Prototype implementation for the research project MCV-DisplayWall (aka divico), which is a large scale MCV (multiple coordinated views) application for visual data exploration.
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README.md

DiViCo

The DiViCo (Distant Visualisation Control) prototype implements large-scale multiple coordinated views on a wall-sized interactive display. Users can interact with visualization views from both close proximity (touch input) as well from a distance (using a pointing approach with mobile devices). The basic ideas and principles behind this research prototype can be found in our publication:

Ricardo Langner, Ulrike Kister and Raimund Dachselt, "Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances" in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, 2018. doi: 10.1109/TVCG.2018.2865235

Note: This prototype does not include all functionality presented in the research article. Due to their relevance for other projects, the node-link diagram and the lens tool are not included.

Project website: Further information, photos, and videos can be found at https://imld.de/mcv-displaywall/.

Questions: If you have any questions or you want to give feedback, please contact Ricardo Langner (institutional website, GitHub) or Marc Satkowski (institutional website, GitHub).

Installing and Running DiViCo

We use this prototyp with Python 2.7. After installing Python you need to install the following libraries:

  • enum34 (>= 1.1.6)
  • enum-compat (>= 0.0.2)
  • pyproj (>= 1.9.5.1)
  • numpy (>= 1.13.1)
  • scipy (>= 0.19.1)
  • pyOSC (>= 0.3.5b5294)
  • networkx (>= 2.1)
  • googlemaps (>= 2.5.1)
  • PyGLM (>=0.4.8b1)

Further it is necessary to install an additional library. This library is libavg which allows us to handle touch input and gesture recognition.

Development

This project is developed by Marc Satkowski, Ulrike Kister and Ricardo Langner at the Interactive Media Lab Dresden, Technische Universität Dresden, Germany. Further development information can be found via the develpoment guide.

If you want to contribute, please check the contribution guide, fork our project, create a feature branch for your changes, and provide us with a pull request.

Acknowledgements

This research prototype uses the Victim Based Crime Dataset data set from Baltimore. Further information can be found in the data set README.