  Spatio-temporal clustering occupies an established role in various fields
dealing with geospatial analysis, spanning from healthcare analysis to
environmental science. One major challenge are applications 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. Meeting
this need, 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. In doing so, we provide the wider research community, as well as
practitioners, with a novel approach 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.
