Paul Vlaswinkel
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Gazemap

Gazemap

Paper

2020

ReactPythonMUIFastAPID3VisualisationsAuthenticationFirebase

Key Learnings

In spearheading the development of an eye movement data visualization tool, I acquired valuable insights into diverse areas, including web application development, backend engineering, authentication integration, and data visualization. Key learnings from this project encompass:

  • Visualization Techniques: Explored and implemented various eye movement data visualizations, enhancing expertise in interpreting visual attention patterns.

  • Interactive Linking: Developed skills in creating interactive links between visualizations, providing users with a flexible and holistic analysis experience.

  • Web-Based Solutions: Gained proficiency in building accessible and user-friendly web applications to facilitate collaborative data analysis.

  • Technological Integration: Successfully integrated React for the frontend, FastAPI for backend processing, Firebase Auth for secure authentication, and D3.js for dynamic data visualizations.

Technologies Used

  • React: Developed the web application frontend for a responsive and user-friendly interface.
  • FastAPI (Python): Engineered the backend using FastAPI, ensuring robust data processing and seamless integration.
  • Firebase Auth: Implemented authentication for secure user access.
  • D3.js: Utilized D3.js for creating rich and interactive data visualizations.

Abstract

This paper introduces an advanced eye movement data visualization tool designed for interpreting visual attention patterns. The project’s primary objective is to create a user-friendly and accessible tool for analyzing eye movement data, culminating in a web-based solution to increase usability and facilitate collaborative data analysis.

Visualization Techniques

The tool incorporates several eye movement data visualizations:

  • 2D Density Plot: Illustrates the concentration of eye movements in a 2D space.
  • Scanpath Representation: Displays the sequential order of fixations and saccades during eye movement.
  • Bee Swarm: Visualizes eye movement data distribution using a bee swarm plot.
  • Scarf Plot: Represents eye movement patterns in a scarf-like visualization.