp(simplenet)
marks(u) <- data.frame(len=lengths_psp(S), ang=angles.psp(S))
u
plot(u)
Run the code above in your browser using DataLab