3 research outputs found
Toward Systematic Design Considerations of Organizing Multiple Views
Multiple-view visualization (MV) has been used for visual analytics in
various fields (e.g., bioinformatics, cybersecurity, and intelligence
analysis). Because each view encodes data from a particular perspective,
analysts often use a set of views laid out in 2D space to link and synthesize
information. The difficulty of this process is impacted by the spatial
organization of these views. For instance, connecting information from views
far from each other can be more challenging than neighboring ones. However,
most visual analysis tools currently either fix the positions of the views or
completely delegate this organization of views to users (who must manually drag
and move views). This either limits user involvement in managing the layout of
MV or is overly flexible without much guidance. Then, a key design challenge in
MV layout is determining the factors in a spatial organization that impact
understanding. To address this, we review a set of MV-based systems and
identify considerations for MV layout rooted in two key concerns: perception,
which considers how users perceive view relationships, and content, which
considers the relationships in the data. We show how these allow us to study
and analyze the design of MV layout systematically.Comment: Short paper with 4 pages + 1 reference page, 2 figures, 1 table,
accepted at IEEE VIS 2022 conferenc
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COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.
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