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 journalArticlepeer-review

2 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 languageEnglish (US)
Pages (from-to)58-70
Number of pages13
JournalVisual Informatics
Volume4
Issue number1
DOIs
StatePublished - Mar 2020

All Science Journal Classification (ASJC) codes

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

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