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 sparse matrices and the 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. If you use the package, please consider citing the following benchmark paper:

@inproceedings{cakmak2021spatio,
        author = {Cakmak, Eren and Plank, Manuel and Calovi, Daniel S. and Jordan, Alex and Keim, Daniel},
        title = {Spatio-Temporal Clustering Benchmark for Collective Animal Behavior},
        year = {2021},
        isbn = {9781450391221},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3486637.3489487},
        doi = {10.1145/3486637.3489487},
        booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility},
        pages = {5–8},
        numpages = {4},
        location = {Beijing, China},
        series = {HANIMOB '21}
}

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“