Simple 3D glyphs for spatial multivariate data

We present an effort to evaluate the possible utility of a new type of 3D glyphs intended for visualizations of multivariate spatial data. They are based on results from vision research suggesting that our perception of metric 3D structure is distorted and imprecise relative to the actual scene before us (e.g., "metric 3D structure in visualizations" by M. Lind et al. (2003)); only a class of qualitative properties of the scene is perceived with accuracy. These properties are best characterized as being invariant over affine but not Euclidean transformations. They are related, but not identical to, the non-accidental properties (NAPs) described by Lowe in "perceptual organization and visual recognition" (1984) on which the notion of geons is based in "recognition by components - a theory of image understanding" by I. Biederman (1987). A large number of possible 3D glyphs for the visualization of spatial data can be constructed using such properties. One group is based on the local sign of surface curvature. We investigated these properties in a visualization experiment. The results are promising and the implications for visualization are discussed.