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Background: Prostate cancer (PCa) is the second most prevalent malignancy among men globally. The diagnosis, treatment, and prognosis of prostate cancer frequently fall short of expectations. In recent years, the connection between inflammation and prostate cancer has attracted considerable attention. However, there is a lack of bibliometric studies analyzing the research on inflammation within the domain of prostate cancer. Research methods: We utilized the Web of Science Core Collection (WOSCC) as our data source to extract articles and reviews related to inflammation in prostate cancer, published up until April 12, 2024. The collected data underwent meticulous manual screening, followed by bibliometric analysis and visualization using the Biblioshiny package in R. Results: This study encompasses an analysis of 2,786 papers focusing on inflammation-related research within the realm of prostate cancer. Recent years have seen a significant proliferation of publications in this area, with the United States and China being the foremost contributors. The most prolific author in this domain is Demarzoam, with Johns Hopkins University standing out as the most influential institution. The leading journal in disseminating these studies is PROSTATE. Keyword co-occurrence analysis reveals that ‘inflammation-related biomarkers’, ‘inflammation index’, and ‘tumor immune microenvironment’ represent the current research hotspots and frontiers. Conclusion: The findings of this bibliometric study serve to illuminate the current landscape of inflammation-related research in the field of prostate cancer, while further augmenting the discourse on inflammation-mediated cancer therapeutics. Of particular note is the potential of these discoveries to facilitate a more nuanced understanding among researchers regarding the interplay between inflammation and prostate cancer. [ABSTRACT FROM AUTHOR]

We propose a novel visual analytics approach based on the Latent Dirichlet Allocation (LDA) model for exploring and interpreting the dynamic evolution of networks. In this approach, we define networks as documents and relationships within networks as words. Using this definition, the LDA model is able to extract a list of structures that fuse relationships and connect the network features. We project networks described by the extracted structures with probabilistic assignments as points into a two-dimensional space via dimensionality reduction techniques. Users can identify evolution states in dynamic networks, including stable states, recurrent states, outlier states, and state transitions. To facilitate the interpretation of evolution states, we provide a novel small multiples view that shows how the extracted structures behave over time. We demonstrate the effectiveness of our work through case studies conducted on two real-world dynamic networks. [ABSTRACT FROM AUTHOR]

The explosive growth in supercomputers capacity has changed simulation paradigms. Simulations have shifted from a few lengthy ones to an ensemble of multiple simulations with varying initial conditions or input parameters. Thus, an ensemble consists of large volumes of multi-dimensional data that could go beyond the exascale boundaries. However, the disparity in growth rates between storage capabilities and computing resources results in I/O bottlenecks. This makes it impractical to utilize conventional post-processing and visualization tools for analyzing such massive simulation ensembles. In situ visualization approaches alleviate I/O constraints by saving predetermined visualizations in image databases during simulation. Nevertheless, the unavailability of output raw data restricts the flexibility of post hoc exploration of in situ approaches. Much research has been conducted to mitigate this limitation, but it falls short when it comes to simultaneously exploring and analyzing parameter and ensemble spaces. In this paper, we propose an expert-in-the-loop visual exploration analytic approach. The proposed approach leverages: feature extraction, deep learning, and human expert–AI collaboration techniques to explore and analyze image-based ensembles. Our approach utilizes local features and deep learning techniques to learn the image features of ensemble members. The extracted features are then combined with simulation input parameters and fed to the visualization pipeline for in-depth exploration and analysis using human expert + AI interaction techniques. We show the effectiveness of our approach using several scientific simulation ensembles. [ABSTRACT FROM AUTHOR]

For a large complex industrial equipment with high-density sensors, exploring the potential influence of generated multiregion monitoring parameters on subsequent control links can be very meaningful to optimize the control process. However, the influencing mechanism and randomness between such numerous monitoring parameters and subsequently influenced parameters are intertwined, and each working condition of the control system has its unique running characteristics and control rules, which makes it challenging to analyze the correlations between these different categories of parameter sets effectively. In this paper, we propose a comprehensive approach that combines parameter fusion and canonical correlation analysis for this kind of high-dimensional industrial control data and constructs a visual analysis framework CAPVis that supports multi-perspective and multi-level exploration of canonical correlation patterns. For a single working condition, we visualize the intricate structure inside of the canonical correlation relationships with a particular tripartite graph and evaluate the redundancy and stability of these relationships with multiple auxiliary views. For multiple working conditions, we design different visual comparison strategies to comprehensively compare the many-to-many canonical correlation patterns from local to global. Experiments on real industrial control datasets and feedback from industry experts demonstrate the effectiveness of CAPVis. [ABSTRACT FROM AUTHOR]

Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks. [ABSTRACT FROM AUTHOR]

Organizing archaeological artefacts under a conceptual system is part and parcel of archaeological research. As an abundant material category, pottery artefacts classified in an effective typological model provide a rich source of information for the discipline. However, building a typological model from scratch, as well as maintaining it, often represents a challenge. To support archaeological research, automated methods are increasingly utilized in sustaining classification models. Yet, there is potential for advancement in creating, rethinking, and updating typological arrangements by means of digital, label-driven, or data-driven algorithmic approaches. In this paper, we take a step towards fulfilling this potential while highlighting the fuzziness involved in typological arrangements. We present a complete research pipeline of pottery form quantification, fuzzy-type description, and fuzzy-type definition which is in principle applicable to any typological model. The methodological pipeline is implemented, first, in rim segments to algorithmically construct polythetic rim descriptors; second, in complete profiles to algorithmically connect the global form with the attributed functional class; and third, in types to investigate within-class form variation and its chronological relevance. This paper provides tools to formalize the ambivalence of typological classification using fuzzy logic and revisit the theoretical model to investigate the vagueness of belonging to a class based on morphological aspects of pottery profiles. [ABSTRACT FROM AUTHOR]

Many industrial companies capture high volume of time series data from their industrial processes. However, to visualize it, regular visualization approaches require specialized hardware. Thus, downsampling algorithms are required to create a simplified view of the original data. Although industrial processes involve synchronized variables that should be visualized together for their analysis, existing downsampling algorithms tackle visualization of univariate data series. This paper proposes an adaptive extension of the M4 algorithm for multivariate datasets. The algorithm has been validated successfully with data from a conventional 3D turning operation and commodity hardware. For validation, a direct image comparison has been performed. [ABSTRACT FROM AUTHOR]

Experimental 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. [ABSTRACT FROM AUTHOR]

As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model‐agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier‐to‐explain. Yet, rule sets can become very lengthy, with many if–else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models by providing surrogate decision stumps (one‐level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity versus fidelity trade‐off by incrementally generating more stumps, creating attribute‐based explanations with weighted stumps to justify decision making, and analysing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case‐by‐case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of running CNN on computation-constrained end devices. A critical component to ensure low-latency and accurate visual analytics offloading over low bandwidth networks is image compression which minimizes the amount of visual data to offload and maximizes the decoding quality of salient pixels for analytics. Despite the wide adoption, JPEG standards and traditional image compression techniques do not address the accuracy of analytics tasks, leading to ineffective compression for visual analytics offloading. Although recent machine-centric image compression techniques leverage sophisticated neural network models or hardware architecture to support the accuracy-bandwidth tradeoff, they introduce excessive latency in the visual analytics offloading pipeline. This article presents CICO, a Context-aware Image Compression Optimization framework to achieve low-bandwidth and low-latency visual analytics offloading. CICO contextualizes image compression for offloading by employing easily-computable low-level image features to understand the importance of different image regions for a visual analytics task. Accordingly, CICO can optimize the tradeoff between compression size and analytics accuracy. Extensive real-world experiments demonstrate that CICO reduces the bandwidth consumption of existing compression methods by up to 40% under comparable analytics accuracy. Regarding the low-latency support, CICO achieves up to a 2× speedup over state-of-the-art compression techniques. [ABSTRACT FROM AUTHOR]

As Internet-based technology spreads to most areas of life, it becomes a challenge to grasp opportunities arising from enormous amounts of data being generated from various sources such as smart homes, smart cities, health care systems and industries. Efficient utilization of these data can enable us to improve many human practices, including those connected to health care. In the present study, we focus on the health care sector, as it consists of large-scale organizations that rely on the processing of big data and complex processing of information. Due to the dynamic nature and complexity of this domain, it is essential to develop sophisticated technologies for the efficient processing of vast amounts of information. There is, for example, a need for interactive tools that can visualise actual care processes being executed in the hospital. A tool visualising real-time data could give a dynamic view of the processes, with accurate quantitative information, which can be used to improve the quality and efficiency of health care provision. These tools should be built on the requirements of practitioners needs and requirements, to ensure their relevance and practical utility. In this paper, we present a user-driven design process for developing therapy data visualisation components of guided Internet-based cognitive behaviour therapy (iCBT) and their evaluation. In order to ensure the reusability of the visual components, we propose to utilise a model-based approach which allows data analysts to adapt domain models by means of model transformation and transform them into visualization. [ABSTRACT FROM AUTHOR]

Gradient boosting decision tree (GBDT) is a mainstream model for advertisement click-through rate (CTR) prediction. Since the complex working mechanism of GBDT, advertising analysts often fail to analyze the decision-making and the iterative evolution process of a large number of decision trees, as well as to understand the impact of different features on the prediction results, which makes the model tuning quite challenging. To address these challenges, we propose a visual analytics system, GBDT4CTRVis, which helps advertising analysts understand the working mechanism of GBDT and facilitate model tuning through intuitive and interactive views. Specifically, we propose instance-level views to hierarchically explore the prediction results of advertising data, feature-level views to analyze the importance of features and their correlations from various perspectives, and model-level views to investigate the structure of representative decision trees and the temporal evolution of information gain during model prediction. We also provide multi-view interactions and panel control for flexible exploration. Finally, we evaluate GBDT4CTRVis through three case studies and expert evaluations. Feedback from experts indicated the usefulness and effectiveness of GBDT4CTRVis in helping to understand the model mechanism and tune the model. [ABSTRACT FROM AUTHOR]

In the field of deep learning, pre-trained BERT models have achieved remarkable success. However, the accompanying problem is that models with more complex structures and more network parameters. The huge parameter size makes the computational cost in terms of time and memory become extremely expensive. Recent work has indicated that BERT models own a significant amount of redundant attention heads. Meanwhile considerable BERT models compression algorithms have been proposed, which can effectively reduce model complexity and redundancy with pruning some attention heads. Nevertheless, existing automated model compression solutions are mainly based on predetermined pruning program, which requires multiple expensive pruning-retraining cycles or heuristic designs to select additional hyperparameters. Furthermore, the training process of BERT models is a black box, and lacks interpretability, which makes researchers cannot intuitively understand the optimization process of the model. In this paper, we propose a visual analysis system, BHPVAS, for pruning BERT models, which helps researchers to incorporate their understanding of model structure and operating mechanism into the model pruning process and generate pruning schemes. We propose three pruning criteria based on the attention data, namely, importance score, stability score, and similarity score, for evaluating the importance of self-attention heads. Additionally, we design multiple collaborative views to display the entire pruning process, guiding users to carry out pruning. Our system supports exploring the role of self-attention heads in the model inference process using text dependency relations and attention weight distribution. Finally, we conduct two case studies to demonstrate how to use the system for Sentiment Classification Sample Analysis and Pruning Scheme Exploration, verifying the effectiveness of the visual analysis system. [ABSTRACT FROM AUTHOR]

Spinel group minerals, found within various rock types, exhibit distinct categorizations based on their host rocks. According to Barnes and Roeder (2001), these minerals can be classified into eight primary groups, each further subdivided into variable numbers of subgroups that can be related to a particular tectonic setting. This classification is based on the cations corresponding to the end-members of the spinel prism and is traditionally analyzed in this prismatic space or using projections of it. In this prismatic representation, several categories tend to overlap, making it impossible to determine which is the tectonic environment in that scenario. An alternative to solve this problem is to generate representations of these groups considering more attributes, making the most of the many values measured during the geochemical analysis. In this paper, we present SpinelVA, a visual exploration tool that integrates Machine Learning techniques and allows the identification of groups using the cations considered by Barnes and Roeder and some additional ones obtained from chemical analysis. SpinelVA allows us to know the tectonic environment of unknown samples by categorizing them according to the Barnes and Roeder classification. Additionally, SpinelVA integrates a collection of visual analysis techniques alongside the already used spinel prism projections and provides a set of interactions that assist geologists in the exploration process. Users can perform a complete data analysis by combining the proposed techniques and associated interactions. [ABSTRACT FROM AUTHOR]

Exploratory data analysis (EDA), data visualization, and visual analytics are essential for understanding and analyzing complex datasets. In this project, we explored these techniques and their applications in data analytics. The case discusses Tableau, a powerful data visualization tool, and Google BigQuery, a cloud-based data warehouse that enables users to store, query, and analyze large datasets. It also explored the benefits and applications of both tools and their integration with other platforms and services. The project offers an introductory insight into Tableau's functionalities, employing a data file from the US Census Bureau via Google BigQuery. [ABSTRACT FROM AUTHOR]

In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study (N = 12), revealing how experts' preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert. [ABSTRACT FROM AUTHOR]

While comprehensive knowledge networks can be instrumental in finding solutions to complex problems or supporting the development of detailed simulation models, their large number of nodes and edges can become a hindrance. When the representation of a network becomes opaque, they stop fulfilling their role as a shared representation of a system between participants and modelers; hence, participants are less engaged in the model-building process. Combating the information overload created by large conceptual models is not merely a matter of changing formats: shifting from an unwieldy diagram to enormous amounts of text does not promote engagement. Rather, we posit that participants need an environment that provides details on demand and where interactions with a model rely primarily on a familiar format (i.e., text). In this study, we developed a visual analytics environment where linked visualizations allow participants to interact with large conceptual models, as shown in a case study with hundreds of nodes and almost a thousand relationships. Our environment leverages several advances in generative AI to automatically transform (i) a conceptual model into detailed paragraphs, (ii) detailed text into an executive summary of a model, (iii) prompts about the model into a safe version that avoids sensitive topics, and (iv) a description of the model into a complementary illustration. By releasing our work open source along with a video of our case study, we encourage other modelers to use this approach with their participants. Their feedback and future usability studies are key to respond to the needs of participants by improving our environment given individual preferences, models, and application domains. [ABSTRACT FROM AUTHOR]

Background: While criteria for the diagnosis of nosocomial pneumonias exist, objective definitions are a challenge and there is no gold standard for diagnosis. We analyzed the impact of the implementation of a logical, consensus-based diagnostic and treatment protocol for managing nosocomial pneumonias in the cardiovascular surgery intensive care unit (CVS-ICU). Methods: We conducted a quasi-experimental, interrupted time series analysis to evaluate the impact of a diagnostic and treatment protocol for nosocomial pneumonias in the CVS-ICU. Impacts were measured relative to patient outcomes, diagnostic processes, and antimicrobial stewardship improvement. Descriptive statistics were used to analyze results. Results: Overall, 35 pre-protocol and 39 post-protocol patients were included. Primary clinical variables suggesting pneumonia in pre- and post-protocol patients were new lung consolidation (50% vs. 71%), new leukocytosis (59% vs. 64%), and positive culture (32% vs. 55%). Appropriate diagnostic testing improved (23% vs. 54%, p = 0.008) after protocol implementation. The proportion of patients meeting the criteria for nosocomial pneumonia (77% vs. 87%) was not statistically significant, though more patients in the post-protocol group met probable diagnostic criteria (51% vs. 77%). Duration of therapy was not significantly different (6 days [IQR = 5.0, 10.0] vs. 7 days [IQR = 6.0, 9.0]). Conclusions: The implementation of a diagnostic and treatment protocol for management of nosocomial pneumonias in the CVS-ICU resulted in improved diagnostic accuracy, advanced antimicrobial and diagnostic stewardship efforts, and laboratory cost savings without an adverse impact on patient-centered outcomes. [ABSTRACT FROM AUTHOR]

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Background: Social Frailty is a significant public health concern affecting the elderly, particularly with the global population aging rapidly. Older adults with social frailty are at significantly higher risk of adverse outcomes such as disability, cognitive impairment, depression, and even death. In recent years, there have been more and more studies on social frailty, but no bibliometrics has been used to analyze and understand the general situation in this field. Therefore, by using CiteSpace, VOSviewer, and Bilioshiny software programs, this study aims to analyze the general situation of the research on social frailties of the older adults and determine the research trends and hot spots. Methods: A bibliometric analysis was conducted by searching relevant literature on the social frailty of the older adults from 2003 to 2022 in the Web of Science core database, using visualization software to map publication volume, country and author cooperation networks, keyword co-occurrences, and word emergence. Results: We analyzed 415 articles from 2003 to 2022. Brazil has the highest number of articles in the field of social frailty of the older adults, and the United States has the highest number of cooperative publications. Andrew MK, from Canada, is the most published and co-cited author, with primary research interests in geriatric assessment, epidemiology, and public health. "Social Vulnerability," "Health," "Frailty," "Mortality," and "Older Adult" are among the research hotspots in this field. "Dementia," "Alzheimer's disease," "Population," and "Covid-19" are emerging research trends in social frailty among the older adults. Conclusion: This scientometric study maps the research hotspots and trends for the past 20 years in social frailty among the older adults. Our findings will enable researchers to better understand trends in this field and find suitable directions and partners for future research. [ABSTRACT FROM AUTHOR]

Growing global concern over greenhouse gas emissions has led to a demand for understanding and addressing carbon emissions, with China being one of the main contributors to global carbon emissions, committed to reach the carbon peak by 2030. As a result, much previous research has delved into the drivers of carbon emissions in China; however, few studies have included new energy factors in the extended STIRPAT model when analysing the data, employed more advanced visualisation techniques such as force-directed diagrams, and explored factors outside of the industrial and energy sectors in determining China's ability to reach their environmental goals. In this study, we use the extended STIRPAT model to analyse a more diverse range of drivers for carbon emissions in China and discuss methods to reach peak carbon emissions through the implementation of environmental policies. Using data from China's 14th Five-Year Plan and Vision 2035 to set up two simulation scenarios, we predict China's carbon emissions, introducing ridge regression to ensure validity, and employing big data and visualisation techniques to aid in interpreting results. Our findings suggest that China needs to implement more stringent environmental policies to meet its commitment to reach peak carbon emissions by 2030, revealing that factors such as per capita arable land area, per capita GDP, the proportion of people living in extreme poverty, the level of tourism development, the use of fossil fuels, and new energy technologies have a significant impact on China's carbon emissions. As such, we can recommend more stringent policies relating to the agricultural, energy, and tourism sectors to help China achieve their goal of carbon peak by 2030. [ABSTRACT FROM AUTHOR]

Distant viewing approaches have typically used image datasets close to the contemporary image data used to train machine learning models. To work with images from other historical periods requires expert annotated data, and the quality of labels is crucial for the quality of results. Especially when working with cultural heritage collections that contain myriad uncertainties, annotating data, or re-annotating, legacy data is an arduous task. In this paper, we describe working with two pre-annotated sets of medieval manuscript images that exhibit conflicting and overlapping metadata. Since a manual reconciliation of the two legacy ontologies would be very expensive, we aim (1) to create a more uniform set of descriptive labels to serve as a "bridge" in the combined dataset, and (2) to establish a high-quality hierarchical classification that can be used as a valuable input for subsequent supervised machine learning. To achieve these goals, we developed visualization and interaction mechanisms, enabling medievalists to combine, regularize and extend the vocabulary used to describe these, and other cognate, image datasets. The visual interfaces provide experts an overview of relationships in the data going beyond the sum total of the metadata. Word and image embeddings as well as co-occurrences of labels across the datasets enable batch re-annotation of images, recommendation of label candidates, and support composing a hierarchical classification of labels. [ABSTRACT FROM AUTHOR]

User evaluation is a common and useful tool for systematically generating knowledge and validating novel approaches in the domain of Immersive Analytics. Since this research domain centres around users, user evaluation is of extraordinary relevance. Additionally, Immersive Analytics is an interdisciplinary field of research where different communities bring in their own methodologies. It is vital to investigate and synchronise these different approaches with the long‐term goal to reach a shared evaluation framework. While there have been several studies focusing on Immersive Analytics as a whole or on certain aspects of the domain, this is the first systematic review of the state of evaluation methodology in Immersive Analytics. The main objective of this systematic literature review is to illustrate methodologies and research areas that are still underrepresented in user studies by identifying current practice in user evaluation in the domain of Immersive Analytics in coherence with the PRISMA protocol. (see https://www.acm.org/publications/class-2012) [ABSTRACT FROM AUTHOR]

Urbanization has amplified the importance of three‐dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine‐grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions. [ABSTRACT FROM AUTHOR]

The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the ai's lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness of ai‐guided va tools. [ABSTRACT FROM AUTHOR]

Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics. [ABSTRACT FROM AUTHOR]

We present CUPID: a visualization method for the contextual understanding of prompt‐conditioned image distributions. CUPID targets the visual analysis of distributions produced by modern text‐to‐image generative models, wherein a user can specify a scene via natural language, and the model generates a set of images, each intended to satisfy the user's description. CUPID is designed to help understand the resulting distribution, using contextual cues to facilitate analysis: objects mentioned in the prompt, novel, synthesized objects not explicitly mentioned, and their potential relationships. Central to CUPID is a novel method for visualizing high‐dimensional distributions, wherein contextualized embeddings of objects, those found within images, are mapped to a low‐dimensional space via density‐based embeddings. We show how such embeddings allows one to discover salient styles of objects within a distribution, as well as identify anomalous, or rare, object styles. Moreover, we introduce conditional density embeddings, whereby conditioning on a given object allows one to compare object dependencies within the distribution. We employ CUPID for analyzing image distributions produced by large‐scale diffusion models, where our experimental results offer insights on language misunderstanding from such models and biases in object composition, while also providing an interface for discovery of typical, or rare, synthesized scenes. [ABSTRACT FROM AUTHOR]

Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real‐world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future. [ABSTRACT FROM AUTHOR]

Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Analyzing 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. [ABSTRACT FROM AUTHOR]

We present concept‐aligned neurons, or CAN, a visualization design for comparing deep neural networks. The goal of CAN is to support users in understanding the similarities and differences between neural networks, with an emphasis on comparing neuron functionality across different models. To make this comparison intuitive, CAN uses concept‐based representations of neurons to visually align models in an interpretable manner. A key feature of CAN is the hierarchical organization of concepts, which permits users to relate sets of neurons at different levels of detail. CAN's visualization is designed to help compare the semantic coverage of neurons, as well as assess the distinctiveness, redundancy, and multi‐semantic alignment of neurons or groups of neurons, all at different concept granularity. We demonstrate the generality and effectiveness of CAN by comparing models trained on different datasets, neural networks with different architectures, and models trained for different objectives, e.g. adversarial robustness, and robustness to out‐of‐distribution data. [ABSTRACT FROM AUTHOR]

Hybrid graph representations combine two or more network visualization techniques in a unique drawing, simultaneously leveraging their strong traits. Since their introduction in the early 2000s, hybrid representations have gained significant research interest, with the introduction of new techniques and comparative user studies. However, all this research has not considered dynamic graphs. In this paper, we investigate hybrid graph representations in a dynamic network context and present DynTrix. Our system uses the NodeTrix representation as a basis, but the research extends this representation to the dynamic network domain. DynTrix supports automatic or manually created clusters/matrices across time. Drawing stability is implemented through aggregation and users can rearrange the nodes/matrix positions and pin them. DynTrix visualizes the temporal dynamics of the network through a combination of movement and element highlighting. We also introduce the concept of volatility, that allows the identification of actors in the network that are the most volatile. Matrices can be ordered such that stable cores gravitate towards the centre of the matrix. We integrate this technique in a visual analytics application for the exploration of offline dynamic networks and evaluate our system through case studies and qualitative expert interviews. Experts agree on the capabilities of the system, noting its potential for the analysis of dynamic networks through hybrid representations. [ABSTRACT FROM AUTHOR]

Universities aim at increasing student retention, with evidence‐based and data‐based governance of universities becoming best practice. In this context, managing large amounts of data in university management is a challenge. Sankey diagram visualizations of student flows per time unit (e.g. semesters) have been used as method for structuring but lacked the ability to go beyond program level. This study aims to advance this method by displaying multiple student lifecycles from school to doctorate, while including user‐based input. Data of 83,264 students enrolled into 140,593 programs between 2012/13 and 2022/23 with 657.615 distinct rows in the raw data were used. A self‐synchronizing web interface was provided, including filter and stratification variables. The diagrams could be mapped to students' lifecycles and extended over all degree levels and fields of study. Limitations were data availability and the lack of indicators for each stage of the student lifecycle. Improvements in the mapping are warranted. [ABSTRACT FROM AUTHOR]

Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitive domains such as healthcare. Visual analytics (VA) combined with DL methods have been widely used to discover data insights, but they often encounter visual clutter (VC) issues. This study presents a compact neural network (NN) view design to reduce the visual clutter in explaining the DL model components for domain experts and end users. We utilized clustering algorithms to group hidden neurons based on their activation similarities. This design supports the overall and detailed view of the neuron clusters. We used a tabular healthcare dataset as a case study. The design for clustered results reduced visual clutter among neuron representations by 54% and connections by 88.7% and helped to observe similar neuron activations learned during the training process. [ABSTRACT FROM AUTHOR]

Visual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well‐designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow‐based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data‐driven modeling capabilities of a flow‐based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real‐time interaction efficiency. [ABSTRACT FROM AUTHOR]

In the field of visualization, understanding users' analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users' reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users' analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users' analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying 'inconclusive' decisions is 83%. Overall, the proposed approach improves the understanding of users' analytical decisions and provides a robust method for evaluating user interactions in visualization tools. [ABSTRACT FROM AUTHOR]

Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the "interestingness" of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset. [ABSTRACT FROM AUTHOR]

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system. [ABSTRACT FROM AUTHOR]

Ranking schemes drive many real-world decisions, like, where to study, whom to hire, what to buy, etc. Many of these decisions often come with high consequences. For example, a university can be deemed less prestigious if not featured in a top-k list, and consumers might not even explore products that do not get recommended to buyers. At the heart of most of these decisions are opaque ranking schemes, which dictate the ordering of data entities, but their internal logic is inaccessible or proprietary. Drawing inferences about the ranking differences is like a guessing game to the stakeholders, like, the rankees (i.e., the entities who are ranked, like product companies) and the decision-makers (i.e., who use the rankings, like buyers). In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods. To realize this aim, we leverage the exploration–explanation paradigm of human–data interaction to let human stakeholders explore subsets and groupings of complex multi-attribute ranking data using visual explanations of model fit and attribute influence on rankings. We realize this explanation paradigm for transparent ranking interpretation in TRIVEA, a visual analytic system that is fueled by: (i) visualizations of model fit derived from algorithmic rankers that learn the associations between attributes and rankings from available data and (ii) visual explanations derived from XAI methods that help abstract important patterns, like, the relative influence of attributes in different ranking ranges. Using TRIVEA, end users not trained in data science have the agency to transparently reason about the global and local behavior of the rankings without the need to open black-box ranking models and develop confidence in the resulting attribute-based inferences. We demonstrate the efficacy of TRIVEA using multiple usage scenarios and subjective feedback from researchers with diverse domain expertise. [ABSTRACT FROM AUTHOR]

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In this study, we examine the design process for a visual learning analytics (VLA) approach in addition to its effect on developing students' epistemic understanding of discourse and relationships to promote knowledge‐building inquiry. Over a 3‐month period, an experimental class of secondary school students took a visual arts course using a knowledge‐building design augmented by VLA, while a comparison class took the same course within a regular knowledge‐building environment. The VLA approach implemented in the experimental class emphasized collective reflections using assessment tools to visualize the students' online and classroom discussions. The analysis of the students' questionnaire responses and Knowledge Forum discourse showed that the VLA approach helped the students develop a deeper epistemic understanding of discourse for productive inquiry and greater domain knowledge. In addition, the students' epistemic understanding was associated with their performance in productive Knowledge Forum inquiry. Qualitative analysis of the classroom and group discussions, interview responses and learning artefacts showed that the VLA design helped them understand the nature of discourse deeply by encouraging them to notice community orientations, increase their awareness of productive discourse moves and advance idea development. The implications of the VLA design in improving epistemic understanding of discourse are discussed. We contribute to the literature by examining the epistemic dimensions of discourse and implementing VLA as an epistemic tool for knowledge building. Practitioner notesWhat is already known about this topic Supporting students to develop a meta‐level understanding, ie, their epistemic understanding of the nature of discourse, is important for promoting productive inquiry but continues to be challenging in education, and limited research has focused on the epistemic aspects of discourse.Little research has examined the role of a computer‐supported knowledge‐building environment augmented by a visual learning analytics approach on students' epistemic understanding of discourse, productive inquiry and domain knowledge.What this paper adds A knowledge‐building design augmented by a visual learning analytics approach linking online and face‐to‐face discourse supported students' epistemic understanding of discourse, productive inquiry and domain knowledge.VLA‐supported collective reflection in knowledge‐building facilitated students' noticing of community orientations, awareness of productive discourse moves and their idea development. These metacognitive processes helped students develop deeper epistemic understanding of discourse, productive inquiry and greater domain knowledge.Implications for practice and/or policy Epistemic understanding of the nature of discourse acts as a medium for promoting productive knowledge‐building inquiry and learning outcomes, and it is critical to help students develop meta‐level understanding by combining discourse content and epistemic components.It is important to create a VLA‐supported community that provides VLA‐related data‐supported collective reflection opportunities to help students engage in productive inquiry and knowledge building.Teachers can scaffold students to link online and face‐to‐face discourse and help them inquire into and reflect on discourse processes. [ABSTRACT FROM AUTHOR]

Data input within mixed reality environments poses significant interaction challenges, notably in immersive visual analytics applications. This study assesses five numerical input techniques: three benchmark methods (Touch-Slider, Keyboard, Pinch-Slider) and two innovative multimodal techniques (Bimanual Scaling, Gesture and Voice). An experimental design was employed to compare these techniques' input efficiency, accuracy, and user experience across varying precision and distance conditions. The findings reveal that multimodal techniques surpass slider methods in input efficiency yet are comparable to keyboards; the voice method excels in reducing cognitive load but falls short in accuracy; and the scaling method marginally leads in user satisfaction but imposes a higher physical load. Furthermore, this study outlines these techniques' pros and cons and offers design guidelines and future research directions. [ABSTRACT FROM AUTHOR]

Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness. [ABSTRACT FROM AUTHOR]

Synchytrium endobioticum (Schilb.) Perc. is a chytrid fungus causing potato wart disease and is one of the most important quarantine diseases on cultivated potato. Infected host tissues develop warts rendering the crop unmarketable. Resting spores, that can remain viable and infectious for decades, are formed in warted tissues and are released into the surrounding soil when host tissue decays. To better understand the pathogen's diversity and to potentially uncover pathways of migrations and introduction events, molecular characterization was performed on the historical S. endobioticum resting spore collection of the Dutch National Plant Protection Organization. Mitochondrial genomes were assembled and annotated, and four novel structural variants were identified from these materials with intronic presence-absence variation in cox1 or cob genes and structural variation in the dpoB – TIR region. Several fungal isolates were shown to contain mixtures of structural variants. We analyzed the mitogenomic sequences obtained from recent potato wart disease findings in Canada and the Netherlands in the context of the historical materials and found that fungal isolates from the new Dutch outbreak contained a specific mixture of mitogenomic variants previously not observed in the Netherlands. Based on the mitogenomic profile, pathotype 38(Nevşehir) was suspected which was later verified with the Spieckermann bioassay. To further facilitate dissemination of data and interactive visual analytics we created a public Nextstrain webpage with S. endobioticum mitogenomic sequences and associated metadata on their geographic origin, pathotype identity and (mixture) of mitogenomic variants (https://nextstrain.nrcnvwa.nl/Sendo). [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are increasingly recognized for their potential to improve efficiency and decision-making in various domains of the built environment. Despite their promise, challenges like cost, complexity, interoperability, and data integration remain. This paper introduces a novel interactive visual analytics system that tackles these issues, using a case study of simulating class distribution and campus building capacity at a large public university. The system leverages enrollment data, converting it into a spatial-temporal format for interactive exploration and analysis of class distribution and resource utilization. Through case studies, we demonstrate the system's effectiveness, adaptability, and real-world applicability, highlighting its role in practical DT implementation for built environments. [ABSTRACT FROM AUTHOR]

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities. [ABSTRACT FROM AUTHOR]

Spatio-temporal evolution analysis has been a critical topic of air pollution research. However, there are still several difficulties caused by the large scale and dimensionality of the data. Specifically, First, traditional methods deal with such data by simplifying and abstracting, resulting in information loss. Second, most existing visualizations, generally focusing on overall evolution, ignore the exploration of multiple time scales and pattern transitions between subsequences. This paper presents AirPollutionViz, a visual analytics system that enables to analyze the spatio-temporal evolution in two manners: sequence mining and clustering analysis. Concretely, we propose sequence merging to shorten the sequence length and construct a weighted directed graph structure, which promotes efficient querying of sequence patterns by combination with dynamic time warping. We design a novel summary view to display the overview of pollution level changes, together with the improved node-link chart, to support the analysis of air pollution spatio-temporal evolution patterns. We also apply K-means clustering to pollutants, and a scatter plot and map reflect the spatial distribution aggregation. The system supports users' free exploration across multiple time scales with rich interactions. Case studies with three domain experts and a user study with ten users demonstrate the usefulness and effectiveness of AirPollutionViz. [ABSTRACT FROM AUTHOR]