ST-DBSCAN

ST-DBSCAN (Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise) is a Python package for clustering spatio-temporal data, extending the traditional DBSCAN algorithm to incorporate both spatial and temporal dimensions. It is particularly useful for analyzing movement patterns, event detection, and identifying clusters in space-time data. The package offers researchers a simple yet powerful method for handling complex spatio-temporal datasets, especially in fields like collective behaviour, urban analytics, and transportation studies. ST-DBSCAN can effectively process data with varying densities and detect clusters of arbitrary shapes, making it versatile for diverse applications. It considers both spatial proximity and temporal relationships between data points, allowing for more nuanced and meaningful cluster identification compared to purely spatial clustering methods. The implementation provides options for customizing clustering parameters, enabling researchers to fine-tune the algorithm for specific dataset characteristics and research objectives.