For decades, uncertainty visualisation has attracted attention in disciplines such as cartography and geographic visualisation, scientific visualisation and information visualisation. Most of this research deals with the development of new approaches to depict uncertainty visually; only a small part is concerned with empirical evaluation of such techniques. This systematic review aims to summarize past user studies and describe their characteristics and findings, focusing on the field of geographic visualisation and cartography and thus on displays containing geospatial uncertainty. From a discussion of the main findings, we derive lessons learned and recommendations for future evaluation in the field of uncertainty visualisation. We highlight the importance of user tasks for successful solutions and recommend moving towards task-centered typologies to support systematic evaluation in the field of uncertainty visualisation.

No abstract

Noise annotation lines are a promising technique to visualize thematic uncertainty in maps. However, their potential has not yet been evaluated in user studies. In two experiments we assessed the usability of this technique with respect to a different number of uncertainty levels as well as the influence of two design aspects of noise annotation lines: the grain and the width of the noise grid. We conducted a web-based study utilizing a qualitative comparison of two areas in 150 different maps. We recruited participants from Amazon Mechanical Turk with the majority being nonexperts with respect to the use of maps.Our findings suggest that for qualitative comparisons of 'constant uncertainty' (i.e., constant uncertainty per area) in thematic maps, noise annotation lines can be used for up to 6 uncertainty levels. During comparison of 4, 6, and 8 levels, the different designs of the technique yielded significantly different accuracies. We propose to use the 'large noise width, coarse grain' design that was most successful. For 'mixed uncertainty' (i.e., uncertainty is not constant per area) we observed a significant decrease in accuracy, but for 4 levels the technique can still be recommended.This article is a follow-up to our conference paper reporting on preliminary results of the first of the two experiments (Kinkeldey et al. 2013).

No abstract

We present BitConduite, a visual analytics approach for explorative analysis of financial activity within the Bitcoin network, offering a view on transactions aggregated by entities, i. e. by individuals, companies or other groups actively using Bitcoin. BitConduite makes Bitcoin data accessible to non-technical experts through a guided workflow around entities analyzed according to several activity metrics. Analyses can be conducted at different scales, from large groups of entities down to single entities. BitConduite also enables analysts to cluster entities to identify groups of similar activities as well as to explore characteristics and temporal patterns of transactions. To assess the value of our approach, we collected feedback from domain experts.