Title:	Spatio-Temporal DBSCAN Clustering
Version:	0.2.0
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, dbscan
Suggests:	knitr, rmarkdown, readr, testthat, ggplot2, plotly, covr, MetBrewer
VignetteBuilder:	knitr
LazyData:	true
NeedsCompilation:	yes
Packaged:	2026-03-14 15:23:16 UTC; miboraminima
Author:	Antoine Le Doeuff [aut, cre]
Maintainer:	Antoine Le Doeuff <antoine.ldoeuff@gmail.com>
Repository:	CRAN
Date/Publication:	2026-03-14 15:40:02 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)

Predict newdata

Description

Assigns each new observation to an existing cluster from a fitted stdbscan object, or marks it as noise if it falls outside any cluster.

Usage

## S3 method for class 'stdbscan'
predict(object, data, newdata, ...)

Arguments

Value

An integer vector of cluster labels, matching the labels of the input stdbscan object.

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)
)
data <- cbind(geolife_traj$x, geolife_traj$y, geolife_traj$t)

res <- st_dbscan(
  data = data,
  eps_spatial = 3,
  eps_temporal = 30,
  min_pts = 5
)

newdata <- cbind(
  c(440160,  440165,  440144,  440130,  440160),
  c(4428129, 4428135, 4428120, 4428123, 4428122),
  c(4617,    4620,    4629,    4635,    4640)
)
predict(res, data, newdata)

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(data, 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.

ST-DBSCAN is implemented using the following approach:

1. Find the spatial neighbors using Fixed Radius Nearest Neighbors (dbscan::frNN())

2. Filter the spatial neighbors by the temporal constraint

3. Apply DBSCAN on the filtered neighbors using dbscan::dbscan()

Value

st_dbscan() returns an object of class stdbscan with the following components:

This class is a simple extension of the dbscan class. For more details, see dbscan documentation.

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)
)
data <- cbind(geolife_traj$x, geolife_traj$y, geolife_traj$t)

st_dbscan(
  data = data,
  eps_spatial = 3,
  eps_temporal = 30,
  min_pts = 3,
  # Extra arguments
  splitRule = "STD",
  search = "kdtree",
  approx = 1
)

Check if points are core points

Description

Check if data points are core points. A core point is a point with more than min_pts points in its neighborhood.

Usage

st_dbscan_corepoint(data, eps_spatial, eps_temporal, min_pts, ...)

Arguments

Value

A boolean vector indicating if data points are core points.

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)
)
data <- cbind(geolife_traj$x, geolife_traj$y, geolife_traj$t)

res <- st_dbscan_corepoint(
  data = data,
  eps_spatial = 3,
  eps_temporal = 30,
  min_pts = 3
)
head(res)