How to evaluate data visualizations across different levels of understanding (original) (raw)

Reviewing data visualization: an analytical taxonomical study

2006

This paper presents an analytical taxonomy that can suitably describe, rather than simply classify, techniques for data presentation. Unlike previous works, we do not consider particular aspects of visualization techniques, but their mechanisms and foundational vision perception. Instead of just adjusting visualization research to a classification system, our aim is to better understand its process. For doing so, we depart from elementary concepts to reach a model that can describe how visualization techniques work and how they convey meaning.

2 A Conversation about Empirical Evaluation of Visualization Approaches

Over the past thirty years, the visualization community has developed theories and models to explain visualization as a technology that augments human cognition by enabling the efficient, accurate, and timely discovery of meaningful information in data. Along the way, practitioners have also debated theories and practices for visualization evaluation: How do we generate durable, reliable evidence that a visualization is effective? Interestingly, there is still no consensus in the visualization research community how to evaluate visualization methods. The goal of this chapter is to rise awareness of still open issues in the visualization evaluation, and to discuss appropriate evaluations suitable for different visualization approaches. This includes user studies and best practices to conduct them but also other approaches for suitable evaluation of visualization. The chapter is structured as a moderated dialogue of two visualization experts.

Measuring comprehension of abstract data visualisations

2011

Common visualisation techniques such as bar-charts and scatter-plots are not sufficient for visual analysis of large sets of complex multidimensional data. Technological advancements have led to a proliferation of novel visualisation tools and techniques that attempt to meet this need. A crucial requirement for efficient visualisation tool design is the development of objective criteria for visualisation quality, informed by research in human perception and cognition.

Aesthetics in Data Visualization

Advances in Data Mining and Database Management, 2014

Data visualization has been one of the major interests among interaction designers thanks to the recent advances of visualization authoring tools. Using such tools including programming languages with Graphics APIs, websites with chart topologies, and open source libraries and component models, interaction designers can more effectively create data visualization harnessing their prototyping skills and aesthetic sensibility. However, there still exist technical and methodological challenges for interaction designers in jumping into the scene. In this article, the authors introduce five case studies of data visualization that highlight different design aspects and issues of the visualization process. The authors also discuss the new roles of designers in this interdisciplinary field and the ways of utilizing, as well as enhancing, visualization tools for the better support of designers.

Evaluating visualizations: using a taxonomic guide

International Journal of Human-computer Studies / International Journal of Man-machine Studies, 2000

Although visualizations are components of many information interfaces, testing of these visual elements is rarely undertaken except as a part of overall usability testing. For this reason, it is unclear what role, if any, visualizations actually perform. Our method involves the creation of simple visual prototypes and task sets based on a visual taxonomy which allows testing of the visualization in isolation from the rest of the system. By de"ning tests using a visual taxonomy rather than customary tasks from the application domain, our method circumvents the problems of restricting evaluation of newer more capable systems to only those tasks which might be accomplished with older, less capable ones. This paper will discuss methods for exhaustively testing the capabilities of a visualization by mapping from a domain-independent taxonomy of visual tasks to a speci"c domain, i.e. information retrieval. Experimental results are presented illustrating this approach to determining the role visualizations may play in supporting users in information-seeking environments. Our methods could easily be extended to other domains including data visualization.

Design and evaluation in visualization research

… , 2005. VIS 05. IEEE, 2005

This panel brings together researchers who have been pioneering quite different approaches to visualization research by integrating evaluation and knowledge of visual design into their work. The panelists will present their views and experiences in using user studies for quantitative evaluation of methods, in integrating the expertise of visually trained designers into the development of methods, and in exploring the parameter space of visualization possibilities using" human-in-the-loop" experiments. A goal of the panel is to ...

16. What we talk about when we talk about beautiful data visualizations

Data Visualization in Society

Beautiful' is an adjective often used in descriptions of well-designed data visualizations. How the concept is used, however, reveals that it is applied to characterize a variety of qualities. Going beyond mere descriptions, the use of the concept also lays bare a certain ambivalence among scholars and practitioners towards how beauty matters, and which means it serves in data visualization. Interrogating 'beautiful' as a characterizing word, combined with a study of cases of 'best practice' used as examples of beautiful visualizations in various discourses, this chapter presents an analysis of what is regarded as beautiful within the field of data visualization design. This, in turn, can inform the understanding of what beauty means in visualizing data, in the purpose of facilitating the viewer's comprehension and engagement.

Rethinking visualization: A high-level taxonomy

2004

Abstract We present the novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user's conceptual model, emphasizing the human aspect of visualization.