# lintess

##### Tessellation on a Linear Network

Create a tessellation on a linear network.

##### Usage

`lintess(L, df, marks=NULL)`

##### Arguments

- L
Linear network (object of class

`"linnet"`

).- df
Data frame of local coordinates for the pieces that make up the tiles of the tessellation. See Details.

- marks
Vector or data frame of marks associated with the tiles of the tessellation.

##### Details

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.

##### Value

An object of class `"lintess"`

.
There are methods for `print`

, `plot`

and
`summary`

for this object.

##### See Also

`linnet`

for linear networks.

`plot.lintess`

for plotting.

`divide.linnet`

to make a tessellation demarcated by
given points.

`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.

##### Examples

```
# NOT RUN {
# 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)
# }
```

*Documentation reproduced from package spatstat, version 1.63-0, License: GPL (>= 2)*