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spatstat.linnet

Spatial analysis on a linear network, for the spatstat family

CRAN_Status_Badge GitHub R package version

The original spatstat package has been split into several sub-packages (See spatstat/spatstat).

This package spatstat.linnet is one of the sub-packages. It contains the subset of the functionality of spatstat that deals with data on linear networks.

There is also an extension package spatstat.Knet which contains additional algorithms for linear networks.

Where to find data

Examples of datasets on linear networks are the point patterns chicago, dendrite and spiders provided in the spatstat.data package (available when spatstat.linnet is loaded) and the point pattern wacrashes provided in the extension package spatstat.Knet (which must be loaded separately).

Overview of spatstat.linnet

spatstat.linnet supports

Network geometry

  • creation of linear networks from coordinate data
  • extraction of networks from tessellations
  • modification of networks
  • interactive editing of networks
  • geometrical operations and measurement on networks
  • construction of the disc in the shortest-path metric
  • trees, tree branch labels, tree pruning

Point patterns on a network

  • creation of point patterns on a network from coordinate data
  • extraction of sub-patterns
  • shortest-path distance measurement

Covariates on a network

  • create pixel images and functions on a network
  • arithmetic operators for pixel images on a network
  • plot pixel images on a network (colour/thickness/perspective)
  • tessellation on a network

Simulation

  • completely random (uniform Poisson) point patterns on a network
  • nonuniform random (Poisson) point patterns on a network
  • Switzer-type point process
  • log-Gaussian Cox process

Exploratory analysis of point patterns on a network

  • kernel density estimation on a network
  • bandwidth selection
  • kernel smoothing on a network
  • estimation of intensity as a function of a covariate
  • ROC curves
  • Berman-Waller-Lawson test
  • CDF test
  • variable selection by Sufficient Dimension Reduction
  • K function on a network (shortest path or Euclidean distance)
  • pair correlation function on a network (shortest path or Euclidean distance)
  • inhomogeneous K function and pair correlation function
  • inhomogeneous F, G and J functions
  • simulation envelopes of summary functions

Parametric modelling and inference on a network

  • fit point process model on a network
  • fitted/predicted intensity
  • analysis of deviance for point process model
  • simulate fitted model