Spatio-Temporal DBSCAN Clustering

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) .

stdbscan

Overview

stdbscan implements the ST-DBSCAN (Spatio-Temporal DBSCAN) algorithm developed by Birant & Kut (2007). It extends DBSCAN by adding a temporal parameter that allows spatio-temporal clustering.

For performance and compatibility, this package heavily relies on dbscan. All CPU-consuming functions are written in C++ via Rcpp.

Installation

You can install the released version of stdbscan from CRAN with:

install.packages("stdbscan")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("MiboraMinima/stdbscan")

Usage

An example of the application of stdbscan is available in the vignette on stop identification.

Breaking changes

• In version 0.2.0, st_dbscan() uses a matrix as input instead of raw x, y and t variables.

Problems and Issues

• Please report any issues or bugs you may encounter on the dedicated page on github.

System Requirements

stdbscan requires R v >= 3.5.0.

Alternatives

R :

• ST-DBSCAN
• stdbscanr

python :

• st_dbscan
• py-st-dbscan

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