Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements

Unveiling How Examples Shape Visualization Design Outcomes

Visualization designers (e.g., journalists or data analysts) often rely on examples to explore the space of possible designs, yet we have little insight into how examples shape data visualization design outcomes. While the effects of examples have been studied in other disciplines, such as web design or engineering, the results are not readily applicable to visualization due to inconsistencies in findings and challenges unique to visualization design. Towards bridging this gap, we conduct an exploratory experiment involving 32 data visualization designers focusing on the influence of five factors (timing, quantity, diversity, data topic similarity, and data schema similarity) on objectively measurable design outcomes (e.g., numbers of designs and idea transfers). Our quantitative analysis shows that when examples are introduced after initial brainstorming, designers curate examples with topics less similar to the dataset they are working on and produce more designs with a high variation in visualization components. Also, designers copy more ideas from examples with higher data schema similarities. Our qualitative analysis of participants' thought processes provides insights into why designers incorporate examples into their designs, revealing potential factors that have not been previously investigated. Finally, we discuss how our results inform how designers may use examples during design ideation as well as future research on quantifying designs and supporting example-based visualization design. All supplemental materials are available in our OSF repo.

Effective data visualization strategies in untargeted metabolomics

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

AutoTitle: An Interactive Title Generator for Visualizations

We propose AutoTitle, an interactive visualization title generator satisfying multifarious user requirements. Factors making a good title, namely, the feature importance, coverage, preciseness, general information richness, conciseness, and non-technicality, are summarized based on the feedback from user interviews. Visualization authors need to trade off among these factors to fit specific scenarios, resulting in a wide design space of visualization titles. AutoTitle generates various titles through the process of visualization facts traversing, deep learning-based fact-to-title generation, and quantitative evaluation of the six factors. AutoTitle also provides users with an interactive interface to explore the desired titles by filtering the metrics. We conduct a user study to validate the quality of generated titles as well as the rationality and helpfulness of these metrics.

Designing an infographic webtool for public health

OBJECTIVE

To create and evaluate a public health informatics tool, Florence, for communicating information to the public.

MATERIALS AND METHODS

This user-centered design study included 3 phases: (1) an interview and survey study with public health practitioners to assess needs for creating infographics; (2) the application of assessment findings and public health-motivated design guidelines to the design and development of a public health-specific infographic design tool; and (3) a feasibility and usability study to evaluate the feasibility and usability of the tool.

RESULTS

In phase 1, participants noted the importance of tailoring infographics to an audience and wanted flexible tools along with design guidance to help make fewer design decisions. In phase 2, we developed a prototype tool with: (1) layout and functionality familiar to PH users, (2) quick and intuitive ways to add and modify data in visualizations, and (3) health-focused visual elements. In phase 3, participants found Florence to be usable, providing an intuitive and straightforward experience, and that the focus on public health was useful.

DISCUSSION

Based on needs assessments and existing literature, we created Florence along with public health practitioners to address their domain specific needs, ultimately leading to a tool that participants in our study deemed useful. Future research can build on our work to develop user-centered tools to meet their needs.

CONCLUSION

Infographics are important for public health communication. Creating user-centered solutions to address the unique needs of public health can support communication efforts.

Graph-Based Information Block Detection in Infographic With Gestalt Organization Principles

An infographic is a type of visualization chart that displays pieces of information through information blocks. Existing information block detection work utilizes spatial proximity to group elements into several information blocks. However, prior studies ignore the chromatic and structural features of the infographic, resulting in incorrect omissions when detecting information blocks. To alleviate this kind of error, we use a scene graph to represent an infographic and propose a graph-based information block detection model to group elements based on Gestalt Organization Principles (spatial proximity, chromatic similarity, and structural similarity principle). We also construct a new dataset for information block detection. Quantitative and qualitative experiments show that our model can detect the information blocks in the infographic more effectively compared with the spatial proximity-based method.

Sporthesia: Augmenting Sports Videos Using Natural Language

Augmented sports videos, which combine visualizations and video effects to present data in actual scenes, can communicate insights engagingly and thus have been increasingly popular for sports enthusiasts around the world. Yet, creating augmented sports videos remains a challenging task, requiring considerable time and video editing skills. On the other hand, sports insights are often communicated using natural language, such as in commentaries, oral presentations, and articles, but usually lack visual cues. Thus, this work aims to facilitate the creation of augmented sports videos by enabling analysts to directly create visualizations embedded in videos using insights expressed in natural language. To achieve this goal, we propose a three-step approach - 1) detecting visualizable entities in the text, 2) mapping these entities into visualizations, and 3) scheduling these visualizations to play with the video - and analyzed 155 sports video clips and the accompanying commentaries for accomplishing these steps. Informed by our analysis, we have designed and implemented Sporthesia, a proof-of-concept system that takes racket-based sports videos and textual commentaries as the input and outputs augmented videos. We demonstrate Sporthesia's applicability in two exemplar scenarios, i.e., authoring augmented sports videos using text and augmenting historical sports videos based on auditory comments. A technical evaluation shows that Sporthesia achieves high accuracy (F1-score of 0.9) in detecting visualizable entities in the text. An expert evaluation with eight sports analysts suggests high utility, effectiveness, and satisfaction with our language-driven authoring method and provides insights for future improvement and opportunities.

FlowNL: Asking the Flow Data in Natural Languages

Flow visualization is essentially a tool to answer domain experts' questions about flow fields using rendered images. Static flow visualization approaches require domain experts to raise their questions to visualization experts, who develop specific techniques to extract and visualize the flow structures of interest. Interactive visualization approaches allow domain experts to ask the system directly through the visual analytic interface, which provides flexibility to support various tasks. However, in practice, the visual analytic interface may require extra learning effort, which often discourages domain experts and limits its usage in real-world scenarios. In this paper, we propose FlowNL, a novel interactive system with a natural language interface. FlowNL allows users to manipulate the flow visualization system using plain English, which greatly reduces the learning effort. We develop a natural language parser to interpret user intention and translate textual input into a declarative language. We design the declarative language as an intermediate layer between the natural language and the programming language specifically for flow visualization. The declarative language provides selection and composition rules to derive relatively complicated flow structures from primitive objects that encode various kinds of information about scalar fields, flow patterns, regions of interest, connectivities, etc. We demonstrate the effectiveness of FlowNL using multiple usage scenarios and an empirical evaluation.

Supporting Expressive and Faithful Pictorial Visualization Design with Visual Style Transfer

Pictorial visualizations portray data with figurative messages and approximate the audience to the visualization. Previous research on pictorial visualizations has developed authoring tools or generation systems, but their methods are restricted to specific visualization types and templates. Instead, we propose to augment pictorial visualization authoring with visual style transfer, enabling a more extensible approach to visualization design. To explore this, our work presents Vistylist, a design support tool that disentangles the visual style of a source pictorial visualization from its content and transfers the visual style to one or more intended pictorial visualizations. We evaluated Vistylist through a survey of example pictorial visualizations, a controlled user study, and a series of expert interviews. The results of our evaluation indicated that Vistylist is useful for creating expressive and faithful pictorial visualizations.

Erato: Cooperative Data Story Editing via Fact Interpolation

As an effective form of narrative visualization, visual data stories are widely used in data-driven storytelling to communicate complex insights and support data understanding. Although important, they are difficult to create, as a variety of interdisciplinary skills, such as data analysis and design, are required. In this work, we introduce Erato, a human-machine cooperative data story editing system, which allows users to generate insightful and fluent data stories together with the computer. Specifically, Erato only requires a number of keyframes provided by the user to briefly describe the topic and structure of a data story. Meanwhile, our system leverages a novel interpolation algorithm to help users insert intermediate frames between the keyframes to smooth the transition. We evaluated the effectiveness and usefulness of the Erato system via a series of evaluations including a Turing test, a controlled user study, a performance validation, and interviews with three expert users. The evaluation results showed that the proposed interpolation technique was able to generate coherent story content and help users create data stories more efficiently.

InfoColorizer: Interactive Recommendation of Color Palettes for Infographics

When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements' spatial arrangement. We propose a data-driven method that provides flexibility by considering users' preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. To validate our method, we conducted a comprehensive four-part evaluation, including case studies, a controlled user study, a survey study, and an interview study. The results indicate that InfoColorizer can provide compelling palette recommendations with adequate flexibility, allowing users to effectively obtain high-quality color design for input infographics with low effort.

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at.

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this article, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems? "This survey reveals seven main processes where the employment of ML techniques can benefit visualizations: Data Processing4VIS, Data-VIS Mapping, Insight Communication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations. Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this article can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io.

The application of graphic language in animation visual guidance system under intelligent environment

With the continuous development of society, the role of the visual guidance system in animation design has also evolved and evolved in its long history, leading to the changes in the values of modern beauty. In the field of modern social and cultural design, the visual guidance system in animation design has unique regional nature and cultural influence. The visual language should correspond to the visual environment and easy to understand and be known by people. It combines animation conception and design technology to capture the cultural charm and beauty, values, and behavioral norms of people in different fields. This article studies and analyzes the visual orientation of graphic language in the design of animation visual guidance system, and injects the graphic language with orientation into its animation design, so that the animation design is more in line with the characteristics of the times. It can be more adapted to the emerging media and better convey the information transfer between the enterprise and the audience. To further understand the audience’s tendency toward elements of graphic expression, this article analyzes the subjective perceptions of the respondents on the importance of color selection, calligraphy fonts, graphic expression, and modeling meaning. The results of the study showed that the respondents aged 21–35 paid more attention to the choice of graphic colors, and the highest number was 69.

Designing Infographics: Visual Representations for Enhancing Education, Communication, and Scientific Research

Infographics are graphic visual representations of educational content, used to deliver complex information, disseminate scientific research, and drive behavioral change. Herein, we review some of the factors pertinent to designing infographics and the potential for automation in the future. To guide high-impact design, it is vital to clearly define the objectives of the infographic and its target audience. Designing an effective infographic necessitates careful consideration of the layout, colors, font, and context. More recently, technical support to develop infographics are increasingly available through online software (Canva, Adobe, and Venngage) and emerging artificial intelligence programs. References can also become a visual representation of trends in scientific discovery. It is crucial for clinicians, researchers and scientists to have the knowledge and skills to design compelling infographics. In the era of social media, the uptake and effects of infographics for disseminating scientific research and public health education need to be further studied to understand their full potential.

Propagating Visual Designs to Numerous Plots and Dashboards

In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-design user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization.

GlyphCreator: Towards Example-based Automatic Generation of Circular Glyphs

Circular glyphs are used across disparate fields to represent multidimensional data. However, although these glyphs are extremely effective, creating them is often laborious, even for those with professional design skills. This paper presents GlyphCreator, an interactive tool for the example-based generation of circular glyphs. Given an example circular glyph and multidimensional input data, GlyphCreator promptly generates a list of design candidates, any of which can be edited to satisfy the requirements of a particular representation. To develop GlyphCreator, we first derive a design space of circular glyphs by summarizing relationships between different visual elements. With this design space, we build a circular glyph dataset and develop a deep learning model for glyph parsing. The model can deconstruct a circular glyph bitmap into a series of visual elements. Next, we introduce an interface that helps users bind the input data attributes to visual elements and customize visual styles. We evaluate the parsing model through a quantitative experiment, demonstrate the use of GlyphCreator through two use scenarios, and validate its effectiveness through user interviews.

Natural Language to Visualization by Neural Machine Translation

Supporting the translation from natural language (NL) query to visualization (NL2VIS) can simplify the creation of data visualizations because if successful, anyone can generate visualizations by their natural language from the tabular data. The state-of-the-art NL2VIS approaches (e.g., NL4DV and FlowSense) are based on semantic parsers and heuristic algorithms, which are not end-to-end and are not designed for supporting (possibly) complex data transformations. Deep neural network powered neural machine translation models have made great strides in many machine translation tasks, which suggests that they might be viable for NL2VIS as well. In this paper, we present ncNet, a Transformer-based sequence-to-sequence model for supporting NL2VIS, with several novel visualization-aware optimizations, including using attention-forcing to optimize the learning process, and visualization-aware rendering to produce better visualization results. To enhance the capability of machine to comprehend natural language queries, ncNet is also designed to take an optional chart template (e.g., a pie chart or a scatter plot) as an additional input, where the chart template will be served as a constraint to limit what could be visualized. We conducted both quantitative evaluation and user study, showing that ncNet achieves good accuracy in the nvBench benchmark and is easy-to-use.

A Mixed-Initiative Approach to Reusing Infographic Charts

Infographic bar charts have been widely adopted for communicating numerical information because of their attractiveness and memorability. However, these infographics are often created manually with general tools, such as PowerPoint and Adobe Illustrator, and merely composed of primitive visual elements, such as text blocks and shapes. With the absence of chart models, updating or reusing these infographics requires tedious and error-prone manual edits. In this paper, we propose a mixed-initiative approach to mitigate this pain point. On one hand, machines are adopted to perform precise and trivial operations, such as mapping numerical values to shape attributes and aligning shapes. On the other hand, we rely on humans to perform subjective and creative tasks, such as changing embellishments or approving the edits made by machines. We encapsulate our technique in a PowerPoint add-in prototype and demonstrate the effectiveness by applying our technique on a diverse set of infographic bar chart examples.

Smile or Scowl? Looking at Infographic Design Through the Affective Lens

Infographics are frequently promoted for their ability to communicate data to audiences affectively. To facilitate the creation of affect-stirring infographics, it is important to characterize and understand people's affective responses to infographics and derive practical design guidelines for designers. To address these research questions, we first conducted two crowdsourcing studies to identify 12 infographic-associated affective responses and collect user feedback explaining what triggered affective responses in infographics. Then, by coding the user feedback, we present a taxonomy of design heuristics that exemplifies the affect-related design factors in infographics. We evaluated the design heuristics with 15 designers. The results showed that our work supports assessing the affective design in infographics and facilitates the ideation and creation of affective infographics.

Calliope: Automatic Visual Data Story Generation from a Spreadsheet

Visual data stories shown in the form of narrative visualizations such as a poster or a data video, are frequently used in data-oriented storytelling to facilitate the understanding and memorization of the story content. Although useful, technique barriers, such as data analysis, visualization, and scripting, make the generation of a visual data story difficult. Existing authoring tools rely on users' skills and experiences, which are usually inefficient and still difficult. In this paper, we introduce a novel visual data story generating system, Calliope, which creates visual data stories from an input spreadsheet through an automatic process and facilities the easy revision of the generated story based on an online story editor. Particularly, Calliope incorporates a new logic-oriented Monte Carlo tree search algorithm that explores the data space given by the input spreadsheet to progressively generate story pieces (i.e., data facts) and organize them in a logical order. The importance of data facts is measured based on information theory, and each data fact is visualized in a chart and captioned by an automatically generated description. We evaluate the proposed technique through three example stories, two controlled experiments, and a series of interviews with 10 domain experts. Our evaluation shows that Calliope is beneficial to efficient visual data story generation.