We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation.

Visual Analytics (VA) supports iterative exploration and insight discovery, but current frameworks lack sufficient provenance management to capture the sequence of analytical actions and reasoning. This limitation hinders the reconstruction of analytical processes and the generation of coherent narratives. We propose the VASTorytelling Framework, which extends existing VA framework by incorporating mechanisms for recording user interactions, annotating visualizations, and utilizing extended data—including logs and external knowledge—for narrative construction. The framework supports metacognitive reflection and integrates large language models to assist narrative generation and reduce cognitive load. This approach enhances traceability, explainability, and communication in long-term VA activities.

Pitch sequences in baseball reflect a pitcher's tactical intent, yet existing tools often overlook their structure by focusing only on frequency or location. This paper presents P3VS, an interactive visualization system that models each at-bat as an ordered trajectory of pitch zones, enriched with pitch type, velocity, and outcome. Using a modified Levenshtein Distance, the system clusters recurring sequence patterns and visualizes them as networks. Users can filter sequences by zone, pitcher, batter stance, and outcome to explore differences across contexts. P3VS supports real-time, outcome-specific analysis and reveals structural variations in pitch sequences that may influence game results.

Many water-related accidents occur not only at sea but also on rivers every year. However, water-related accidents are caused by multiple factors, making it difficult to understand the causes. Therefore, it is necessary to develop a system that automatically extracts areas of high occurrence of water-related accidents from a large amount of data and analyzes them in combination with other data to understand the cause of the accidents. In this paper, we first utilize DBSCAN to extract areas of high occurrence of water-related accidents in rivers. We then construct a feature analysis system to visualize and analyze these areas in combination with other data such as population and presence/absence of bridges. From the analysis results, we found that when bridges and campgrounds are located around high accident areas, people often dive from bridges and drink alcohol while camping.

Traffic accidents are still troubling our society. The number of drive recorders sold has increased, and therefore we can collect large-scale vehicle recorder data to be used to support traffic safety. We have developed a system for detecting potentially risky crossroads on the basis of vehicle recorder data, road shapes, and weather information. Visualization combining space and time in a single display called a “space time cube (STC)” helps us to understand and analyze spatio-temporal mobility data on caution crossroads. The STC enables us to simultaneously explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find good viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoint selection method for visualizing spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. Major contributions of this paper are as follows: (1) We provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of a viewpoint quality on a projected 2D image. (2) We demonstrate our solution can be adapted to crossroads with different trajectory shapes. We also extend the proposed method to find an optimal viewpoint for multiple crossroads. (3) We verify the proposed method through users' evaluations. (4) We construct an overviewing catalog of potentially risky crossroads detected from real vehicle recorder big data to discuss and analyze them with stakeholders.

We present a method for estimating road safety using large-scale and heterogeneous vehicle recorder data. Our assumption in this study is that occurrences of traffic accidents on crossings could be estimated by their road shape, driving behaviors over them, and photos taken by dash cams. Utilizing 3 years dataset from 1,500 drivers in a fleet delivery company, we try to detect risky crossings based on supervised machine learning. Results show that our method provides good performance, and can extract potentially risky crossings.

Various events, such as public gatherings, traffic accidents, and natural disasters, occur every day in mega-cities. Although understanding such ever-changing events over all these cities is important for urban planning, traffic management, and disaster response, this is quite a huge challenge. This paper proposes a method of visualizing spatio-temporal events with a multi-layered geo-locational word-cloud representation from a geo-parsed microblog stream. Real-time geo-parsing first geo-locates posts in the stream, using geo-tags and mentioned places and facilities as clues. Temporal local events are then identified and represented by a set of words specifically observed in a certain location and time grid, and then displayed above a map as word-clouds. We detect the locality of events to split them into multiple layers to avoid occlusions between local (e.g., music concerts) and global (e.g., earthquakes and marathon races) events. Users can thereby distinguish local from global events, and see their interactions over the layered maps. We demonstrate the effectiveness of our method by applying it to real events extracted from our archive accumulated from five years of Twitter posts.

Transportation systems in mega-cities are often affected by various kinds of events such as natural disasters, accidents, and public gatherings. Highly dense and complicated networks in the transportation systems propagate confusion in the network because they offer various possible transfer routes to passengers. Visualization is one of the most important techniques for examining such cascades of unusual situations in the huge networks. This paper proposes visual integration of traffic analysis and social media analysis using two forms of big data: smart card data on the Tokyo Metro and social media data on Twitter. Our system provides multiple coordinated views to visually, intuitively, and simultaneously explore changes in passengers' behavior and abnormal situations extracted from smart card data and situational explanations from real voices of passengers such as complaints about services extracted from social media data. We demonstrate the possibilities and usefulness of our novel visualization environment using a series of real data case studies and domain experts' feedbacks about various kinds of events.

It is vital for the transportation industry, which performs most of their work by automobiles, to reduce its number of traffic accidents. Many local governments in Japan have made potential risk maps of traffic accident spots. However, making such maps in wide areas and with the time information had been difficult because most of them are made based on an investigation. Utilizing long-term driving records can extract wide area spatio-temporal caution spots. This paper proposes a visual interaction method for exploring caution spots from large-scale vehicle recorder data. Our method provides (i) a flexible filtering interface for driving operations using various combinations of attribute values such as velocity and acceleration, and (ii) a 3D visual environment for spatio-temporal exploration of caution spots. We demonstrate the usefulness of our novel visual exploration environment using real data given by one of the biggest transportation companies in Japan. Exploration results show our environments can extract caution spots where some accidents have actually occurred or that are on very narrow roads with bad visibility.

To understand recent societal behavior, it is important to compare how multiple media are affected by real-world events and how each medium affects other media. This paper proposes a novel framework for inter-media comparison through visualizing images extracted from different types of media. We extract blog image clusters from our six-year blog archive and search for similar TV shots in each cluster from a broadcast news video archive by using image similarities. We then visualize such image flows on a timeline in 3D space to visually and interactively explore time sequential changes in influences among media resources and differences and/or similarities between them such as topics that become popular on only blogs or that become popular on blogs earlier than on TV.

Transportation systems in mega-cities are often affected by various kinds of events such as natural disasters, accidents, and public gatherings. Highly dense and complicated networks in the transportation systems propagate confusion in the network because they offer various possible transfer routes to passengers. Visualization is one of the most important techniques for examining such cascades of unusual situations in the huge networks. This paper proposes visual integration of traffic analysis and social media analysis using two forms of big data: smart card data on the Tokyo Metro and social media data on Twitter. Our system provides multiple coordinated views to visually, intuitively, and simultaneously explore changes in passengers' behavior and abnormal situations extracted from smart card data and situational explanations from real voices of passengers such as complaints about services extracted from social media data. We demonstrate the possibilities and usefulness of our novel visualization environment using a series of real data case studies about various kinds of events.

This paper proposes a system for analyzing societal behaviors by visualizing time-varying topics in multiple media. Various types of content such as text, images, and videos have spread throughout multiple media, such as TV and the Web, that have complementary information and influence one another. It is important to compare how these media react to real world events to understand recent societal behaviors and how each medium reacts to other media. Our system visualizes flows of content in multiple media in 3D space enabling us to simultaneously explore them. We present two example applications using our system. The first involves the visualization of inter-media events comparing the exposure of topics in TV news and the activities of bloggers. The second example application is a system for visualizing visual trends on social media that chronologically displays extracted clusters of images on blogs. The proposed systems enable users to visually monitor changes in thought, activities, and interests of people, and differences between media through interactively exploring flows of texts and images extracted from the media.

Social media such as blogs and microblogs enable users to easily and rapidly publish information on their personal activities and interests. They are considered to provide valuable information from the viewpoints of sociology, linguistics, and marketing. This paper proposes a novel system for analyzing temporal changes in the activities and interests of bloggers through a 3D visualization of phrase dependency structures in sentences. We first extract events that represent bloggers' activities and interests through analyzing the phrase dependencies of sentences in a blog archive. We roughly categorize the retrieved events according to the thematic roles (such as the experiencer, agent, and location) of the noun within the events, and then store them in a dependency database so that we can retrieve events that involve a given topic. Second, we present a 3D visualization framework for exploring temporal changes in events related to a topic. Our framework enables users to find events about a topic that appear within a specific timing, and drill down details of the events. It also enables users to compare events with different timings and/or on multiple topics. Moreover, it allows them to observe an overview of temporal changes in sets of events, and long-term changes in the frequency of events to assist users in finding trends. We implement the proposed system on our own five-year blog archive that focused on Japanese, and we report the usefulness of our system by using various examples.

A huge number of historical documents that have been accumulated for a long time are currently being digitized. However, it is difficult for us to analyze and obtain insight into the past from such documents. This paper proposes an interactive visualization system to extract networks of historical figures from historical data and to show time-varying changes in their relationships. It enables users to explore changes in the structure of the network interactively. Moreover, it extracts characteristics of each relationship, such as hostile or friendly relations, and visualizes them on the network. It enables us to understand changes in past society better by exploring changes in relationships between people.

We propose an interactive framework for the 3D visualization of the time-series of Web graphs. The purpose of our framework is to enable users to examine the evolution of Web graphs by comparing multiple graphs that have different timings and topics. To achieve this, we provide TimeSlices that are 2D planes to visualize Web graphs in a 3D environment. Users can interactively add new TimeSlices along the timeline, and they can manipulate them to animate Web graphs. Visualized Web graphs on TimeSlices are snapshots of different timings. Moreover, we provide mechanisms to generate and manipulate overlay views and parallel views in the 3D environment to enable Web graphs to be compared. Users can seamlessly change these viewing modes. Our system can also visualize two Web graphs created from different keywords to display the evolutional correlation between them. This system enables us to observe structural and temporal changes in multiple Web graphs such as shifts in influential blogs or power shifts in the diffusion of blogs between topics.

The aim of our research is to provide an application framework for end-users, who do not have enough programming skills, to dynamically generate applications using interactive, coordinated, multiple 3D visualization techniques. In our framework, each 3D application for visualization is constructed by users themselves or already exists, and consists of sets of interactive 3D components. Users can embed such 3D applications into cells on Excel, and define the coordination between them. To define coordination, we provide interactive 2D/3D components and coordination rules that enable users to use operations for coordination such as brushing and linking, retrieving related or detailed information one after another, synchronizing viewpoints between 3D visualizations, and sharing table values between 3D components for visualizations and cells on Excel.

We propose a spreadsheet-based visualization framework for end-users to generate and modify multiple 3D visualizations of data-sets from various Web resources. In this paper, first, we provide a 3D component-based access mechanism to Web resources. It allows users to access various Web resources using only 3D visual components interactively. Second, we provide an interactive 3D visualization mechanism. It enables users to construct multiple 3D visualizations of data-sets from various Web resources just by combining 3D visual components. Third, we provide 2D components for communicating between a spreadsheet and 3D components. Users can export necessary functions of 3D components and define synchronization between these components and cells on a spreadsheet. A spreadsheet environment allows us to define relationships among cells, and copy these relationships through a copy and paste manipulation. By using these mechanisms, users can create multiple visualizations in parallel in order to compare different visualization results simultaneously for different visualization parameters just through a user's direct manipulation.

This paper proposes a new concept and mechanism for the user and content interaction between multiple spaces in a 3D virtual environment. We introduce two types of visual components; a flat, window-like WorldMirror, and a volumetric WorldBottle. These components embed other spaces inside and render them on their surface, so that users can look into the contents of other spaces from their current working one and choose to enter. They also allow content manipulation in embedded spaces from the current space. Users can move an object from an embedded space to the current working space, or drop an object from the current space into an embedded space. In contrast to conventional approaches, the proposed mechanism enables us not only to define a spatial connection between different spaces, but also to define a functional linkage among components in different spaces.

In this paper, we propose a component-based framework for generating simple 3D applications that aid users in the use of Web services without the need for programming. These days we can get lots of information through Web services. By using Web services, we can reduce the need to buy data packages and construct special databases in order to generate 3D information visualization systems or 3D simulation systems. Basically, we need to write some scripts or programs to access Web services. Therefore, it is difficult for users to use interactively through a graphical user interface, especially in a 3D virtual environment. Our framework uses the 3D meme media system IntelligentBox as its platform. All functions for accessing Web services are provided by 3D proxy components. Users have only to operate 3D interactive components and create composite components in order to create applications that use Web services. Users input values by means of specialized interface components, thereby creating requests to Web services, and changing visualization parameters. In this way, users can easily compose simple applications with a combination of arbitrary 3D components