A model of clutter for complex, multivariate geospatial displays.
OBJECTIVE
A novel model of measuring clutter in complex geospatial displays was compared with human ratings of subjective clutter as a measure of convergent validity. The new model is called the color-clustering clutter (C3) model.


BACKGROUND
Clutter is a known problem in displays of complex data and has been shown to affect target search performance. Previous clutter models are discussed and compared with the C3 model.


METHODS
Two experiments were performed. In Experiment 1, participants performed subjective clutter ratings on six classes of information visualizations. Empirical results were used to set two free parameters in the model. In Experiment 2, participants performed subjective clutter ratings on aeronautical charts. Both experiments compared and correlated empirical data to model predictions.


RESULTS
The first experiment resulted in a .76 correlation between ratings and C3. The second experiment resulted in a .86 correlation, significantly better than results from a model developed by Rosenholtz et al. Outliers to our correlation suggest further improvements to C3.


CONCLUSIONS
We suggest that (a) the C3 model is a good predictor of subjective impressions of clutter in geospatial displays, (b) geospatial clutter is a function of color density and saliency (primary C3 components), and (c) pattern analysis techniques could further improve C3.


CONCLUSIONS
The C3 model could be used to improve the design of electronic geospatial displays by suggesting when a display will be too cluttered for its intended audience.
Lohrenz
Maura C
MC
Naval Research Laboratory, Code 7440.1, Stennis Space Center, MS 39529, USA. mlohrenz@nrlssc.navy.mil
Trafton
J Gregory
JG
Beck
R Melissa
RM
Gendron
Marlin L
ML
D000293:Adolescent; D000328:Adult; D003118:Color Perception; D003196:Computer Graphics; D003198:Computer Simulation; D005843:Geography; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010470:Perceptual Masking; D055815:Young Adult