  Multiple-view visualization (MV) is a layout design technique often employed
to help users see a large number of data attributes and values in a single
cohesive representation. Because of its generalizability, the MV design has
been widely adopted by the visualization community to help users examine and
interact with large, complex, and high-dimensional data. However, although
ubiquitous, there has been little work to categorize and analyze MVs in order
to better understand its design space. As a result, there has been little to no
guideline in how to use the MV design effectively. In this paper, we present an
in-depth study of how MVs are designed in practice. We focus on two fundamental
measures of multiple-view patterns: composition, which quantifies what view
types and how many are there; and configuration, which characterizes spatial
arrangement of view layouts in the display space. We build a new dataset
containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis
publications 2011 to 2019, and make fine-grained annotations of view types and
layouts for these visualization images. From this data we conduct composition
and configuration analyses using quantitative metrics of term frequency and
layout topology. We identify common practices around MVs, including
relationship of view types, popular view layouts, and correlation between view
types and layouts. We combine the findings into a MV recommendation system,
providing interactive tools to explore the design space, and support
example-based design.
