Chin, Wei-Chien-BennyWei-Chien-BennyChinWen, Tzai-HungTzai-HungWenSabel, Clive E.Clive E.SabelI-HSIANG WANGWen T.-H.2020-06-042020-06-0420172045-2322https://scholars.lib.ntu.edu.tw/handle/123456789/497185A diffusion process can be considered as the movement of linked events through space and time. Therefore, space-time locations of events are key to identify any diffusion process. However, previous clustering analysis methods have focused only on space-time proximity characteristics, neglecting the temporal lag of the movement of events. We argue that the temporal lag between events is a key to understand the process of diffusion movement. Using the temporal lag could help to clarify the types of close relationships. This study aims to develop a data exploration algorithm, namely the TrAcking Progression In Time And Space (TaPiTaS) algorithm, for understanding diffusion processes. Based on the spatial distance and temporal interval between cases, TaPiTaS detects sub-clusters, a group of events that have high probability of having common sources, identifies progression links, the relationships between sub-clusters, and tracks progression chains, the connected components of sub-clusters. Dengue Fever cases data was used as an illustrative case study. The location and temporal range of sub-clusters are presented, along with the progression links. TaPiTaS algorithm contributes a more detailed and in-depth understanding of the development of progression chains, namely the geographic diffusion process. ? 2017 The Author(s).[SDGs]SDG3journal article10.1038/s41598-017-12852-zWOS:000412138800001