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Design Space of Origin‐Destination Data Visualization
Author(s) -
Tennekes Martijn,
Chen Min
Publication year - 2021
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14310
Subject(s) - abstraction , computer science , visualization , information visualization , data visualization , categorization , visual analytics , data mining , space (punctuation) , interactive visual analysis , data science , geovisualization , human–computer interaction , information retrieval , theoretical computer science , artificial intelligence , philosophy , epistemology , operating system
Abstract Visualization is an essential tool for observing and analyzing origin‐destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin‐destination data visualization (ODDV). We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.