adaptive intensity estimation for spatio-temporal point patterns on linear networks using Voronoi-Dirichlet tessellation.
# S3 method for stlpp
densityVoronoi(X, f = 1, nrep = 1,
separable=FALSE,at=c("points","pixels"),
dimt=128,...)If at="points": a vector of intensity values at the data points of X.
If at="pixels": a list of images on a linear network. Each image represents an estimated saptio-temporal intensity at a fixed time.
an object of class stlpp
fraction (between 0 and 1 inclusive) of the data points that will be used to build a tessellation for the intensity estimate
number of independent repetitions of the randomised procedure
logical. If FALSE, it then calculates a pseudo-separable estimate
string specifying whether to return the intensity values at a grid of pixel locations and time (at="pixels") or only at the points of X (at="points"). default is to estimate the intensity at pixels
the number of equally spaced points at which the temporal density is to be estimated. see density
arguments passed to densityVoronoi.lpp
Mehdi Moradi <m2.moradi@yahoo.com>, and Ottmar Cronie.
This function computes intensity estimates for spatio-temporal point patterns on linear networks using Voronoi-Dirichlet tessellation. Both first-order separability and pseudo-separability assumptions are accommodated in the function.
If separable=TRUE, the estimated intensities will be a product of the estimated intensities on the network and those on time. Each will be obtained using densityVoronoi.lpp. If f=1, the function calculates the estimations based on the original Voronoi intensity estimator.
If separable=FALSE, the estimated intensities will be calculated based on a subsampling technique explained in Mateu et al. (2019). nrep subsamples will be obtained from X based on a given retention probability f, the function densityVoronoi.stlpp, considering separable=TRUE and f=1, will be applied to each obtained subsample, and finally, the estimated intensities will be the sum of all obtained estimated intensities from all subsamples divided by the (f * nrep).
Mateu, J., Moradi, M., & Cronie, O. (2019). Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation. Spatial Statistics, 100400.
densityVoronoi.lpp, density.stlpp
X <- rpoistlpp(0.2,a=0,b=5,L=easynet)
densityVoronoi(X)
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