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MotionGlyphs: Visual Abstraction of Spatio‐Temporal Networks in Collective Animal Behavior
Author(s) -
Cakmak E.,
Schäfer H.,
Buchmüller J.,
Fuchs J.,
Schreck T.,
Jordan A.,
Keim D.
Publication year - 2020
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.13963
Subject(s) - computer science , glyph (data visualization) , abstraction , domain (mathematical analysis) , collective behavior , node (physics) , animal behavior , cluster analysis , visualization , enhanced data rates for gsm evolution , process (computing) , artificial intelligence , data mining , human–computer interaction , theoretical computer science , mathematical analysis , zoology , philosophy , mathematics , structural engineering , epistemology , sociology , anthropology , engineering , operating system , biology
Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio‐temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node‐link diagrams, resulting in issues of node‐overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio‐temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio‐temporal networks of collective animal behavior.