linearkd: Kernel Density on Linear Network

In secrlinear: Spatially Explicit Capture-Recapture for Linear Habitats

Kernel Density on Linear Network

Description

This function applies the kernel density methods of McSwiggan et al. (2016) as implemented in spatstat (Baddeley et al. 2015). The default method solves the heat equation McSwiggan et al. (2016).

Usage

linearkd(X, linmask, sigma, which = NULL, ...)

Arguments

X	2-column matrix of coordinates
linmask	linear habitat mask
sigma	numeric spatial scale of kernel
which	index vector to select subset of edges (optional)
...	other arguments passed to spatstat.linnet::density.lpp

Details

The density along the network of the points in X is saved as covariate ‘density’ of the linear mask.

Setting to FALSE the argument ‘finespacing’ of densityHeat.lpp speeds up estimation.

Value

A linear habitat mask identical to the input except for the additional covariate.

Warning

This function is still in development: there are details to resolve concerning terminal vertices etc.

References

Baddeley, A., Rubak, E., and Turner, R. 2015. Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press, London. ISBN 9781482210200, https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/p/book/9781482210200/.

McSwiggan, G., Baddeley, A. and Nair, G. 2016. Kernel density estimation on a linear network. Scandinavian Journal of Statistics 44, 324–345.

See Also

read.linearmask

Examples

# simulate some points
set.seed(123)
pop <- sim.linearpopn(glymemask, N = 40)

# restrict edges to overcome a glitch in this particular linearmask

tmp <- linearkd(X = pop, linmask = glymemask, sigma = 30, 
    which = 1:325, finespacing = FALSE)

plot(tmp, cov = 'density', cex = 1.7)
plot (pop, add = TRUE, cex = 1.4)

# view covariates
head(covariates(tmp))

secrlinear documentation built on Oct. 22, 2024, 9:08 a.m.