Title:	Spatio-Temporal DBSCAN Clustering
Version:	0.1.0
Description:	Implements the ST-DBSCAN (spatio-temporal density-based spatial clustering of applications with noise) clustering algorithm for detecting spatially and temporally dense regions in point data, with a fast C++ backend via 'Rcpp'. Birant and Kut (2007) <doi:10.1016/j.datak.2006.01.013>.
License:	GPL (≥ 3)
Depends:	R (≥ 3.5.0)
Encoding:	UTF-8
RoxygenNote:	7.3.3
URL:	https://github.com/MiboraMinima/stdbscan/, https://miboraminima.github.io/stdbscan/
BugReports:	https://github.com/MiboraMinima/stdbscan/issues/
LinkingTo:	Rcpp
Imports:	Rcpp
Suggests:	knitr, rmarkdown, readr, testthat, ggplot2, lubridate, plotly, covr, MetBrewer
VignetteBuilder:	knitr
LazyData:	true
NeedsCompilation:	yes
Packaged:	2026-01-22 18:02:50 UTC; miboraminima
Author:	Antoine Le Doeuff [aut, cre]
Maintainer:	Antoine Le Doeuff <antoine.ldoeuff@gmail.com>
Repository:	CRAN
Date/Publication:	2026-01-27 09:20:08 UTC

stdbscan: Spatio-Temporal DBSCAN Clustering

Description

Implements the ST-DBSCAN (spatio-temporal density-based spatial clustering of applications with noise) clustering algorithm for detecting spatially and temporally dense regions in point data, with a fast C++ backend via 'Rcpp'. Birant and Kut (2007) doi:10.1016/j.datak.2006.01.013.

Author(s)

Maintainer: Antoine Le Doeuff antoine.ldoeuff@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/MiboraMinima/stdbscan/

• https://miboraminima.github.io/stdbscan/

• Report bugs at https://github.com/MiboraMinima/stdbscan/issues/

GPS pings from the GeoLife GPS Trajectories dataset

Description

Extraction of the GeoLife GPS Trajectories dataset. The selected trajectory id is 000-20081023025304.

Data manipulation applied to the raw data :

• Conversion to EPSG:4586

• Manual selection of the pings

• Selection of relevant variables

Usage

geolife_traj

Format

A data.frame with one row per ping and the following columns:

• date (chr): The date

• time (chr): The time

• x (dbl): Longitude (EPSG:4586)

• y (dbl): Latitude (EPSG:4586)

Source

https://www.microsoft.com/en-us/download/details.aspx?id=52367

Examples

data(geolife_traj)
head(geolife_traj)

Spatio-Temporal DBSCAN

Description

Perform ST-DBSCAN clustering on points with spatial and temporal coordinates. This algorithm identifies clusters of points that are close both in space and time.

Usage

st_dbscan(x, y, t, eps_spatial, eps_temporal, min_pts)

Arguments

Details

ST-DBSCAN extends classical DBSCAN by incorporating a temporal constraint. Two points are considered neighbors if they are within eps_spatial in space and within eps_temporal in time. Clusters are expanded from core points recursively following the DBSCAN algorithm.

This function is implemented in C++ via Rcpp for performance.

Value

An integer vector of length length(x) with cluster assignments:

• -1: noise point

• ⁠>=1⁠: cluster ID

References

Birant, D., & Kut, A. (2007). ST-DBSCAN: An algorithm for clustering spatial–temporal data. Data & Knowledge Engineering, 60(1), 208–221. https://doi.org/10.1016/j.datak.2006.01.013

Examples

data(geolife_traj)

geolife_traj$date_time <- as.POSIXct(
  paste(geolife_traj$date, geolife_traj$time),
  format = "%Y-%m-%d %H:%M:%S",
  tz = "GMT"
)

geolife_traj$t <- as.numeric(
  geolife_traj$date_time - min(geolife_traj$date_time)
)

st_dbscan(
  x = geolife_traj$x,
  y = geolife_traj$y,
  t = geolife_traj$t,
  eps_spatial = 3, # meters
  eps_temporal = 30, # seconds
  min_pts = 3
)