R: Tessellation on a Linear Network

lintess {spatstat.linnet} | R Documentation

## Tessellation on a Linear Network

### Description

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.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Greg McSwiggan.

### 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

       # 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)

[Package spatstat.linnet version 2.0-0 Index]