relpnet {DRHotNet}

Computes the relative probability of observing a type of event along a linear network

Description

Given a marked point pattern lying on a linear network structure, this function uses kernel density estimation (KDE) to estimate a relative probability of occurrence for a type of event specified by the user through the marks of the pattern. The marks of a point pattern represent additional information of the events that are part of the pattern

Usage

relpnet(X, lixel_length, h, mark, category_mark, finespacing = F)

Arguments

X

- A lpp object representing a marked point pattern lying on a linear network (linnet object)

lixel_length

- A numeric value representing a lixel length that will be used for creating a split version of the network contained in X. Then, the length of all the segments of the split network is below lixel_length

h

- A numeric value representing the bandwidth parameter (in meters)

mark

- Mark of X that is used to characterize the type of event. The algorithm searches microzones of the network where this mark is over- or underrepresented

category_mark

- A numeric/character value from the set allowed in the chosen mark to compute the relative probability in relation to it

finespacing

- A logical value specifying whether to use a finer spatial resolution (with longer computation time but higher accuracy). It is set to FALSE by default

Value

Returns a list that contains the relative probability values estimated along the network for the type of event specified by mark and category_mark

References

Baddeley, A., Rubak, E., & Turner, R. (2015). Spatial point patterns: methodology and applications with R. Chapman and Hall/CRC.

Briz-Redon, A., Martinez-Ruiz, F., & Montes, F. (2019). Identification of differential risk hotspots for collision and vehicle type in a directed linear network. Accident Analysis & Prevention, 132, 105278.

Diggle, P. J. (2013). Statistical analysis of spatial and spatio-temporal point patterns. Chapman and Hall/CRC.

Kelsall, J. E., & Diggle, P. J. (1995). Kernel estimation of relative risk. Bernoulli, 1(1-2), 3-16.

McSwiggan, G., Baddeley, A., & Nair, G. (2017). Kernel density estimation on a linear network. Scandinavian Journal of Statistics, 44(2), 324-345.

Examples

library(DRHotNet)
library(spatstat.geom)
library(spatstat.linnet)
library(spdep)
library(raster)
rel_assault <- relpnet(X = chicago, 
lixel_length = 50, h = 50, mark = "marks", category_mark = "assault")