TriSpace: An Immersive 3D Data Visualization System Based on Trilateration - A Case Study of Multi-Subject Student Performance Analysis

With the advent of the big data era, the dramatic increase in data volume and dimensionality has posed higher demands on visualization techniques.Traditional 2D charts often require dimensionality reduction when representing high-dimensional data, leading to information loss or visual clutter.This paper proposes an immersive 3D information visualization method that maps multi-dimensional data points into spheres in 3D space, encoding data attributes through multiple visual channels such as position, color, and size.Taking students' scores in Mathematics, Chinese, and English as an example, we design an equilateral triangle layout with three colored cubes as reference points.Through a linear score-to-distance mapping and trilateration algorithm, we accurately determine the spatial position of each student sphere, making the distance inversely proportional to the score.Additionally, the sphere's color is determined by the student's highest-scoring subject and its score, while the sphere's size is positively correlated with the total score.The system provides first-person navigation and a crosshair-based selection mechanism, allowing users to explore the data immersively.Experimental results show that this method clearly reveals the distribution patterns, subject preferences, and overall performance levels of students.It is highly extensible and can be applied to any data set with three groups of indicators (e.g., professional courses, basic courses, practical skills).This paper offers a reusable technical framework for high-dimensional data visualization in immersive environments.