A Comparison of Animated Maps with Static Small-Multiple Maps for Visually Identifying Space-Time Clusters

Although animated maps are widely promoted as ideal vehicles for learning and scientific discovery, there has been little empirical work that demonstrates their relative effectiveness in relation to static small-multiple alternatives. In this article, we attempt to clarify the issues related to the potential of animation from an explicitly geographic perspective, but one that is also grounded in broader cognitive science and human-computer interaction considerations. We compared the effectiveness of animated with static small-multiple maps, specifically in relation to map readers' ability to identify clusters that move over space and through time. In this study, we focused on several factors that might impact (or help explain) map readers' ability to correctly identify clusters. These factors included animation pace, cluster coherence, and gender. We found that map readers answer more quickly and identify more patterns correctly when using animated maps than when using static small-multiple maps. We also found that pace and cluster coherence interact so that different paces are more effective for identifying certain types of clusters (none vs. subtle vs. strong), and that there are some gender differences in the animated condition. This study is one of a small number of controlled experiments directed to the relative advantages of animated and static small-multiple maps. It provides the basis for further research that is needed to better understand the cognitive load involved in reading animated maps, to better describe and understand gender differences, and to investigate the efficacy of animated maps for other types of map reading tasks.