Amid rapid data streaming and declining mental health, there is a critical need to enhance the public’s ability to self-reflect and work on their mental wellbeing. Holding Your Mind is a series of artist-made personal data physicalizations that engage young students to record data relevant to their mental activity (electroencephalograph (EEG), heart rate, and narrative) during familiar tasks, and returned to them as personalized art sculptures to hold, interact with, and reflect. Our poster outlines the material characteristics through which Holding Your Mind sculptures embody mind data into body-relatable and perceptually enticing objects, procuring an embodied experience of self-cognition and reflection From the case of our sculptures, we draw preliminary insights on the use of artistic data embodiments to mediate the users’ relationship to self-tracking technology, reaching beyond quantification, to facilitate reflective practice for mental wellbeing. We lay groundwork for further study of data physicalization’s materiality in a mental wellbeing space, and its use in pre-clinical interventions helping foster a more aware and compassionate society.

Abstract Visualization is routinely used in control rooms to maintain situational awareness of complex sociotechnical systems. While control room design often incorporates human factors engineering to identify the information presented, it is not unusual for control room visual encodings to rely closely on operator experience and historical encodings, which may be contrary to recommended guidelines from the visualization literature. Such approaches may contribute to the development of sub‐optimal operator mental models of the systems they monitor. In this article, we describe a design study in which we assessed the control room context from a visualization perspective and identified a set of 15 visualization guidelines, matching each of these to the domain requirements of the control room. Inspired by these guidelines, we design a visual encoding for alerts occurring on a power system schematic single line diagram, which is commonly used to identify and respond to system alerts arising from faults and conditions that require operator attention in power system control rooms. We report on the results of an experiment conducted with both domain experts and non‐experts. Our results show support for designs that follow our visualization guidelines via faster response times. Accuracy is improved in cases where information is directly encoded by our designs. We also find increased accuracy for domain experts, but expert response times were biased towards more familiar visual encodings in comparison to non‐experts. This emphasizes the importance of established mental maps for control room operators and the need for careful change management in updating control room visualizations.

Abstract Visualization studies that examine age effects on task performance often attribute the performance gap between young adults (YA) and people in late adulthood (PLA) to declines in cognitive abilities, especially reduced visual working memory (VWM) capacity. However, the tasks and visualizations used in prior work typically involve many additional perceptual and cognitive processes, making it difficult to isolate the contribution of VWM decline to age differences in visualization performance. Here, we used a glyph‐based discrimination paradigm in which set size (number of glyphs) was manipulated to vary VWM demand systematically. Young adults (YA), mid‐age adults (MA), and PLA completed a two‐alternative forced‐choice task in which they judged which of two glyph‐based maps presented a higher overall risk level encoded by the glyphs. For each participant, we fitted psychometric functions to obtain accuracy and precision, and analyzed the response time. The results show that set size impaired performance similarly across age groups, and the marked accuracy drop at the largest set size indicated a practical upper limit on meaningful set size for this task. Contrary to expectations based on cognitive aging, PLA did not perform worse overall or show disproportionate costs at higher VWM demands. Instead, the MA group showed higher precision and was more strongly influenced by the distribution of individual glyph values across the visualization, raising the possibility that strategic or effortful processing may partly compensate for capacity limits. These findings indicate that, for this glyph‐based estimation task, age‐related VWM decline alone is unlikely to fully explain age differences in performance. Therefore, our findings offer little evidence for treating reduced VWM demand as the primary design goal for visualizations targeting PLA, in the absence of evidence that age‐related VWM limitations substantially constrain task performance.

Recent work suggests that shape can encode quantitative data via a mapping between value and spatial frequency (SF). However, the set-size effect when perceiving multiple SF based items remains unclear. While automatic feature extraction has been found to be less affected by set size (number of items in a group), higher-level processes for making perceptual decisions tend to require increased cognitive demand. To investigate the set-size effect on comparing integrated SF based items, we used a risk-based scenario to assess discrimination performance. Participants were asked to discriminate between pairs of maps containing multiple SF glyphs, in which each glyph represents one of four discrete levels (none, low, medium, high), forming an aggregate "risk strength" per map. The set size was also adjusted across conditions, ranging from small (3 items) to large (7 items). Discrimination sensitivity is modeled with a logistic function and response time with a mixed-effect linear model. Results show that smaller set sizes and lower overall strength enable more precise discrimination, with faster response times for larger differences between maps. Incorporating set size and overall strength into the logistic model, we found that these variables both independently and jointly influence discrimination sensitivity. We suggest these results point towards capacity-limited processes rather than purely automatic ensemble coding. Our findings highlight the importance of set size and overall signal strength when presenting multiple SF glyphs in data visualization.

With the acceleration of global ageing and the widespread adoption of electronic devices, modern visualization design must address the varying perceptual abilities across different age groups. Spatial frequency (SF) is commonly used as a stimulus dimension to assess perceptual ability in vision science, but its potential role as a guiding variable in visualization design, particularly for addressing age-related differences across the lifespan, remains largely unexplored. Many vision science studies have examined age-related spatial frequency perception, but with varying results. This review systematically analyses differences in experimental design and the reasons behind selecting experimental methods, which may be key to these differences. This study developed search terms and systematically searched the ACM, IEEE, PubMed, APA PsycINFO, SAGE, ScienceDirect, and Springer Nature databases. After rigorous screening, 33 studies were included and evaluated, with their similarities and differences compared across five dimensions. Cluster analysis further categorizes the literature into five groups, confirming that discrepancies in experimental outcomes largely stem from variations in psychological methods and participant characteristics. By considering the underlying reasons for these choices, we propose guidelines for designing perceptual experiments in which spatial frequency and age are manipulated factors. These recommendations provide theoretical support and practical insights for visualization design research that considers age-related perception of spatial frequency.

Visualization design study is a widely adopted approach for developing tailored visual solutions to domain-specific problems through close interdisciplinary collaboration. While the visualization community has proposed generalizable frameworks, there is a growing need for domain-aware methodologies that address discipline-specific challenges and refine design study practices. To investigate how domain characteristics and collaborator roles influence the design study process, we conducted interviews with 15 experts, including domain specialists from the humanities, arts, applied sciences, and artificial intelligence, as well as visualization researchers and developers, with direct experience in design studies. Our findings reveal tensions and opportunities that arise from differing expectations, communication styles, and levels of engagement among collaborators at various stages of the design process, including problem formulation, co-design, and evaluation. We highlight how domain-specific norms and role dynamics shape collaboration and influence the trajectory of visualization projects. Based on these insights, we offer practical considerations to help visualization researchers anticipate domain-specific challenges, foster mutual understanding, and adapt their methods accordingly. Our study contributes to ongoing efforts to support more context-sensitive, sustainable, and inclusive design study practices across diverse application domains.

This paper examines how link colors influence the discriminability of quan- titative node color encoding in node-link diagrams. Node colors in node-link diagrams can represent quantitative attributes, such as value, frequency, or intensity. Although links themselves do not necessarily represent values, the relational aspect must be displayed with a color. This color has the potential to influence the discriminability of the node colors. Extensive research has been conducted on quantitative encoding for scatterplots; however, there is currently no research on which color combinations between nodes and links should be employed and how the quantitative values in nodes should be encoded to ensure their discriminability. This paper addresses two cases: 1. Comparing two highlighted nodes while all other nodes are gray, and 2. Comparing two nodes in a fully colored graph. We conducted two stud- ies, in which participants were required to identify color differences between two nodes in a node-link diagram with varying link properties. In the first study, conducted online, two nodes were colored, and the surrounding link topologies and link hues were varied. The node hues were also varied, while the quantitative values were encoded through saturation. The most discrim- inable node hues were used in the second study, conducted in a laboratory setting, where all nodes encoded quantitative data. Our findings indicate that the use of complementary-colored links has the effect of enhancing the discriminability of node colors, irrespective of the underlying topology. In contrast, links with a hue that is similar to the node hues leads to a reduction in discriminability. The findings of both studies were found to be consistent. In sum, our results indicate guidelines for the use of colors to enhance the discriminability of nodes in small graphs with quantitative node encoding. We recommend using shades of blue rather than yellow to quantitatively en- code nodes. To highlight the connections between nodes, pair these colors with complementary colors for the links, rather than coloring the links the same color as the nodes. Alternatively, use neutral colors, such as gray, to support the discriminability of node colors.

Generative Artificial Intelligence (GenAI) applications in artistic and creative domains have gained substantial attention of late. These intelligent interactive systems, shaped by innovations in Large Language Models (LLMs) and Vision Language Models (VLMs), are materially impacting digital creative domains. While initial work to understand this space has highlighted new models and architectures, we lack a holistic view of how interactive GenAI systems are designed for user interactions across various artistic and creative domains. In this paper, we present a systematic review of interactive GenAI system designs for art and creativity in the HCI literature (N = 189), and a detailed taxonomy of interaction paradigms with design components. We shed light on the communities of design focus and decompose the system interaction designs, mapping these characteristics to creative domains, user interaction patterns, GenAI technologies, detailing under-represented spaces, and future directions of designing interactions for GenAI creativity.

Videoconferencing is integral to modern work and living. Recently, technologists have sought to leverage data captured -- e.g. from cameras and microphones -- to augment communication. This might mean capturing communication information about verbal (e.g. speech, chat messages), or non-verbal exchanges (e.g. body language, gestures, tone of voice) and using this to mediate -- and potentially improve -- communication. However, such tracking has implications for user experience and raises wider concerns (e.g. privacy). To design tools which account for user needs and preferences, this study investigates perspectives on communication tracking through a global survey and interviews, exploring how daily behaviours and the impact of specific features influence user perspectives. We examine user preferences on non-verbal communication tracking, preferred methods of how this information is conveyed and to whom this should be communicated. Our findings aim to guide the development of non-verbal communication tools which augment videoconferencing that prioritise user needs.

Visualizing event timelines for collaborative text writing is an important application for navigating and understanding such data, as time passes and the size and complexity of both text and timeline increase. They are often employed by applications such as code repositories and collaborative text editors. In this article, we present a visualization tool to explore historical records of writing of legislative texts, which were discussed and voted on by an assembly of representatives. Our visualization focuses on event timelines from text documents that involve multiple people and different topics, allowing for observation of different proposed versions of said text or tracking data provenance of given text sections, while highlighting the connections between all elements involved. We also describe the process of designing such a tool alongside domain experts, with three steps of evaluation being conducted to verify the effectiveness of our design.

This article presents a review and analysis of evaluation practices within the visualization and visual analytics (VIS) domain, with a focus on domain application work accepted at the IEEE VIS conference from 2018 to 2022. Through the analysis of 140 pertinent article, we establish a detailed classification principle for evaluation practices, using the Who, When, What, and How indicators. This principle covers facets such as analysis methods, targets, scenarios, participant expertise, and stages of occurrence. By systematically categorizing the application domains presented in these works, we apply our established classification principle to discern and categorize the evaluation practices within them, identifying the prevailing characteristics and trends. The article explores the variety of evaluation methods employed across different application domains and observes the distinctions in their usage. In conclusion, we provide insights and highlight concerns for conducting evaluations in upcoming domain application research. Our findings are intended to inform and guide subsequent studies in a similar context.

Fine-grained text classification requires models to distinguish between many fine-grained classes that are hard to tell apart. However, despite the increased risk of models relying on confounding features and predictions being especially difficult to interpret in this context, existing work on the interpretability of fine-grained text classification is severely limited. Therefore, we introduce our visual analysis system, SemLa, which incorporates novel visualization techniques that are tailored to this challenge. Our evaluation based on case studies and expert feedback shows that SemLa can be a powerful tool for identifying model weaknesses, making decisions about data annotation, and understanding the root cause of errors.

Essay writing is a skill commonly taught and practised in schools. The ability to write a fluent and persuasive essay is often a major component of formal assessment. In natural language processing and education technology we may work with essays in their final form, for example to carry out automated assessment or grammatical error correction. In this work we collect and analyse data representing the essay writing process from start to finish, by recording every key stroke from multiple writers participating in our study. We describe our data collection methodology, the characteristics of the resulting dataset, and the assignment of proficiency levels to the texts. We discuss the ways the keystroke data can be used – for instance seeking to identify patterns in the keystrokes which might act as features in automated assessment or may enable further advancements in writing assistance – and the writing support technology which could be built with such information, if we can detect when writers are struggling to compose a section of their essay and offer appropriate intervention. We frame this work in the context of English language learning, but we note that keystroke logging is relevant more broadly to text authoring scenarios as well as cognitive or linguistic analyses of the writing process.

This study builds on past research bridging spatial visualization, psychology, and information visualization to holistically inform visualization design. We investigate the effects of chosen disciplines in psychology and math & computer science, combined with cognitive abilities and demographic differences, on visual tasks by measuring estimation of Pearson's correlation coefficient in scatterplots. Results reveal mathematicians demonstrate greater accuracy, benefiting from domain expertise. However, psychologists with high spatial skills outperform some mathematicians with lower spatial skills. Spatial visualization, level of education, and age (inversely) correlated with quicker and more accurate responses. Findings prove that domain expertise and spatial cognition affect correlation judgments in scatterplots, supporting that individual differences should inform visualization design. This work introduces psychologists as a new target domain for visualization research and reveals the impact of combined effects of cognitive abilities and domain on the estimation and manipulation of Pearson's correlation coefficient.

Many papers make claims about specific visualization techniques that are said to enhance or calibrate trust in AI systems. But a design choice that enhances trust in some cases appears to damage it in others. In this paper, we explore this inherent duality through an analogy with "knobs". Turning a knob too far in one direction may result in under-trust, too far in the other, over-trust or, turned up further still, in a confusing distortion. While the designs or so-called "knobs" are not inherently evil, they can be misused or used in an adversarial context and thereby manipulated to mislead users or promote unwarranted levels of trust in AI systems. When a visualization that has no meaningful connection with the underlying model or data is employed to enhance trust, we refer to the result as "trust junk." From a review of 65 papers, we identify nine commonly made claims about trust calibration. We synthesize them into a framework of knobs that can be used for good or "evil," and distill our findings into observed pitfalls for the responsible design of human-AI systems.

Abstract In natural language processing (NLP), text classification tasks are increasingly fine‐grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse‐grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design‐and‐evaluation process to characterize and tackle the growing requirements in their workflow of developing fine‐grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine‐grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissamand exponenteof a valuev = m 10eWe evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyze error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.

The circulation of historical books has always been an area of interest for historians. However, the data used to represent the journey of a book across different places and times can be difficult for domain experts to digest due to buried geographical and chronological features within text-based presentations. This situation provides an opportunity for collaboration between visualization researchers and historians. This paper describes a design study where a variant of the Nine-Stage Framework [46] was employed to develop a Visual Analytics (VA) tool called DanteExploreVis. This tool was designed to aid domain experts in exploring, explaining, and presenting book trade data from multiple perspectives. We discuss the design choices made and how each panel in the interface meets the domain requirements. We also present the results of a qualitative evaluation conducted with domain experts. The main contributions of this paper include: 1) the development of a VA tool to support domain experts in exploring, explaining, and presenting book trade data; 2) a comprehensive documentation of the iterative design, development, and evaluation process following the variant Nine-Stage Framework; 3) a summary of the insights gained and lessons learned from this design study in the context of the humanities field; and 4) reflections on how our approach could be applied in a more generalizable way.

This study builds on past research to present a domain-specific empirical investigation of artists and math & computer scientists on their respective relationships to, perceptions of, and interactions with data visualization. We conducted a three-phase study utilizing mixed-methods to investigate performance on visual and text representations of data between domains. Our findings evidenced how math & computer scientists are proficient utilizing text representations of data while artists benefit more from visual chart representations. Finally, we present perspectives from artists to gain an understanding of their approach to visual and mathematical tasks. Our findings indicate that artists are especially adept at statistical visual tasks and that development of cognitive skills could be fostered by individuals to potentially benefit visualization task performance.

Abstract Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.

Computational modeling is a commonly used technology in many scientific disciplines and has played a noticeable role in combating the COVID-19 pandemic. Modeling scientists conduct sensitivity analysis frequently to observe and monitor the behavior of a model during its development and deployment. The traditional algorithmic ranking of sensitivity of different parameters usually does not provide modeling scientists with sufficient information to understand the interactions between different parameters and model outputs, while modeling scientists need to observe a large number of model runs in order to gain actionable information for parameter optimization. To address the above challenge, we developed and compared two visual analytics approaches, namely: algorithm-centric and visualization-assisted, and visualization-centric and algorithm-assisted. We evaluated the two approaches based on a structured analysis of different tasks in visual sensitivity analysis as well as the feedback of domain experts. While the work was carried out in the context of epidemiological modeling, the two approaches developed in this work are directly applicable to a variety of modeling processes featuring time series outputs, and can be extended to work with models with other types of outputs.

Understanding one's audience is foundational to creating high impact visualization designs. However, individual differences and cognitive abilities influence interactions with information visualization. Different user needs and abilities suggest that an individual's background could influence cognitive performance and interactions with visuals in a systematic way. This study builds on current research in domain-specific visualization and cognition to address if domain and spatial visualization ability combine to affect performance on information visualization tasks. We measure spatial visualization and visual task performance between those with tertiary education and professional profile in business, law & political science, and math & computer science. We conducted an online study with 90 participants using an established psychometric test to assess spatial visualization ability, and bar chart layouts rotated along Cartesian and polar coordinates to assess performance on spatially rotated data. Accuracy and response times varied with domain across chart types and task difficulty. We found that accuracy and time correlate with spatial visualization level, and education in math & computer science can indicate higher spatial visualization. Additionally, we found that motivational differences between domains could contribute to increased levels of accuracy. Our findings indicate discipline not only affects user needs and interactions with data visualization, but also cognitive traits. Our results can advance inclusive practices in visualization design and add to knowledge in domain-specific visual research that can empower designers across disciplines to create effective visualizations.

Abstract Visual analytics systems integrate interactive visualizations and machine learning to enable expert users to solve complex analysis tasks. Applications combine techniques from various fields of research and are consequently not trivial to evaluate. The result is a lack of structure and comparability between evaluations. In this survey, we provide a comprehensive overview of evaluations in the field of human‐centered machine learning. We particularly focus on human‐related factors that influence trust, interpretability, and explainability. We analyze the evaluations presented in papers from top conferences and journals in information visualization and human‐computer interaction to provide a systematic review of their setup and findings. From this survey, we distill design dimensions for structured evaluations, identify evaluation gaps, and derive future research opportunities.

The objective of this work was to investigate from a user perspective linkage between a 1D time-series view of data and a 2D representation provided by dimension reduction techniques. Our hypothesis is that when such interaction happens seamlessly, the use of these linked views, compared to only interacting with the 1D time-series view, for the ubiquitous task of selection and labelling, is more efficient and effective both in terms of performance and user experience. To this end we examine different dimension reduction techniques (UMAP, t-SNE, PCA and Autoencoder) and evaluate each technique within our experimental setting. Results demonstrate that there is a positive impact on speed and accuracy through augmenting 1D views with a dimension reduction 2D view when these views are linked and linkage is supported through coordinated interaction.

We seek to answer the question on whether different geometrical attributes within a glyph can bias interpretation of data. We focus on a specific visual encoding, the Emoji, and evaluate its effectiveness at encoding multidimensional features. Given the anthropomorphic nature of the encoding we seek to quantify the amount of bias the encoding itself introduces, and use this to balance the Emoji glyph to remove that bias. We perform our analysis by comparing Emoji with Chernoff faces, of which they can be seen as direct descendant. Results shed light on how this new approach of feature-tuning in glyph design can influence overall effectiveness of novel multidimensional encodings.

Abstract Visualization researchers have been increasingly leveraging crowdsourcing approaches to overcome a number of limitations of controlled laboratory experiments, including small participant sample sizes and narrow demographic backgrounds of study participants. However, as a community, we have little understanding on when, where, and how researchers use crowdsourcing approaches for visualization research. In this paper, we review the use of crowdsourcing for evaluation in visualization research. We analyzed 190 crowdsourcing experiments, reported in 82 papers that were published in major visualization conferences and journals between 2006 and 2017. We tagged each experiment along 36 dimensions that we identified for crowdsourcing experiments. We grouped our dimensions into six important aspects: study design & procedure, task type, participants, measures & metrics, quality assurance, and reproducibility. We report on the main findings of our review and discuss challenges and opportunities for improvements in conducting crowdsourcing studies for visualization research.

Abstract The design of effective glyphs for visualisation involves a number of different visual encodings. Since spatial position is usually already specified in advance, we must rely on other visual channels to convey additional relationships for multivariate analysis. One such relationship is the apparent order present in the data. This paper presents two crowdsourcing empirical studies that focus on the perceptual evaluation of orderability for visual channels, namely Bertin's retinal variables. The first study investigates the perception of order in a sequence of elements encoded with different visual channels. We found evidence that certain visual channels are perceived as more ordered (for example, value) while others are perceived as less ordered (for example, hue) than the measured order present in the data. As a result, certain visual channels are more/less sensitive to disorder. The second study evaluates how visual orderability affects min and max judgements of elements in the sequence. We found that visual channels that tend to be perceived as ordered, improve the accuracy of identifying these values.

Collecting sensor data results in large temporal data sets which need to be visualized, analyzed, and presented. Onedimensional time-series charts are used, but these present problems when screen resolution is small in comparison to the data. This can result in severe over-plotting, giving rise for the requirement to provide effective rendering and methods to allow interaction with the detailed data. Common solutions can be categorized as multi-scale representations, frequency based, and lens based interaction techniques. In this paper, we comparatively evaluate existing methods, such as Stack Zoom [15] and ChronoLenses [38], giving a graphical overview of each and classifying their ability to explore and interact with data. We propose new visualizations and other extensions to the existing approaches. We undertake and report an empirical study and a field study using these techniques.

In this paper we introduce Order of Magnitude Markers (OOMMs) as a new technique for number representation. The motivation for this work is that many data sets require the depiction and comparison of numbers that have varying orders of magnitude. Existing techniques for representation use bar charts, plots and colour on linear or logarithmic scales. These all suffer from related problems. There is a limit to the dynamic range available for plotting numbers, and so the required dynamic range of the plot can exceed that of the depiction method. When that occurs, resolving, comparing and relating values across the display becomes problematical or even impossible for the user. With this in mind, we present an empirical study in which we compare logarithmic, linear, scale-stack bars and our new markers for 11 different stimuli grouped into 4 different tasks across all 8 marker types.

Natural image statistics is an important area of research in cognitive sciences and computer vision. Visualization of statistical results can help identify clusters and anomalies as well as analyze deviation, distribution, and correlation. Furthermore, they can provide visual abstractions and symbolism for categorized data. In this paper, we begin our study of visualization of image statistics by considering visual representations of power spectra, which are commonly used to visualize different categories of images. We show that they convey a limited amount of statistical information about image categories and their support for analytical tasks is ineffective. We then introduce several new visual representations, which convey different or more information about image statistics. We apply ANOVA to the image statistics to help select statistically more meaningful measurements in our design process. A task-based user evaluation was carried out to compare the new visual representations with the conventional power spectra plots. Based on the results of the evaluation, we made further improvement of visualizations by introducing composite visual representations of image statistics.

Abstract In recent years, a collection of new techniques which deal with video as input data, emerged in computer graphics and visualization. In this survey, we report the state of the art in video‐based graphics and video visualization. We provide a review of techniques for making photo‐realistic or artistic computer‐generated imagery from videos, as well as methods for creating summary and/or abstract visual representations to reveal important features and events in videos. We provide a new taxonomy to categorize the concepts and techniques in this newly emerged body of knowledge. To support this review, we also give a concise overview of the major advances in automated video analysis, as some techniques in this field (e.g. feature extraction, detection, tracking and so on) have been featured in video‐based modelling and rendering pipelines for graphics and visualization.

In written and spoken communications, figures of speech (e.g., metaphors and synecdoche) are often used as an aid to help convey abstract or less tangible concepts. However, the benefits of using rhetorical illustrations or embellishments in visualization have so far been inconclusive. In this work, we report an empirical study to evaluate hypotheses that visual embellishments may aid memorization, visual search and concept comprehension. One major departure from related experiments in the literature is that we make use of a dual-task methodology in our experiment. This design offers an abstraction of typical situations where viewers do not have their full attention focused on visualization (e.g., in meetings and lectures). The secondary task introduces "divided attention", and makes the effects of visual embellishments more observable. In addition, it also serves as additional masking in memory-based trials. The results of this study show that visual embellishments can help participants better remember the information depicted in visualization. On the other hand, visual embellishments can have a negative impact on the speed of visual search. The results show a complex pattern as to the benefits of visual embellishments in helping participants grasp key concepts from visualization.

Abstract This work is concerned with a design study by an interdisciplinary team on visualizing a 10‐year record of seasonal and inter‐annual changes in frontal position (advance/retreat) of nearly 200 marine terminating glaciers in Greenland. Whilst the spatiotemporal nature of the raw data presents a challenge to develop a compact and intuitive visual design, the focus on coastal boundaries provides an opportunity for dimensional reduction. In this paper, we report the user‐centered design process carried out by the team, and present several visual encoding schemes that have met the requirements including compactness, intuitiveness, and ability to depict temporal changes and spatial relations. In particular, we designed a family of radial visualization, where radial lines correspond to different coastal locations, and nested rings represent the evolution of the temporal dimension from inner to outer circles. We developed an algorithm for mapping glacier terminus positions from Cartesian coordinates to angular coordinates. Instead of a naive uniform mapping, the algorithm maintains consistent spatial perception of the visually‐sensitive geographical references between their Cartesian and angular coordinates, and distributes other termini positions between primary locations based on coastal distance. This work has provided a useful solution to address the problem of inaccuracy in change evaluation based on pixel‐based visualization [ BPC*10 ].

Abstract We present a feasibility study on using video visualization to aid snooker skill training. By involving the coaches and players in the loop of intelligent reasoning, our approach addresses the difficulties of automated semantic reasoning, while benefiting from mature video processing techniques. This work was conducted in conjunction with a snooker club and a sports scientist. In particular, we utilized the principal design of the VideoPerpetuoGram (VPG) to convey spatiotemporal information to the viewers through static visualization, removing the burden of repeated video viewing. We extended the VPG design to accommodate the need for depicting multiple video streams and respective temporal attribute fields, including silhouette extrusion, spatial attributes, and non‐spatial attributes. Our results and evaluation have shown that video visualization can provide snooker coaching with visually quantifiable and comparable summary records, and is thus a cost‐effective means for assessing skill levels and monitoring progress objectively and consistently.

Abstract Sound is an integral part of most movies and videos. In many situations, viewers of a video are unable to hear the sound track, for example, when watching it in a fast forward mode, viewing it by hearing‐impaired viewers or when the plot is given as a storyboard. In this paper, we present an automated visualization solution to such problems. The system first detects the common components (such as music, speech, rain, explosions, and so on) from a sound track, then maps them to a collection of programmable visual metaphors, and generates a composite visualization. This form of sound visualization, which is referred to as SoundRiver, can be also used to augment various forms of video abstraction and annotated key frames and to enhance graphical user interfaces for video handling software. The SoundRiver conveys more semantic information to the viewer than traditional graphical representations of sound illustration, such as phonoautographs, spectrograms or artistic audiovisual animations.

Pixel-based visualization is a popular method of conveying large amounts of numerical data graphically. Application scenarios include business and finance, bioinformatics and remote sensing. In this work, we examined how the usability of such visual representations varied across different tasks and block resolutions. The main stimuli consisted of temporal pixel-based visualization with a white-red color map, simulating monthly temperature variation over a six-year period. In the first study, we included 5 separate tasks to exert different perceptual loads. We found that performance varied considerably as a function of task, ranging from 75% correct in low-load tasks to below 40% in high-load tasks. There was a small but consistent effect of resolution, with the uniform patch improving performance by around 6% relative to higher block resolution. In the second user study, we focused on a high-load task for evaluating month-to-month changes across different regions of the temperature range. We tested both CIE L*u*v* and RGB color spaces. We found that the nature of the change-evaluation errors related directly to the distance between the compared regions in the mapped color space. We were able to reduce such errors by using multiple color bands for the same data range. In a final study, we examined more fully the influence of block resolution on performance, and found block resolution had a limited impact on the effectiveness of pixel-based visualization.

The pipeline model in visualization has evolved from a conceptual model of data processing into a widely used architecture for implementing visualization systems. In the process, a number of capabilities have been introduced, including streaming of data in chunks, distributed pipelines, and demand-driven processing. Visualization systems have invariably built on stateful programming technologies, and these capabilities have had to be implemented explicitly within the lower layers of a complex hierarchy of services. The good news for developers is that applications built on top of this hierarchy can access these capabilities without concern for how they are implemented. The bad news is that by freezing capabilities into low-level services expressive power and flexibility is lost. In this paper we express visualization systems in a programming language that more naturally supports this kind of processing model. Lazy functional languages support fine-grained demand-driven processing, a natural form of streaming, and pipeline-like function composition for assembling applications. The technology thus appears well suited to visualization applications. Using surface extraction algorithms as illustrative examples, and the lazy functional language Haskell, we argue the benefits of clear and concise expression combined with fine-grained, demand-driven computation. Just as visualization provides insight into data, functional abstraction provides new insight into visualization.

Data coming from complex simulation models reach easily dimensions much greater than available computational resources. Visualization of such data still represents the most intuitive and effective tool for scientific inspection of simulated phenomena. To ease this process several techniques have been adopted mainly concerning the use of hierarchical multi-resolution representations. In this paper we present the implementation of a hierarchical indexing schema for multiresolution data tailored to overwork the computational power of distributed environments.