spatstat (version 1.55-0)

density.lpp: Kernel Estimate of Intensity on a Linear Network

Description

Estimates the intensity of a point process on a linear network by applying kernel smoothing to the point pattern data.

Usage

# S3 method for lpp
density(x, sigma, …,
        weights=NULL,
        kernel="gaussian",
        continuous=TRUE,
        epsilon = 1e-06, verbose = TRUE,
        debug = FALSE, savehistory = TRUE,
        old=FALSE)
# S3 method for splitppx
density(x, sigma, …)

Arguments

x

Point pattern on a linear network (object of class "lpp") to be smoothed.

sigma

Smoothing bandwidth (standard deviation of the kernel) in the same units as the spatial coordinates of x.

…

Arguments passed to as.mask determining the resolution of the result.

weights

Optional. Numeric vector of weights associated with the points of x. Weights may be positive, negative or zero.

kernel

Character string specifying the smoothing kernel. See dkernel for possible options.

continuous

Logical value indicating whether to compute the “equal-split continuous” smoother (continuous=TRUE, the default) or the “equal-split discontinuous” smoother (continuous=FALSE).

epsilon

Tolerance value. A tail of the kernel with total mass less than epsilon may be deleted.

verbose

Logical value indicating whether to print progress reports.

debug

Logical value indicating whether to print debugging information.

savehistory

Logical value indicating whether to save the entire history of the algorithm, for the purposes of evaluating performance.

old

Logical value indicating whether to use the old, very slow algorithm for the equal-split continuous estimator.

Value

A pixel image on the linear network (object of class "linim").

Details

Kernel smoothing is applied to the points of x using one of the rules described in Okabe and Sugihara (2012) and McSwiggan et al (2016). The result is a pixel image on the linear network (class "linim") which can be plotted.

If continuous=TRUE (the default), smoothing is performed using the “equal-split continuous” rule described in Section 9.2.3 of Okabe and Sugihara (2012). The resulting function is continuous on the linear network.

If continuous=FALSE, smoothing is performed using the “equal-split discontinuous” rule described in Section 9.2.2 of Okabe and Sugihara (2012). The resulting function is not continuous.

In the default case (where continuous=TRUE and kernel="gaussian" and old=FALSE), computation is performed rapidly by solving the classical heat equation on the network, as described in McSwiggan et al (2016). Computational time is short, but increases quadratically with sigma. The arguments epsilon,debug,verbose,savehistory are ignored.

In all other cases, computation is performed by path-tracing as described in Okabe and Sugihara (2012); computation can be extremely slow, and time increases exponentially with sigma.

There is also a method for split point patterns on a linear network (class "splitppx") which will return a list of pixel images.

References

McSwiggan, G., Baddeley, A. and Nair, G. (2016) Kernel density estimation on a linear network. Scandinavian Journal of Statistics, In press.

Okabe, A. and Sugihara, K. (2012) Spatial analysis along networks. Wiley.

See Also

lpp, linim

Examples

# NOT RUN {
  X <- runiflpp(3, simplenet)
  D <- density(X, 0.2, verbose=FALSE)
  plot(D, style="w", main="", adjust=2)
  Dw <- density(X, 0.2, weights=c(1,2,-1), verbose=FALSE)
  De <- density(X, 0.2, kernel="epanechnikov", verbose=FALSE)
  Ded <- density(X, 0.2, kernel="epanechnikov", continuous=FALSE, verbose=FALSE)
# }