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.

翻译：摘要：时空聚类在地理空间分析相关领域（从医疗分析到环境科学）中占据着既定地位。其中一项主要挑战是聚类分配依赖于局部密度的应用场景，即高密度区域应在空间聚类中受到更严格的对待，反之亦然。为满足这一需求，我们描述并实现了一种扩展方法，该方法涵盖了基于核密度估计和正交距离的连续自适应距离重新缩放，以及通过动态时间规整实现的时间序列间距离计算。通过此工作，我们为更广泛的研究界及实践者提供了一种解决现有挑战的新颖方法，以及一个易于使用且稳健的开源软件工具。所实现的成果具有高度可定制性，适用于不同应用场景。我们在理想化场景和先前关于苏格兰广谱抗生素处方研究的复现中对其实施验证和测试，以展示其表现良好的比较性能。随后，我们利用地球观测卫星的数据，将该方法应用于撒哈拉以南非洲的火灾排放，并展示了我们的实施成果在揭示由时间序列驱动聚类分裂所导致的子组碳排放季节性变化方面的能力。