Boundary Blending: Reconsidering the Design of Multi-View Visualizations (2306.09812v1)

Abstract: Multiple-view visualizations (MVs) have been widely used for visual analysis. Each view shows some part of the data in a usable way, and together multiple views enable a holistic understanding of the data under investigation. For example, an analyst may check a social network graph, a map of sensitive locations, a table of transaction records, and a collection of reports to identify suspicious activities. While each view is designed to preserve its own visual context with visible borders or perceivable spatial distance from others, the key to solving real-world analysis problems often requires "breaking" such boundaries, and further integrating and synthesizing the data scattered across multiple views. This calls for blending the boundaries in MVs, instead of simply breaking them, which brings key questions: what are possible boundaries in MVs, and what are design options that can support the boundary blending in MVs? To answer these questions, we present three boundaries in MVs: 1) data boundary, 2) representation boundary, and 3) semantic boundary, corresponding to three major aspects regarding the usage of MVs: encoded information, visual representation, and interpretation. Then, we discuss four design strategies (highlighting, linking, embedding, and extending) and their pros and cons for supporting boundary blending in MVs. We conclude our discussion with future research opportunities.