Quite a few examples in the cartographic and information visualisation literature suggest that multi-component animated maps may be appropriate for examining complex spatio-temporal phenomena. Such space–time visualizations typically consist of multiple dynamic map or data windows, linked by means of interactive tools. Little empirical evidence exists, however, providing support of the potential advantages of such complex visual space–time displays. This research aimed at filling this gap.\ud An empirical study was carried out to obtain insight on how multi-component animated maps are used to explore dynamic spatio-temporal phenomena. We examined which particular components attract users’ attention and in what sequence, and whether display effectiveness can be characterized by users’ viewing behaviours. Based on behavioural data collected with the eye-tracking method, we find that component size, and employed dynamic variables attracted users’ attention most. We are also able to identify visual behaviour patterns that result in performance differences between participants, using\ud multi-component animated map. Finally, we highlight component layout design issues that should be further examined empirically, in order to reduce potential split attention effects

After demonstrating that rainbow colors are still commonly used in scientific publications, we comparatively evaluate the rainbow and sequential color schemes on choropleth and isarithmic maps in an empirical user study with 544 participants to examine if a) people intuitively associate order for the colors in these schemes, b) they can successfully conduct perceptual and semantic map reading and recall tasks with quantitative data where order may have implicit or explicit importance. We find that there is little to no agreement in ordering of rainbow colors while sequential colors are indeed intuitively ordered by the participants with a strong dark is more bias. Sequential colors facilitate most quantitative map reading tasks better than the rainbow colors, whereas rainbow colors competitively facilitate extracting specific values from a map, and may support hue recall better than sequential. We thus contribute to dark- versus light is more bias debate, demonstrate why and when rainbow colors may impair performance, and add further nuance to our understanding of this highly popular, yet highly criticized color scheme.

Geographic visualization tools with coordinated and multiple views (CMV) typically provide sets of visualization methods. Such configuration gives users the possibility of investigating data in various visual contexts; however, it can be confusing due to the multiplicity of visual components and interactive functions. We addressed this challenge and conducted an empirical study on how a CMV tool, consisting of a map, a parallel coordinate plot (PCP), and a table, is used to acquire information. We combined a task-based approach with eye-tracking and usability metrics since these methods provide comprehensive insights into users’ behaviour. Our empirical study revealed that the freedom to choose visualization components is appreciated by users. The individuals worked with all the available visualization methods and they often used more than one visualization method when executing tasks. Different views were used in different ways by various individuals, but in a similarly effective way. Even PCP, which is claimed to be problematic, was found to be a handy way of exploring data when accompanied by interactive functions.

It is acknowledged that various types of thematic maps emphasize different aspects of mapped phenomena and thus support different map users’ tasks. To provide empirical evidence, a user study with 366 participants was carried out comparing three map types showing the same input data. The aim of the study is to compare the effect of using choropleth, graduated symbols, and isoline maps to solve basic map user tasks. Three metrics were examined: two performance metrics (answer accuracy and time) and one subjective metric (difficulty). The results showed that the performance metrics differed between the analyzed map types, and better performances were recorded using the choropleth map. It was also proven that map users find the most commonly applied type of the map, choropleth map, as the easiest. In addition, the subjective metric matched the performance metrics. We conclude with the statement that the choropleth map can be a sufficient solution for solving various tasks. However, it should be remembered that making this type of map correctly may seem easy, but it is not. Moreover, we believe that the richness of thematic cartography should not be abandoned, and work should not be limited to one favorable map type only.

Thematic map analysis is a complex and challenging task that might result in map user failure for many reasons. In the study reported here, we wanted to search for differences between successful and unsuccessful map users, focusing—unlike many similar studies—on strategies applied by users who give incorrect answers. In the eye-tracking study, followed by a questionnaire survey, we collected data from 39 participants. The eye-tracking data were analyzed both qualitatively and quantitatively to compare participants’ strategies from various perspectives. Unlike the results of some other studies, it turned out that unsuccessful participants show some similarities that are consistent across most analyzed tasks. The main issues that characterize bad solvers relate to improper use of the thematic legend, the inability to focus on relevant map layout elements, as well as on adequate map content. Moreover, they differed in the general problem-solving approach used as they, for example, tended to choose fast, less cautious, strategies. Based on the collected results, we developed tips that could help prevent unsuccessful participants ending with an incorrect answer and therefore be beneficial in map use education.