  Composite visualization is a popular design strategy that represents complex
datasets by integrating multiple visualizations in a meaningful and aesthetic
layout, such as juxtaposition, overlay, and nesting. With this strategy,
numerous novel designs have been proposed in visualization publications to
accomplish various visual analytic tasks. These well-crafted composite
visualizations have formed a valuable collection for designers and researchers
to address real-world problems and inspire new research topics and designs.
However, there is a lack of understanding of design patterns of composite
visualization, thus failing to provide holistic design space and concrete
examples for practical use. In this paper, we opted to revisit the composite
visualizations in VIS publications and answered what and how visualizations of
different types are composed together. To achieve this, we first constructed a
corpus of composite visualizations from IEEE VIS publications and decomposed
them into a series of basic visualization types (e.g., bar chart, map, and
matrix). With this corpus, we studied the spatial (e.g., separated or
overlaying) and semantic relationships (e.g., with same types or shared axis)
between visualizations and proposed a taxonomy consisting of eight different
design patterns (e.g., repeated, stacked, accompanied, and nested).
Furthermore, we analyzed and discussed common practices of composite
visualizations, such as the distribution of different patterns and correlations
between visualization types. From the analysis and examples, we obtained
insights into different design patterns on the utilities, advantages, and
disadvantages. Finally, we developed an interactive system to help
visualization developers and researchers conveniently explore collected
examples and design patterns.
