pois.lppm: Fit a Poisson point process model to a point pattern dataset...

In myllym/GET: Global Envelopes

Fit a Poisson point process model to a point pattern dataset on a linear network

Description

Fit a Poisson point process model to a point pattern dataset on a linear network. This function is provided in GET to support hotspots.poislpp and hotspots.MatClustlpp. See the hotspots vignette available by starting R, typing library("GET") and vignette("GET").

Usage

pois.lppm(PP, formula, data, subwin = NULL, r_max = NULL)

Arguments

PP	Input, a point pattern object (ppp) of spatstat.
formula	An R formula to estimate the first order model. This formula can contain objects of full size. PP should be on the left side of the formula.
data	Data from where the formula takes objects. Must be acceptable by the function lppm of spatstat.linnet.
subwin	A part of the observation window of PP to be used for estimating the second order structure. NULL means that the full point pattern is used. Typically this is feasible (not too time consuming).
r_max	The maximum distance on which the K-function is evaluated. Default is computed as \sqrt{A}/10 where A is the area of the window of observation of X.

Details

The function pois.lppm, can be used to estimate the inhomogeneous Poisson point process model on linear network. This function provides the firstordermodel, i.e. the regression model of dependence of crashes on the spatial covariates, EIP, i.e. estimated inhomogeneous intensity from the data and secondorder, i.e. estimation of the inhomogeneous K-function. The plot of the secondorder provides diagnostics, if the model is adequate for the data. If the estimated $K$-function lies close to the theoretical line, the data does not report any clustering, and the function hotspots.poislpp can be used for final hotspots detection. If the estimated K-function does not lie close to the theoretical line, and it is above, the data report clustering, and the a clustered point pattern model must be fitted to the data and hotspots detected using this clustered model instead.

myllym/GET documentation built on July 3, 2025, 8:48 p.m.