This paper examines how link colors influence the discriminability of quan- titative node color encoding in node-link diagrams. Node colors in node-link diagrams can represent quantitative attributes, such as value, frequency, or intensity. Although links themselves do not necessarily represent values, the relational aspect must be displayed with a color. This color has the potential to influence the discriminability of the node colors. Extensive research has been conducted on quantitative encoding for scatterplots; however, there is currently no research on which color combinations between nodes and links should be employed and how the quantitative values in nodes should be encoded to ensure their discriminability. This paper addresses two cases: 1. Comparing two highlighted nodes while all other nodes are gray, and 2. Comparing two nodes in a fully colored graph. We conducted two stud- ies, in which participants were required to identify color differences between two nodes in a node-link diagram with varying link properties. In the first study, conducted online, two nodes were colored, and the surrounding link topologies and link hues were varied. The node hues were also varied, while the quantitative values were encoded through saturation. The most discrim- inable node hues were used in the second study, conducted in a laboratory setting, where all nodes encoded quantitative data. Our findings indicate that the use of complementary-colored links has the effect of enhancing the discriminability of node colors, irrespective of the underlying topology. In contrast, links with a hue that is similar to the node hues leads to a reduction in discriminability. The findings of both studies were found to be consistent. In sum, our results indicate guidelines for the use of colors to enhance the discriminability of nodes in small graphs with quantitative node encoding. We recommend using shades of blue rather than yellow to quantitatively en- code nodes. To highlight the connections between nodes, pair these colors with complementary colors for the links, rather than coloring the links the same color as the nodes. Alternatively, use neutral colors, such as gray, to support the discriminability of node colors.

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of visualization.

Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.

Adding temporal information to social network visualizations is still a challenging task despite previous research efforts. The dimension time is of great interest to analysts as it offers insights into various trends and patterns such as changing relationships between different actors or economic opportunities for businesses. Yet current approaches suffer from limitations that can be improved with the visualization design presented in this work. The visualization was developed considering aesthetic criteria and characteristics of adjacency matrices and node-link diagrams. In a formative evaluation process an artificial dataset was specifically created to examine dynamic social networks. A qualitative user study with observation and think-aloud protocols was conducted and analyzed with regard to the user's strategies, limitations of the visualization and potential additional features. The visualization appears to be suitable for all of the evaluated network tasks, however, path-related tasks were more challenging similar to the characteristics of matrix-based representations.

Visualising how social networks evolve is important in intelligence analysis in order to detect and monitor issues, such as emerging crime patterns or rapidly growing groups of offenders. It remains an open research question how this type of information should be presented for visual exploration. To get a sense of how users work with different types of visualisations, we evaluate a matrix and a node-link diagram in a controlled thinking aloud study. We describe the sense-making strategies that users adopted during explorative and realistic tasks. Thereby, we focus on the user behaviour in switching between the two visualisations and propose a set of nine strategies. Based on a qualitative and quantitative content analysis we show which visualisation supports which strategy better. We find that the two visualisations clearly support intelligence tasks and that for some tasks the combined use is more advantageous than the use of an individual visualisation.

In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game's community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period.

Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms.

Due to the high potential of digital media to support learning processes and outcomes, educational games have gained wide acceptance over the years. The combination of mobile devices with location-based technologies offers new options and possibilities for the development of educational games in consideration of learners' environment with the positive side effect to promote learner's physical activities. This paper introduces a mobile educational game for promoting a better understanding of concepts related to route problems and route optimization on the basis of real world examples in a playful manner. The game combines problem-solving tasks with a quiz to teach concepts related to the Traveling Salesman Problem (TSP) by using the Global Positioning System (GPS) technology.

With the rising interest in multiplayer gaming, gameplay statistics have become an increasingly important aspect of the overall game experience for many players. As a part of this trend, visualizations have gained great popularity among players, in particular heatmaps since they allow them to reenact the course of a game and to develop new strategies. In this paper we report results of a user study conducted with 29 players (i) to investigate how players use heatmaps and two further graphical representations that use clustering algorithms to interpret gameplay and (ii) to assess the three representations in regard to time efficiency, correctness, suitability, and player preference. Our results show that heatmaps were mainly used to detect hot spots while the cluster representations proved useful to compare variables, allowing the players to uncover relationships between them and in turn allowing a deeper insight into the gameplay data.

The design of traffic management systems is challenging because of the large amount of dynamic data which has to be analysed by operators. Systems have to be designed appropriately to enable operators to react efficiently and quickly. The following paper gives an overview of guidelines derived from empirical research in cognitive psychology and HCI. These guidelines specifically address the design of traffic management systems. The following topics seem to be especially relevant: visual perception, geographic data, perception of motion, monitoring of tasks and interaction. These guidelines can help to design usable and useful systems.

Current visualization methods of gaze data may lead to unspecific interpretation in form of ambiguous or inconsistent results during the final analysis process. It is not infrequent that inappropriate visualizations mislead researchers and provoke false conclusions of academic as well as economic studies. The most common visualization methods for cumulative gaze data are heatmaps as well as gaze plots. The former is suitable for an overview of eye movements' intensities, density as well as the general distribution of the learner's visual attention. However, this visualization method neglects fundamental information for example about the temporal distribution (order of fixation/saccades), general traceability of gaze, (cumulative) start as well as end of learner's visual explorations and additional information about visual transitions between areas of interest (AOI). The second considers most of the above mentioned aspects, however cannot be fully applied due its insufficient form of representation for multiuser-gaze data. The second topic of my PhD concerns the linking of eye tracking studies' results to e-learning technologies. Eye tracking research has been largely adapted to marketing or human-computer-interaction in general - however it has not been commonly used for learning technologies or e-learning applications in particular. Furthermore the reliability of existing eye tracking studies (within both and economic settings) may be impaired due to ambiguous interpretation. It is a general issue that the analysis process of these eye tracking results is not carried out by basic guidelines or (internationally approved) standards. An aim of my dissertation is to develop a practical framework respectively a set of guidelines for the interpretation process in order to minimize ambiguity during analysis of gaze data. As a side effect this framework should contribute to the improvement of the learning technologies' quality.

The usage of visualizations to aid the analysis of time oriented data plays an important role in various fields of applications. The need to visualize such data was decisive for the development of different visualization techniques over the last years. One of the frequently applied techniques is animation in order to illustrate the movements in such a way to make changes in the data transparent. However, evaluation studies of such animated interfaces for time-oriented data with potential users are still difficult to find. In this paper, we present our observations based on a systematic literature review with the motivation to support researchers and designers to identify future directions for their research. The literature review is split in two parts: (1) research on animation from the field of psychology, and (2) evaluation studies with the focus on animation of time-oriented data.

Visual analytics emphasizes the interplay between visualization, analytical procedures performed by computers and human perceptual and cognitive activities. Human reasoning is an important element in this context. There are several theories in psychology and HCI explaining open-ended and exploratory reasoning. Five of these theories (sensemaking theories, gestalt theories, distributed cognition, graph comprehension theories and skill-rule-knowledge models) are described in this paper. We discuss their relevance for visual analytics. In order to do this more systematically, we developed a schema of categories relevant for visual analytics research and evaluation. All these theories have strengths but also weaknesses in explaining interaction with visual analytics systems. A possibility to overcome the weaknesses would be to combine two or more of these theories.

The evaluation of Information Visualization (InfoVis) techniques can help to identify specific strengths and weaknesses of these methods. The following article describes the results of an empirical study assessing the contribution of an interactive InfoVis method based on a spring metaphor (GRAVI), exploratory data analysis (EDA) and machine learning (ML) to ease understanding. The application domain is the psychotherapeutic treatment of anorectic young women. The three methods are supposed to support the therapists in finding the variables which influence success or failure of the therapy. To conduct the evaluation we developed a report system which helped subjects to formulate and document in a self-directed manner the insights they gained when using the three methods. The results indicate that the three methods are complementary and should be used in conjunction.

Comparing different information visualization (InfoVis) techniques is a challenging task and a necessary step to reach the users and their tasks. We evaluated the effectiveness in visualizing psychotherapeutic data of two InfoVis techniques, namely the stardinates and the parallel coordinates by a comparative study with 22 participants. Based on three research questions we interpret the results of our study in order to derive statements on both visualization techniques. We evaluated (1) the time participants were engaged in testing our material; (2) the number of correct answers; (3) subjects' statements, which were categorized by the type of statement; and (4) the subjects' key statements in comparison to those defined by an expert. Our empirical results indicate that the stardinates are a more appropriate method for interpreting such highly structured data in detail whereas parallel coordinates show advantages for gaining information at the first glance.

The Stardinates are a novel interactive information visualization (InfoVis) technique, which aims at visualizing highly structured data. They represent some Gestalt principles very well, especially the principles of Closure and 'Pragnanz'. As a consequence, Stardinates form very distinct and memorable patterns, which make abstraction and aggregation much easier. We give a formal description of the Stardinates as a basis for implementation. Furthermore, we show an application by visualizing psychotherapeutic data derived from a clinical study on anorectic girls.