Li, J.Chen, S.Zhang, KangAndrienko, G.Andrienko, N.2019-06-282019-06-282018-06-291077-2626https://hdl.handle.net/10735.1/6655Full text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided link to the article). Non UTD affiliates will find the web address for this item by clicking the Show full item record link and copying the "relation.uri" metadata.Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term ‘event’ refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets.en©2018 IEEEData miningInformation visualizationEconomicsShapesTime-series analysisVisual analyticsVisualizationarticleLi, J., S. Chen, K. Zhang, G. Andrienko, et al. 2018. "COPE: Interactive exploration of co-occurrence patterns in spatial time series." IEEE Transactions on Visualization and Computer Graphics, doi:10.1109/TVCG.2018.2851227