SCADDA: Spatiotemporal Cluster Analysis With Density-Based Distance Augmentation and Its Application to Fire Carbon Emissions

Abstract

Spatiotemporal clustering occupies an established role in various fields dealing with geospatial analysis, spanning from healthcare analysis to environmental science. One major challenge is application in which cluster assignments are dependent on local densities, meaning that higher-density areas should be treated more strictly for spatial clustering and vice versa. We describe and implement an extended method that covers continuous and adaptive distance rescaling based on kernel density estimates and the orthodromic metric, as well as the distance between time series via dynamic time warping (DTW). In doing so, we provide the wider research community, as well as practitioners, with a way to solve an existing challenge as well as an easy-to-handle and robust open-source software tool. The resulting implementation is highly customizable to suit different application cases, and we verify and test the latter on both an idealized scenario and the recreation of prior work on broadband antibiotics prescriptions in Scotland to demonstrate well-behaved comparative performance. Following this, we apply our approach to fire emissions in Sub-Saharan Africa using data from Earth-observing satellites, and show our implementation's ability to uncover seasonality shifts in carbon emissions of subgroups as a result of time series-driven cluster splits.