merge_clusters: Spatially merge clusters

In wolfeclw/circleclust: Spatiotemporal Clustering for Personal Location Data

Spatially merge clusters

Description

merge_clusters() aggregates spatiotemporal clusters within a specified distance using the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. After merging, the remaining clusters are not temporally unique.

Usage

merge_clusters(
  df,
  dt_field = NULL,
  radius = 100,
  minPts = 5,
  borderPoints = TRUE,
  keep_noise = FALSE,
  noise_threshold = 1
)

Arguments

df	a data frame created by circleclust() with a sp_temporal_cluster column and datetime column.
dt_field	POSIXct; name of datetime field.
radius	numeric; distance threshold (meters) used to aggregate clusters.
minPts	numeric; minimum number of points points required in each cluster.
borderPoints	logical; should border points be assigned to clusters. Default = TRUE. If FALSE, border points are considered noise.
keep_noise	logical; should noise points be retained? Default = FALSE.
noise_threshold	numeric; threshold value (%) to determine if noise points should be retained. If the percentage of noise points is above this value, noise points are retained and column noise is appended to the output data frame. Noise points are deleted otherwise. This argument is ignored if 'keep_noise' is set to FALSE.

Details

merge_clusters() spatially combines clusters based on the Euclidean distance between points. Because the Earth is sphere, the calculated distances are not exact. See here.

Value

a data frame. The original spatiotemporal cluster values are retained in a column called sp_temporal_cluster. New spatially merged cluster values are listed under spatial_cluster.

Examples

## Not run: 

merge_clusters(
     df, dt_field = NULL, radius = 100, minPts = 5, borderPoints = TRUE,
     keep_noise = FALSE, noise_threshold = 1)

## End(Not run)

wolfeclw/circleclust documentation built on Aug. 13, 2024, 3:33 a.m.