Static Visualization Research Articles

Multicore memristor from electrically readable nanoscopic racetracks.

The manipulation and detection of mobile domain walls in nanoscopic magnetic wires underlies the development of multibit memories. The studies of such domain walls have focused on macroscopic wires that allow for optical detection by using magneto-optic effects. In this study, we demonstrated the electrical tracking with a spatial resolution of better than 40 nm of multiple mobile domain walls in nanoscopic racetracks, using a set of anomalous Hall detectors integrated into the racetracks. Electrical time-series signals from the Hall detectors allow for the static and dynamic phase space visualization of the dynamics of a domain wall or multiple domain walls that can be described by a multicore memristor model. The domain wall dynamics and stochasticity can be controlled in racetracks even to deep submicron dimensions.

Visualizing education: infographics and pop-up edutainment exhibitions

This article embarks on an exploration of the transformative influence of infographics within the educational landscape, tracing their impact from the Gen-Alpha era onwards. Infographics emerge as dynamic visual tools, transcending generational boundaries and establishing a universal language of comprehension. From catering to the digital fluency of Gen-Alpha to resonating with earlier and subsequent generations, infographics serve as enduring conduits, adapting to diverse learning styles and cultivating a culture of visual literacy. This article delves into a unique dimension, proposing infographics as suitable aids for teaching and learning through the innovative concept of ‘pop-up edutainment exhibitions’. These exhibitions represent dynamic, interactive showcases that bring educational content to life, creating an engaging and immersive experience for learners. From static visuals to dynamic formats like animated and interactive infographics, this evolution revolutionizes the pedagogical experience, making education a visually stimulating journey. Within the collaborative realm, infographics evolve into platforms fostering multigenerational wisdom, where minds converge, co-create, and contribute to a collective reservoir of knowledge. Through a critical lens, this article scrutinizes how infographics, presented as pop-up edutainment exhibitions, amplify critical thinking, digital literacy, and creative expression across generational spectra, exemplifying their instrumental role in crafting an inclusive and engaging learning environment.

A Spatial Constraint Model for Manipulating Static Visualizations

We introduce a spatial constraint model to characterize the positioning and interactions in visualizations, thereby facilitating the activation of static visualizations. Our model provides users with the capability to manipulate visualizations through operations such as selection, filtering, navigation, arrangement, and aggregation. Building upon this conceptual framework, we propose a prototype system designed to activate pre-existing visualizations by imbuing them with intelligent interactions. This augmentation is accomplished through the integration of visual objects with forces. The instantiation of our spatial constraint model enables seamless animated transitions between distinct visualization layouts. To demonstrate the efficacy of our approach, we present usage scenarios that involve the activation of visualizations within real-world contexts.

Insufficient evidence for interactive or animated graphics for communicating probability.

We sought to analyze interactive visualizations and animations of health probability data (such as chances of disease or side effects) that have been studied in head-to-head comparisons with either static graphics or numerical communications. Secondary analysis of a large systematic review on ways to communicate numbers in health. We group the research to show that 4 types of animated or interactive visualizations have been studied by multiple researchers: those that simulate experience of probabilistic events; those that demonstrate the randomness of those events; those that reduce information overload by directing attention sequentially to different items of information; and those that promote elaborative thinking. Overall, these 4 types of visualizations do not show strong evidence of improving comprehension, risk perception, or health behaviors over static graphics. Evidence is not yet strong that interactivity or animation is more effective than static graphics for communicating probabilities in health. We discuss 2 possibilities: that the most effective visualizations haven't been studied, and that the visualizations aren't effective. Future studies should rigorously compare participant performance with novel interactive or animated visualizations against their performance with static visualizations. Such evidence would help determine whether health communicators should emphasize novel interactive visualizations or rely on older forms of visual communication, which may be accessible to broader audiences, including those with limited digital access.

Beyond visual primes: Audiovisual integration of racial identity and sentence intelligibility

There is no biological basis for racial categories. Yet, the presentation of racialized stimuli impacts psycholinguistic processing, suggesting that a talker’s racial identity affects measures of intelligibility (Babel & Russell, 2015; McGowan, 2015). Previous work has used static visual primes, presenting images accompanied by acoustic sentence stimuli. Static visual primes may prime expectations of the person’s speech, with unexpected pairings decreasing intelligibility. These findings show that social expectations affect speech intelligibility and have broad implications for real-world speech perception in high-stakes educational (Evans, Munson, & Edwards, 2017), social (Purnell, Baugh, & Idsardi, 1999), and legal (Rickford & King, 2016) contexts. Expanding our understanding of the psycholinguistic and psychophysical mechanisms behind these phenomena requires experimentation with dynamic, audiovisual speech rather than static visuals. The current experiment presents conflicting visual and auditory cues indexing the white and Black racial identities of the talkers. The talker who is seen (the ‘visual’ talker) and the racial identity of the talker who is heard (the ‘audio’ talker) are fully crossed. This design allows us to examine how intelligibility is influenced by visual racial identity, audio racial identity, and the audiovisual (mis)match between them. Data collection is currently in progress and includes groups of both white and Black listeners.

The Shape of Mobile Health: A Systematic Review of Health Visualization on Mobile Devices

mHealth apps and tracking technology are becoming popular because they help people adopt a healthier lifestyle and form healthy habits. One way mHealth apps can help users is by presenting visuals to help them better understand their health data. Commendable efforts have been carried out to personalize health-promoting interventions to help users become more aware of their own health, and health practitioners for providing healthcare services. For example, digital self-tracking apps nowadays can be used to monitor physical activity, nutrition and sleep patterns. This systematic review aims to investigate the current trends, challenges, gaps, and opportunities in health visualizations on mobile devices. Peer-reviewed papers in English collected using online databases (ACM Digital Library, PubMed, and Web of Science) from 2012 to 2022 were considered, and 56 studies were selected out of 1,168 studies. Results showed that among 11 different health domains, general health and physical health were the most heavily studied. Relatedly, results also showed that physical fitness data is the most frequently collected data type automatically from sensors/trackers. Furthermore, bar and line charts are the most popular type of visualizations used for presenting a variety of health data, and while most apps present static visualizations, interactive visualizations, as well as a combination of both static and interactive visualizations, are becoming more common. Based on our results, we offer recommendations for future research as designers and researchers continue to improve the presentation of data visualizations in mHealth apps.

An empirical study of counterfactual visualization to support visual causal inference

Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users’ understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users’ understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants’ interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations.

A Holistic Approach to Ransomware Classification: Leveraging Static and Dynamic Analysis with Visualization

Ransomware is a type of malicious software that encrypts a victim’s files and demands payment in exchange for the decryption key. It is a rapidly growing and evolving threat that has caused significant damage and disruption to individuals and organizations around the world. In this paper, we propose a comprehensive ransomware classification approach based on the comparison of similarity matrices derived from static, dynamic analysis, and visualization. Our approach involves the use of multiple analysis techniques to extract features from ransomware samples and to generate similarity matrices based on these features. These matrices are then compared using a variety of comparison algorithms to identify similarities and differences between the samples. The resulting similarity scores are then used to classify the samples into different categories, such as families, variants, and versions. We evaluate our approach using a dataset of ransomware samples and demonstrate that it can accurately classify the samples with a high degree of accuracy. One advantage of our approach is the use of visualization, which allows us to classify and cluster large datasets of ransomware in a more intuitive and effective way. In addition, static analysis has the advantage of being fast and accurate, while dynamic analysis allows us to classify and cluster packed ransomware samples. We also compare our approach to other classification approaches based on single analysis techniques and show that our approach outperforms these approaches in terms of classification accuracy. Overall, our study demonstrates the potential of using a comprehensive approach based on the comparison of multiple analysis techniques, including static analysis, dynamic analysis, and visualization, for the accurate and efficient classification of ransomware. It also highlights the importance of considering multiple analysis techniques in the development of effective ransomware classification methods, especially when dealing with large datasets and packed samples.

Charting EDA: Characterizing Interactive Visualization Use in Computational Notebooks with a Mixed-Methods Formalism.

Interactive visualizations are powerful tools for Exploratory Data Analysis (EDA), but how do they affect the observations analysts make about their data? We conducted a qualitative experiment with 13 professional data scientists analyzing two datasets with Jupyter notebooks, collecting a rich dataset of interaction traces and think-aloud utterances. By qualitatively coding participant utterances, we introduce a formalism that describes EDA as a sequence of analysis states, where each state is comprised of either a representation an analyst constructs (e.g., the output of a data frame, an interactive visualization, etc.) or an observation the analyst makes (e.g., about missing data, the relationship between variables, etc.). By applying our formalism to our dataset, we identify that interactive visualizations, on average, lead to earlier and more complex insights about relationships between dataset attributes compared to static visualizations. Moreover, by calculating metrics such as revisit count and representational diversity, we uncover that some representations serve more as "planning aids" during EDA rather than tools strictly for hypothesis-answering. We show how these measures help identify other patterns of analysis behavior, such as the "80-20 rule", where a small subset of representations drove the majority of observations. Based on these fndings, we offer design guidelines for interactive exploratory analysis tooling and refect on future directions for studying the role that visualizations play in EDA.

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

Visualizing interferential stimulation of human brains.

Transcranial electrical stimulation (TES) is limited in focally stimulating deep-brain regions, even with optimized stimulation montages. Recently, interferential stimulation (IFS), also known as transcranial temporal interference stimulation (TI, TIS, or tTIS), has drawn much attention in the TES community as both computational and experimental studies show that IFS can reach deep-brain areas. However, the underlying electrodynamics of IFS is complicated and difficult to visualize. Existing literature only shows static visualization of the interfered electric field induced by IFS. These could result in a simplified understanding that there is always one static focal spot between the two pairs of stimulation electrodes. This static visualization can be frequently found in the IFS literature. Here, we aimed to systematically visualize the entire dynamics of IFS. Following the previous study, the lead field was solved for the MNI-152 head, and optimal montages using either two pairs of electrodes or two arrays of electrodes were found to stimulate a deep-brain region close to the left striatum with the highest possible focality. We then visualized the two stimulating electrical currents injected with similar frequencies. We animated the instant electric field vector at the target and one exemplary off-target location both in 3D space and as a 2D Lissajous curve. We finally visualized the distribution of the interfered electric field and the amplitude modulation envelope at an axial slice going through the target location. These two quantities were visualized in two directions: radial-in and posterior-anterior. We hope that with intuitive visualization, this study can contribute as an educational resource to the community's understanding of IFS as a powerful modality for non-invasive focal deep-brain stimulation.

The effect of dynamic versus static visualizations on acquisition of basketball game actions: a diurnal study

This study aimed to examine the effect of time of day (TOD) on the acquisition of basketball game actions from dynamic and static visualizations in physical education students (novice practitioners). Participants were quasi-randomly assigned to three treatments (static pictures, enriched static-pictures, or video). Morning and late-afternoon sessions were conducted, involving study phases and immediate-recall tests [game comprehension (GC) test and game performance (GP) test]. Oral temperature (OT) and mood states (MS) were also measured. Compared to the morning, the results revealed that afternoon resulted in higher OT, higher negative MS (e.g., anxiety and fatigue), and lower positive MS (i.e., vigor) in all experimental conditions. Moreover, the results showed that: (a) GC and GP decreased throughout the day (regardless of treatments), (b) GC and GP were better with enriched static-pictures (with arrows) than with static pictures, at both TOD, and (c) the video resulted in better GC and GP than the two static presentations, at both TOD. This study (a) highlights the morning's superiority in the acquisition of motor skills from dynamic and static visualizations, due to mood disturbances and lower arousal levels, and (b) encourages basketball teachers to use video modeling by experts, particularly in the morning, for explaining tactical skills.

FPGA-Based Hardware-in-the-Loop (HIL) Emulation of Power Electronics Circuit Using Device-Level Behavioral Modeling

Accurate models of power electronic converters can greatly enhance the accuracy of hardware-in-the-loop (HIL) simulators. This can result in faster and more cost-effective design cycles in industrial applications. This paper presents a detailed hardware model of the IGBT and power diode at the device level suggested for emulating power electronic converters on a field programmable gate array (FPGA). The static visualization of the IGBT component involves an arrangement of equivalent models for both the MOSFET and bipolar transistor in a cascading configuration. The dynamic aspect is represented by inter-electrode nonlinear capacitances. In an effort to expedite the development process while still producing reliable results, the algorithm for the simulation system was built utilizing FPGA-based rapid prototyping via the HDL Coder in MATLAB software (R2019b). Essentially, the HDL Coder transforms the Simulink blocks of these devices within MATLAB into a hardware description language (HDL) suitable for implementation on an FPGA. To evaluate the suggested IGBT hardware model and the nonlinear circuit simulation technique, a chopper circuit is replicated, and an FPGA-in-the-loop simulation is carried out to compare the efficacy and accuracy of the model with both offline simulation results and real-time simulation results using MATLAB Simulink software and the Altera FPGA Cyclone IV GX development board.

Vivid and Engaging: Effects of Interactive Data Visualization on Perceptions and Attitudes about Social Issues

With the rising availability and volume of data, journalists are finding ways to integrate data-rich information into news content and make them accessible to everyday news readers by accompanying them with exemplifying cases. However, these exemplars do not always fully capture the complexity of the data patterns, leading to significant biases in readers’ issue perceptions. Drawing on the theoretical frameworks of exemplification theory and interactivity, this study examines the effectiveness of interactivity in counteracting the exemplification effect through perceived vividness of and user engagement with data-rich information. A mixed-design experiment revealed that, compared to textual description and static visualization, interactive data visualization is effective in motivating systematic processing of baserate information, which positively influences accuracy of issue perception as well as personal attitudes toward two controversial issues—climate change and same-sex marriage legalization. These findings have theoretical and practical implications for journalistic practice and interface design for communicating data information.

Metanetwork: A R package dedicated to handling and representing trophic metanetworks.

Trophic networks describe interactions between species at a given location and time. Due to environmental changes, anthropogenic perturbations or sampling effects, trophic networks may vary in space and time. The collection of network time series or networks in different sites thus constitutes a metanetwork. We present here the R package metanetwork, which will ease the representation, the exploration and analysis of trophic metanetwork data sets that are increasingly available. Our main methodological advance consists in suitable layout algorithm for trophic networks, which is based on trophic levels and dimension reduction in a graph diffusion kernel. In particular, it highlights relevant features of trophic networks (trophic levels, energetic channels). In addition, we developed tools to handle, compare visually and quantitatively and aggregate those networks. Static and dynamic visualisation functions have been developed to represent large networks. We apply our package workflow to several trophic network data sets.

Straightforward Stereoscopic Techniques for Archaeometric Interpretation of Archeological Artifacts

Stereoscopic visualization plays a significant role in the detailed and accurate interpretation of various geometric features on the surface of archaeological artifacts, which can be challenging to perceive using conventional two-dimensional visualizations. Moreover, virtual 3D models can be shared with other archaeologists for interpretation and the exchange of opinions. The hardware requirements for rendering stereoscopic 3D models are often readily available on desktop computers, or require only a minimal investment for implementation. This article focuses on creating stereoscopic visualizations of a stylized dove-shaped cult vessel for a virtual museum project. The term “visualization” is defined, emphasizing its significance and everyday applications. The camerawork techniques and processes involved in stereoscopic image production, including anaglyph imaging and polarization, are described. Blender (community-driven project under the GNU General Public License (GPL), Blender Foundation is a member of Open Invention Network, Khronos, Linux Foundation and the Academy Software Foundation) and StereoPhoto Maker (Muttyan, Japan) are reviewed as they relate to the production process of stereoscopic visualizations using open-source software. A series of static stereoscopic visualizations, along with two dynamic stereoscopic examples, are created, one using the anaglyph process, and the other using polarization. Lastly, the article discusses the contribution of stereoscopic visualizations to the interpretation of archaeological artifacts and suggests the optimal parameters for creating stereoscopic visualizations.

The effects of animated versus static metaphor with 3D images on EFL learners' acquisition of degrees of certainty

AbstractBackgroundJapanese EFL learners often fail to identify and categorise the different degrees of certainty associated with CERTAIN, PROBABLE, and POSSIBLE items. This computer‐mediated study evaluated the relative effects of animated versus static illustrations on stimulating the spatial concept of distance for the acquisition of this knowledge.ObjectivesThis study aimed to: (1) discover whether the spatial concept of distance captured by three‐dimensional (3D) images in animated or static illustrations help learners understand spatial relationships more clearly, and (2) whether a static illustration is sufficient for learning the different degrees of certainty.MethodAnimated and static visualisation groups used 3D images of animated versus static illustrations that capture the spatial concept of distance, and a contrasting group did not use illustrations. The former applied the spatial concept of distance to learn the degrees of certainty, using animated or static illustrations. A spatial concept‐oriented approach that visualises concepts of distance enabled participants to understand the degrees of certainty. The contrasting approach involved rote learning of a list of target expressions for the different degrees of certainty in English.Results and ConclusionsBoth the animated and static versions of the spatial concept‐oriented approaches were equally effective. The difference between the two versions did not significantly impact the overall performance of the groups.TakeawaysRegardless of the type of effect, the spatial concept of distance captured in words and illustrations helped the participants (1) process the different degrees of certainty cognitively in terms of distances and (2) retain them in long‐term memory.

Joint visualization of seasonal influenza serology and phylogeny to inform vaccine composition.

Seasonal influenza vaccines must be updated regularly to account for mutations that allow influenza viruses to escape our existing immunity. A successful vaccine should represent the genetic diversity of recently circulating viruses and induce antibodies that effectively prevent infection by those recent viruses. Thus, linking the genetic composition of circulating viruses and the serological experimental results measuring antibody efficacy is crucial to the vaccine design decision. Historically, genetic and serological data have been presented separately in the form of static visualizations of phylogenetic trees and tabular serological results to identify vaccine candidates. To simplify this decision-making process, we have created an interactive tool for visualizing serological data that has been integrated into Nextstrain's real-time phylogenetic visualization framework, Auspice. We show how the combined interactive visualizations may be used by decision makers to explore the relationships between complex data sets for both prospective vaccine virus selection and retrospectively exploring the performance of vaccine viruses.

Using dashboards to verify coronavirus (COVID-19) vaccinations can reduce fatality rates in countries/regions: Development and usability study.

The new coronavirus disease 2019 (COVID-19) pandemic is raging worldwide. The administered vaccination has become a significant vehicle against the virus. Three hypotheses were made and required for validation: the number of vaccines administered is related to the country gross domestic product (GDP), vaccines can reduce the fatality rate (FR), and dashboards can present more meaningful information than traditionally static visualizations. Research data were downloaded from the GitHub website. The aims of this study are to verify that the number of vaccination uptakes is related to the country GDP, that vaccines can reduce FR, and that dashboards can provide more meaningful information than traditionally static visualizations. The COVID-19 cumulative number of confirmed cases (CNCCs) and deaths were downloaded from the GitHub website for countries/regions on November 6, 2021. Four variables between January 1, 2021, and November 6, 2021, were collected, including CNCCs and deaths, GDP per capita, and vaccine doses administered per 100 people (VD100) in countries/regions. We applied the Kano model, forest plot, and choropleth map to demonstrate and verify the 3 hypotheses using correlation coefficients (CC) between vaccination and FRs. Dashboards used to display the vaccination effects were on Google Maps. We observed that the higher the GDP, the more vaccines are administered (Association = 0.68, t = 13.14, P < .001) in countries, the FR can be reduced by administering vaccinations that are proven except for the 4 groups of Asia, Low income, Lower middle income, and South America, as well as the application (app) with dashboard-type choropleth map can be used to show the comparison of vaccination rates for countries/regions using line charts. This research uses the Kano map, forest plot, and choropleth map to verify the 3 hypotheses and provides insights into the vaccination effect against the FR for relevant epidemic studies in the future.

Mobile augmented reality-based visualization framework for lifecycle O&M support of urban underground pipe networks

The structural deterioration of urban underground pipe networks is the accumulated combined effects of physical, environmental and operational factors during the whole lifecycle. Accurate on-site locating and mapping of pipes and their conditions has been a great challenge for construction, maintenance and emergency response in a dynamic manner. Existing approaches, mostly based on design-centric and static visualization, are unsuitable for implementation within the operation and maintenance (O&M) phase because they are performed in a completely virtual environment and are unable to interact with buried assets on site. An integrated hyperrealistic visualization framework based on mobile augmented reality (MAR) is developed to support various real-life project tasks for O&M support of urban underground pipe networks. The proposed approach consists of three parts: a cloud-based data management platform, a 3D visualization development platform, and a location-based registration method for MAR. A cloud–edge–mobile layer is proposed to manage, analyze, process and visualize multisource data, especially for dynamic data. Field experiments on a real-life urban gas pipe network were carried out to investigate the feasibility of the proposed framework to achieve informed O&M support. The results demonstrate that (1) a smartphone linked to a Lite RTK device allows for both accurate locating and easy operation; (2) both the 3D virtual digital model and the multisource data visually superimpose onto the realistic environment, enabling on-site human–machine interaction in a hyperrealistic manner; and (3) MAR-based immersive visualization contributes to avoiding excavation damage, improving O&M efficiency and assisting emergency responders to make timely and better–informed decisions based on multisource information fusion.