Rajasagi, Damaruka PriyaBoot, LeeWilson, LucyKing, TristanZuber, JamesStockwell, IanKomlodi, Anita2024-07-262024-07-262024-06-07Rajasagi, Priya, Lee Boot, Lucy E Wilson, Tristan King, James Zuber, Ian Stockwell, and Anita Komlodi. “SEEe Immersive Analytics System: Enhancing Data Analysis Experience within Complex Data Visualization Environments.” In Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, 408–15. IMX ’24. New York, NY, USA: Association for Computing Machinery, 2024. https://doi.org/10.1145/3639701.3663645.https://doi.org/10.1145/3639701.3663645http://hdl.handle.net/11603/35016IMX '24: Proceedings of the 2024 ACM International Conference on Interactive Media Experiences-June 20204 IMX '24: ACM International Conference on Interactive Media Experiences Stockholm Sweden June 12 - 14, 2024The current state-of-the-art 2D data visualizations fall short in capturing the intricate complexity and depth of available information crucial for integrated decision-making. In response to this limitation, the Systems Exploration and Engagement environment (SEEe) emerges as a cutting-edge virtual immersive analytics data experience. We developed this system through a user-centered design process involving an interdisciplinary design and development team. Through virtual reality, SEEe seamlessly integrates geo-referenced spatial data, abstract data visualization, and qualitative data encompassing text, images, videos, and conceptual diagrams to support sensemaking from large amounts of multiformat data and integrated decision making. We aim to redefine the experience of analyzing extensive amounts of abstract data by creating an environment that accommodates both quantitative and qualitative data for visualization and analysis. How these novel immersive analytics experiences fit into data analysis workflows in various domains have not been studied widely. We carried out a user study with 10 public health graduate students to test the usability, learnability, and utility of the SEEe experience and to explore how these immersive data visualization experiences can fit into traditional data analysis processes. While SEEe is designed to be adaptable across various domains, we evaluated its performance within the public health context. The results of the evaluation affirm that SEEe is not only usable and useful but also provides a learnable environment conducive to immersive analytics.8 pagesen-USGeo-referenced data visualizationImmersive analyticsVirtual realityText