Chart Types Research Articles

Rank Ordered Design Attributes for Health Care Dashboards Including Artificial Intelligence: Usability Study.

On average, people in the United States visit a doctor 4 times a year, and many of them have chronic illnesses. Because of the increased use of technology, people frequently rely on the internet to access health information and statistics. People use health care information to make better-educated decisions for themselves and others. Health care dashboards should provide pertinent and easily understood data, such as information on timely cancer screenings, so the public can make better-informed decisions. In order to enhance health outcomes, effective dashboards should provide precise data in an accessible and easily digestible manner. This study identifies the top 15 attributes of a health care dashboard. The objective of this research is to enhance health care dashboards to benefit the public by making better health care information available for more informed decisions by the public and to improve population-level health care outcomes. The authors conducted a survey of health care dashboards with 218 individuals identifying the best practices to consider when creating a public health care dashboard. The data collection was conducted from June 2023 to August 2023. The analyses performed were descriptive statistics, frequencies, and a comparison to a prior study. From May 2023 to June 2023, we collected 3259 responses in multiple different states around the United States from 218 people aged 18 years or older. The features ranking in descending order of importance are as follows: (1) easy navigation, (2) historical data, (3) simplicity of design, (4) high usability, (5) use of clear descriptions, (6) consistency of data, (7) use of diverse chart types, (8) compliance with the Americans with Disabilities Act, (9) incorporated user feedback, (10) mobile compatibility, (11) comparison data with other entities, (12) storytelling, (13) predictive analytics with artificial intelligence, (14) adjustable thresholds, and (15) charts with tabulated data. Future studies can extend the research to other types of dashboards such as bioinformatics, financial, and managerial dashboards as well as confirm these top 15 best practices for medical dashboards with further evidentiary support. The medical informatics community may benefit from standardization to improve efficiency and effectiveness as dashboards can communicate vital information to patients worldwide on critically prominent issues. Furthermore, health care professionals should use these best practices to help increase population health care outcomes by informing health care consumers to make better decisions with better data.

A Systematic Review of Data Visualization

Data visualization involves presenting data in graphical or pictorial form that in turn helps with decision support. This study addresses the importance of data visualizations in today’s data world, and its role in effective communication in various industry sectors. This study applied the PRISMA methodology to conduct the literature review. Four major themes are identified in this paper: data visualization principles, methods, contemporary chart types, and data visualization challenges. Popularly applied data visualization principles such as Gestalt Principles that help make data visualizations more effective are examined. Advanced chart types include doughnut chart, chord diagram, sankey diagram, and violin plot. Some challenges associated with data visualizations are data accuracy, complexity and uncertainty. Given the challenges, visualizations still benefit any field of study that requires interpreting and presenting complex information.

Toward Constructing Frameworks for Task- and Design-Optimized Visualizations.

Visualization is crucial to augment and enhance human understanding and decision-making in today's data-driven world. However, the way data are visualized can influence and drastically change the conclusions people draw using data. The findings around visualization effectiveness are nuanced, and guidelines for effective visualization design depend on the visual channels used, chart types, and analysis tasks. This points to a significant need to understand the intersection of these factors to create optimized visualizations. We need a framework to define this intersection that fills the gap by providing a task-optimized visualization design for better quality and higher decision-making confidence that gives designers objective guidance. A task-optimized visualization design framework strategically integrates visual channels, visualization types, and specific low-level tasks to enhance data interpretation and optimize user task performance. We discuss constructing a visualization framework that considers both human perception for encoding techniques and the task being performed, enabling optimizing visualization design to maximize efficiency. Furthermore, we highlight a task-optimized framework's impact and potential applications.

Business Data Visualization, Beyond the Boring.

Business uses of charts and visualizations, and by extension business users, are usually considered mundane and boring. But they, too, want to get their audience's attention, emphasize a point they are making, or simply break out of the monotony of the limited palette of common chart types. I believe that there is ample opportunity to develop new approaches and build better tools that go far beyond the current one-size-fits-all approach to creating charts-much more than is currently recognized in the visualization community. The first step is to reexamine our notions of who business users are, and what they actually want and need.

Swin-chart: An efficient approach for chart classification

Charts are a visualization tool used in scientific documents to facilitate easy comprehension of complex relationships underlying data and experiments. Researchers use various chart types to convey scientific information, so the problem of data extraction and subsequent chart understanding becomes very challenging. Many studies have been taken up in the literature to address the problem of chart mining, whose motivation is to facilitate the editing of existing charts, carry out extrapolative studies, and provide a deeper understanding of the underlying data. The first step towards chart understanding is chart classification, for which traditional ML and CNN-based deep learning models have been used in the literature. In this paper, we propose Swin-Chart, a Swin transformer-based deep learning approach for chart classification, which generalizes well across multiple datasets with a wide range of chart categories. Swin-Chart comprises a pre-trained Swin Transformer, a finetuning component, and a weight averaging component. The proposed approach is tested on a five-chart image benchmark dataset. We observed that the Swin-Chart model outperformers existing state-of-the-art models on all the datasets. Furthermore, we also provide an ablation study of the Swin-Chart model with all five datasets to understand the importance of various sub-parts such as the back-bone Swin transformer model, the value of several best weights selected for the weight averaging component, and the presence of the weight averaging component itself.The Swin-Chart model also received first position in the chart classification task on the latest dataset in the CHART Infographics competition at ICDAR 2023 - chartinfo.github.io.

Are pie charts evil? An assessment of the value of pie and donut charts compared to bar charts

Many data visualization experts recommend the use of bar charts over pie charts because they consider comparing the area or angle of segments to be less accurate than comparing bars on a bar chart. However, many studies show that when the pie chart is used to estimate proportions (arguably its main function) it is as accurate as the bar chart. A major issue is that most previous studies have only looked at one method of extracting information from pie charts, for example either by comparing the segment to the circle (the part-whole relationship) or one segment to another (relative magnitude estimation). Therefore, in this study I test multiple metrics to provide a more holistic assessment of the pie and donut chart against the bar chart. I also measured cognitive load through pupillometry. In summary, bar charts were more precise than pie and donut charts for ranking elements, but all charts were equally accurate for extracting the part-whole relationship. There was little difference in cognitive load between chart types, although bar charts were consistently faster to use on average. Overall, the bar chart was more flexible, but where there were statistically significant differences between charts, the effect sizes were often small, and unlikely to prevent effective extraction of quantitative information. That is, as long as they were used appropriately, all chart types were arguably acceptable for displaying simple, categorical data.

Same Data, Diverging Perspectives: The Power of Visualizations to Elicit Competing Interpretations.

People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how the data is visually represented or what the viewer finds salient. We also demonstrate that viewer interpretation of data is similar to that of 'ambiguous figures' such that two people looking at the same data can come to different decisions. In our studies, participants read visualizations depicting competitions between two entities, where one has a historical lead (A) but the other has been gaining momentum (B) and predicted a winner, across two chart types and three annotation approaches. They either saw the historical lead as salient and predicted that A would win, or saw the increasing momentum as salient and predicted B to win. These results suggest that decisions can be influenced by both how data are presented and what patterns people find visually salient.

DATA VISUALIZATION USING POWER BI

This paper discusses Power BI, a Microsoft-developed business intelligence tool, for data visualization. It discusses its components, such as charts, maps, matrices, and custom visuals, and its process of connecting and importing data from various sources. The paper also discusses the advanced data modeling and transformation features, allowing users to shape and prepare their data for effective visualization. It also discusses the collaboration and sharing capabilities of Power BI, enabling teams to work together on data visualizations and distribute them to stakeholders. The paper also highlights best practices for creating effective data visualizations, such as selecting appropriate chart types, applying proper formatting, and incorporating interactivity and drill-down capabilities.

Causal Priors and Their Influence on Judgements of Causality in Visualized Data.

"Correlation does not imply causation' is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations. We collected a corpus of concept pairs from variables in widely used datasets and created visualizations that depict varying correlative associations using three typical statistical chart types. We conducted two MTurk studies on (1) preconceived notions on causal relations without charts, and (2) perceived causal relations with charts, for each concept pair. Our results indicate that people make assumptions about causal relationships between pairs of concepts even without seeing any visualized data. Moreover, our results suggest that these assumptions constitute causal priors that, in combination with visualized association, impact how data visualizations are interpreted. The results also suggest that causal priors may lead to over- or under-estimation in perceived causal relations in different circumstances, and that those priors can also impact users' confidence in their causal assessments. In addition, our results align with prior work, indicating that chart type may also affect causal inference. Using data from the studies, we develop a model to capture the interaction between causal priors and visualized associations as they combine to impact a user's perceived causal relations. In addition to reporting the study results and analyses, we provide an open dataset of causal priors for 56 specific concept pairs that can serve as a potential benchmark for future studies. We also suggest remaining challenges and heuristic-based guidelines to help designers improve visualization design choices to better support visual causal inference.

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space.

Annotations play a vital role in highlighting critical aspects of visualizations, aiding in data externalization and exploration, collaborative sensemaking, and visual storytelling. However, despite their widespread use, we identified a lack of a design space for common practices for annotations. In this paper, we evaluated over 1,800 static annotated charts to understand how people annotate visualizations in practice. Through qualitative coding of these diverse real-world annotated charts, we explored three primary aspects of annotation usage patterns: analytic purposes for chart annotations (e.g., present, identify, summarize, or compare data features), mechanisms for chart annotations (e.g., types and combinations of annotations used, frequency of different annotation types across chart types, etc.), and the data source used to generate the annotations. We then synthesized our findings into a design space of annotations, highlighting key design choices for chart annotations. We presented three case studies illustrating our design space as a practical framework for chart annotations to enhance the communication of visualization insights. All supplemental materials are available at https://shorturl.at/bAGM1.

D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding.

Onboarding a user to a visualization dashboard entails explaining its various components, including the chart types used, the data loaded, and the interactions available. Authoring such an onboarding experience is time-consuming and requires significant knowledge and little guidance on how best to complete this task. Depending on their levels of expertise, end users being onboarded to a new dashboard can be either confused and overwhelmed or disinterested and disengaged. We propose interactive dashboard tours (D-Tours) as semi-automated onboarding experiences that preserve the agency of users with various levels of expertise to keep them interested and engaged. Our interactive tours concept draws from open-world game design to give the user freedom in choosing their path through onboarding. We have implemented the concept in a tool called D-TOUR PROTOTYPE, which allows authors to craft custom interactive dashboard tours from scratch or using automatic templates. Automatically generated tours can still be customized to use different media (e.g., video, audio, and highlighting) or new narratives to produce an onboarding experience tailored to an individual user. We demonstrate the usefulness of interactive dashboard tours through use cases and expert interviews. Our evaluation shows that authors found the automation in the D-Tour Prototype helpful and time-saving, and users found the created tours engaging and intuitive. This paper and all supplemental materials are available at https://osf.io/6fbjp/.

Mystique: Deconstructing SVG Charts for Layout Reuse.

To facilitate the reuse of existing charts, previous research has examined how to obtain a semantic understanding of a chart by deconstructing its visual representation into reusable components, such as encodings. However, existing deconstruction approaches primarily focus on chart styles, handling only basic layouts. In this paper, we investigate how to deconstruct chart layouts, focusing on rectangle-based ones, as they cover not only 17 chart types but also advanced layouts (e.g., small multiples, nested layouts). We develop an interactive tool, called Mystique, adopting a mixed-initiative approach to extract the axes and legend, and deconstruct a chart's layout into four semantic components: mark groups, spatial relationships, data encodings, and graphical constraints. Mystique employs a wizard interface that guides chart authors through a series of steps to specify how the deconstructed components map to their own data. On 150 rectangle-based SVG charts, Mystique achieves above 85% accuracy for axis and legend extraction and 96% accuracy for layout deconstruction. In a chart reproduction study, participants could easily reuse existing charts on new datasets. We discuss the current limitations of Mystique and future research directions.

Promises and Pitfalls: Using Large Language Models to Generate Visualization Items.

Visualization items-factual questions about visualizations that ask viewers to accomplish visualization tasks-are regularly used in the field of information visualization as educational and evaluative materials. For example, researchers of visualization literacy require large, diverse banks of items to conduct studies where the same skill is measured repeatedly on the same participants. Yet, generating a large number of high-quality, diverse items requires significant time and expertise. To address the critical need for a large number of diverse visualization items in education and research, this paper investigates the potential for large language models (LLMs) to automate the generation of multiple-choice visualization items. Through an iterative design process, we develop the VILA (Visualization Items Generated by Large LAnguage Models) pipeline, for efficiently generating visualization items that measure people's ability to accomplish visualization tasks. We use the VILA pipeline to generate 1,404 candidate items across 12 chart types and 13 visualization tasks. In collaboration with 11 visualization experts, we develop an evaluation rulebook which we then use to rate the quality of all candidate items. The result is the VILA bank of ∼1,100 items. From this evaluation, we also identify and classify current limitations of the VILA pipeline, and discuss the role of human oversight in ensuring quality. In addition, we demonstrate an application of our work by creating a visualization literacy test, VILA-VLAT, which measures people's ability to complete a diverse set of tasks on various types of visualizations; comparing it to the existing VLAT, VILA-VLAT shows moderate to high convergent validity (R = 0.70). Lastly, we discuss the application areas of the VILA pipeline and the VILA bank and provide practical recommendations for their use. All supplemental materials are available at https://osf.io/ysrhq/.

Simulation Study for Parametric EWMA and NPWEWPA-SR Control Charts Against Non-Normality Assumptions

Common control chart types such as EWMA require assumptions to have valid information. The study compares IC robustness and OOC performance for parametric EWMA and NPEWMA-SR control charts in violation of symmetrical assumption. The Monte Carlo simulation study held scale parameters with various shape parameters in Weibull distribution. First finding in this paper was both parametric EWMA and NPEWMA-SR control charts were not suitable for the application in asymmetrical distribution due to weak IC robustness and frequent false alarm will be occurred. Although EWMA-X ̅ The control chart showed a most stable OOC performance; the weak IC robustness made the control chart unacceptable. Whereas, NPEWMA-SR control chart lost the ability in small shift detection when symmetrical assumption violated. Moreover, two different weightage of current sample for both parametric EWMA and NPEWMA-SR control charts were also investigated. The results showed that weightage of current sample for both parametric EWMA and NPEWMA-SR control charts did not affect the ARL value trend in different skewness of Weibull distribution.

Awareness and Use of ChatGPT and Large Language Models: A Prospective Cross-sectional Global Survey in Urology

BackgroundSince its release in November 2022, ChatGPT has captivated society and shown potential for various aspects of health care. ObjectiveTo investigate potential use of ChatGPT, a large language model (LLM), in urology by gathering opinions from urologists worldwide. Design, setting, and participantsAn open web-based survey was distributed via social media and e-mail chains to urologists between April 20, 2023 and May 5, 2023. Participants were asked to answer questions related to their knowledge and experience with artificial intelligence, as well as their opinions of potential use of ChatGPT/LLMs in research and clinical practice. Outcome measurements and statistical analysisData are reported as the mean and standard deviation for continuous variables, and the frequency and percentage for categorical variables. Charts and tables are used as appropriate, with descriptions of the chart types and the measures used. The data are reported in accordance with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). Results and limitationsA total of 456 individuals completed the survey (64% completion rate). Nearly half (47.7%) reported that they use ChatGPT/LLMs in their academic practice, with fewer using the technology in clinical practice (19.8%). More than half (62.2%) believe there are potential ethical concerns when using ChatGPT for scientific or academic writing, and 53% reported that they have experienced limitations when using ChatGPT in academic practice. ConclusionsUrologists recognise the potential of ChatGPT/LLMs in research but have concerns regarding ethics and patient acceptance. There is a desire for regulations and guidelines to ensure appropriate use. In addition, measures should be taken to establish rules and guidelines to maximise safety and efficiency when using this novel technology. Patient summaryA survey asked 456 urologists from around the world about using an artificial intelligence tool called ChatGPT in their work. Almost half of them use ChatGPT for research, but not many use it for patients care. The resonders think ChatGPT could be helpful, but they worry about problems like ethics and want rules to make sure it's used safely.

How to visualize electricity consumption anomalies: The impact of chart types on triggered emotions and eye movements

Home Energy Management Systems (HEMS) show anomalies and unusual patterns of the electricity consumption of households, usually in the form of charts. Charts that show anomalies are very difficult to design in a way that is both comprehensible and visually appealing to users. However, visually appealing charts direct the users’ gaze to the central chart elements and thus trigger positive emotions, which in turn increase the intention to use HEMS. Therefore, this study focuses on the emotions and eye movements that different chart types showing anomalous electricity consumption trigger. An experimental eye-tracking and facial expression study was carried out with 50 participants that were randomly assigned to one of three commonly used chart types (bar, line or rose chart). The participants had to complete three search tasks during which the eye movements and the facial expressions were observed. The results show that different emotions are triggered depending on the chart type. Participants showed the highest number of sad and contempt facial expressions while viewing bar charts. Line charts triggered the most positive and, overall, the least negative facial expressions, followed by the rose charts. Bar charts triggered the most negative emotions among all three chart types. Although most eye-tracking metrics in this study differed by chart type, most results did not show significant differences. However, the later participants saw the area in demand in the rose charts, the sadder facial expressions occurred. HEMS providers can use these findings to design emotionally appealing charts for potential users, which in turn can increase their motivation to use these systems.

Mapping News Visualization Pattern on Katadata.id and Tirto.id

This study addresses the research gap in understanding the types and patterns of visualization used in Covid-19 infographics. By analyzing infographics from popular media platforms in Indonesia, such as Katadata.co.id and Tirto.id, this research aims to fill the existing gap and find visual trends and preferences relevant to health communication. The theoretical framework draws upon concepts from visual communication, information design theory, and health communication. Using a qualitative approach and content analysis techniques, the study examines a sample of infographics from the aforementioned media platforms, categorizing the visualization and showing key trends and preferences. The findings reveal that both media outlets predominantly utilize illustrations, graphics, and text elements rather than charts. Bar graphs and column charts are the most widely used chart types, with Katadata.co.id showing higher frequency of chart elements compared to Tirto.id. Additionally, Katadata.co.id presents a greater number of interactive charts. The colors commonly employed for charts include blue, yellow, orange, and brown. However, the study does not delve into underlying reasons for these visual trends, whether they stem from specific visual strategies employed by the media or are influenced by other factors. The research findings contribute to the development of visually appealing and easily understandable infographics for policymakers and graphic designers.

Integrating human-centered design in public health data dashboards: lessons from the development of a data dashboard of sexually transmitted infections in New York State.

The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs. We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data. Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data. Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic. Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere.

ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries

Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user. A large and well-structured dataset is always a key part for data driven models. In this paper, we propose ChartSumm: a large-scale benchmark dataset consisting of a total of 84,363 charts along with their metadata and descriptions covering a wide range of topics and chart types to generate short and long summaries. Extensive experiments with strong baseline models show that even though these models generate fluent and informative summaries by achieving decent scores in various automatic evaluation metrics, they often face issues like suffering from hallucination, missing out important data points, in addition to incorrect explanation of complex trends in the charts. We also investigated the potential of expanding ChartSumm to other languages using automated translation tools. These make our dataset a challenging benchmark for future research.

Design of adaptive EWMA control charts using the conditional false alarm rate

AbstractDynamic control limits can be useful in designing control charts, especially when sample sizes, risk scores, or other covariate values change over time. Computer simulation can be used to control the conditional false alarm rate and thus the in‐control run length properties. We show that this approach can be useful in designing adaptive exponentially weighted moving average (AEWMA) control charts for which the control chart smoothing parameter at a given time point depends on the observed value at that time point. We use AEWMA charts as examples, but the approach can be applied to the adaptive cumulative sum (CUSUM) chart and other types of adaptive charts.