Visual Abstraction of Spatio-Temporal Networks in Collective Animal Behavior

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.

This repository provides a JavaScript implementation of MotionGlyphs prototype as described in the paper:

@article{CaScBu 2020MotionGlyphs,
    author = {Cakmak, Eren and Schäfer, Hanna and Buchmüller, Juri and Fuchs, Johannes and Schreck, Tobias and Jordan, Alex and Keim, Daniel A.},
    journal = {Computer Graphics Forum},
    number = {3},
    title = {MotionGlyphs: Visual Abstraction of Spatio-Temporal Networks in Collective Animal Behavior},
    volume = {39},
    year = {2020}
}

The prototype is online: Demo

First, install the node.js modules and run wepack.

    > npm install
    > npm run watch

After that, start a webserver of your choice and move generated project dist folder

The following datasets are avaiable:

• fish-5.csv a part of real-world lab experiment with five stickleback fish
• data-1.csv artificially generate data with the Reynolds model
• data-2.csv artificially generate data with the Couzin model
• data-3.csv artificially generate data with the Reynolds model and clusters
• fish-151.csv a part of a real-world lab experiment with 151 golden shiner fish

The datasets were preprocessed and clustered with the following packages:

• Movekit
• ST-DBSCAN
• ST-OPTICS - the usage of the package is optional

Released under GPL-3 License. See the LICENSE file for details. The package was developed by Eren Cakmak from the Data Analysis and Visualization Group and the Department of Collective Behaviour at the University Konstanz funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2117 – 422037984.