cluster_points: Point clustering methods for spatial data with plotting...

In gmcirco/crimtools:

Point clustering methods for spatial data with plotting functions

Description

Calculates cluster membership using DBScan

Usage

cluster_points(x, region, dist, minPts, verbose = T, plot = T, return_pts = T)

Arguments

x	an 'sf' point pattern. Should be the point pattern of interest.
region	an 'sf' polygon feature that defines the study region.
dist	a distance measure that defines the maximum neighborhood radius within points will be considered for cluster assignment.
minPts	The minimum number of points required within the dist measure for core points.
verbose	Should 'dbscan' results be printed? Defaults to TRUE.
plot	Should a plot be generated? Defaults to TRUE.
return_pts	Should the original x value be returned? Defaults to TRUE. The returned x value will contain a new column 'K' which contains integer identifiers for DBSCAN clusters, where 'K = 0' is a noise point, and 'K > 0 ' is a cluster ID.

Details

The 'cluster_points' function applies the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN is a fast clustering method that distinguishes clusters of points from "noise" points based on a user-defined neighborhood distance and a minimum points threshold. The 'cluster_points' function simplifies the fitting and visualization of spatial point features. Users can return the original data with cluster IDs attached for further analysis.

#' @examples data("newhaven") data("nh_hom")

cluster_out <- cluster_points(x = nh_hom, region = newhaven, dist = 2000, minPts = 5)

References

Hahsler M, Piekenbrock M, Doran D (2019). dbscan: Fast Density-Based Clustering with R. Journal of Statistical Software, 91(1), 1-30. doi: 10.18637/jss.v091.i01

gmcirco/crimtools documentation built on May 22, 2022, 1:31 a.m.