Hinrichs, Ute
; Perin, Charles
(Ed.)
As scientific data continues to grow in size, complexity, and density,
the representation scope of three-dimensional spaces, data sampling
methods, and transfer functions have improved in parallel,
allowing visualization practitioners to produce richer multidimensional
encodings. Glyphs, in particular, have become an essential
encoding tool due to their versatile applications in co-located multivariate
volumetric datasets. While prior work has been conducted
investigating the perceptual attributes of computationally-generated
three-dimensional glyph-forms for scientific visualization, their affective
and expressive qualities have yet to be examined. Further,
our prior work has demonstrated the benefits of artist hand-created
glyph forms in contrast to commonly-used synthetic forms in increasing
visual diversity, discrimination, and expressive association
in complex environmental datasets. In order to begin to address this
gap, we establish preliminary groundwork for an affective design
space for hand-created glyph forms, produce a novel set of glyph forms
based on this design space, describe a non-verbal method
for discovering affective classifications of glyph-forms adopted
from current affect theory, and report the results of two studies
that explore how these three-dimensional forms produce consistent
affective responses across assorted study cohorts.
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