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VIS Metrics Platform

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Description: VIS Metrics Platform plans to pool several empirically validated models and metrics of user perception and attention into an easy-to-use online service for the evaluation of infovis designs. Users input a infovis design via either Vega-Lite json or a screenshot, and select from a list of different infovis metrics, such as white space ratio, color preference, and visual saliency.

Goal:

  • Full investigation of existing infovis metrics in previous literature
  • Select, implement and test infovis metrics
  • Web application design, integration of infovis metrics.
  • Test the usability of the application, collect user feedback.

Supervisor: Yao Wang

Distribution: 20% Literature Review, 70% Implementation, 10% Evaluation & analysis

Requirements: good Python web programming skill, ideally some knowledge of a framework such as Flask or Django.

Literature: [1] Antti, O., et al. "Aalto Interface Metrics (AIM): A Service and Codebase for Computational GUI Evaluation". UIST '18 Adjunct. Paper link.

[2] Sungbok Shin, Sanghyun Hong, Niklas Elmqvist. "Perceptual Pat: A Virtual Human Visual System for Iterative Visualization Design." Proceedings of 2023 CHI Conference on Human Factors in Computing Systems. Paper link.