A Characterization of Interactive Visual Data Stories With a Spatio-Temporal Context

Large-scale issues with a spatial and temporal context such as the COVID-19 pandemic, the war against Ukraine, and climate change have given visual storytelling with data a lot of attention in online journalism, confirming its high effectiveness and relevance for conveying stories. Thus, new ways have emerged that expand the space of visual storytelling techniques. However, interactive visual data stories with a spatio-temporal context have not been extensively studied yet. Particularly quantitative information about the used layout and media, the visual storytelling techniques, and the visual encoding of space-time is relevant to get a deeper understanding of how such stories are commonly built to convey complex information in a comprehensible way. Covering these three aspects, we propose a design space derived by merging and adjusting existing approaches, which we used to categorize 130 collected web-based visual data stories with a spatio-temporal context from between 2018 and 2022. An analyzis of the collected data reveals the power of large-scale issues to shape the landscape of storytelling techniques and a trend towards a simplified consumability of stories. Taken together, our findings can serve story authors as inspiration regarding which storytelling techniques to include in their own spatio-temporal data stories.     

Visualization rhetoric: framing effects in narrative visualization

Narrative visualizations combine conventions of communicative and exploratory information visualization to convey an intended story. We demonstrate visualization rhetoric as an analytical framework for understanding how design techniques that prioritize particular interpretations in visualizations that "tell a story" can significantly affect end-user interpretation. We draw a parallel between narrative visualization interpretation and evidence from framing studies in political messaging, decision-making, and literary studies. Devices for understanding the rhetorical nature of narrative information visualizations are presented, informed by the rigorous application of concepts from critical theory, semiotics, journalism, and political theory. We draw attention to how design tactics represent additions or omissions of information at various levels-the data, visual representation, textual annotations, and interactivity-and how visualizations denote and connote phenomena with reference to unstated viewing conventions and codes. Classes of rhetorical techniques identified via a systematic analysis of recent narrative visualizations are presented, and characterized according to their rhetorical contribution to the visualization. We describe how designers and researchers can benefit from the potentially positive aspects of visualization rhetoric in designing engaging, layered narrative visualizations and how our framework can shed light on how a visualization design prioritizes specific interpretations. We identify areas where future inquiry into visualization rhetoric can improve understanding of visualization interpretation.     

Narrative visualization: telling stories with data

Data visualization is regularly promoted for its ability to reveal stories within data, yet these “data stories” differ in important ways from traditional forms of storytelling. Storytellers, especially online journalists, have increasingly been integrating visualizations into their narratives, in some cases allowing the visualization to function in place of a written story. In this paper, we systematically review the design space of this emerging class of visualizations. Drawing on case studies from news media to visualization research, we identify distinct genres of narrative visualization. We characterize these design differences, together with interactivity and messaging, in terms of the balance between the narrative flow intended by the author (imposed by graphical elements and the interface) and story discovery on the part of the reader (often through interactive exploration). Our framework suggests design strategies for narrative visualization, including promising under-explored approaches to journalistic storytelling and educational media.     

The Role of Visual Perception in Data Visualization

This paper presents a perceptually motivated formal framework for effective visualization of relational data. In this framework, the intended structure of data and the perceptual structure of visualizations are formally and uniformly defined in terms of relations that are induced on data and visual elements by data and visual attributes, respectively. Visual attributes are analyzed and classified from the perceptual point of view and in terms of perceptual relations that they induce on visual elements. The presented framework satisfies a necessary condition for effective data visualizations. This condition is formulated in terms of a structure preserving map between the intended structure of data and the perceptual structure of visualization.     

Topic Tomographies (TopTom): a visual approach to distill information from media streams

In this paper we present Top Tom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user‐generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low‐dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. Top Tom implements a batch processing pipeline able to run both in near‐real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast‐like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.     

Do Disease Stories Need a Hero? Effects of Human Protagonists on a Narrative Visualization about Cerebral Small Vessel Disease

Authors use various media formats to convey disease information to a broad audience, from articles and videos to interviews or documentaries. These media often include human characters, such as patients or treating physicians, who are involved with the disease. While artistic media, such as hand-crafted illustrations and animations are used for health communication in many cases, our goal is to focus on data-driven visualizations. Over the last decade, narrative visualization has experienced increasing prominence, employing storytelling techniques to present data in an understandable way. Similar to classic storytelling formats, narrative medical visualizations may also take a human character-centered design approach. However, the impact of this form of data communication on the user is largely unexplored. This study investigates the protagonist's influence on user experience in terms of engagement, identification, self-referencing, emotional response, perceived credibility, and time spent in the story. Our experimental setup utilizes a character-driven story structure for disease stories derived from Joseph Campbell's Hero's Journey. Using this structure, we generated three conditions for a cerebral small vessel disease story that vary by their protagonist: (1) a patient, (2) a physician, and (3) a base condition with no human protagonist. These story variants formed the basis for our hypotheses on the effect of a human protagonist in disease stories, which we evaluated in an online study with 30 participants. Our findings indicate that a human protagonist exerts various influences on the story perception and that these also vary depending on the type of protagonist.     

Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude

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/.     

Visual mining of time series using a tubular visualization

In this paper, we study the visual mining of time series, and we contribute to the study and evaluation of 3D tubular visualizations. We describe the state of the art in the visual mining of time-dependent data, and we concentrate on visualizations that use a tubular shape to represent data. After analyzing the motivations for studying such a representation, we present an extended tubular visualization. We propose new visual encodings of the time and data, new interactions for knowledge discovery, and the use of rearrangement clustering. We show how this visualization can be used in several real-world domains and that it can address large datasets. We present a comparative user study. We conclude with the advantages and the drawbacks of our method (especially the tubular shape).     

Visual Exploration of Time‐Series Data with Shape Space Projections

Time‐series data is a common target for visual analytics, as they appear in a wide range of application domains. Typical tasks in analyzing time‐series data include identifying cyclic behavior, outliers, trends, and periods of time that share distinctive shape characteristics. Many methods for visualizing time series data exist, generally mapping the data values to positions or colors. While each can be used to perform a subset of the above tasks, none to date is a complete solution. In this paper we present a novel approach to time‐series data visualization, namely creating multivariate data records out of short subsequences of the data and then using multivariate visualization methods to display and explore the data in the resulting shape space . We borrow ideas from text analysis, where the use of N‐grams is a common approach to decomposing and processing unstructured text. By mapping each temporal N‐gram to a glyph, and then positioning the glyphs via PCA (basically a projection in shape space), many different kinds of patterns in the sequence can be readily identified. Interactive selection via brushing, in conjunction with linking to other visualizations, provides a wide range of tools for exploring the data. We validate the usefulness of this approach with examples from several application domains and tasks, comparing our methods with traditional time‐series visualizations.     

Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences

Many factors can shape the flow of visual data‐driven stories, and thereby the way readers experience those stories. Through the analysis of 80 existing stories found on popular websites, we systematically investigate and identify seven characteristics of these stories, which we name “flow‐factors,” and we illustrate how they feed into the broader concept of “visual narrative flow.” These flow‐factors are navigation input, level of control, navigation progress, story layout, role of visualization, story progression, and navigation feedback. We also describe a series of studies we conducted, which shed initial light on how different visual narrative flows impact the reading experience. We report on two exploratory studies, in which we gathered reactions and preferences of readers for stepper‐ vs. scroller‐driven flows. We then report on a crowdsourced study with 240 participants, in which we explore the effect of the combination of different flow‐factors on readers’ engagement. Our results indicate that visuals and navigation feedback (e.g., static vs. animated transitions) have an impact on readers’ engagement, while level of control (e.g., discrete vs. continuous) may not.     

Context-preserving visual links

Evaluating, comparing, and interpreting related pieces of information are tasks that are commonly performed during visual data analysis and in many kinds of information-intensive work. Synchronized visual highlighting of related elements is a well-known technique used to assist this task. An alternative approach, which is more invasive but also more expressive is visual linking in which line connections are rendered between related elements. In this work, we present context-preserving visual links as a new method for generating visual links. The method specifically aims to fulfill the following two goals: first, visual links should minimize the occlusion of important information; second, links should visually stand out from surrounding information by minimizing visual interference. We employ an image-based analysis of visual saliency to determine the important regions in the original representation. A consequence of the image-based approach is that our technique is application-independent and can be employed in a large number of visual data analysis scenarios in which the underlying content cannot or should not be altered. We conducted a controlled experiment that indicates that users can find linked elements in complex visualizations more quickly and with greater subjective satisfaction than in complex visualizations in which plain highlighting is used. Context-preserving visual links were perceived as visually more attractive than traditional visual links that do not account for the context information.     

behaviorism: a framework for dynamic data visualization

While a number of information visualization software frameworks exist, creating new visualizations, especially those that involve novel visualization metaphors, interaction techniques, data analysis strategies, and specialized rendering algorithms, is still often a difficult process. To facilitate the creation of novel visualizations we present a new software framework, behaviorism, which provides a wide range of flexibility when working with dynamic information on visual, temporal, and ontological levels, but at the same time providing appropriate abstractions which allow developers to create prototypes quickly which can then easily be turned into robust systems. The core of the framework is a set of three interconnected graphs, each with associated operators: a scene graph for high-performance 3D rendering, a data graph for different layers of semantically linked heterogeneous data, and a timing graph for sophisticated control of scheduling, interaction, and animation. In particular, the timing graph provides a unified system to add behaviors to both data and visual elements, as well as to the behaviors themselves. To evaluate the framework we look briefly at three different projects all of which required novel visualizations in different domains, and all of which worked with dynamic data in different ways: an interactive ecological simulation, an information art installation, and an information visualization technique.     

An enhanced concept map approach to improving children’s storytelling ability

Storytelling is an imperative and innovative pathway to enhance learning due to the fact that such activity prompts learners to reflect to construct meaning based on their observations and knowledge. Therefore, to develop and enhance students’ storytelling ability has become an important issue for both educators and researchers. Since storytelling involves complex cognitive processes, this study proposed an enhanced concept map approach featured with story grammars to help children develop stories. By gathering the stories developed by 114 third-graders, this study aimed to investigate whether the concept map approach can enhance their storytelling ability. It is found that the children, who used the concept map with story grammars, can develop stories with more complex structure, clearer subjects, more creative ideas, and abundant contents than those only using the concept map without the grammars. Such findings support that the concept map and story grammars can be an effective approach for developing learner-centered storytelling tools which help students develop and apply the knowledge about storytelling.     

Automatic Animation for Time‐Varying Data Visualization

This paper presents a digital storytelling approach that generates automatic animations for time‐varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree‐like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time‐varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time‐varying data and easy animation generation for users without special knowledge about the underlying visualization techniques.     

Data visualization optimization via computational modeling of perception

We present a method for automatically evaluating and optimizing visualizations using a computational model of human vision. The method relies on a neural network simulation of early perceptual processing in the retina and primary visual cortex. The neural activity resulting from viewing flow visualizations is simulated and evaluated to produce a metric of visualization effectiveness. Visualization optimization is achieved by applying this effectiveness metric as the utility function in a hill-climbing algorithm. We apply this method to the evaluation and optimization of 2D flow visualizations, using two visualization parameterizations: streaklet-based and pixel-based. An emergent property of the streaklet-based optimization is head-to-tail streaklet alignment. It had been previously hypothesized the effectiveness of head-to-tail alignment results from the perceptual processing of the visual system, but this theory had not been computationally modeled. A second optimization using a pixel-based parameterization resulted in a LIC-like result. The implications in terms of the selection of primitives is discussed. We argue that computational models can be used for optimizing complex visualizations. In addition, we argue that they can provide a means of computationally evaluating perceptual theories of visualization, and as a method for quality control of display methods.     

Reading Traces: Scalable Exploration in Elastic Visualizations of Cultural Heritage Data

Through a design study, we develop an approach to data exploration that utilizes elastic visualizations designed to support varying degrees of detail and abstraction. Examining the notions of scalability and elasticity in interactive visualizations, we introduce a visualization of personal reading traces such as marginalia or markings inside the reference library of German realist author Theodor Fontane. To explore such a rich and extensive collection, meaningful visual forms of abstraction and detail are as important as the transitions between those states. Following a growing research interest in the role of fluid interactivity and animations between views, we are particularly interested in the potential of carefully designed transitions and consistent representations across scales. The resulting prototype addresses humanistic research questions about the interplay of distant and close reading with visualization research on continuous navigation along several granularity levels, using scrolling as one of the main interaction mechanisms. In addition to presenting the design process and resulting prototype, we present findings from a qualitative evaluation of the tool, which suggest that bridging between distant and close views can enhance exploration, but that transitions between views need to be crafted very carefully to facilitate comprehension.     

A Survey on Visual Analysis Approaches for Financial Data

Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.     

An approach to the perceptual optimization of complex visualizations

This paper proposes a new experimental framework within which evidence regarding the perceptual characteristics of a visualization method can be collected, and describes how this evidence can be explored to discover principles and insights to guide the design of perceptually near-optimal visualizations. We make the case that each of the current approaches for evaluating visualizations is limited in what it can tell us about optimal tuning and visual design. We go on to argue that our new approach is better suited to optimizing the kinds of complex visual displays that are commonly created in visualization. Our method uses human-in-the-loop experiments to selectively search through the parameter space of a visualization method, generating large databases of rated visualization solutions. Data mining is then used to extract results from the database, ranging from highly specific exemplar visualizations for a particular data set, to more broadly applicable guidelines for visualization design. We illustrate our approach using a recent study of optimal texturing for layered surfaces viewed in stereo and in motion. We show that a genetic algorithm is a valuable way of guiding the human-in-the-loop search through visualization parameter space. We also demonstrate several useful data mining methods including clustering, principal component analysis, neural networks, and statistical comparisons of functions of parameters.     

Crowd‐Funded Journalism

Crowd‐funded journalism is a novel business model in which journalists rely on micropayments from ordinary people to finance their reporting. Based on analyses of the database of Spot.us, a pioneering crowd‐funded journalism website, we examine the impact of crowd‐funded journalism on the news produced. We apply a uses and gratifications approach to study consumers' choices when they donate to crowd‐funded journalism and find that consumers are more likely to donate to stories that provide them with practical guidance for daily living (e.g., stories about public health or local city infrastructure), as opposed to stories from which they gain a general awareness of the world (e.g., cultural diversity, or government and politics). We discuss the implications for the future of news.