Our next finalist is Jennifer Smith who is finishing her Master’s degree at San Diego State, however she will soon ditch the gray skies of San Diego in favor of the optimal climes of beautiful Happy Valley, Pennsylvania. Pack your sunscreen, Jennifer! Jennifer is starting in the Ph.D. program at Penn State this fall, and we’re all anticipating big things from her. Her current project examines how people perceive cartographic depictions of changes in urban scenes – but again, why read my description when you can read Jennifer’s?
Effective Color Schemes for 3D Animations of Urban Landscapes with a Spatial and Temporal Dimension
By: Jennifer Smith
This research compares different color schemes for 3D urban spatio-temporal dynamic maps. It identifies methods of map designs facilitating acquisition of spatio-temporal information by map users. The following two questions guide the framework of this study: (1) How do different colors and schemes (red vs. blue and sequential vs. random) compare in affecting user abilities to acquire spatio-temporal information? (2) How do findings from eye-tracking analysis reinforce or contradict observed results of specific color schemes?
Three animations have been created using ArcScene, each re-illustrating urban growth of San Diego State University campus buildings over eight decades. Each animation is presented through a different color scheme (red sequential, blue sequential and random hue), where campus buildings vary in hue or value for each subsequent decade. As time moves forward, buildings appear in their actual height in scale.
Ninety-nine participants were utilized to view a randomly assigned animation and take a survey testing their ability to recognize and comprehend spatio-temporal information presented. One-tailed and two-tailed T-tests were computed, revealing no difference in survey accuracy among animations. Eye-tracking analysis yielded similar results, a result impacted by the small sample size of only nine students. Further statistical analyses will be computed to explore other demographic connections with survey accuracy (eg. Grade level, gender, age, map experience, etc.).