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spatstat.linnet-package

The spatstat.linnet Package


Description

The spatstat.linnet package belongs to the spatstat family of packages. It contains the functionality for analysing spatial data on a linear network.

Details

spatstat is a family of R packages for the statistical analysis of spatial data. Its main focus is the analysis of spatial patterns of points in two-dimensional space.

The original spatstat package has now been split into several sub-packages.

This sub-package spatstat.linnet contains the user-level functions from spatstat that are concerned with spatial data on a linear network.

Structure of the spatstat family

The orginal spatstat package grew to be very large. It has now been divided into several sub-packages:

  • spatstat.utils containing basic utilities

  • spatstat.sparse containing linear algebra utilities

  • spatstat.data containing datasets

  • spatstat.geom containing geometrical objects and geometrical operations

  • spatstat.core containing the main functionality for statistical analysis and modelling of spatial data

  • spatstat.linnet containing functions for spatial data on a linear network

  • spatstat, which simply loads the other sub-packages listed above, and provides documentation.

When you install spatstat, these sub-packages are also installed. Then if you load the spatstat package by typing library(spatstat), the other sub-packages listed above will automatically be loaded or imported. For an overview of all the functions available in these sub-packages, see the help file for spatstat in the spatstat package,

Additionally there are several extension packages:

  • spatstat.gui for interactive graphics

  • spatstat.local for local likelihood (including geographically weighted regression)

  • spatstat.Knet for additional, computationally efficient code for linear networks

  • spatstat.sphere (under development) for spatial data on a sphere, including spatial data on the earth's surface

The extension packages must be installed separately and loaded explicitly if needed. They also have separate documentation.

Overview of functionality

Here is a list of the main functionality in spatstat.linnet.

Point patterns on a linear network

An object of class "linnet" represents a linear network (for example, a road network).

linnet create a linear network
clickjoin interactively join vertices in network
spatstat.gui::iplot.linnet interactively plot network
simplenet simple example of network
lineardisc disc in a linear network
delaunayNetwork network of Delaunay triangulation
dirichletNetwork network of Dirichlet edges
methods.linnet methods for linnet objects
vertices.linnet nodes of network
joinVertices join existing vertices in a network
insertVertices insert new vertices at positions along a network
addVertices add new vertices, extending a network
thinNetwork remove vertices or lines from a network
repairNetwork repair internal format
pixellate.linnet approximate by pixel image

An object of class "lpp" represents a point pattern on a linear network (for example, road accidents on a road network).

lpp create a point pattern on a linear network
methods.lpp methods for lpp objects
subset.lpp method for subset
rpoislpp simulate Poisson points on linear network
runiflpp simulate random points on a linear network
chicago Chicago crime data
dendrite Dendritic spines data
spiders Spider webs on mortar lines of brick wall

Summary statistics for a point pattern on a linear network:

These are for point patterns on a linear network (class lpp). For unmarked patterns:

linearK K function on linear network
linearKinhom inhomogeneous K function on linear network
linearpcf pair correlation function on linear network
linearpcfinhom inhomogeneous pair correlation on linear network

For multitype patterns:

linearKcross K function between two types of points
linearKdot K function from one type to any type
linearKcross.inhom Inhomogeneous version of linearKcross
linearKdot.inhom Inhomogeneous version of linearKdot
linearmarkconnect Mark connection function on linear network
linearmarkequal Mark equality function on linear network
linearpcfcross Pair correlation between two types of points
linearpcfdot Pair correlation from one type to any type
linearpcfcross.inhom Inhomogeneous version of linearpcfcross
linearpcfdot.inhom Inhomogeneous version of linearpcfdot

Related facilities:

pairdist.lpp distances between pairs
crossdist.lpp distances between pairs
nndist.lpp nearest neighbour distances
nncross.lpp nearest neighbour distances
nnwhich.lpp find nearest neighbours
nnfun.lpp find nearest data point
density.lpp kernel smoothing estimator of intensity
distfun.lpp distance transform
envelope.lpp simulation envelopes
rpoislpp simulate Poisson points on linear network
runiflpp simulate random points on a linear network

It is also possible to fit point process models to lpp objects.

Point process models on a linear network:

An object of class "lpp" represents a pattern of points on a linear network. Point process models can also be fitted to these objects. Currently only Poisson models can be fitted.

lppm point process model on linear network
anova.lppm analysis of deviance for
point process model on linear network
envelope.lppm simulation envelopes for
point process model on linear network
fitted.lppm fitted intensity values
predict.lppm model prediction on linear network
linim pixel image on linear network
plot.linim plot a pixel image on linear network
eval.linim evaluate expression involving images
linfun function defined on linear network
methods.linfun conversion facilities

Licence

This library and its documentation are usable under the terms of the "GNU General Public License", a copy of which is distributed with the package.

Acknowledgements

Ottmar Cronie, Tilman Davies, Greg McSwiggan and Suman Rakshit made substantial contributions of code.

Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.


spatstat.linnet

Linear Networks Functionality of the 'spatstat' Family

v2.1-1
GPL (>= 2)
Authors
Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut], Ottmar Cronie [ctb], Tilman Davies [ctb], Greg McSwiggan [ctb], Suman Rakshit [ctb]
Initial release
2021-03-28

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