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Spatio-temporal clustering occupies an established role in various fieldsdealing with geospatial analysis, spanning from healthcare analysis toenvironmental science. One major challenge are applications in which clusterassignments are dependent on local densities, meaning that higher-density areasshould be treated more strictly for spatial clustering and vice versa. Meetingthis need, we describe and implement an extended method that covers continuousand adaptive distance rescaling based on kernel density estimates and theorthodromic metric, as well as the distance between time series via dynamictime warping. In doing so, we provide the wider research community, as well aspractitioners, with a novel approach to solve an existing challenge as well asan easy-to-handle and robust open-source software tool. The resultingimplementation is highly customizable to suit different application cases, andwe verify and test the latter on both an idealized scenario and the recreationof prior work on broadband antibiotics prescriptions in Scotland to demonstratewell-behaved comparative performance. Following this, we apply our approach tofire emissions in Sub-Saharan Africa using data from Earth-observingsatellites, and show our implementation's ability to uncover seasonality shiftsin carbon emissions of subgroups as a result of time series-driven clustersplits.

SCADDA: Spatio-temporal cluster analysis with density-based distance augmentation and its application to fire carbon emissions