Salla Multimäki

User profiling for maritime situational picture

When map visualisation and a user interface are being designed, knowledge about the user groups and the users needs is one of the main principles. In practice, in most cases the available data direct the visualisation. Another thing that must be taken into consideration is the relationship between the amount of information and the method used for its visualisation. This turns out to be much more complicated when the temporal aspect is taken into account; one can no longer claim that when visualising bigger areas, the information must be filtered or generalised. A case with both these features is the maritime situational picture in Finland. This study was made to find out whether a single software version can produce an effective and informative visualisation for three different user groups representing different authorities. The information needs and tasks of these groups were studied and found varying from each other, even though they use the same real-time data. The results suggest that designing different user profiles for these user groups can improve not only the user satisfaction but also their situational awareness. The results confirm the importance of the thorough research of the user needs.

Comparison of Temporally Classified and Unclassified Map Animations

While animation is a natural and, under certain circumstances, effective way to present spatio-temporal information, it has its limitations. Studying animations of large point datasets can be cognitively very demanding. Aiming to help users to comprehend such data, this study presents a new concept of temporal classification. A phenomenon is classified into periods of increasing, decreasing, and steady intensity, and each is assigned different colours in an animation. This concept was tested with a group of experts in the field of the phenomenon. The results suggest that this kind of classified animation, together with a traditional animation presenting the same dataset, supports users in their analysis process and adds to the impression they get of the phenomenon. It also seems that the viewing order of the animations matters: the full potential of the tested method is reached by viewing the traditional version first and temporally classified version after that.

Experiencing bivariate colour scales on animated maps

ABSTRACT We examined animated maps on which two different datasets are presented together in order to test the applicability of the bivariate colour scales to moving phenomena. Experimental software, combining meteorological datasets, was implemented to study two different use cases: the comparison between a predicted model and the actual observations of a single phenomenon, and the causality between two independent phenomena. The characteristics of different datasets and tasks were taken into account when designing the visualizations for these two combinations. These visualizations were then evaluated in group interviews. The results from the interviews indicate that designing the colours for animated bivariate maps differs remarkably from the colours of static bivariate maps, and the applicability of previous bivariate data visualization guidelines can be questioned. The spatial coverage, geometrical complexity and movement of the phenomena have a significant effect on bivariate animations.