  Visualizations support rapid analysis of scientific datasets, allowing
viewers to glean aggregate information (e.g., the mean) within split-seconds.
While prior research has explored this ability in conventional charts, it is
unclear if spatial visualizations used by computational scientists afford a
similar ensemble perception capacity. We investigate people's ability to
estimate two summary statistics, mean and variance, from pseudocolor scalar
fields. In a crowdsourced experiment, we find that participants can reliably
characterize both statistics, although variance discrimination requires a much
stronger signal. Multi-hue and diverging colormaps outperformed monochromatic,
luminance ramps in aiding this extraction. Analysis of qualitative responses
suggests that participants often estimate the distribution of hotspots and
valleys as visual proxies for data statistics. These findings suggest that
people's summary interpretation of spatial datasets is likely driven by the
appearance of discrete color segments, rather than assessments of overall
luminance. Implicit color segmentation in quantitative displays could thus
prove more useful than previously assumed by facilitating quick, gist-level
judgments about color-coded visualizations.
