Abstract Visualization in historical research is shifting from isolated attempts to systematic practices. However, data‐driven evidence about how historians actually use visualization remains scarce. We present a corpus‐driven, mixed‐methods study that combines analysis of images from 4,142 research articles across history and digital humanities journals with a collaboratively developed visualization taxonomy and a semi‐automatic labeling pipeline. We construct a corpus of 14,021 images, classify 4,831 visualization instances using a hierarchical, domain‐informed taxonomy, and analyze patterns of visualization adoption across venues, history subfields, and time. To interpret these patterns, we conduct interviews with 11 historians and use Hi‐FigAtlas system as a boundary object to support joint inspection of the corpus. We identify distinct roles for visualizations in historical research: primary‐source, evidence‐synthesis, communicative, confirmative, and exploratory. We further find that while historians pursue diverse goals with figures, persistent epistemological and practical barriers, such as uncertainty, provenance, justification burden, and publication constraints, impede the adoption of visualization. This work contributes a grounded account of visualization use in historical scholarship and points to opportunities to better support domain‐specific needs.

Chinese calligraphy is a quintessential element of Chinese cultural heritage. Analyzing and comparing calligraphic styles not only enhances the appreciation, learning, and advancement of calligraphy but also provides valuable insights into ancient China. However, such analysis remains challenging due to the limited scalability and possible inconsistencies of qualitative methods, as well as usability and misalignment issues in conventional quantitative approaches. We propose Calli-VA, a visual analytics system, to address these challenges. Calli-VA extracts character images and their corresponding strokes from original works and characterizes each character using systematic criteria. During analysis, the system defines the analysis scope by overview and uncovers relationships between characters. Explanation and recommendation mechanisms are integrated to help users understand patterns and guide further exploration. A documentation feature allows users to record and share their findings. We demonstrate the effectiveness of Calli-VA through three case studies and expert feedback.

The course of history is inseparable from the actions of historical figures. From the perspective of contemporaneity, an important challenge is how to visualize large numbers of historical figures and their dynamic associations on a unified temporal scale. Yet existing large-scale data solutions often rely on aggregation strategies that diminish the visibility of key figures, while failing to provide smooth contextual transitions between overview and detail. Moreover, current approaches to representing inter-figure associations lack effective integration of the surrounding historical context. To address these challenges, we propose TimeScape, a large-scale biographical data exploration system supporting multi-resolution analysis. Anchored in absolute time, the system highlights the evolving dynamics of historical development. Through multi-level layouts, sampling, and semantic zooming, it enables efficient navigation of large-scale data, allowing users to move seamlessly between overview and detail and to "wander" freely across the historical landscape. The system further allows iterative switching of focal figures to explore concrete inter-personal associations, aligning them on the temporal axis so that these connections are grounded in traceable evidence and explicit context. Case studies and expert interviews demonstrate that TimeScape effectively supports multi-perspective understanding and exploration of large-scale biographical data, offering historians a new visualization pathway for research.

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions.

Biological networks describe complex relationships in biological systems, which represent biological entities as vertices and their underlying connectivity as edges. Ideally, for a complete analysis of such systems, domain experts need to visually integrate multiple sources of heterogeneous data , and visually, as well as numerically, probe said data in order to explore or validate (mechanistic) hypotheses. Such visual analyses require the coming together of biological domain experts, bioinformaticians, as well as network scientists to create useful visualization tools. Owing to the underlying graph data becoming ever larger and more complex, the visual representation of such biological networks has become challenging in its own right. This introduction and survey aims to describe the current state of biological network visualization in order to identify scientific gaps for visualization experts, network scientists, bioinformaticians, and domain experts, such as biologists, or biochemists, alike. Specifically, we revisit the classic visualization pipeline, upon which we base this paper’s taxonomy and structure, which in turn forms the basis of our literature classification. This pipeline describes the process of visualizing data, starting with the raw data itself, through the construction of data tables, to the actual creation of visual structures and views, as a function of task-driven user interaction. Literature was systematically surveyed using API-driven querying where possible, and the collected papers were manually read and categorized based on the identified sub-components of this visualization pipeline’s individual steps. From this survey, we highlight a number of exemplary visualization tools from multiple biological sub-domains in order to explore how they adapt these discussed techniques and why. Additionally, this taxonomic classification of the collected set of papers allows us to identify existing gaps in biological network visualization practices. We finally conclude this report with a list of open challenges and potential research directions. Examples of such gaps include (i) the overabundance of visualization tools using schematic or straight-line node-link diagrams, despite the availability of powerful alternatives, or (ii) the lack of visualization tools that also integrate more advanced network analysis techniques beyond basic graph descriptive statistics.

Empathic medical conversation is central to patient-centered care within Narrative Medicine. However, difficulties, such as physicians’ limited empathic capabilities and lack of time, impede the practice. Research on real-time, on-site empathic medical exchanges has been limited in exploring technology to assist and enhance physicians’ capabilities. This paper proposed the Empathic Opportunity Perception and Distinction (EOPD) framework for building physician-AI collaboration based on Intelligence Augmentation (IA) for empathic conversations. The EOPD integrates two multi-modal machine learning (ML) models based on facial and verbal cues, presenting a physician-AI interaction framework and three distinctive visualization components: emotional reference, opportunity reminding and keyword collection, and situation understanding. To assess EOPD's effectiveness and gauge physicians’ and patients’ receptiveness, a prototype system named EMVIS ( EM otional VIS ualization ) was designed and developed. Results from the study demonstrated improvements in physicians’ empathy efforts and perceived empathy performance when using EMVIS, particularly for junior physicians. Physicians and patients held positive attitudes towards EMVIS, with patients expressing a high expectation that EMVIS would improve the physician-patient relationship. The research showed the efficacy of the multi-modal ML models in supporting complex affective empathy and EMVIS in facilitating and complementing empathy concerns. It highlighted the tailored support to junior and senior physicians and emphasized physician-AI collaboration to maintain user autonomy and mitigate potential biases. Future research should explore extensive system applications, tailor visual and interactive support for physicians, and implement adaptive and reflective ML models to improve the effectiveness and efficiency of empathy communications.

The deep neural networks combined with the statistical shape model have enabled efficient deformable 2D-3D registration and recovery of 3D anatomical structures from a single radiograph. However, the recovered volumetric image tends to lack the volumetric fidelity of fine-grained anatomical structures and explicit consideration of cross-dimensional semantic correspondence. In this paper, we introduce a simple but effective solution for semantically-consistent deformable 2D-3D registration and detailed volumetric image recovery by inferring a voxel-wise registration field between the cone-beam computed tomography and a single lateral cephalometric radiograph (LC). The key idea is to refine the initial statistical model-based registration field with craniofacial structural details and semantic consistency from the LC. Specifically, our framework employs a self-supervised scheme to learn a voxel-level refiner of registration fields to provide fine-grained craniofacial structural details and volumetric fidelity. We also present a weakly supervised semantic consistency measure for semantic correspondence, relieving the requirements of volumetric image collections and annotations. Experiments showcase that our method achieves deformable 2D-3D registration with performance gains over state-of-the-art registration and radiograph-based volumetric reconstruction methods. The source code is available at https://github.com/Jyk-122/SC-DREG.

Generative text-to-image models, which allow users to create appealing images through a text prompt, have seen a dramatic increase in popularity in recent years. However, most users have a limited understanding of how such models work and often rely on trial and error strategies to achieve satisfactory results. The prompt history contains a wealth of information that could provide users with insights into what has been explored and how the prompt changes impact the output image, yet little research attention has been paid to the visual analysis of such process to support users. We propose the Image Variant Graph, a novel visual representation designed to support comparing prompt-image pairs and exploring the editing history. The Image Variant Graph models prompt differences as edges between corresponding images and presents the distances between images through projection. Based on the graph, we developed the PrompTHis system through co-design with artists. Based on the review and analysis of the prompting history, users can better understand the impact of prompt changes and have a more effective control of image generation. A quantitative user study and qualitative interviews demonstrate that PrompTHis can help users review the prompt history, make sense of the model, and plan their creative process.

Classical bibliography, by researching preserved catalogs from both official archives and personal collections of accumulated books, examines the books throughout history, thereby revealing cultural development across historical periods. In this work, we collaborate with domain experts to accomplish the task of data annotation concerning Chinese ancient catalogs. We introduce the CataAnno system that facilitates users in completing annotations more efficiently through cross-linked views, recommendation methods and convenient annotation interactions. The recommendation method can learn the background knowledge and annotation patterns that experts subconsciously integrate into the data during prior annotation processes. CataAnno searches for the most relevant examples previously annotated and recommends to the user. Meanwhile, the cross-linked views assist users in comprehending the correlations between entries and offer explanations for these recommendations. Evaluation and expert feedback confirm that the CataAnno system, by offering high-quality recommendations and visualizing the relationships between entries, can mitigate the necessity for specialized knowledge during the annotation process. This results in enhanced accuracy and consistency in annotations, thereby enhancing the overall efficiency.

Historical visualizations are a rich resource for visualization research. While taxonomy is commonly used to structure and understand the design space of visualizations, existing taxonomies primarily focus on contemporary visualizations and largely overlook historical visualizations. To address this gap, we describe an empirical method for taxonomy development. We introduce a coding protocol and the VisTaxa system for taxonomy labeling and comparison. We demonstrate using our method to develop a historical visualization taxonomy by coding 400 images of historical visualizations. We analyze the coding result and reflect on the coding process. Our work is an initial step toward a systematic investigation of the design space of historical visualizations.

Animated hand-drawing sketches are a common way to communicate concepts and information. Sketches are also used to query charts, interact with visualizations, or express rough designs. However, there is little work investigating how people manually create visualization sketches and whether animated sketches can help users understand charts. We first conduct a user study that collects the sketching processes of people with visualization knowledge and then summarize the patterns in the sketch order. Based on the sketch patterns, we conduct a between-subject study to evaluate whether animated sketches can improve users’ performance of visualization tasks. We discuss the results of the user study and future work on evaluating the effectiveness of animated sketches.

Railway transportation is closely linked to everyday lives while also aiding domain experts in analyzing national or regional development. However, discrepancies between travel time reduced by railways and actual distance pose challenges in visualizing the accessibility. Existing methods either struggle to simultaneously depict the accessibility relationships among all cities or disregard genuine geographical positions, leading to spatial cognitive confusion. In this work, we propose a novel approach to balance between the geographical positions and travel time between cities. We construct linear cartograms to represent accessibility while preserving better local railway network structures compared with existing methods. We further propose the A-Map system, enabling users to interactively explore the extensive development of China’s railway system over decades. We validate the effectiveness of our proposed layout algorithm from qualitative metric evaluation and illustrate the applicability of our system with results.

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

Books act as a crucial carrier of cultural dissemination in ancient times. This work involves joint efforts between visualization and humanities researchers, aiming at building a holistic view of the cultural exchange and integration between China and Japan brought about by the overseas circulation of Chinese classics. Book circulation data consist of uncertain spatiotemporal trajectories, with multiple dimensions, and movement across hierarchical spaces forms a compound network. LiberRoad visualizes the circulation of books collected in the Imperial Household Agency of Japan, and can be generalized to other book movement data. The LiberRoad system enables a smooth transition between three views (Location Graph, map, and timeline) according to the desired perspectives (spatial or temporal), as well as flexible filtering and selection. The Location Graph is a novel uncertainty-aware visualization method that employs improved circle packing to represent spatial hierarchy. The map view intuitively shows the overall circulation by clustering and allows zooming into single book trajectory with lenses magnifying local movements. The timeline view ranks dynamically in response to user interaction to facilitate the discovery of temporal events. The evaluation and feedback from the expert users demonstrate that LiberRoad is helpful in revealing movement patterns and comparing circulation characteristics of different times and spaces.

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.

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.

We propose AutoTitle, an interactive visualization title generator satisfying multifarious user requirements. Factors making a good title, namely, the feature importance, coverage, preciseness, general information richness, conciseness, and non-technicality, are summarized based on the feedback from user interviews. Visualization authors need to trade off among these factors to fit specific scenarios, resulting in a wide design space of visualization titles. AutoTitle generates various titles through the process of visualization facts traversing, deep learning-based fact-to-title generation, and quantitative evaluation of the six factors. AutoTitle also provides users with an interactive interface to explore the desired titles by filtering the metrics. We conduct a user study to validate the quality of generated titles as well as the rationality and helpfulness of these metrics.

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset.

We propose Edit-History Vis, a visual analytics system designed to facilitate interactive exploration on Wikipedia edit history at a fine-grained level. The examination of detailed changes in Wikipedia articles is crucial for understanding how authors’ perspectives vary and conflict during the collaborative editing process. However, it is challenging to reveal the details while preserving the heterogeneous attributes of revisions, namely the time, content, and editor. The Edit-History Vis system integrates editor and textual changes of revisions by utilizing a force-directed revision graph that groups revisions based on standpoints. Through this revision graph, users can identify and analyze editing events such as edit wars, vandalism, repair, and normal updates. The effectiveness of the system in analyzing the edit history is validated through a qualitative comparison with prior work and a quantitative rating from a user study.

Visual analytic systems usually provide multiple coordinated views (MCVs) to support data analysis and exploration. Coordination in visual graphics plays an important role in facilitating comprehensive analytical tasks, such as data comparison and cognitive inference. However, individual views in MCVs are probably designed for a specific purpose based on a particular type of data, and insufficient consideration of the intricate relationships among views may lead to inconsistency in visual representation and user interaction across different views. To better understand the inconsistency issues in MCVs and their impacts on user behaviors, this paper reports a study on the analysis and classification of visualization inconsistency based on the reviews of interactive visualization designs and visual analytic systems, and the interviews with stakeholders. We find that inconsistencies are prevalent in MCVs and frequently lead to misleading or even incorrect results. We classify the discovered inconsistencies based on a coordination model of MCVs, and develop an empirical guide for systematic and efficient visualization consistency checking in the design, implementation, and evaluation stage.

Declarative grammar is becoming an increasingly important technique for understanding visualization design spaces. The GoTreeScape system presented in the paper allows users to navigate and explore the vast design space implied by GoTree, a declarative grammar for visualizing tree structures. To provide an overview of the design space, GoTreeScape, which is based on an encoder-decoder architecture, projects the tree visualizations onto a 2D landscape. Significantly, this landscape takes the relationships between different design features into account. GoTreeScape also includes an exploratory framework that allows top-down, bottom-up, and hybrid modes of exploration to support the inherently undirected nature of exploratory searches. Two case studies demonstrate the diversity with which GoTreeScape expands the universe of designed tree visualizations for users. The source code associated with GoTreeScape is available at https://github.com/bitvis2021/gotreescape.

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.

Ensemble simulation is a crucial method to handle potential uncertainty in modern simulation and has been widely applied in many disciplines. Many ensemble contour visualization methods have been introduced to facilitate ensemble data analysis. On the basis of deep exploration and summarization of existing techniques and domain requirements, we propose a unified framework of ensemble contour visualization, EnConVis (Ensemble Contour Visualization), which systematically combines state-of-the-art methods. We model ensemble contour visualization as a four-step pipeline consisting of four essential procedures: member filtering, point-wise modeling, uncertainty band extraction, and visual mapping. For each of the four essential procedures, we compare different methods they use, analyze their pros and cons, highlight research gaps, and attempt to fill them. Specifically, we add Kernel Density Estimation in the point-wise modeling procedure and multi-layer extraction in the uncertainty band extraction procedure. This step shows the ensemble data's details accurately and provides abstract levels. We also analyze existing methods from a global perspective. We investigate their mechanisms and compare their effects, on the basis of which, we offer selection guidelines for them. From the overall perspective of this framework, we find choices and combinations that have not been tried before, which can be well compensated by our method. Synthetic data and real-world data are leveraged to verify the efficacy of our method. Domain experts' feedback suggests that our approach helps them better understand ensemble data analysis.

We propose UNICON, a UNIform CONstraint based graph layout framework that supports both soft and hard constraints. We extend the stress model to accommodate soft constraints by incorporating them in the objective functions, optimized by stochastic gradient descent. For hard constraints, such as inequalities or equalities in the layout space, we utilize a gradient projection method to satisfy them. A visualization prototype system is implemented based on this framework for the user to interactively add or remove constraints to generate the desired layouts. We demonstrate the efficiency, quality, and flexibility of the framework and the system on a number of datasets with a wide range of user-defined constraints.

Abstract Danmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.

Biases inevitably occur in numerical weather prediction (NWP) due to an idealized numerical assumption for modeling chaotic atmospheric systems. Therefore, the rapid and accurate identification and calibration of biases is crucial for NWP in weather forecasting. Conventional approaches, such as various analog post-processing forecast methods, have been designed to aid in bias calibration. However, these approaches fail to consider the spatiotemporal correlations of forecast bias, which can considerably affect calibration efficacy. In this article, we propose a novel bias pattern extraction approach based on forecasting-observation probability density by merging historical forecasting and observation datasets. Given a spatiotemporal scope, our approach extracts and fuses bias patterns and automatically divides regions with similar bias patterns. Termed BicaVis, our spatiotemporal bias pattern visual analytics system is proposed to assist experts in drafting calibration curves on the basis of these bias patterns. To verify the effectiveness of our approach, we conduct two case studies with real-world reanalysis datasets. The feedback collected from domain experts confirms the efficacy of our approach.

This February 2022 issue of theIEEE Transactions on Visualization and Computer Graphics (TVCG)contains the proceedings of IEEE VIS 2021, held online on October 24-29, 2021, with General Chairs from Tulane University and Universidade de Sao Paulo. With IEEE VIS 2021, the conference series is in its 32nd year.

We propose an automatic pipeline to generate visualization with annotations to answer natural-language questions raised by the public on tabular data. With a pre-trained language representation model, the input natural language questions and table headers are first encoded into vectors. According to these vectors, a multi-task end-to-end deep neural network extracts related data areas and corresponding aggregation type. We present the result with carefully designed visualization and annotations for different attribute types and tasks. We conducted a comparison experiment with state-of-the-art works and the best commercial tools. The results show that our method outperforms those works with higher accuracy and more effective visualization.

In various domains, there are abundant streams or sequences of multi-item data of various kinds, e.g. streams of news and social media texts, sequences of genes and sports events, etc. Comparison is an important and general task in data analysis. For comparing data streams involving multiple items (e.g., words in texts, actors or action types in action sequences, visited places in itineraries, etc.), we propose Co-Bridges, a visual design involving connection and comparison techniques that reveal similarities and differences between two streams. Co-Bridges use river and bridge metaphors, where two sides of a river represent data streams, and bridges connect temporally or sequentially aligned segments of streams. Commonalities and differences between these segments in terms of involvement of various items are shown on the bridges. Interactive query tools support the selection of particular stream subsets for focused exploration. The visualization supports both qualitative (common and distinct items) and quantitative (stream volume, amount of item involvement) comparisons. We further propose Comparison-of-Comparisons, in which two or more Co-Bridges corresponding to different selections are juxtaposed. We test the applicability of the Co-Bridges in different domains, including social media text streams and sports event sequences. We perform an evaluation of the users' capability to understand and use Co-Bridges. The results confirm that Co-Bridges is effective for supporting pair-wise visual comparisons in a wide range of applications.

As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework.

Creating thematic sessions based on accepted papers is important to the success of a conference. Facing a large number of papers from multiple topics, conference organizers need to identify the topics of papers and group them into sessions by considering the constraints on session numbers and paper numbers in individual sessions. In this paper, we present a system using visualization and topic modeling to help the construction of conference sessions. The system provides multiple automatically generated session schemes and allows users to create, evaluate, and manipulate paper sessions with given constraints. A case study based on our system on the VAST papers shows that our method can help users successfully construct coherent conference sessions. In addition to conference session management, our method can be extended to other tasks, such as event and class schedule.

In this paper, we propose a novel approach to generate captions for visualization charts automatically. In the proposed method, visual marks and visual channels, together with the associated text information in the original charts, are first extracted and identified with a multilayer perceptron classifier. Meanwhile, data information can also be retrieved by parsing visual marks with extracted mapping relationships. Then a 1-D convolutional residual network is employed to analyze the relationship between visual elements, and recognize significant features of the visualization charts, with both data and visual information as input. In the final step, the full description of the visual charts can be generated through a template-based approach. The generated captions can effectively cover the main visual features of the visual charts and support major feature types in commons charts. We further demonstrate the effectiveness of our approach through several cases.

We propose an interactive visual analytical approach to exploring and diagnosing the dynamic load balance (data and task partition) process of parallel particle tracing in flow visualization. To understand the complex nature of the parallel processes, it is necessary to integrate the information of the behaviors and patterns of the computing processes, data changes and movements, task status and exchanges, and gain the insight of the relationships among them. In our proposed approach, the data and task behaviors are visualized through a graph with a fine-designed layout, in which node glyphs are dedicated to showing the status of processes and the links represent the data or task transfer between different computation rounds and processes. User interactions are supported to facilitate the exploration of performance analysis. We provide a case study to demonstrate that the proposed approach enables users to identify the bottlenecks during this process, and thus help optimize the related algorithms.

We present GoTree, a declarative grammar allowing users to instantiate tree visualizations by specifying three aspects: visual elements, layout, and coordinate system. Within the set of all possible tree visualization techniques, we identify a subset of techniques that are both "unit-decomposable" and "axis-decomposable" (terms we define). For tree visualizations within this subset, GoTree gives the user flexible and fine-grained control over the parameters of the techniques, supporting both explicit and implicit tree visualizations. We developed Tree Illustrator, an interactive authoring tool based on GoTree grammar. Tree Illustrator allows users to create a considerable number of tree visualizations, including not only existing techniques but also undiscovered and hybrid visualizations. We demonstrate the expressiveness and generative power of GoTree with a gallery of examples and conduct a qualitative study to validate the usability of Tree Illustrator.

In this paper, we propose a technique for automatically annotating visualizations according to the textual description. In our approach, visual elements in the target visualization, along with their visual properties, are identified and extracted with a Mask R-CNN model. Meanwhile, the description is parsed to generate visual search requests. Based on the identification results and search requests, each descriptive sentence is displayed beside the described focal areas as annotations. Different sentences are presented in various scenes of the generated animation to promote a vivid step-by-step presentation. With a user-customized style, the animation can guide the audience's attention via proper highlighting such as emphasizing specific features or isolating part of the data. We demonstrate the utility and usability of our method through a user study with use cases.

Abstract Despite the significance of tracking human mobility dynamics in a large‐scale earthquake evacuation for an effective first response and disaster relief, the general understanding of evacuation behaviors remains limited. Numerous individual movement trajectories, disaster damages of civil engineering, associated heterogeneous data attributes, as well as complex urban environment all obscure disaster evacuation analysis. Although visualization methods have demonstrated promising performance in emergency evacuation analysis, they cannot effectively identify and deliver the major features like speed or density, as well as the resulting evacuation events like congestion or turn‐back. In this study, we propose a shot design approach to generate customized and narrative animations to track different evacuation features with different exploration purposes of users. Particularly, an intuitive scene feature graph that identifies the most dominating evacuation events is first constructed based on user‐specific regions or their tracking purposes on a certain feature. An optimal camera route, i.e., a storyboard is then calculated based on the previous user‐specific regions or features. For different evacuation events along this route, we employ the corresponding shot design to reveal the underlying feature evolution and its correlation with the environment. Several case studies confirm the efficacy of our system. The feedback from experts and users with different backgrounds suggests that our approach indeed helps them better embrace a comprehensive understanding of the earthquake evacuation.

We propose R-Map (Reposting Map), a visual analytical approach with a map metaphor to support interactive exploration and analysis of the information reposting process in social media. A single original social media post can cause large cascades of repostings (i.e., retweets) on online networks, involving thousands, even millions of people with different opinions. Such reposting behaviors form the reposting tree, in which a node represents a message and a link represents the reposting relation. In R-Map, the reposting tree structure can be spatialized with highlighted key players and tiled nodes. The important reposting behaviors, the following relations and the semantics relations are represented as rivers, routes and bridges, respectively, in a virtual geographical space. R-Map supports a scalable overview of a large number of information repostings with semantics. Additional interactions on the map are provided to support the investigation of temporal patterns and user behaviors in the information diffusion process. We evaluate the usability and effectiveness of our system with two use cases and a formal user study.

We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT's utility.

Interactive visualization and exploration of large spatiotemporal data sets is difficult without carefully-designed data pre-processing and management tools. We propose a novel architecture for spatiotemporal data management. The architecture can dynamically update itself based on user queries. Datasets is stored in a tree-like structure to support memory sharing among cuboids in a logical structure of data cubes. An update mechanism is designed to create or remove cuboids on it, according to the analysis of the user queries, with the consideration of memory size limitation. Data structure is dynamically optimized according to different user queries. During a query process, user queries are recorded to predict the performance increment of the new cuboid. The creation or deletion of a cuboid is determined by performance increment. Experiment results show that our prototype system deliveries good performance towards user queries on different spatiotemporal datasets, which costing small memory size with comparable performance compared with other state-of-the-art algorithms.

In this work, we propose a novel approach of volume visualization without explicit traditional rendering pipeline. In our proposed method, volumetric images can be interactively `reversed' given the volumetric data and a static volume rendered image under the desired rendering effect. Our pipeline enables 3D-navigation on it for exploring the given volumetric data without explicit transfer function. In our approach, deep neural networks, combined usage of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) are employed to synthesize high-resolution and perceptually authentic images directly, inheriting the desired transfer function and viewing parameter implicitly given by the input images respectively.

Information diffusion analysis is important in social media. In this work, we present a coherent ego-centric and event-centric model to investigate diffusion patterns and user behaviors. Applying the model, we propose Diffusion Map+ (D-Maps+), a novel visualization method to support exploration and analysis of user behaviors and diffusion patterns through a map metaphor. For ego-centric analysis, users who participated in reposting (i.e., resending a message initially posted by others) one central user’s posts (i.e., a series of original tweets) are collected. Event-centric analysis focuses on multiple central users discussing a specific event, with all the people participating and reposting messages about it. Social media users are mapped to a hexagonal grid based on their behavior similarities and in the chronological order of repostings. With the additional interactions and linkings, D-Map+ is capable of providing visual profiling of influential users, describing their social behaviors and analyzing the evolution of significant events in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real-world social media data and find interesting patterns among users and events. We also perform evaluations including user studies and expert feedback to certify the capabilities of our method.

In this work, we present a novel access pattern estimation approach for parallel particle tracing in flow field visualization based on deep neural networks. With strong generalization ability, we develop a Long Short-term Memory (LSTM)-based model, which is capable of learning accurate access patterns with only a few training samples and representing the learned patterns with small storage overhead. Equipped with prediction and prefetching functions driven by the developed model, our parallel particle tracing framework employs CPUs and GPUs together for particle tracing tasks. We demonstrate the accuracy and time efficiency of our approach with various flow visualization applications in three different flow datasets.

We present a novel dynamic load-balancing algorithm based on data repartitioning for parallel particle tracing in flow visualization. Instead of static data assignment, we dynamically repartition the data into blocks and reassign the blocks to processes to balance the workload distribution among the processes. Block repartitioning is performed based on a dynamic workload estimation method that predicts the workload in the flow field on the fly as the input. In our approach, we allow data duplication in the repartitioning, enabling the same data blocks to be assigned to multiple processes. Load balance is achieved by regularly exchanging the blocks (together with the particles in the blocks) among processes according to the output of the data repartitioning. Compared with other load-balancing algorithms, our approach does not need any preprocessing on the raw data and does not require any dedicated process for work scheduling, while it has the capability to balance uneven workload efficiently. Results show improved load balance and high efficiency of our method on tracing particles in both steady and unsteady flow.

User behavior analysis is complex and especially crucial in the cyber security domain. Understanding dynamic and multi-variate user behavior are challenging. Traditional sequential and timeline based method cannot easily address the complexity of temporal and relational features of user behaviors. We propose a map-based visual metaphor and create an interactive map for encoding user behaviors. It enables analysts to explore and identify user behavior patterns and helps them to understand why some behaviors are regarded as anomalous. We experiment with a real dataset containing multiple user sessions, consisting of sequences of diverse types of actions. In the behavior map, we encode an action as a city and user sessions as trajectories going through the cities. The position of the cities is determined by the sequential and temporal relationship of actions. Spatial and temporal patterns on the map reflect behavior patterns in the action space. In the case study, we illustrate how we explore relationships between actions, identify patterns of the typical session and detect anomaly behaviors.

EditorsThis January 2018 issue of the IEEE Transactions on Visualization and Computer Graphics contains the proceedings of IEEE VIS 2017, held during 1-6 October 2017. In 2017, IEEE VIS returns to the city of Phoenix, AZ, USA, for the conference's 28th year. The conference will be held at the Hyatt Regency Phoenix hotel. VIS consists of three conferences, held concurrently: the IEEE Visual Analytics Science and Technology Conference (VAST 2017), the IEEE Information Visualization Conference (InfoVis 2017), and the IEEE Scientific Visualization Conference (SciVis 2017). Information on the paper review process is provided along with an overview of each conference.

We propose a dynamically load-balanced algorithm for parallel particle tracing, which periodically attempts to evenly redistribute particles across processes based on k-d tree decomposition. Each process is assigned with (1) a statically partitioned, axis-aligned data block that partially overlaps with neighboring blocks in other processes and (2) a dynamically determined k-d tree leaf node that bounds the active particles for computation; the bounds of the k-d tree nodes are constrained by the geometries of data blocks. Given a certain degree of overlap between blocks, our method can balance the number of particles as much as possible. Compared with other load-balancing algorithms for parallel particle tracing, the proposed method does not require any preanalysis, does not use any heuristics based on flow features, does not make any assumptions about seed distribution, does not move any data blocks during the run, and does not need any master process for work redistribution. Based on a comprehensive performance study up to 8K processes on a Blue Gene/Q system, the proposed algorithm outperforms baseline approaches in both load balance and scalability on various flow visualization and analysis problems.

With the development of cities and the explosion of information, vast amounts of geo-tagged textural data about Points of Interests (POIs) have been generated. Extracting useful information and discovering text spatial distributions from the data are challenging and meaningful. Also, the huge numbers of POIs in modern cities make it important to have efficient approaches to retrieve and choose a destination. This paper provides a visual design combing metro map and wordles to meet the needs. In this visualization, metro lines serve as the divider lines splitting the city into several subareas and the boundaries to constrain wordles within each subarea. The wordles are generated from keywords extracted from the text about POIs (including reviews, descriptions, etc.) and embedded into the subareas based on their geographical locations. By generating intuitive results and providing an interactive visualization to support exploring text distribution patterns, our strategy can guide the users to explore urban spatial characteristics and retrieve a location efficiently. Finally, we implement a visual analysis of the restaurants data in Shanghai, China as a case study to evaluate our strategy.

In this work, we present Interaction+, a tool that enhances the interactive capability of existing web-based visualizations. Different from the toolkits for authoring interactions during the visualization construction, Interaction+ takes existing visualizations as input, analyzes the visual objects, and provides users with a suite of interactions to facilitate the visual exploration, including selection, aggregation, arrangement, comparison, filtering, and annotation. Without accessing the underlying data or process how the visualization is constructed, Interaction+ is application-independent and can be employed in various visualizations on the web. We demonstrate its usage in two scenarios and evaluate its effectiveness with a qualitative user study.

Abstract With the development of social media (e.g. Twitter, Flickr, Foursquare, Sina Weibo, etc.), a large number of people are now using them and post microblogs, messages and multi‐media information. The everyday usage of social media results in big open social media data. The data offer fruitful information and reflect social behaviors of people. There is much visualization and visual analytics research on such data. We collect state‐of‐the‐art research and put it into three main categories: social network, spatial temporal information and text analysis. We further summarize the visual analytics pipeline for the social media, combining the above categories and supporting complex tasks. With these techniques, social media analytics can apply to multiple disciplines. We summarize the applications and public tools to further investigate the challenges and trends.

There are often multiple routes between regions. Drivers choose different routes with different considerations. Such considerations, have always been a point of interest in the transportation area. Studies of route choice behaviour are usually based on small range experiments with a group of volunteers. However, the experiment data is quite limited in its spatial and temporal scale as well as the practical reliability. In this work, we explore the possibility of studying route choice behaviour based on general trajectory dataset, which is more realistic in a wider scale. We develop a visual analytic system to help users handle the large-scale trajectory data, compare different route choices, and explore the underlying reasons. Specifically, the system consists of: 1. the interactive trajectory filtering which supports graphical trajectory query; 2. the spatial visualization which gives an overview of all feasible routes extracted from filtered trajectories; 3. the factor visual analytics which provides the exploration and hypothesis construction of different factors' impact on route choice behaviour, and the verification with an integrated route choice model. Applying to real taxi GPS dataset, we report the system's performance and demonstrate its effectiveness with three cases.

We propose a longest common subsequence (LCSS)-based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble pathlines pass through, the LCSS distance defines the similarity among vector field ensembles by counting the number of shared domain data blocks. Compared with traditional methods (e.g., pointwise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outliers, missing data, and the sampling rate of the pathline timesteps. Taking advantage of smaller and reusable intermediate output, visualization based on the proposed LCSS approach reveals temporal trends in the data at low storage cost and avoids tracing pathlines repeatedly. We evaluate our method on both synthetic data and simulation data, demonstrating the robustness of the proposed approach.

This paper presents a novel visual analysis tool, EnsembleGraph, which aims at helping scientists understand spatiotemporal similarities across runs in time-varying ensemble simulation data. We abstract the input data into a graph, where each node represents a region with similar behaviors across runs and nodes in adjacent time frames are linked if their regions overlap spatially. The visualization of this graph, combined with multiple-linked views showing details, enables users to explore, select, and compare the extracted regions that have similar behaviors. The driving application of this paper is the study of regional emission influences over tropospheric ozone, based on the ensemble simulations conducted with different anthropogenic emission absences using MOZART-4. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations.

We present a novel high-order access dependencies-based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly seeded pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation.

Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews.

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

The papers in this special section were presented at the 2016 IEEE Pacific Visualization Symposium (IEEE PacificVis’16) which was held at the National Taiwan University of Science and Technology, Taipei, Taiwan during April 19 to 22, 2016.

Visualizing dynamic graphs are challenging due to the difficulty to preserving a coherent mental map of the changing graphs. In this paper, we propose a novel layout algorithm which is capable of maintaining the overall structure of a sequence graphs. Through Laplacian constrained distance embedding, our method works online and maintains the aesthetic of individual graphs and the shape similarity between adjacent graphs in the sequence. By preserving the shape of the same graph components across different time steps, our method can effectively help users track and gain insights into the graph changes. Two datasets are tested to demonstrate the effectiveness of our algorithm.

Understanding the Origin-Destination (OD) patterns between different regions of a city is important in urban planning. In this work, based on taxi GPS data, we propose OD-Wheel, a novel visual design and associated analysis tool, to explore OD patterns. Once users define a region, all taxi trips starting from or ending to that region are selected and grouped into OD clusters. With a hybrid circular-linear visual design, OD-Wheel allows users to explore the dynamic patterns of each OD cluster, including the variation of traffic flow volume and traveling time. The proposed tool supports convenient interactions and allows users to compare and correlate the patterns between different OD clusters. A use study with real data sets demonstrates the effectiveness of the proposed OD-Wheel.

In this paper, we propose a novel visual analysis method TrajRank to study the travel behaviour of vehicles along one route. We focus on the spatial-temporal distribution of travel time, i.e., the time spent on each road segment and the travel time variation in rush/non-rush hours. TrajRank first allows users to interactively select a route, and segment it into several road segments. Then trajectories passing this route are automatically extracted. These trajectories are ranked on each road segment according to travel time and further clustered according to the rankings on all road segments. Based on the above ranking analysis, we provide a temporal distribution view showing the temporal distribution of travel time and a ranking diagram view showing the spatial variation of travel time. With real taxi GPS data, we present three use cases and an informal user study to show the effectiveness and usability of our method.

The papers in this special section present extended versions of four selected papers from the 2014 IEEE Pacific Visualization Symposium (PacificVis’14).

In this paper, we present a novel scalable approach for visualizing multivariate unsteady flow data with Lagrangian-based Attribute Space Projection (LASP). The distances between spatial temporal samples are evaluated by their attribute values along the advection directions in the flow field. The massive samples are then projected into 2D screen space for feature identification and selection. A hybrid parallel system, which tightly integrates a MapReduce-style particle tracer with a scalable algorithm for massive projection, is designed to support the large scale analysis. Results show that the proposed methods and system are capable of visualizing features in the unsteady flow, which couples multivariate analysis of vector and scalar attributes with projection.

Transfer function design in volume visualization has been a challenging problem due to the huge design space. In this work, we present a system which is capable of integrating the transfer function design results from a group of users. For a specified volume dataset, intermediate and final transfer function designs for many users with different backgrounds are collected. A 2D representation of the transfer function feature space, called transfer function map, is then constructed for each volume data set by MDS projection of the collected transfer function samples. With the proposed transfer function map, interactions, including flexible navigation in the transfer function feature space and transfer function design recommendation, have been developed.

In this work, we propose a visual analytic system for analyzing events of Weibo, a Chinese-version microblog service. We build a system which consists of two interfaces: a web-based online visualization interface for public users and an offline expert visual analytic system which wraps the online one and provides additional analysis functions. The online interface provides an intuitive and powerful retweet tree visualization which inspires users' creativity. The expert system adopts public users' analysis results collected from the web interface, and can visualize and analyze Weibo events to a deeper extent.

Visualization and interactive analysis can help network administrators and security analysts analyze the network flow and log data. The complexity of such an analysis requires a combination of knowledge and experience from more domain experts to solve difficult problems faster and with higher reliability. We developed an online visual analysis system called OCEANS to address this topic by allowing close collaboration among security analysts to create deeper insights in detecting network events. Loading the heterogeneous data source (netflow, IPS log and host status log), OCEANS provides a multi-level visualization showing temporal overview, IP connections and detailed connections. Participants can submit their findings through the visual interface and refer to others' existing findings. Users can gain inspiration from each other and collaborate on finding subtle events and targeting multi-phase attacks. Our case study confirms that OCEANS is intuitive to use and can improve efficiency. The crowd collaboration helps the users comprehend the situation and reduce false alarms.

When computing integral curves and integral surfaces for large-scale unsteady flow fields, a major bottleneck is the widening gap between data access demands and the available bandwidth (both I/O and in-memory). In this work, we explore a novel advection-based scheme to manage flow field data for both efficiency and scalability. The key is to first partition flow field into blocklets (e.g. cells or very fine-grained blocks of cells), and then (pre)fetch and manage blocklets on-demand using a parallel key-value store. The benefits are (1) greatly increasing the scale of local-range analysis (e.g. source-destination queries, streak surface generation) that can fit within any given limit of hardware resources; (2) improving memory and I/O bandwidth-efficiencies as well as the scalability of naive task-parallel particle advection. We demonstrate our method using a prototype system that works on workstation and also in supercomputing environments. Results show significantly reduced I/O overhead compared to accessing raw flow data, and also high scalability on a supercomputer for a variety of applications.

In this paper, we present a novel feature extraction approach called FLDA for unsteady flow fields based on Latent Dirichlet allocation (LDA) model. Analogous to topic modeling in text analysis, in our approach, pathlines and features in a given flow field are defined as documents and words respectively. Flow topics are then extracted based on Latent Dirichlet allocation. Different from other feature extraction methods, our approach clusters pathlines with probabilistic assignment, and aggregates features to meaningful topics at the same time. We build a prototype system to support exploration of unsteady flow field with our proposed LDA-based method. Interactive techniques are also developed to explore the extracted topics and to gain insight from the data. We conduct case studies to demonstrate the effectiveness of our proposed approach.

We present the design, implementation and evaluation of iVisDesigner, a web-based system that enables users to design information visualizations for complex datasets interactively, without the need for textual programming. Our system achieves high interactive expressiveness through conceptual modularity, covering a broad information visualization design space. iVisDesigner supports the interactive design of interactive visualizations, such as provisioning for responsive graph layouts and different types of brushing and linking interactions. We present the system design and implementation, exemplify it through a variety of illustrative visualization designs and discuss its limitations. A performance analysis and an informal user study are presented to evaluate the system.

In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system.

Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions.

In this paper, we propose a novel volume visualization system enabling local transfer function specification through direct painting or sketching on the rendered image, in a WYSIWYG style. Localized transfer functions are defined on scalar topology regions specified by the user. Intelligent and fast feature inference algorithms have been developed to convert user's input to the region specification and to achieve desirable feature styles with the local transfer functions. In our system, users can not only manipulate the color appearance of the object volume, but also apply style transfer and generate various illustration styles with a unified input gesture. Without manual transfer function editing and without parameter specification, our system is capable of generating informative illustrations that intuitively highlight user specified local features.

Ensemble run simulations are becoming increasingly widespread. In this work, we couple particle advection with pathline analysis to visualize and reveal the differences among the flow fields of ensemble runs. Our method first constructs a variation field using a Lagrangian-based distance metric. The variation field characterizes the variation between vector fields of the ensemble runs, by extracting and visualizing the variation of pathlines within ensemble. Parallelism in a MapReduce style is leveraged to handle data processing and computing at scale. Using our prototype system, we demonstrate how scientists can effectively explore and investigate differences within ensemble simulations.

In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system.

Community structure is an important characteristic of many real networks, which shows high concentrations of edges within special groups of vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships among communities, identifying attribute-structure relationships, and selecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks. The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated, uncluttered visualizations, and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.

For high-dimensional data, this work proposes two novel visual exploration methods to gain insights into the data aspect and the dimension aspect of the data. The first is a Dimension Projection Matrix, as an extension of a scatterplot matrix. In the matrix, each row or column represents a group of dimensions, and each cell shows a dimension projection (such as MDS) of the data with the corresponding dimensions. The second is a Dimension Projection Tree, where every node is either a dimension projection plot or a Dimension Projection Matrix. Nodes are connected with links and each child node in the tree covers a subset of the parent node's dimensions or a subset of the parent node's data items. While the tree nodes visualize the subspaces of dimensions or subsets of the data items under exploration, the matrix nodes enable cross-comparison between different combinations of subspaces. Both Dimension Projection Matrix and Dimension Project Tree can be constructed algorithmically through automation, or manually through user interaction. Our implementation enables interactions such as drilling down to explore different levels of the data, merging or splitting the subspaces to adjust the matrix, and applying brushing to select data clusters. Our method enables simultaneously exploring data correlation and dimension correlation for data with high dimensions.

In this paper, we propose a novel volume illustration technique inspired by interference microscopy, which has been successfully used in biological, medical and material science over decades. Our approach simulates the optical phenomenon in interference microscopy that accounts light interference over transparent specimens, in order to generate contrast enhanced and illustrative volume visualization results. Specifically, we propose PCVR (Phase- Contrast Volume Rendering) and DICVR (Differential Interference Contrast Volume Rendering) corresponding to Phase-Contrast microscopy and Differential Interference Contrast (DIC) microscopy respectively. Without complex transfer function design, our proposed method can enhance the image contrast and structure details according to the subtle change of Optical Path Differences (OPD), and illustrate the thickness change and occluded structures with interferometry metaphors. In addition, we also develop a user interface to enable slicing specimen sections in volume data. Focus+ context lens are also included in the system for convenient data navigation and exploration. As the proposed methods are based upon widely applied microscopy techniques, they are intuitive for domain experts to explore and analyze the volume data with the proposed methods. The feedbacks from domain users suggest our proposed techniques are useful volume visualization approaches complimentary to the traditional ones.

Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency.

In this paper, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel transfer function interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate (dimension) and the MDS plots project features. They are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties with lassos, magic wand, and other tools. Furthermore, direct sketching on the volume rendered images has been implemented to probe and edit features. With our system, users can interactively analyze multivariate volumetric data sets by navigating and exploring feature spaces in unified PCP and MDS plots. To further support large-scale multivariate volume data visualization and analysis, Scalable Pivot MDS (SPMDS), parallel adaptive continuous PCP rendering, as well as parallel rendering techniques are developed and integrated into our visualization system. Our experiments show that the system is effective in multivariate volume data visualization and its performance is highly scalable for data sets with different sizes and number of variates.

In this paper, we propose a new strategy for graph drawing utilizing layouts of many sub-graphs supplied by a large group of people in a crowd sourcing manner. We developed an algorithm based on Laplacian constrained distance embedding to merge subgraphs submitted by different users, while attempting to maintain the topological information of the individual input layouts. To facilitate collection of layouts from many people, a light-weight interactive system has been designed to enable convenient dynamic viewing, modification and traversing between layouts. Compared with other existing graph layout algorithms, our approach can achieve more aesthetic and meaningful layouts with high user preference.

In this paper, we present an interactive visual analytics system, Triple Perspective Visual Trajectory Analytics (TripVista), for exploring and analyzing complex traffic trajectory data. The users are equipped with a carefully designed interface to inspect data interactively from three perspectives (spatial, temporal and multi-dimensional views). While most previous works, in both visualization and transportation research, focused on the macro aspects of traffic flows, we develop visualization methods to investigate and analyze microscopic traffic patterns and abnormal behaviors. In the spatial view of our system, traffic trajectories with various presentation styles are directly interactive with user brushing, together with convenient pattern exploration and selection through ring-style sliders. Improved ThemeRiver, embedded with glyphs indicating directional information, and multiple scatterplots with time as horizontal axes illustrate temporal information of the traffic flows. Our system also harnesses the power of parallel coordinates to visualize the multi-dimensional aspects of the traffic trajectory data. The above three view components are linked closely and interactively to provide access to multiple perspectives for users. Experiments show that our system is capable of effectively finding both regular and abnormal traffic flow patterns.

In this paper, we present an effective transfer function (TF) design for multivariate volume, providing tightly coupled views of parallel coordinates plot (PCP), MDS-based dimension projection plots, and volume rendered image space. In our design, the PCP showing the data distribution of each variate dimension and the MDS showing reduced dimensional features are integrated seamlessly to provide flexible feature classification for the user without context switching between different data presentations. Our proposed interface enables users to identify interested clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, sketching directly on the volume rendered images has been implemented to probe and edit features. To achieve interactivity, octree partitioning with Gaussian Mixture Model (GMM), and other data reduction techniques are applied. Our experiments show that the proposed method is effective for multidimensional TF design and data exploration.

In this paper, we propose a volume visualization system that accepts direct manipulation through a sketch-based What You See Is What You Get (WYSIWYG) approach. Similar to the operations in painting applications for 2D images, in our system, a full set of tools have been developed to enable direct volume rendering manipulation of color, transparency, contrast, brightness, and other optical properties by brushing a few strokes on top of the rendered volume image. To be able to smartly identify the targeted features of the volume, our system matches the sparse sketching input with the clustered features both in image space and volume space. To achieve interactivity, both special algorithms to accelerate the input identification and feature matching have been developed and implemented in our system. Without resorting to tuning transfer function parameters, our proposed system accepts sparse stroke inputs and provides users with intuitive, flexible and effective interaction during volume data exploration and visualization.

In this paper, we propose an approach of clustering data in parallel coordinates through interactive local operations. Different from many other methods in which clustering is globally applied to the whole dataset, our interactive scheme allows users to directly apply attractive and repulsive operators at regions of interests, taking advantages of an electricity interaction metaphor, for clutter reduction and cluster detection. Our design enables users to interact directly with the parallel coordinate plots and provides great flexibility in exploring and revealing underlying patterns. With instant feedback, our work allows users to dynamically adjust the clustering parameters to reach an optimum. We also supply the user with a graph indicating the logical relationship between clusters. Our experiments show that our scheme is more efficient than traditional methods in performing visual analysis tasks.

Over the past few years, large human populations around the world have been affected by an increase in significant seismic activities. For both conducting basic scientific research and for setting critical government policies, it is crucial to be able to explore and understand seismic and geographical information obtained through all scientific instruments. In this work, we present a visual analytics system that enables explorative visualization of seismic data together with satellite-based observational data, and introduce a suite of visual analytical tools. Seismic and satellite data are integrated temporally and spatially. Users can select temporal ;and spatial ranges to zoom in on specific seismic events, as well as to inspect changes both during and after the events. Tools for designing high dimensional transfer functions have been developed to enable efficient and intuitive comprehension of the multi-modal data. Spread-sheet style comparisons are used for data drill-down as well as presentation. Comparisons between distinct seismic events are also provided for characterizing event-wise differences. Our system has been designed for scalability in terms of data size, complexity (i.e. number of modalities), and varying form factors of display environments.

Abstract In this paper, we propose a novel splatting framework for clutter reduction and pattern revealing in parallel coordinates. Our framework consists of two major components: a polyline splatter for cluster detection and a segment splatter for clutter reduction. The cluster detection is performed by splatting the lines one by one into the parallel coordinates plots, and for each splatted line we enhance its neighboring lines and suppress irrelevant ones. To reduce visual clutter caused by line crossings and overlappings in the clustered results, we provide a segment splatter which represents each polyline by one segment and splats these segments with different speeds, colors, and lengths from the leftmost axis to the rightmost axis. Users can interactively control both the polyline splatting and the segment splatting processes to emphasize the features they are interested in. The experimental results demonstrate that our framework can effectively reveal some hidden patterns in parallel coordinates.

In this paper, we present a novel parallel coordinates design integrated with points (Scattering Points in Parallel Coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordinates where each visualization type occupies an individual window, we convert two selected neighboring coordinate axes into a scatterplot directly. Multidimensional scaling is adopted to allow converting multiple axes into a single subplot. The transition between two visual types is designed in a seamless way. In our work, a series of interaction tools has been developed. Uniform brushing functionality is implemented to allow the user to perform data selection on both points and parallel coordinate polylines without explicitly switching tools. A GPU accelerated Dimensional Incremental Multidimensional Scaling (DIMDS) has been developed to significantly improve the system performance. Our case study shows that our scheme is more efficient than traditional multi-view methods in performing visual analysis tasks.

Radio frequency identification (RFID) is a powerful automatic remote identification technique that has wide applications. To facilitate RFID deployment, an RFID benchmarking instrument called aGate has been invented to identify the strengths and weaknesses of different RFID technologies in various environments. However, the data acquired by aGate are usually complex time varying multidimensional 3D volumetric data, which are extremely challenging for engineers to analyze. In this paper, we introduce a set of visualization techniques, namely, parallel coordinate plots, orientation plots, a visual history mechanism, and a 3D spatial viewer, to help RFID engineers analyze benchmark data visually and intuitively. With the techniques, we further introduce two workflow procedures (a visual optimization procedure for finding the optimum reader antenna configuration and a visual analysis procedure for comparing the performance and identifying the flaws of RFID devices) for the RFID benchmarking, with focus on the performance analysis of the aGate system. The usefulness and usability of the system are demonstrated in the user evaluation.

Facial emotions and expressive facial motions have become an intrinsic part of many graphics systems and human computer interaction applications. The dynamics and high dimensionality of facial motion data make its exploration and processing challenging. In this paper, we propose a novel visualization system for expressive facial motion data exploration. Based on Principal Component Analysis (PCA) dimensionality reduction on anatomical facial sub regions, high dimensional facial motion data is mapped to 3D spaces. We further rendered it as colored 3D trajectories and color represents different emotion. We design an intuitive interface to allow users effectively explore and analyze high dimensional facial motion spaces. The applications of our visualization system on novel facial motion synthesis and emotion recognition are demonstrated.

Effectively visualizing complex node-link graphs which depict relationships among data nodes is a challenging task due to the clutter and occlusion resulting from an excessive amount of edges. In this paper, we propose a novel energy-based hierarchical edge clustering method for node-link graphs. Taking into the consideration of the graph topology, our method first samples graph edges into segments using Delaunay triangulation to generate the control points, which are then hierarchically clustered by energy-based optimization. The edges are grouped according to their positions and directions to improve comprehensibility through abstraction and thus reduce visual clutter. The experimental results demonstrate the effectiveness of our proposed method in clustering edges and providing good high level abstractions of complex graphs.

Abstract Parallel coordinates have been widely applied to visualize high‐dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.

In this paper, we present an interactive high dynamic range volume visualization framework (HDR VolVis) for visualizing volumetric data with both high spatial and intensity resolutions. Volumes with high dynamic range values require high precision computing during the rendering process to preserve data precision. Furthermore, it is desirable to render high resolution volumes with low opacity values to reveal detailed internal structures, which also requires high precision compositing. High precision rendering will result in a high precision intermediate image (also known as high dynamic range image). Simply rounding up pixel values to regular display scales will result in loss of computed details. Our method performs high precision compositing followed by dynamic tone mapping to preserve details on regular display devices. Rendering high precision volume data requires corresponding resolution in the transfer function. To assist the users in designing a high resolution transfer function on a limited resolution display device, we propose a novel transfer function specification interface with nonlinear magnification of the density range and logarithmic scaling of the color/ opacity range. By leveraging modern commodity graphics hardware, multiresolution rendering techniques and out-of-core acceleration, our system can effectively produce an interactive visualization of large volume data, such as 2,048(3).

High resolution volumes require high precision compositing to preserve detailed structures. This is even more desirable for volumes with high dynamic range values. After the high precision intermediate image has been computed, simply rounding up pixel values to regular display scales loses the computed details. In this paper, we present a novel high dynamic range volume visualization method for rendering volume data with both high spatial and intensity resolutions. Our method performs high precision volume rendering followed by dynamic tone mapping to preserve details on regular display devices. By leveraging available high dynamic range image display algorithms, this dynamic tone mapping can be automatically adjusted to enhance selected features for the final display. We also present a novel transfer function design interface with nonlinear magnification of the density range and logarithmic scaling of the color/opacity range to facilitate high dynamic range volume visualization. By leveraging modern commodity graphics hardware and out-of-core acceleration, our system can produce an effective visualization of huge volume data.

We present a GPU supported interactive non-photorealistic rendering system, INSPIRE, which performs feature extraction in both image space, on intermediately rendered images, and object space, on models of various representations, e.g., point, polygon, or hybrid models, without needing connectivity information. INSPIRE obtains interactive NPR rendering with most styles of existing NPR systems, but offers more flexibility on model representations and compromises little on rendering speed.