Andrienko, NataliaAndrienko, GennadyEl-Assady, MennatallahSchulz, Hans-Jörg2024-05-212024-05-212024978-3-03868-253-0https://doi.org/10.2312/eurova.20241114https://diglib.eg.org/handle/10.2312/eurova20241114We propose an approach to exploring interrelationships between two or more sequences of events when events occurring in one sequence both affect and are affected by events occurring in another sequences. We present the approach by example of exploring the dynamic relationships between COVID pandemic events and changes in population mobility behaviours across various countries. The key idea is to generate data capturing the temporal context of each event, i.e., what types of events occurred in different sequences within a specified time buffer around this event. An application of 2D space embedding to the context data reveals groups of events occurring in similar contexts. We can investigate the types of events each group consists of and see when and where these events and these contexts took place. By interactive or algorithmic clustering of the context data, we categorise event contexts based on their similarities, which allows us to compute, visualise, explore, and compare summary statistics of the context clusters, as well as exploring their distribution over time and other data dimensions.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visual AnalyticsHuman centered computing → Visual Analytics10.2312/eurova.202411146 pages