Simple and effective method for spatial-temporal clustering

st_dbscan is an open-source software package for the spatial-temporal clustering of movement data:

• Implemnted using numpy and sklearn
• Scales to memory - using chuncking see st_dbscan.fit_frame_split

The easiest way to install st_dbscan is by using pip :

pip install st-dbscan

from st_dbscan import ST_DBSCAN

st_dbscan = ST_DBSCAN(eps1 = 0.05, eps2 = 10, min_samples = 5)
st_dbscan.fit(data)

• Demo Notebook: the following noteboook shows a demo of common features in this package - see Jupyter Notebook

A package to perform the ST_DBSCAN clustering. For more details please see the following papers:

* Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise". In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
* Birant, Derya, and Alp Kut. "ST-DBSCAN: An algorithm for clustering spatial–temporal data." Data & Knowledge Engineering 60.1 (2007): 208-221.
* Peca, I., Fuchs, G., Vrotsou, K., Andrienko, N. V., & Andrienko, G. L. (2012). Scalable Cluster Analysis of Spatial Events. In EuroVA@ EuroVis.

Released under MIT License. See the LICENSE file for details. The package was developed by Eren Cakmak from the Data Analysis and Visualization Group and the Department of Collective Behaviour at the University Konstanz funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2117 – 422037984“