TY - JOUR
T1 - AntVis
T2 - A web-based visual analytics tool for exploring ant movement data
AU - Hu, Tianxiao
AU - Zheng, Hao
AU - Liang, Chen
AU - Zhu, Sirou
AU - Imirzian, Natalie
AU - Zhang, Yizhe
AU - Wang, Chaoli
AU - Hughes, David P.
AU - Chen, Danny Z.
N1 - Funding Information:
This research was supported in part by the US National Science Foundation through grants IIS-1456763, IIS-1455886, CNS-1629914, CCF-1617735, and DUE-1833129, and by the US National Institutes of Health through grant R01 GM116927. T. Hu, S. Zhu, and C. Liang conducted this work as iSURE (International Summer Undergraduate Research Experience) students at the University of Notre Dame during Summer 2017.
Funding Information:
This research was supported in part by the US National Science Foundation through grants IIS-1456763 , IIS-1455886 , CNS-1629914 , CCF-1617735 , and DUE-1833129 , and by the US National Institutes of Health through grant R01 GM116927 . T. Hu, S. Zhu, and C. Liang conducted this work as iSURE (International Summer Undergraduate Research Experience) students at the University of Notre Dame during Summer 2017.
Publisher Copyright:
© 2020 Zhejiang University and Zhejiang University Press
PY - 2020/3
Y1 - 2020/3
N2 - 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.
AB - 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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U2 - 10.1016/j.visinf.2020.02.001
DO - 10.1016/j.visinf.2020.02.001
M3 - Article
AN - SCOPUS:85081237825
SN - 2543-2656
VL - 4
SP - 58
EP - 70
JO - Visual Informatics
JF - Visual Informatics
IS - 1
ER -