density.stlpp {stlnpp}

Kernel estimation of intensity of spatio-temporal point patterns on a linear network

Description

Kernel density estimation of a spatio-temporal point pattern on a linear network.

Usage

## S3 method for class 'stlpp'
density(x,lbw,tbw,at=c("points","pixels"),dimt=512,...)

Arguments

x

an object of class stlpp

lbw

network smoothing bandwidth

tbw

time smoothing bandwidth

at

string specifying whether to compute the intensity values at a grid of pixel locations and times (at="pixels") or only at the points of x (at="points"). default is to estimate the intensity at pixels

dimt

the number of equally spaced points at which the temporal density is to be estimated. see density

...

arguments passed to density.lpp

Details

Kernel smoothing is applied to the spatio-temporal point pattern x using methods in Moradi et al (2019). The function computes estimated intensities assuming first-order separability. Estimated intensity values of the marginal spatial point pattern on the linear network will be obtained using the fast kernel smoothing technique of Rakshit et al. (2019) and function densityQuick.lpp, whereas the estimated intensity values of the marginal temporal point pattern will be estimated using the function density.

If lbw and tbw are not given, then they will be selected using bw.nrd0 and bw.scott.iso respectively.

Value

If at="points": a vector of intensity values at the data points of x. If at="pixels": a list of images on linear network. Each image represents an estimated spatio-temporal intensity at a fixed time. Check the attributes for more accommodated outputs.

Author(s)

Mehdi Moradi <[e-mail]>

References

Moradi, M., & Mateu, J. (2020). First-and second-order characteristics of spatio-temporal point processes on linear networks. Journal of Computational and Graphical Statistics, 29(3), 432-443.

See Also

density, density.lpp, bw.nrd0, bw.scott.iso

Examples

 
X <- rpoistlpp(.2,a=0,b=5,L=easynet)
density(X)