Visual Analytics System Research Articles

CATOM : Causal Topology Map for Spatiotemporal Traffic Analysis with Granger Causality in Urban Areas.

The transportation network is an important element in an urban system that supports daily activities, enabling people to travel from one place to another. One of the key challenges is the network complexity, which is composed of many node pairs distributed over the area. This spatial characteristic results in the high dimensional network problem in understanding the 'cause' of problems such as traffic congestion. Recent studies have proposed visual analytics systems aimed at understanding these underlying causes. Despite these efforts, the analysis of such causes is limited to identified patterns. However, given the intricate distribution of roads and their mutual influence, new patterns continuously emerge across all roads within urban transportation. At this stage, a well-defined visual analytics system can be a good solution for transportation practitioners. In this paper, we propose a system, CATOM (Causal Topology Map), for the cause-effect analysis of traffic patterns based on Granger causality for extracting causal topology maps. CATOM discovers causal relationships between roads through the Granger causality test and quantifies these relationships through the causal density. During the design process, the system was developed to fully utilize spatial information with visualization techniques to overcome the previous problems in the literature. We also evaluate the usability of our approach by conducting a SUS(System Usability Scale) test and traffic cause analysis with the real-world data from two study sites in collaboration with domain experts.

Visualization Analysis of Integrating College Sports Training and Psychology into Basketball Physical Education Teaching System Based on Image Recognition Algorithm

ABSTRACT On this basis, a basketball movement model based on ORB (Oriented FAST and Rotated BRIEF) local feature extraction has been proposed, and a basketball teaching visualization analysis system has been constructed. By extracting images from athletes’ videos in basketball, and using their training as a standard, tracking the frequency of their various celebration postures appearing and improving their scores in the game, corresponding feature points are extracted, and then the BP (back propagation) neural network algorithm is used to classify feature points. Ten different participants were selected from 10 games. Their movements were tracked, and then the extracted feature actions were imported into the visualization system for visual analysis, comparing the changes before and after psychological intervention. The results showed that there were significant differences in scores, errors, hit rates, steals, and motivation among subjects who received psychological cues and incentives compared to the data before psychological counseling. The scores and positive and negative values increased to 2–4 points, which had a certain positive impact on the competitive results.

MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.

This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical exploratory sensor data analysis. Following a user-centered design approach, we learned that clinicians have 3 priorities for generating insights for the Barka-MS study data: exploration and search for, and contextualization of, sequences and patterns in patient sleep and activity. We compute meaningful sequences for patients using clustering and proximity search, displaying these with an interactive visual interface composed of coordinated views. Our evaluation posed both closed and open-ended tasks to participants, utilizing a scoring system to gauge the tool's usability, and effectiveness in supporting insight generation across 15 clinicians, data scientists, and non-experts. We present MS Pattern Explorer, a visual analytics system that helps clinicians better address complex data-centric challenges by facilitating the understanding of activity patterns. It enables innovative analysis that leads to rapid insight generation and contextualization of temporal activity data, both within and between patients of a cohort. Our evaluation results indicate consistent performance across participant groups and effective support for insight generation in MS patient fitness tracker data. Our implementation offers broad applicability in clinical research, allowing for potential expansion into cohort-wide comparisons or studies of other chronic conditions. MS Pattern Explorer successfully reduces the signal overload clinicians currently experience with activity data, introducing novel opportunities for data exploration, sense-making, and hypothesis generation.

CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective.

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

EBPVis: Visual Analytics of Economic Behavior Patterns in a Virtual Experimental Environment

AbstractExperimental economics is an important branch of economics to study human behaviours in a controlled laboratory setting or out in the field. Scientific experiments are conducted in experimental economics to collect what decisions people make in specific circumstances and verify economic theories. As a significant couple of variables in the virtual experimental environment, decisions and outcomes change with the subjective factors of participants and objective circumstances, making it a difficult task to capture human behaviour patterns and establish correlations to verify economic theories. In this paper, we present a visual analytics system, EBPVis, which enables economists to visually explore human behaviour patterns and faithfully verify economic theories, e.g. the vicious cycle of poverty and poverty trap. We utilize a Doc2Vec model to transform the economic behaviours of participants into a vectorized space according to their sequential decisions, where frequent sequences can be easily perceived and extracted to represent human behaviour patterns. To explore the correlation between decisions and outcomes, an Outcome View is designed to display the outcome variables for behaviour patterns. We also provide a Comparison View to support an efficient comparison between multiple behaviour patterns by revealing their differences in terms of decision combinations and time‐varying profits. Moreover, an Individual View is designed to illustrate the outcome accumulation and behaviour patterns of subjects. Case studies, expert feedback and user studies based on a real‐world dataset have demonstrated the effectiveness and practicability of EBPVis in the representation of economic behaviour patterns and certification of economic theories.

KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts.

Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVIS, a human-in-the-loop visual analytics system for interpreting language models using fill-in-the-blank sentences as prompts. By comparing predictions between sentences, KnowledgeVIS reveals learned associations that intuitively connect what language models learn during training to natural language tasks downstream, helping users create and test multiple prompt variations, analyze predicted words using a novel semantic clustering technique, and discover insights using interactive visualizations. Collectively, these visualizations help users identify the likelihood and uniqueness of individual predictions, compare sets of predictions between prompts, and summarize patterns and relationships between predictions across all prompts. We demonstrate the capabilities of KnowledgeVIS with feedback from six NLP experts as well as three different use cases: (1) probing biomedical knowledge in two domain-adapted models; and (2) evaluating harmful identity stereotypes and (3) discovering facts and relationships between three general-purpose models.

SenseMap: Urban Performance Visualization and Analytics Via Semantic Textual Similarity.

As urban populations grow, effectively accessing urban performance measures such as livability and comfort becomes increasingly important due to their significant socioeconomic impacts. While Point of Interest (POI) data has been utilized for various applications in location-based services, its potential for urban performance analytics remains unexplored. In this article, we present SenseMap, a novel approach for analyzing urban performance by leveraging POI data as a semantic representation of urban functions. We quantify the contribution of POIs to different urban performance measures by calculating semantic textual similarities on our constructed corpus. We propose Semantic-adaptive Kernel Density Estimation which takes into account POIs' influential areas across different Traffic Analysis Zones and semantic contributions to generate semantic density maps for measures. We design and implement a feature-rich, real-time visual analytics system for users to explore the urban performance of their surroundings. Evaluations with human judgment and reference data demonstrate the feasibility and validity of our method. Usage scenarios and user studies demonstrate the capability, usability and explainability of our system.

SingVisio: Visual analytics of diffusion model for singing voice conversion

In this study, we present SingVisio, an interactive visual analysis system that aims to explain the diffusion model used in singing voice conversion. SingVisio provides a visual display of the generation process in diffusion models, showcasing the step-by-step denoising of the noisy spectrum and its transformation into a clean spectrum that captures the desired singer’s timbre. The system also facilitates side-by-side comparisons of different conditions, such as source content, melody, and target timbre, highlighting the impact of these conditions on the diffusion generation process and resulting conversions. Through comparative and comprehensive evaluations, SingVisio demonstrates its effectiveness in terms of system design, functionality, explainability, and user-friendliness. It offers users of various backgrounds valuable learning experiences and insights into the diffusion model for singing voice conversion.

Evaluation of visual quality and refractive outcomes of myopia with presbyopia after laser blended vision surgery

Objective: To investigate the postoperative outcomes and visual quality following laser blended vision (LBV) surgery for the correction of myopia with presbyopia. Methods: This is a prospective study. A total of 50 patients (100 eyes) who underwent LBV surgery for myopia with presbyopia at Beijing Tongren Hospital, Capital Medical University, between August 2021 and March 2022 were included. Uncorrected distance visual acuity (UDVA), uncorrected near visual acuity (UNVA), best-corrected distance visual acuity (BCDVA), manifest refraction spherical equivalent (MRSE) were measured preoperatively and at 1, 3, 6, 12, and 24 months postoperatively for the dominant eye, non-dominant eye, and both eyes. Effectiveness Index (EI) and Safety Index (SI) were calculated to evaluate refractive outcomes. Accommodation function and contrast sensitivity were assessed to evaluate functional vision. Objective visual quality was assessed using higher-order aberrations (HOAs) and a dual-channel visual quality analysis system. Subjective visual quality was evaluated using a questionnaire. Results: At 24 months postoperatively, the EI and SI for the dominant eye were 1.04±0.23 and 1.14±0.14, respectively, while for the non-dominant eye, they were 0.85±0.21 and 1.06±0.08, respectively. Although there were statistically significant differences in UNVA between the eyes at 1 and 3 months postoperatively (all P<0.05), no significant differences were observed from 6 months onward (all P>0.05). At 6 months postoperatively, the UNVA for the dominant and non-dominant eyes were 0.07±0.13 and 0.03±0.13, respectively, with no significant difference (P>0.05). MRSE showed no significant differences at any follow-up time points up to 24 months. Accommodation function did not decrease compared to preoperative levels at any postoperative follow-up. Increases in HOAs were primarily due to spherical aberrations, and there were no significant differences in objective visual quality before and after surgery. Contrast sensitivity improved significantly at 24 months postoperatively compared to preoperative levels. At 24 months postoperatively, 11 out of 12 patients reported good visual quality in the questionnaire. Conclusions: LBV surgery gradually achieves good binocular blended vision within 6 months postoperatively and demonstrates good safety, efficacy, and stability at 24 months. It maintains good functional vision and visual quality with high postoperative patient satisfaction.

A machine vision tool for multi-color H2O2 sensing by MoOx nanoparticles with oxygen vacancies

Improving the ease of operation and portability of hydrogen peroxide (H2O2) detection in daily production and life holds significant application value. However, it remains a challenge to achieve rapid colorimetric detection of H2O2 and color change quantification. In this study, we achieved rapid and visual detection of H2O2 by MoOx (2 ≤ x ≤ 3) nanoparticles with rich oxygen vacancies using machine vision. As the concentration of H2O2 increases, the detection system exhibited a visible multi-color change from blue to green and then yellow and the absorption peak near 680 nm measured by the UV–visible spectrophotometer gradually decreased. With excellent sensitivity, a wide linear range of 0.1–600 μmol/L, concentrations as low as 0.1 μmol/L can be detected with good selectivity towards H2O2. The sensing mechanism of detecting H2O2 by the change of oxygen vacancies in MoOx was revealed through characterization methods such as XPS, EPR, and DFT. In addition, the Hue, Saturation, Value (HSV) visual analysis system based on MoOx was constructed to assist in the rapid, portable, and sensitive monitoring of H2O2 in practical application scenarios. This work offers an easy-to operate, low cost, and convenience for achieving rapid colorimetric determination of H2O2 and has broad application prospects in daily life and industrial production.

A framework to improve causal inferences from visualizations using counterfactual operators

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems.

Towards Better Modeling With Missing Data: A Contrastive Learning-Based Visual Analytics Perspective.

Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to enhance ML performance. These approaches rely on the observed data to estimate the missing values and therefore encounter three main shortcomings in imputation, including the need for different imputation methods for various missing data mechanisms, heavy dependence on the assumption of data distribution, and potential introduction of bias. This study proposes a Contrastive Learning (CL) framework to model observed data with missing values, where the ML model learns the similarity between an incomplete sample and its complete counterpart and the dissimilarity between other samples. Our proposed approach demonstrates the advantages of CL without requiring any imputation. To enhance interpretability, we introduce CIVis, a visual analytics system that incorporates interpretable techniques to visualize the learning process and diagnose the model status. Users can leverage their domain knowledge through interactive sampling to identify negative and positive pairs in CL. The output of CIVis is an optimized model that takes specified features and predicts downstream tasks. We provide two usage scenarios in regression and classification tasks and conduct quantitative experiments, expert interviews, and a qualitative user study to demonstrate the effectiveness of our approach. In short, this study offers a valuable contribution to addressing the challenges associated with ML modeling in the presence of missing data by providing a practical solution that achieves high predictive accuracy and model interpretability.

SpectrumVA: Visual Analysis of Astronomical Spectra for Facilitating Classification Inspection.

In astronomical spectral analysis, class recognition is essential and fundamental for subsequent scientific research. The experts often perform the visual inspection after automatic classification to deal with low-quality spectra to improve accuracy. However, given the enormous spectral volume and inadequacy of the current inspection practice, such inspection is tedious and time-consuming. This article presents a visual analytics system named SpectrumVA to promote the efficiency of visual inspection while guaranteeing accuracy. We abstract inspection as a visual parameter space analysis process, using redshifts and spectral lines as parameters. Different navigation strategies are employed in the "selection-inspection-promotion" workflow. At the selection stage, we help the experts identify a spectrum of interest through spectral representations and auxiliary information. Several possible redshifts and corresponding important spectral lines are also recommended through a global-to-local strategy to provide an appropriate entry point for the inspection. The inspection stage adopts a variety of instant visual feedback to help the experts adjust the redshift and select spectral lines in an informed trial-and-error manner. Similar spectra to the inspected one rather than different ones are visualized at the promotion stage, making the inspection process more fluent. We demonstrate the effectiveness of SpectrumVA through a quantitative algorithmic assessment, a case study, interviews with domain experts, and a user study.

ViSSR: A visual analytics system for student high-order social relationships at campus

Social relationships among students at campus are closely related to their mental health and academic performance. Therefore, it is a very important task for educators to analyze students’ social relationships. However, existing studies have focused on one-to-one social relationships between students, few ones have explored the high-order community relationships hidden in social networks, especially in a visual manner. To solve this problem, a visual analysis system called ViSSR is proposed in this paper, which utilizes the Louvain algorithm to detect the hierarchical community structure of students’ social network at campus, and then provides four coordinated views to visualize the detection results. Among the views, the hierarchical hypergraph view is to visualize the hierarchical community structure that greatly breaks through the limitations of first-order relationships available in a traditional node-link social network, the community analysis view and individual analysis view show the social characteristics of a community and individual student respectively, and the matrix view displays the behavioral features of students. Case studies and experts evaluation have been conducted to demonstrate the usability of the system.

XNLI: Explaining and Diagnosing NLI-Based Visual Data Analysis.

Natural language interfaces (NLIs) enable users to flexibly specify analytical intentions in data visualization. However, diagnosing the visualization results without understanding the underlying generation process is challenging. Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries. We present XNLI, an explainable NLI system for visual data analysis. The system introduces a Provenance Generator to reveal the detailed process of visual transformations, a suite of interactive widgets to support error adjustments, and a Hint Generator to provide query revision hints based on the analysis of user queries and interactions. Two usage scenarios of XNLI and a user study verify the effectiveness and usability of the system. Results suggest that XNLI can significantly enhance task accuracy without interrupting the NLI-based analysis process.

RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences

AbstractAnalyzing the preference of route choice not only facilitates the understanding of individuals' decision‐making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed‐loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio‐temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status.

Visual Analytics for Fine‐grained Text Classification Models and Datasets

AbstractIn 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.

Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning.

Recent advancements in pre-trained language-image models have ushered in a new era of visual comprehension. Leveraging the power of these models, this article tackles two issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of data biases within them; (2) the evaluation of image captions and steering of their generation process. On the one hand, by visually examining the captions generated from language-image models for an image dataset, we gain deeper insights into the visual contents, unearthing data biases that may be entrenched within the dataset. On the other hand, by depicting the association between visual features and textual captions, we expose the weaknesses of pre-trained language-image models in their captioning capability and propose an interactive interface to steer caption generation. The two parts have been coalesced into a coordinated visual analytics system, fostering the mutual enrichment of visual and textual contents. We validate the effectiveness of the system with domain practitioners through concrete case studies with large-scale image datasets.

PM-Vis: A Visual Analytics System for Tracing and Analyzing the Evolution of Pottery Motifs.

In Chinese archaeological research, analyzing the evolution of motifs in ancient pottery is crucial for studying the spread and growth of cultures across various eras and regions. However, such analyses are often challenging due to the complexities of identifying motifs with evolutionary connections that may manifest concurrent changes in appearance, space, and time, compounded by ineffective documentation. We propose PM-Vis, a visual analytics system for tracing and analyzing the evolution of pottery motifs. PM-Vis is anchored in a "selection-organization-documentation" workflow. In the selection stage, we design a three-fold projection paired with a motif-based search mechanism, displaying the appearance similarity and temporal and spatial proximities of all motifs or a specific motif, aiding users in selecting motifs with evolutionary connections. The organization stage helps users establish the evolutionary sequence and segment the selected motifs into distinct evolutionary phases. Finally, the documentation stage enables users to record their observations and insights through various forms of annotation. We demonstrate the usefulness and effectiveness of PM-Vis through two case studies, expert feedback, and a user study.

Designing interactive visualizations for analyzing chronic lung diseases in a user-centered approach.

Medical practitioners analyze numerous types of data, often using archaic representations that do not meet their needs. Pneumologists who analyze lung function exams must often consult multiple exam records manually, making comparisons cumbersome. Such shortcomings can be addressed with interactive visualizations, but these must be designed carefully with practitioners' needs in mind. A workshop with experts was conducted to gather user requirements and common tasks. Based on the workshop results, we iteratively designed a web-based prototype, continuously consulting experts along the way. The resulting application was evaluated in a formative study via expert interviews with 3 medical practitioners. Participants in our study were able to solve all tasks in accordance with experts' expectations and generally viewed our system positively, though there were some usability and utility issues in the initial prototype. An improved version of our system solves these issues and includes additional customization functionalities. The study results showed that participants were able to use our system effectively to solve domain-relevant tasks, even though some shortcomings could be observed. Using a different framework with more fine-grained control over interactions and visual elements, we implemented design changes in an improved version of our prototype that needs to be evaluated in future work. Employing a user-centered design approach, we developed a visual analytics system for lung function data that allows medical practitioners to more easily analyze the progression of several key parameters over time.