Existing toolkits for data physicalization prioritize ease of use and adoption by novices. This is often achieved by limiting the affordances of the materials used and constraining design possibilities. The result is limited opportunities for creating expressive physicalizations. To address this limitation and to better understand how to support the creation process of expressive physicalizations, we created ClayPhys, a low-fidelity data physicalization toolkit designed to encourage making expressive data physicalizations. Our toolkit consists of clay, clay work tools, instruction and documentation handbooks, and warm-up activities that scaffold the design process. We studied the use of ClayPhys in a one-day workshop with nine expert participants. From our analysis of participants’ created data physicalizations, we observed that using ClayPhys, participants could map data to different visual and physical variables, and that their designs incorporated various data interaction styles. Informed by our findings, we discuss implications for designing higher-fidelity expressive data physicalization toolkits.

This work investigates the current research on in-situ visualisations for running: visualisations about data that are referred to during the running activity. We analyse 47 papers from 33 Human-Computer Interaction and Visualisation venues and identify six dimensions of a design space of in-situ running visualisations. Our analysis of this design space highlights an emerging trend: a shift from on-body, peripersonal visualisations (i.e., in the space within direct reach, such as visualisations on a smartwatch or a mobile phone display) towards extrapersonal displays (i.e., in the space beyond immediate reach, such as visualisations in immersive augmented reality displays) that integrate data in the runner's surrounding environment. We explore this opportunity by conducting a series of workshops with 10 active runners in total, eliciting design concepts for running visualisations and interactions beyond conventional 2D displays. We find that runners show a strong interest for visualisation designs that favour more context-aware, interactive, and unobtrusive experiences that seamlessly integrate with their run. These findings inform a set of design considerations for future immersive running visualisations and highlight directions for further research.

Finding a particular object in a display is important for viewers in many visualizations, for example, when reacting to brushing or to a highlighted object. This can be enabled by making the target object different in one of the visual variables that determine the object's appearance; for example, by changing its color or size. Certain interpretations of the visual search literature have promoted the view that using visual variables such as hue-often labeled as preattentive-would make the target object automatically "popout," implying that an object can be located almost instantly, regardless of the number of objects in the display. In this paper we present a study that serves as a bridge between the extensive visual search literature and visualization, establishing empirical base measurements for the localization task. By testing displays with up to hundreds of objects, we are able to show that none of the common visual variables is immune to the increase in the number of objects. We also provide the first empirically informed comparisons between visual variables for this task in the context of visualization, and show how different visual variables have varying robustness with respect to two additional dimensions: the location of the target and the overall visual arrangement (layout). A free copy of this paper and all supplemental materials are available on our online repository: https://osf.io/z68ak/overview.

Stacked-dimension tables (SDTs) represent multidimensional data with nested tables, where each table shows two of the dataset’s dimensions. SDTs provide a comprehensive overview of all of the data and all of its dimensions – but overviews are just the starting point for exploration, and there is little information available to designers about how to support further interactions with SDTs. We worked with a crop-breeding research group to develop a stacked-dimension system that suited their complex multidimensional datasets, and to identify requirements for their analyses of differential gene expression across multiple genomes. Based on the requirements, we developed a new SDT system and several new interaction techniques that support the researchers’ needs to filter the data, reconfigure the visualization, provide data context, revisit previous configurations, and integrate findings into a broader workflow. To test the generalizability of our designs, we then extended the requirements and techniques in a second SDT system for a new domain (outcomes from a retirement-planning model) and carried out a small usability study with this system. Our evaluations show that the techniques are easily learned and understood by both domain experts and everyday users, and that they provide support for real-world exploration in stacked-dimension visualizations.

Although we know a lot about how adults read and utilize visualizations, we barely know anything about how children do so. We report the results of a qualitative study in which children aged 4–6 were asked to map, encode, and derive insights from creating physical data representations of their emotions using art and craft materials over a period of two weeks. We found that young children can encode and decode data, derive insights and self-reflect when engaging in a constructive physicalization activity.

Unlike digital visualization, physicalization design introduces structural and material uncertainties, which require immediate feedback and often rapid iteration. This makes physicalization design a speculative, labor-intensive, and expertise-dependent activity. While speculative reasoning can be central to physicalization, authoring tools remain scarce and there is a lack of computational support that can help in anticipating physicalization outcomes. We explore how feedforward mechanisms (predictive features) might help physicalization designers anticipate post-fabrication qualities like tactile feel and expandability before fabrication, by iteratively developing and studying DataCuts, a physicalization authoring tool for designing Kirigami-based data physicalizations. Our study findings show that DataCuts’ simple feedforward functionalities can inform design decisions, shape people’s understanding of physical properties and encourage tactile representation over conventional mappings. Building on these results, we discuss implications for designing data physicalization support tools that integrate feedforward.

There is evidence that constructing a data physicalization can empower a diverse audience to engage in data understanding and decision making. Although the hands-on nature of data physicalization construction enables a familiar way for people to collaboratively construct and interact with representations, we do not know how this collaboration happens. We conducted a qualitative study with 12 participants (six couples) to better understand how people collaborate to construct a physicalization, using a combination of elicitation diary and semi-structured interviews over a period of several weeks. We found that participants employed three main styles of collaboration - mutual, exclusive and dictator, when working together to construct a physicalization. We found that such collaboration styles are facilitated through actions such as discussion, distributing tasks, and preparing and assembling tokens. Informed by our findings, we discuss: i) similarities and differences between previously proposed physicalization workflows in the literature and ours, and ii) research directions to foster collaboration in data physicalization processes.

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Abstract We investigate the use of mixed reality visualisations to help pace tracking for interval running. We introduce three immersive visual designs to support pace tracking. Our designs leverage two properties afforded by mixed reality environments to display information: the space in front of the user and the physical environment to embed pace visualisation. In this paper, we report on the first design exploration and controlled study of mixed reality technology to support pacing tracking during interval running on an outdoor running track. Our results show that mixed reality and immersive visualisation designs for interval training offer a viable option to help runners (a) maintain regular pace, (b) maintain running flow, and (c) reduce mental task load.

Emotion is an important factor to consider when designing visualizations as it can impact the amount of trust viewers place in a visualization, how well they can retrieve information and understand the underlying data, and how much they engage with or connect to a visualization. We conducted five crowdsourced experiments to quantify the effects of color, chart type, data trend, data variability and data density on emotion (measured through self-reported arousal and valence). Results from our experiments show that there are multiple design elements which influence the emotion induced by a visualization and, more surprisingly, that certain data characteristics influence the emotion of viewers even when the data has no meaning. In light of these findings, we offer guidelines on how to use color, scale, and chart type to counterbalance and emphasize the emotional impact of immutable data characteristics.

Data tables are one of the most common ways in which people encounter data. Although mostly built with text and numbers, data tables have a spatial layout and often exhibit visual elements meant to facilitate their reading. Surprisingly, there is an empirical knowledge gap on how people read tables and how different visual aids affect people's reading of tables. In this work, we seek to address this vacuum through a controlled study. We asked participants to repeatedly perform four different tasks with four table representation conditions (plain tables, tables with zebra striping, tables with cell background color encoding cell value, and tables with in-cell bars with lengths encoding cell value). We analyzed completion time, error rate, gaze-tracking data, mouse movement and participant preferences. We found that color and bar encodings help for finding maximum values. For a more complex task (comparison of proportional differences) color and bar helped less than zebra striping. We also characterize typical human behavior for the four tasks. These findings inform the design of tables and research directions for improving presentation of data in tabular form.

Data physicalization is a research area that explores representing data attributes through manipulating the geometric and physical properties of tangible objects. We introduce VISMOCK, a data physicalization approach that leverages a fabric manipulation technique called “smocking”. VISMOCK supports the creation of interactive and dynamic data physi-calizations by extending the smocking technique with programmable components such as thermochromic pigments and shape memory alloys. Using a research-through-design methodology, we develop an initial design space for VISMOCK that shows how data can be represented using visual and tactile variables, as well as the affordances of VISMOCK. We demonstrate the generative power of our design space through four exemplars, created using VISMOCK. We use these exemplars to discuss the advantages and limitations of VISMOCK as a tool for data physicalization.

Millions of individuals with diabetes use glucose monitors to track blood sugar levels. Research shows that such individuals seek to customize different aspects of their interactions with these devices, including how they engage with, decorate, and wear them. However, it remains challenging to tailor both device form and function to accommodate individual needs. To address this challenge, we introduce GlucoMaker, a system for collaboratively customizing physical design aspects of glucose monitors. Prior to designing GlucoMaker, we conducted a prototyping and focus group study to understand customization preferences and collaboration benefits. GlucoMaker provides individuals with the ability to a) select monitor form and function preferences, b) alter predefined and downloadable digital model files, c) receive feedback on monitor designs from stakeholders, and d) learn technical design aspects. We further demonstrate the versatility and design space of GlucoMaker with three examples of different form and function use cases.

Researchers have found that hobbyist-sewers seek to create new or adapted clothing designs that foster self-expression through communicating ideas, opinions and emotions. Although existing sewing technologies enable designing new patterns, they focus only on the technical aspects of pattern drafting and not on how information can be expressed. To address this gap, we conducted a qualitative diary study with 12 hobbyist-sewers to better understand how they envision creating expressive clothing. From our analysis of the 24 expressive clothing sketches participants created and participant interviews, we identified i) five distinctive multifaceted approaches participants used for self-expression; and ii) four challenges participants identified from their design process. Informed by these insights, we present a set of implications for the design of future technologies that can better support hobbyist-sewers in designing and creating expressive clothing.

Millions of runners rely on smart watches that display running-related metrics such as pace, heart rate and distance for training and racing — mostly with text and numbers. Although research tells us that visualizations are a good alternative to text on smart watches, we know little about how visualizations can help in realistic running scenarios. We conducted a study in which 20 runners completed running-related tasks on an outdoor track using both text and visualizations. Our results show that runners are 1.5 to 8 times faster in completing those tasks with visualizations than with text, prefer visualizations to text, and would use such visualizations while running — if available on their smart watch.

This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals & Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.

We present KiriPhys, a new type of data physicalization based on kirigami, a traditional Japanese art form that uses paper-cutting. Within the kirigami possibilities, we investigate how different aspects of cutting patterns offer opportunities for mapping data to both independent and dependent physical variables. As a first step towards understanding the data physicalization opportunities in KiriPhys, we conducted a qualitative study in which 12 participants interacted with four KiriPhys examples. Our observations of how people interact with, understand, and respond to KiriPhys suggest that KiriPhys: 1) provides new opportunities for interactive, layered data exploration, 2) introduces elastic expansion as a new sensation that can reveal data, and 3) offers data mapping possibilities while providing a pleasurable experience that stimulates curiosity and engagement.

We present Activity River, a personal visualization tool which enables individuals to plan, log, and reflect on their self-defined activities. We are interested in supporting this type of reflective practice as prior work has shown that reflection can help people plan and manage their time effectively. Hence, we designed Activity River based on five design goals (visualize historical and contextual data, facilitate comparison of goals and achievements, engage viewers with delightful visuals, support authorship, and enable flexible planning and logging) which we distilled from the Information Visualization and Human-Computer Interaction literature. To explore our approach's strengths and limitations, we conducted a qualitative study of Activity River using a role-playing method. Through this qualitative exploration, we illustrate how our participants envisioned using our visualization to perform dynamic and continuous reflection on their activities. We observed that they were able to assess their progress towards their plans and adapt to unforeseen circumstances using our tool.

We were approached by a group of healthcare providers who are involved in the care of chronic patients looking for potential technologies to facilitate the process of reviewing patient-generated data during clinical visits. Aiming at understanding the healthcare providers' attitudes towards reviewing patient-generated data, we (1) conducted a focus group with a mixed group of healthcare providers. Next, to gain the patients' perspectives, we (2) interviewed eight chronic patients, collected a sample of their data and designed a series of visualizations representing patient data we collected. Last, we (3) sought feedback on the visualization designs from healthcare providers who requested this exploration. We found four factors shaping patient-generated data: data & context, patient's motivation, patient's time commitment, and patient's support circle. Informed by the results of our studies, we discussed the importance of designing patient-generated visualizations for individuals by considering both patient and healthcare provider rather than designing with the purpose of generalization and provided guidelines for designing future patient-generated data visualizations.

Abstract In the visualization community, it is often assumed that visual data storytelling increases memorability and engagement, making it more effective at communicating information. However, many assumptions about the efficacy of storytelling in visualization lack empirical evaluation. Contributing to an emerging body of work, we study whether selected techniques commonly used in visual data storytelling influence people's attitudes towards immigration. We compare (a) personal visual narratives designed to generate empathy; (b) structured visual narratives of aggregates of people; and (c) an exploratory visualization without narrative acting as a control condition. We conducted two crowdsourced between‐subject studies comparing the three conditions, each with 300 participants. To assess the differences in attitudes between conditions, we adopted established scales from the social sciences used in the European Social Survey (ESS). Although we found some differences between conditions, the effects on people's attitudes are smaller than we expected. Our findings suggest that we need to be more careful when it comes to our expectations about the effects visual data storytelling can have on attitudes. Additional material: https://flowstory.github.io/attitudes/.

Interaction is fundamental to data visualization, but what "interaction" means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community - including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization. Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them.

We investigate direct manipulation of graphical encodings as a method for interacting with visualizations. There is an increasing interest in developing visualization tools that enable users to perform operations by directly manipulating graphical encodings rather than external widgets such as checkboxes and sliders. Designers of such tools must decide which direct manipulation operations should be supported, and identify how each operation can be invoked. However, we lack empirical guidelines for how people convey their intended operations using direct manipulation of graphical encodings. We address this issue by conducting a qualitative study that examines how participants perform 15 operations using direct manipulation of standard graphical encodings. From this study, we 1) identify a list of strategies people employ to perform each operation, 2) observe commonalities in strategies across operations, and 3) derive implications to help designers leverage direct manipulation of graphical encoding as a method for user interaction.

We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality.

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work.

Abstract In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially‐growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real‐world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in‐game actions and trajectories), and meta‐data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high‐level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/ .

We extend the popular brushing and linking technique by incorporating personal agency in the interaction. We map existing research related to brushing and linking into a design space that deconstructs the interaction technique into three components: source (what is being brushed), link (the expression of relationship between source and target), and target (what is revealed as related to the source). Using this design space, we created MyBrush, a unified interface that offers personal agency over brushing and linking by giving people the flexibility to configure the source, link, and target of multiple brushes. The results of three focus groups demonstrate that people with different backgrounds leveraged personal agency in different ways, including performing complex tasks and showing links explicitly. We reflect on these results, paving the way for future research on the role of personal agency in information visualization.

We empirically evaluate the extent to which people perceive non-constant time and speed encoded on 2D paths. In our graphical perception study, we evaluate nine encodings from the literature for both straight and curved paths. Visualizing time and speed information is a challenge when the x and y axes already encode other data dimensions, for example when plotting a trip on a map. This is particularly true in disciplines such as time-geography and movement analytics that often require visualizing spatio-temporal trajectories. A common approach is to use 2D+time trajectories, which are 2D paths for which time is an additional dimension. However, there are currently no guidelines regarding how to represent time and speed on such paths. Our study results provide InfoVis designers with clear guidance regarding which encodings to use and which ones to avoid; in particular, we suggest using color value to encode speed and segment length to encode time whenever possible.

We investigate whether the notion of active reading for text might be usefully applied to visualizations. Through a qualitative study we explored whether people apply observable active reading techniques when reading paper-based node-link visualizations. Participants used a range of physical actions while reading, and from these we synthesized an initial set of active reading techniques for visualizations. To learn more about the potential impact such techniques may have on visualization reading, we implemented support for one type of physical action from our observations (making freeform marks) in an interactive node-link visualization. Results from our quantitative study of this implementation show that interactive support for active reading techniques can improve the accuracy of performing low-level visualization tasks. Together, our studies suggest that the active reading space is ripe for research exploration within visualization and can lead to new interactions that make for a more flexible and effective visualization reading experience.

Heterogeneous multi-dimensional data are now sufficiently common that they can be referred to as ubiquitous. The most frequent approach to visualizing these data has been to propose new visualizations for representing these data. These new solutions are often inventive but tend to be unfamiliar. We take a different approach. We explore the possibility of extending well-known and familiar visualizations through including Heterogeneous Embedded Data Attributes (HEDA) in order to make familiar visualizations more powerful. We demonstrate how HEDA is a generic, interactive visualization component that can extend common visualization techniques while respecting the structure of the familiar layout. HEDA is a tabular visualization building block that enables individuals to visually observe, explore, and query their familiar visualizations through manipulation of embedded multivariate data. We describe the design space of HEDA by exploring its application to familiar visualizations in the D3 gallery. We characterize these familiar visualizations by the extent to which HEDA can facilitate data queries based on attribute reordering.

Motivation is a key factor for introducing and maintaining healthy changes in behaviour. However, typical visualization methods (e.g., bar-, pie-, and line charts) hardly motivate individuals. We investigate how a plant---a living visualization---whose health relies on the plant owner's level of activity, can engage people in tracking and self-reflecting on their fitness data. To address this question, we designed, implemented, and studied Go & Grow, a living plant that receives water proportionally to its owner's activity. Our six-week qualitative study with ten participants suggests that living visualizations have qualities that their digital counterparts do not have. This includes people feeling: emotionally connected to their plant; sentiments such as pride and guilt; and responsibility towards their plant. Based on this study, we introduce the Goal Motivation Model, a model considering the diversity of individuals, thus supporting and encouraging a diversity of strategies for accomplishing goals.

We introduce Flexible Trees, a sketch-based layout adjustment technique. Although numerous tree layout algorithms exist, these algorithms are usually bound to fit within standard shapes such as rectangles, circles and triangles. In order to provide the possibility of interactively customizing a tree layout, we offer a free-form sketch-based interaction through which one can re-define the boundary constraints for the tree layouts by combining ray-line intersection and line segment intersection. Flexible Trees offer topology preserving adjustments; can be used with a variety of tree layouts; and offer a simple way of authoring tree layouts for infographic purposes.

Stacked graphs are a visualization technique popular in casual scenarios for representing multiple time-series. Variations of stacked graphs have been focused on reducing the distortion of individual streams because foundational perceptual studies suggest that variably curved slopes may make it difficult to accurately read and compare values. We contribute to this discussion by formally comparing the relative readability of basic stacked area charts, ThemeRivers, streamgraphs and our own interactive technique for straightening baselines of individual streams in a ThemeRiver. We used both real-world and randomly generated datasets and covered tasks at the elementary, intermediate and overall information levels. Results indicate that the decreased distortion of the newer techniques does appear to improve their readability, with streamgraphs performing best for value comparison tasks. We also found that when a variety of tasks is expected to be performed, using the interactive version of the themeriver leads to more correctness at the cost of being slower for value comparison tasks.

Abstract We provide afresh look at the use and prevalence of emphasis effects in Infovis. Through a survey of existing emphasis frameworks, we extract a set‐based approach that uses visual prominence to link visually and algorithmically diverse emphasis effects. Visual prominence provides a basis for describing, comparing and generating emphasis effects when combined with a set of general features of emphasis effects. Therefore, we use visual prominence and these general features to construct a new mathematical Framework for Information Visualization Emphasis, FIVE. The concepts we introduce to describe FIVE unite the emphasis literature and point to several new research directions for emphasis in information visualization.

We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan.

We introduce a novel time navigation technique to update ranking tables by direct manipulation. The technique allows users to drag a table's cells to change the time period, while a line chart overlays on top of the table to provide an overview of the changes. The line chart is also a visual hint to control the pace at which data are updated. We explore the design and usability of this technique for table variations in size, time spans and data variability. We report the results of a usability study, using academic citation rankings and economic complexity datasets, and discuss design implications coming with real-world scenarios such as missing data and affordance.

This article introduces A Table!, an enhanced soccer ranking table providing temporal navigation by combining two novel interaction techniques. Ranking tables order soccer teams represented as rows, according to values of columns containing attributes e.g., accumulated points, or number of scored goals. Because they represent a snapshot of a championship at a time t, tables are regularly updated with new results. Such updates usually change the rows order, which makes the tracking of a specified team over time difficult. We observed that the tables available on the web do not support tracking such changes very well, are generally hard to read, and lack interactions. This contrasts with the extensive use of comments on temporal trends found in soccer analysts articles. To better support such analyzes, the two interactive techniques presented allow exploration of time, and are designed to preserve users' flow: DRAG-CELL is based on direct manipulation of values to browse ranks; VIZ-RANK uses a transient line chart of team ranks to visually explore a championship. An on-line evaluation with 143 participants shows that each technique efficiently supports a set of important temporal tasks not supported by current ranking tables. This paves the way for introducing efficient advanced visual exploration techniques to millions of soccer enthusiasts who use tables everyday.

We present Bertifier, a web app for rapidly creating tabular visualizations from spreadsheets. Bertifier draws from Jacques Bertin's matrix analysis method, whose goal was to "simplify without destroying" by encoding cell values visually and grouping similar rows and columns. Although there were several attempts to bring this method to computers, no implementation exists today that is both exhaustive and accessible to a large audience. Bertifier remains faithful to Bertin's method while leveraging the power of today's interactive computers. Tables are formatted and manipulated through crossets, a new interaction technique for rapidly applying operations on rows and columns. We also introduce visual reordering, a semi-interactive reordering approach that lets users apply and tune automatic reordering algorithms in a WYSIWYG manner. Sessions with eight users from different backgrounds suggest that Bertifier has the potential to bring Bertin's method to a wider audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists.

Many approaches have been proposed for the visualization of multiple time series. Two prominent approaches are reduced line charts (RLC), which display small multiples for time series, and the more recent horizon graphs (HG). We propose to unify RLC and HG using a new technique - interactive horizon graphs (IHG) - which uses pan and zoom interaction to increase the number of time series that can be analysed in parallel. In a user study we compared RLC, HG, and IHG across several tasks and numbers of time series, focusing on datasets with both large scale and small scale variations. Our results show that IHG outperform the other two techniques in complex comparison and matching tasks where the number of charts is large. In the hardest task PHG have a significantly higher number of good answers (correctness) than HG (+14%) and RLC (+51%) and a lower error magnitude than HG (-64%) and RLC (-86%).

This article presents SoccerStories, a visualization interface to support analysts in exploring soccer data and communicating interesting insights. Currently, most analyses on such data relate to statistics on individual players or teams. However, soccer analysts we collaborated with consider that quantitative analysis alone does not convey the right picture of the game, as context, player positions and phases of player actions are the most relevant aspects. We designed SoccerStories to support the current practice of soccer analysts and to enrich it, both in the analysis and communication stages. Our system provides an overview+detail interface of game phases, and their aggregation into a series of connected visualizations, each visualization being tailored for actions such as a series of passes or a goal attempt. To evaluate our tool, we ran two qualitative user studies on recent games using SoccerStories with data from one of the world's leading live sports data providers. The first study resulted in a series of four articles on soccer tactics, by a tactics analyst, who said he would not have been able to write these otherwise. The second study consisted in an exploratory follow-up to investigate design alternatives for embedding soccer phases into word-sized graphics. For both experiments, we received a very enthusiastic feedback and participants consider further use of SoccerStories to enhance their current workflow.