DBSCAN density clusters on lat/long

Requires the optional dbscan package. Coordinates are projected to km via the small-angle latitude factor so eps_km is interpretable as a kilometre-scale radius.

Usage

morie_tps_dbscan_clusters(
  df,
  ds_name = "?",
  eps_km = 0.25,
  min_samples = 30L,
  max_n = 30000L,
  lat_col = "LAT_WGS84",
  lon_col = "LONG_WGS84"
)

Arguments

df

Incident-level data.frame.

ds_name

Tag for the result title.

eps_km

Neighbourhood radius in km.

min_samples

DBSCAN minPts parameter.

max_n

Subsample cap to keep DBSCAN tractable.

lat_col, lon_col

WGS84 column names.

Value

A named list with the per-cluster table, the count of noise points, and the largest-cluster size.

Examples

if (requireNamespace("dbscan", quietly = TRUE)) {
  set.seed(2026)
  df <- data.frame(
    LAT_WGS84 = c(rnorm(60, 43.65, 0.005), rnorm(60, 43.70, 0.005)),
    LONG_WGS84 = c(rnorm(60, -79.40, 0.005), rnorm(60, -79.38, 0.005))
  )
  morie_tps_dbscan_clusters(df, eps_km = 0.5, min_samples = 5L)
}
#> DBSCAN density clusters -- ?
#> ============================
#> Call: morie_tps_dbscan_clusters(df=<120r>, eps_km=0.5, min_samples=5) 
#> 
#>   Points clustered      120
#>   eps (km)              0.5
#>   min_samples           5
#>   Clusters discovered   2
#>   Noise points          5
#>   Largest cluster size  58
#> 
#> DBSCAN with eps=0.5 km, minPts=5 on 120 point(s) found 2 cluster(s); 5 point(s) classified as noise (no nearby cluster). Largest cluster has 58 member(s).