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.

翻译：多视图可视化(MV)在各个领域(例如生物信息学、网络安全和情报分析)用于视觉分析。由于每种观点都从特定角度对数据进行编码,分析师往往使用2D空间中列出的一系列观点来连接和综合信息。这一过程的困难受到这些观点的空间组织的影响。例如,从不同观点中相互连接信息比相邻观点更具挑战性。然而,大多数视觉分析工具目前要么确定观点的位置,要么将观点组织完全委托给用户(必须手动拖动和移动观点)。这要么限制了用户对MV布局管理的参与,要么过于灵活,没有多少指导。然后,MV布局中的关键设计挑战就是确定影响理解的空间组织中的各种因素。为了解决这一问题,我们审查一组基于MV的系统,并找出基于以下两个关键问题的MV布局考虑:认知,它考虑用户如何看待观点关系,内容如何考虑数据中的关系。我们展示了这些如何使我们能够系统地研究和分析MV布局的设计。