Many techniques have been proposed for visualizing uncertainty in geospatial data. Previous empirical research on the effectiveness of visualizations of geospatial uncertainty has focused primarily on user intuitions rather than objective measures of performance when reasoning under uncertainty. Framed in the context of Google's blue dot, we examined the effectiveness of four alternative visualizations for representing positional uncertainty when reasoning about self-location data. Our task presents a mobile mapping scenario in which GPS satellite location readings produce location estimates with varying levels of uncertainty. Given a known location and two smartphone estimates of that known location, participants were asked to judge which smartphone produces the better location reading, taking uncertainty into account. We produced visualizations that vary by glyph type (uniform blue circle with border vs. Gaussian fade) and visibility of a centroid dot (visible vs. not visible) to produce the four visualization formats. Participants viewing the uniform blue circle are most likely to respond in accordance with the actual probability density of points sampled from bivariate normal distributions and additionally respond most rapidly. Participants reported a number of simple heuristics on which they based their judgments, and consistency with these heuristics was highly predictive of their judgments.

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Virtual models are increasingly employed in STEM education to foster learning about spatial phenomena. However, the roles of the computer interface and students' cognitive abilities in moderating learning and performance with virtual models are not yet well understood. In two experiments students solved spatial organic chemistry problems using a virtual model system.Two aspects of the virtual model interface were manipulated: display dimensionality (stereoscopic vs. monoscopic displays) and the location of the hand-held device used to manipulate the virtual molecules (co-located with the visual display vs. displaced). The experimental task required participants to interpret the spatial structure of organic molecules and to manipulate the models to align them with orientations and configurations depicted by diagrams in Experiment 1 and three-dimensional models in Experiment 2. Co-locating the interaction device with the virtual image led to better performance in both experiments and stereoscopic viewing led to better performance in Experiment 2. The effect of co-location on performance was moderated by spatial ability in Experiment 1, and the effect of providing stereo viewing was moderated by spatial ability in Experiment 2. The results are in line with the abilityas-compensator hypothesis: participants with lower ability uniquely benefited from the treatment, while those with higher ability were not affected by stereo or co-location. The findings suggest that increased fidelity in a virtual model system may be one way of alleviating difficulties of low-spatial participants in learning spatially demanding content in STEM domains.

Two experiments revealed how nonexperts interpret visualizations of positional uncertainty on GPS-like displays and how the visual representation of uncertainty affects their judgments. Participants were shown maps with representations of their current location; locational uncertainty was visualized as either a circle (confidence interval) or a faded glyph (indicating the probability density function directly). When shown a single circle or faded glyph, participants assumed they were located at the center of the uncertain region. In a task that required combining 2 uncertain estimates of their location, the most common strategy-integration-was to take both estimates into account, with more weight given to the more certain estimate. Participants' strategies were not affected by how uncertainty was visualized, but visualization affected the consistency of responses, both within individuals and in relation to models of individual's preferred strategies. The results indicate that nonexperts have an intuitive understanding of uncertainty. Rather than arguing for a particular method of visualizing uncertainty, the data suggest that the best visualization method is task dependent. (PsycINFO Database Record