drhot: Identifies differential risk hotspots along a linear network...

In DRHotNet: Differential Risk Hotspots in a Linear Network

Identifies differential risk hotspots along a linear network given a vector of relative probabilities computed over the middle points of the segments of the network

Description

Given a relative probability surface corresponding to the occurrence of a type of event along a linear network, this function filters and groups in hotspots those segments satisfying two conditions: 1) the relative probability in the segment exceeds the average relative probability per segment in k times the standard deviation of the complete set of probabilities estimated across all the segments of the network, and 2) there are n or more events at a distance below h from the middle point of the segment (h is obtained from the object rel_probs computed with the function relpnet). In summary, k and n control the formation of differential risk hotspots along the network, given a set of relative probabilities covering the network. The choice of a higher value for k or n (or both) represents a more strict criterion and leads to a lower number of differential risk hotspots being detected. Users should test several values of k and n (sensitivity analysis on k and n) in order to reach reasonable choices for the research or practical purposes of their data analyses. This sensitivity analysis can be carried out with the drsens function

Usage

drhot(X, rel_probs, k, n, dist = "path", event_distances = NULL)

Arguments

X	- A lpp object representing a marked point pattern lying on a road network (linnet object)
rel_probs	- An object containing the relative probabilities of a specific type of event along the linear network contained in X, generated through the function relpnet
k	- A numeric value that controls the procedure of detecting differential risk hotspots (departure from average relative probability), as described above
n	- A numeric value that controls the procedure of detecting differential risk hotspots (minimum size for the sample of events implicated in the computation of the relative probabilities), as described above
dist	- A character indicating which distance to use. Two values are allowed: path (shortest-path distance) and euclidean (Euclidean distance). By default, the shortest-path distance is used. Change to euclidean to reduce the computation time or skip memory issues
event_distances	- A matrix that contains the distances between the middle points of the segments satisfying the condition on parameter k and the events o X. By default it is set to NULL

Value

Returns a list that contains the differential risk hotspots found for X and the type of event specified by rel_probs

References

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.

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")
hotspots_assault <- drhot(X = chicago, rel_probs = rel_assault, 
k = 0.5, n = 4)

DRHotNet documentation built on July 26, 2023, 5:18 p.m.