GraSp: Combining Spatially‐aware Mobile Devices and a Display Wall for Graph Visualization and Interaction

Going beyond established desktop interfaces, researchers have begun re‐thinking visualization approaches to make use of alternative display environments and more natural interaction modalities. In this paper, we investigate how spatially‐aware mobile displays and a large display wall can be coupled to support graph visualization and interaction. For that purpose, we distribute typical visualization views of classic node‐link and matrix representations between displays. The focus of our work lies in novel interaction techniques that enable users to work with personal mobile devices in combination with the wall. We devised and implemented a comprehensive interaction repertoire that supports basic and advanced graph exploration and manipulation tasks, including selection, details‐on‐demand, focus transitions, interactive lenses, and data editing. A qualitative study has been conducted to identify strengths and weaknesses of our techniques. Feedback showed that combining mobile devices and a wall‐sized display is useful for diverse graph‐related tasks. We also gained valuable insights regarding the distribution of visualization views and interactive tools among the combined displays.     

Visual Debugging Techniques for Reactive Data Visualization

Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the “spaghetti code” of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step‐by‐step. On‐demand annotations overlay the output visualization to expose relevant state and scale mappings in‐situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first‐time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification.     

Finding a Clear Path: Structuring Strategies for Visualization Sequences

Little is known about how people structure sets of visualizations to support sequential viewing. We contribute findings from several studies examining visualization sequencing and reception. In our first study, people made decisions between various possible structures as they ordered a set of related visualizations (consisting of either bar charts or thematic maps) into what they considered the clearest sequence for showing the data. We find that most people structure visualization sequences hierarchically: they create high level groupings based on shared data properties like time period, measure, level of aggregation, and spatial region, then order the views within these groupings. We also observe a tendency for certain types of similarities between views, like a common spatial region or aggregation level, to be seen as more appropriate categories for organizing views in a sequence than others, like a common time period or measure. In a second study, we find that viewers’ perceptions of the quality and intention of different sequences are largely consistent with the perceptions of the users who created them. The understanding of sequence preferences and perceptions that emerges from our studies has implications for the development of visualization authoring tools and sequence recommendations for guided analysis.     

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.     

An Exploratory Study of Data Sketching for Visual Representation

Hand‐drawn sketching on napkins or whiteboards is a common, accessible method for generating visual representations. This practice is shared by experts and non‐experts and is probably one of the faster and more expressive ways to draft a visual representation of data. In order to better understand the types of and variations in what people produce when sketching data, we conducted a qualitative study. We asked people with varying degrees of visualization expertise, from novices to experts, to manually sketch representations of a small, easily understandable dataset using pencils and paper and to report on what they learned or found interesting about the data. From this study, we extract a data sketching representation continuum from numeracy to abstraction; a data report spectrum from individual data items to speculative data hypothesis; and show the correspondence between the representation types and the data reports from our results set. From these observations we discuss the participants’ representations in relation to their data reports, indicating implications for design and potentially fruitful directions for research.     

Understanding Indirect Causal Relationships in Node‐Link Graphs

To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node‐link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi‐attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.     

LoVis: Local Pattern Visualization for Model Refinement

Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.     

Lyra: An Interactive Visualization Design Environment

We present Lyra, an interactive environment for designing customized visualizations without writing code. Using drag‐and‐drop interactions, designers can bind data to the properties of graphical marks to author expressive visualization designs. Marks can be moved, rotated and resized using handles; relatively positioned using connectors; and parameterized by data fields using property drop zones. Lyra also provides a data pipeline interface for iterative, visual specification of data transformations and layout algorithms. Visualizations created with Lyra are represented as specifications in Vega, a declarative visualization grammar that enables sharing and reuse. We evaluate Lyra's expressivity and accessibility through diverse examples and studies with journalists and visualization designers. We find that Lyra enables users to rapidly develop customized visualizations, covering a design space comparable to existing programming‐based tools.     

BubbleNet: A Cyber Security Dashboard for Visualizing Patterns

The field of cyber security is faced with ever‐expanding amounts of data and a constant barrage of cyber attacks. Within this space, we have designed BubbleNet as a cyber security dashboard to help network analysts identify and summarize patterns within the data. This design study faced a range of interesting constraints from limited time with various expert users and working with users beyond the network analyst, such as network managers. To overcome these constraints, the design study employed a user‐centered design process and a variety of methods to incorporate user feedback throughout the design of BubbleNet. This approach resulted in a successfully evaluated dashboard with users and further deployments of these ideas in both research and operational environments. By explaining these methods and the process, it can benefit future visualization designers to help overcome similar challenges in cyber security or alternative domains.     

Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics

Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called Insights Feed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression.     

Towards a smooth design process for static communicative node‐link diagrams

Node‐link infographics are visually very rich and can communicate messages effectively, but can be very difficult to create, often involving a painstaking and artisanal process. In this paper we present an investigation of node‐link visualizations for communication and how to better support their creation. We begin by breaking down these images into their basic elements and analyzing how they are created. We then present a set of techniques aimed at improving the creation workflow by bringing more flexibility and power to users, letting them manipulate all aspects of a node‐link diagram (layout, visual attributes, etc.) while taking into account the context in which it will appear. These techniques were implemented in a proof‐of‐concept prototype called GraphCoiffure, which was designed as an intermediary step between graph drawing/editing software and image authoring applications. We describe how GraphCoiffure improves the workflow and illustrate its benefits through practical examples.     

Dynamic Change Arcs to Explore Model Forecasts

In many planning applications, a computational model is used to make predictions about the effects of management or engineering decisions. To understand the implications of alternative scenarios, a user typically adjusts one or more of the input parameters, runs the model, and examines the outcomes using simple charts. For example, a time series showing changes in productivity or revenue might be generated. While this approach can be effective in showing the projected effects of changes to the model's input parameters, it fails to show the mechanisms that cause those changes. In order to promote understanding of model mechanics using a simple graphical device, we propose dynamic change arcs. Dynamic change arcs graphically reveal the internal model structure as cause and effect linkages. They are signed to show both positive and negative effects. We implemented this concept using a species interaction model developed for fisheries management based on a system of Lotka‐Volterra equations. The model has 10 economically important fish species and incorporates both predation and competition between species. The model predicts that changing the catch of one species can sometimes result in changes in biomass of another species through multi‐step causal chains. The dynamic change arcs make it possible to interpret the resulting complex causal chains and interaction effects. We carried out an experiment to evaluate three alternative forms of arcs for portraying causal connections in the model. The results show that all linkage representations enabled participants to reason better about complex chains of causality than not showing linkages. However, none of them were significantly better than the others.     

Small MultiPiles: Piling Time to Explore Temporal Patterns in Dynamic Networks

We introduce MultiPiles, a visualization to explore time‐series of dense, weighted networks. MultiPiles is based on the physical analogy of piling adjacency matrices, each one representing a single temporal snapshot. Common interfaces for visualizing dynamic networks use techniques such as: flipping/animation; small multiples; or summary views in isolation. Our proposed ‘piling’ metaphor presents a hybrid of these techniques, leveraging each one's advantages, as well as offering the ability to scale to networks with hundreds of temporal snapshots. While the MultiPiles technique is applicable to many domains, our prototype was initially designed to help neuroscientists investigate changes in brain connectivity networks over several hundred snapshots. The piling metaphor and associated interaction and visual encodings allowed neuroscientists to explore their data, prior to a statistical analysis. They detected high‐level temporal patterns in individual networks and this helped them to formulate and reject several hypotheses.     

A Shot at Visual Vulnerability Analysis

Increasing the safety of vehicles is an important goal for vehicle manufacturers. These manufacturers often turn to simulations to understand how to improve a vehicle's design as real‐world safety tests are expensive and time consuming. Understanding the results of these simulations, however, is challenging due to the complexity of the data, which often includes both spatial and nonspatial data types. In this design study we collaborated with analysts who are trying to understand the vulnerability of military vehicles. From this design study we contribute a problem characterization, data abstraction, and task analysis for vehicle vulnerability analysis, as well as a validated and deployed tool called Shotviewer. Shotviewer links 3D spatial views with abstract 2D views to support a broad range of analysis needs. Furthermore, reflection on our design study process elucidates a strategy of view‐design parallelism for creating multiview visualizations, as well as four recommendations for conducting design studies in large organizations with sensitive data.     

Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings

In addition to the choice of visual encodings, the effectiveness of a data visualization may vary with the analytical task being performed and the distribution of data values. To better assess these effects and create refined rankings of visual encodings, we conduct an experiment measuring subject performance across task types (e.g., comparing individual versus aggregate values) and data distributions (e.g., with varied cardinalities and entropies). We compare performance across 12 encoding specifications of trivariate data involving 1 categorical and 2 quantitative fields, including the use of x, y, color, size, and spatial subdivision (i.e., faceting). Our results extend existing models of encoding effectiveness and suggest improved approaches for automated design. For example, we find that colored scatterplots (with positionally‐coded quantities and color‐coded categories) perform well for comparing individual points, but perform poorly for summary tasks as the number of categories increases.     

Perceptually‐based Color Assignment

Color assignment is a complex task of incorporating and balancing area configuration, color harmony, and user's intent. In this paper, we present a novel method for automatic color assignment based on theories of color perception. We define color assignment as an optimization problem with respect to the color relationships as well as the spatial configuration of input segments. We also suggest possible constraints that are suitable for task‐specific purposes and for enhancing visual appeal. Our colorization scheme is useful in many applications such as infographics, computer‐aided design, and visual presentation. The user study shows that our method generates perceptually pleasing results over a variety of data sets.     

TimeArcs: Visualizing Fluctuations in Dynamic Networks

In this paper we introduce TimeArcs, a novel visualization technique for representing dynamic relationships between entities in a network. Force‐directed layouts provide a way to highlight related entities by positioning them near to each other Entities are brought closer to each other (forming clusters) by forces applied on nodes and connections between nodes. In many application domains, relationships between entities are not temporally stable, which means that cluster structures and cluster memberships also may vary across time. Our approach merges multiple force‐directed layouts at different time points into a single comprehensive visualization that provides a big picture overview of the most significant clusters within a user‐defined period of time. TimeArcs also supports a range of interactive features, such as allowing users to drill‐down in order to see details about a particular cluster. To highlight the benefits of this technique, we demonstrate its application to various datasets, including the IMDB co‐star network, a dataset showing conflicting evidences within biomedical literature of protein interactions, and collocated popular phrases obtained from political blogs.     

Exploring Items and Features with IF,FI‐Tables

The exploration of high‐dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an I^F‐Table, showing all items with a selection of features; and an F^I‐Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.     

Authoring Narrative Visualizations with Ellipsis

Data visualization is now a popular medium for journalistic storytelling. However, current visualization tools either lack support for storytelling or require significant technical expertise. Informed by interviews with journalists, we introduce a model of storytelling abstractions that includes state‐based scene structure, dynamic annotations and decoupled coordination of multiple visualization components. We instantiate our model in Ellipsis: a system that combines a domain‐specific language (DSL) for storytelling with a graphical interface for story authoring. User interactions are automatically translated into statements in the Ellipsis DSL. By enabling storytelling without programming, the Ellipsis interface lowers the threshold for authoring narrative visualizations. We evaluate Ellipsis through example applications and user studies with award‐winning journalists. Study participants find Ellipsis to be a valuable prototyping tool that can empower journalists in the creation of interactive narratives.     

Oui! Outlier Interpretation on Multi‐dimensional Data via Visual Analytics

Outliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.