How Ordered Is It? On the Perceptual Orderability of Visual Channels

The design of effective glyphs for visualisation involves a number of different visual encodings. Since spatial position is usually already specified in advance, we must rely on other visual channels to convey additional relationships for multivariate analysis. One such relationship is the apparent order present in the data. This paper presents two crowdsourcing empirical studies that focus on the perceptual evaluation of orderability for visual channels, namely Bertin’s retinal variables. The first study investigates the perception of order in a sequence of elements encoded with different visual channels. We found evidence that certain visual channels are perceived as more ordered (for example, value) while others are perceived as less ordered (for example, hue) than the measured order present in the data. As a result, certain visual channels are more/less sensitive to disorder. The second study evaluates how visual orderability affects min and max judgements of elements in the sequence. We found that visual channels that tend to be perceived as ordered, improve the accuracy of identifying these value

Empirically measuring soft knowledge in visualization

In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes

Design and Interpretability of Contour Lines for Visualizing Multivariate Data

Multivariate geospatial data are commonly visualized using contour plots, where the plots for various attributes are often examined side by side, or using color blending. As the number of attributes grows, however, these approaches become less efficient. This limitation motivated the use of glyphs, where different attributes are mapped to different pre-attentive features of the glyphs. Since both contour plot overlays and glyphs clutter the underlying map, in this paper we examine whether contour lines, which are already present in map space, can be leveraged to visualize multivariate geospatial data. We present five different designs for stylizing contour lines, and investigate their interpretability using three crowdsourced studies. We evaluated the designs through a set of common geospatial data analysis tasks on a four-dimensional dataset. Our first two studies examined how the contour line width and the number of contour intervals affect interpretability, using synthetic datasets where we controlled the underlying data distribution. Study 1 revealed that the increase of width improves the task performance in most of the designs, specially in completion time, except some scenarios where reducing width does not affect performance where the visibility of the background is critical. In Study 2, we found out that fewer contour intervals lead to less visual clutter, hence improved performance. We then compared the designs in a third study that used both synthetic and real-life meteorological data. The study revealed that the results found using synthetic data were generalizable to the real-life data, as hypothesized. Moreover, we formulated a design recommendation table tuned to give users task- and category-specific design suggestions under various environment constraints. At last, we discuss the comparison between the lab and online versions of study 1 with respect to display size (lab study was done on big screen and vice versa). Our studies show the effectiveness of stylizing contour lines to represent multivariate data, reveal trade-offs among design parameters, and provide designers with important insights into the factors that influence multivariate interpretability. We also show some real-life scenarios where our visualization approach may improve decision making

A Multi-Faceted Approach for Evaluating Visualization Recommendation Algorithms

Data visualizations allow analysts to quickly understand data trends, outliers, and patterns. However, designing the "best" visualizations for a given dataset is complicated. Multiple factors need to be considered, such as the data size, data types, target analysis tasks being supported, and even how the visualization needs to be personalized to the audience. In response, many visualization recommendation algorithms are being proposed to reduce user effort by automatically making some or all of these design decisions for analysts. However, existing visualization recommendation algorithms are evaluated in isolation, or the comparisons do not measure user performance. In other words, existing algorithms are not tested in a way that aligns with how they are used in practice. The lack of evaluation approaches makes it impossible to know how functional an algorithm is compared to another across various analysis tasks, hindering our ability to design new algorithms that provide significantly more benefits than the existing ones.This dissertation contributes a multi-faceted approach for evaluating visualization recommendation algorithms to investigate factors affecting an algorithm's performance and ways to improve it. It first proposes an evaluation-focused framework and then demonstrates how the framework can evaluate strategic behaviors and user performance among a broad range of existing algorithms. The case study results show that newly proposed algorithms might not significantly improve user performance. One way to improve the algorithm performance is by integrating more established theoretical rules or empirical results on how people perceive different visualization designs, i.e., graphical perception guidelines, to guide the recommendation ranking process. Thus, this dissertation next presents a thorough literature review of existing graphical perception literature that can inform visualization recommendation algorithms. It contributes a systematic dataset that collates existing theoretical and experimental visualization comparison results and summarizes key study outcomes. Further, this dissertation conducts an exploratory analysis to investigate the influence of each piece of graphical perception study in changing a visualization recommendation algorithm's behavior and outputs. The analysis results show that some graphical perception studies can alter the behavior of visualization recommendation algorithms dominantly, while others have little influence. Based on the analysis findings, this dissertation opens avenues at the intersection of graphical perception and visualization research, like how to evaluate the effectiveness of new graphical perception work in guiding visualization recommendations

Exploring the potential of physical visualizations

The goal of an external representation of abstract data is to provide insights and convey information about the structure of the underlying data, therefore helping people execute tasks and solve problems more effectively. Apart from the popular and well-studied digital visualization of abstract data there are other scarcely studied perceptual channels to represent data such as taste, sound or haptic. My thesis focuses on the latter and explores in which ways human knowledge and ability to sense and interact with the physical non-digital world can be used to enhance the way in which people analyze and explore abstract data. Emerging technological progress in digital fabrication allow an easy, fast and inexpensive production of physical objects. Machines such as laser cutters and 3D printers enable an accurate fabrication of physical visualizations with different form factors as well as materials. This creates, for the first time, the opportunity to study the potential of physical visualizations in a broad range. The thesis starts with the description of six prototypes of physical visualizations from static examples to digitally augmented variations to interactive artifacts. Based on these explorations, three promising areas of potential for physical visualizations were identified and investigated in more detail: perception & memorability, communication & collaboration, and motivation & self-reflection. The results of two studies in the area of information recall showed that participants who used a physical bar chart retained more information compared to the digital counterpart. Particularly facts about maximum and minimum values were be remembered more efficiently, when they were perceived from a physical visualization. Two explorative studies dealt with the potential of physical visualizations regarding communication and collaboration. The observations revealed the importance on the design and aesthetic of physical visualizations and indicated a great potential for their utilization by audiences with less interest in technology. The results also exposed the current limitations of physical visualizations, especially in contrast to their well-researched digital counterparts. In the area of motivation we present the design and evaluation of the Activity Sculptures project. We conducted a field study, in which we investigated physical visualizations of personal running activity. It was discovered that these sculptures generated curiosity and experimentation regarding the personal running behavior as well as evoked social dynamics such as discussions and competition. Based on the findings of the aforementioned studies this thesis concludes with two theoretical contributions on the design and potential of physical visualizations. On the one hand, it proposes a conceptual framework for material representations of personal data by describing a production and consumption lens. The goal is to encourage artists and designers working in the field of personal informatics to harness the interactive capabilities afforded by digital fabrication and the potential of material representations. On the other hand we give a first classification and performance rating of physical variables including 14 dimensions grouped into four categories. This complements the undertaking of providing researchers and designers with guidance and inspiration to uncover alternative strategies for representing data physically and building effective physical visualizations.Um aus abstrakten Daten konkrete Aussagen, komplexe Zusammenhänge oder überraschende Einsichten gewinnen zu können, müssen diese oftmals in eine, für den Menschen, anschauliche Form gebracht werden. Eine weitverbreitete und gut erforschte Möglichkeiten ist die Darstellung von Daten in visueller Form. Weniger erforschte Varianten sind das Verkörpern von Daten durch Geräusche, Gerüche oder physisch ertastbare Objekte und Formen. Diese Arbeit konzentriert sich auf die letztgenannte Variante und untersucht wie die menschlichen Fähigkeiten mit der physischenWelt zu interagieren dafür genutzt werden können, das Analysieren und Explorieren von Daten zu unterstützen. Der technische Fortschritt in der digitalen Fertigung vereinfacht und beschleunigt die Produktion von physischen Objekten und reduziert dabei deren Kosten. Lasercutter und 3D Drucker ermöglichen beispielsweise eine maßgerechte Fertigung physischer Visualisierungen verschiedenster Ausprägungen hinsichtlich Größe und Material. Dadurch ergibt sich zum ersten Mal die Gelegenheit, das Potenzial von physischen Visualisierungen in größerem Umfang zu erforschen. Der erste Teil der Arbeit skizziert insgesamt sechs Prototypen physischer Visualisierungen, wobei sowohl statische Beispiele beschrieben werden, als auch Exemplare die durch digital Inhalte erweitert werden oder dynamisch auf Interaktionen reagieren können. Basierend auf den Untersuchungen dieser Prototypen wurden drei vielversprechende Bereiche für das Potenzial physischer Visualisierungen ermittelt und genauer untersucht: Wahrnehmung & Einprägsamkeit, Kommunikation & Zusammenarbeit sowie Motivation & Selbstreflexion. Die Ergebnisse zweier Studien zur Wahrnehmung und Einprägsamkeit von Informationen zeigten, dass sich Teilnehmer mit einem physischen Balkendiagramm an deutlich mehr Informationen erinnern konnten, als Teilnehmer, die eine digitale Visualisierung nutzten. Insbesondere Fakten über Maximal- und Minimalwerte konnten besser im Gedächtnis behalten werden, wenn diese mit Hilfe einer physischen Visualisierung wahrgenommen wurden. Zwei explorative Studien untersuchten das Potenzial von physischen Visualisierungen im Bereich der Kommunikation mit Informationen sowie der Zusammenarbeit. Die Ergebnisse legten einerseits offen wie wichtig ein ausgereiftes Design und die Ästhetik von physischen Visualisierungen ist, deuteten anderseits aber auch darauf hin, dass Menschen mit geringem Interesse an neuen Technologien eine interessante Zielgruppe darstellen. Die Studien offenbarten allerdings auch die derzeitigen Grenzen von physischen Visualisierungen, insbesondere im Vergleich zu ihren gut erforschten digitalen Pendants. Im Bereich der Motivation und Selbstreflexion präsentieren wir die Entwicklung und Auswertung des Projekts Activity Sculptures. In einer Feldstudie über drei Wochen erforschten wir physische Visualisierungen, die persönliche Laufdaten repräsentieren. Unsere Beobachtungen und die Aussagen der Teilnehmer ließen darauf schließen, dass die Skulpturen Neugierde weckten und zum Experimentieren mit dem eigenen Laufverhalten einluden. Zudem konnten soziale Dynamiken entdeckt werden, die beispielsweise durch Diskussion aber auch Wettbewerbsgedanken zum Ausdruck kamen. Basierend auf den gewonnen Erkenntnissen durch die erwähnten Studien schließt diese Arbeit mit zwei theoretischen Beiträgen, hinsichtlich des Designs und des Potenzials von physischen Visualisierungen, ab. Zuerst wird ein konzeptionelles Framework vorgestellt, welches die Möglichkeiten und den Nutzen physischer Visualisierungen von persönlichen Daten veranschaulicht. Für Designer und Künstler kann dies zudem als Inspirationsquelle dienen, wie das Potenzial neuer Technologien, wie der digitalen Fabrikation, zur Darstellung persönlicher Daten in physischer Form genutzt werden kann. Des Weiteren wird eine initiale Klassifizierung von physischen Variablen vorgeschlagen mit insgesamt 14 Dimensionen, welche in vier Kategorien gruppiert sind. Damit vervollständigen wir unser Ziel, Forschern und Designern Inspiration und Orientierung zu bieten, um neuartige und effektvolle physische Visualisierungen zu erschaffen