AntVis: A web-based visual analytics tool for exploring ant movement data

Tianxiao Hu; Hao Zheng; Chen Liang; Sirou Zhu; Natalie Imirzian; Yizhe Zhang; Chaoli Wang; David P. Hughes; Danny Z. Chen

doi:10.1016/j.visinf.2020.02.001

AntVis: A web-based visual analytics tool for exploring ant movement data

Tianxiao Hu, Hao Zheng, Chen Liang, Sirou Zhu, Natalie Imirzian, Yizhe Zhang, Chaoli Wang, David P. Hughes, Danny Z. Chen

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	58-70
Number of pages	13
Journal	Visual Informatics
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/j.visinf.2020.02.001
State	Published - Mar 2020

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Graphics and Computer-Aided Design

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10.1016/j.visinf.2020.02.001

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title = "AntVis: A web-based visual analytics tool for exploring ant movement data",

abstract = "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.",

author = "Tianxiao Hu and Hao Zheng and Chen Liang and Sirou Zhu and Natalie Imirzian and Yizhe Zhang and Chaoli Wang and Hughes, {David P.} and Chen, {Danny Z.}",

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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.

PY - 2020/3

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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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AntVis: A web-based visual analytics tool for exploring ant movement data

Abstract

All Science Journal Classification (ASJC) codes

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