The Social Morphology Ball Model: A Proof-of-Concept for Spherical Representation of Multivariate Social Data

This proof-of-concept study introduces the Social Morphology Ball, a framework for visualizing multivariate social data as three-dimensional morphological structures. The method normalizes variables using the median and interquartile range (IQR), followed by nonlinear compression with a hyperbolic tangent function to reduce the impact of extreme outliers. Each normalized variable is mapped onto spherical basis functions, transforming numerical relationships into geometric deformations—bulges or concavities—on a sphere. When all variables align with their long-term medians, the model yields a perfect sphere representing a morphologically neutral, structurally balanced state. Implemented in Python, the pipeline automates normalization, shape generation, and visualization, exporting results in STL, PNG, and GIF formats. Applied to Japanese national statistics from 2015–2023, the model captured temporal variations in the balance and distortion of societal indicators through intuitive 3D forms. By interpreting data as morphology rather than numbers, this approach extends social data visualization into a sensory and cognitive domain, providing a conceptual foundation for morphological visualization of social phenomena and potential applications in education, policy, and data-driven art.