AntVis: A web-based visual analytics tool for exploring ant movement data
Author(s) -
Tianxiao Hu,
Hao Zheng,
Liang Chen,
Sirou Zhu,
Natalie Imirzian,
Yizhe Zhang,
Chaoli Wang,
David Hughes,
Danny Z. Chen
Publication year - 2020
Publication title -
visual informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 11
eISSN - 2543-2656
pISSN - 2468-502X
DOI - 10.1016/j.visinf.2020.02.001
Subject(s) - computer science , visual analytics , timeline , visualization , analytics , domain (mathematical analysis) , similarity (geometry) , movement (music) , artificial intelligence , cluster analysis , segmentation , data mining , data science , geography , aesthetics , mathematical analysis , philosophy , image (mathematics) , mathematics , archaeology
We present AntVis, a web-based visual analytics tool for exploring ant movement data collected from the video recording of ants moving on tree branches. Our goal is to enable domain experts to visually explore massive ant movement data and gain valuable insights via effective visualization, filtering, and comparison. This is achieved through a deep learning framework for automatic detection, segmentation, and labeling of ants, ant movement clustering based on their trace similarity, and the design and development of five coordinated views (the movement, similarity, timeline, statistical, and attribute views) for user interaction and exploration. We demonstrate the effectiveness of AntVis with several case studies developed in close collaboration with domain experts. Finally, we report the expert evaluation conducted by an entomologist and point out future directions of this study.
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