density.lpp

0th

Percentile

Kernel Estimate of Intensity on a Linear Network

Estimates the intensity of a point process on a linear network by applying kernel smoothing to the point pattern data, using the equal-split continuous algorithm.

Keywords
methods, smooth, spatial
Usage
## S3 method for class 'lpp':
density(x, sigma, \dots,
        epsilon = 1e-06, verbose = TRUE, debug = FALSE, savehistory = TRUE)

## S3 method for class 'splitppx': density(x, sigma, \dots)

Arguments
x
Point pattern on a linear network (object of class "lpp") to be smoothed.
sigma
Smoothing bandwidth (standard deviation of the Gaussian kernel) in the same units as the spatial coordinates of x.
...
Arguments passed to as.mask determining the resolution of the result.
epsilon
Tolerance value. A tail of the Gaussian 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.
Details

Kernel smoothing using the Gaussian kernel with the equal-split continuous rule is applied to the points of x. The result is a pixel image on the linear network (class "linim") which can be plotted.

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

Value

  • Pixel image on the linear network (class "linim").

WARNING

THIS ALGORITHM CAN BE EXTREMELY SLOW for large values of sigma.

The computational complexity increases exponentially with sigma. You Have Been Warned.

References

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

See Also

lpp, linim

Aliases
  • density.lpp
  • density.splitppx
Examples
X <- runiflpp(3, simplenet)
  D <- density(X, 0.2, verbose=FALSE)
  plot(D, style="w", main="", adjust=2)
Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)

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