Interest in visualization design has increased in recent years. While there is a large body of existing work from which visualization designers can draw, much of the past research has focused on developing new tools and techniques that are aimed at specific contexts. Less focus has been placed on developing holistic frameworks, models, and theories that can guide visualization design at a general level—a level that transcends domains, data types, users, and other contextual factors. In addition, little emphasis has been placed on the thinking processes of designers, including the concepts that designers use, while they are engaged in a visualization design activity. In this book we present a general, holistic framework that is intended to support visualization design for human-information interaction. The framework is composed of a number of conceptual elements that can aid in design thinking. The core of the framework is a pattern language—consisting of a set of 14 basic, abstract patterns—and a simple syntax for describing how the patterns are blended. We also present a design process, made up of four main stages, for creating static or interactive visualizations. The 4-stage design process places the patterns at the core of designers’ thinking, and employs a number of conceptual tools that help designers think systematically about creating visualizations based on the information they intend to represent. Although the framework can be used to design static visualizations for simple tasks, its real utility can be found when designing visualizations with interactive possibilities in mind—in other words, designing to support a human-information interactive discourse. This is especially true in contexts where interactive visualizations need to support complex tasks and activities involving large and complex information spaces. The framework is intended to be general and can thus be used to design visualizations for diverse domains, users, information spaces, and tasks in different fields such as business intelligence, health and medical informatics, digital libraries, journalism, education, scientific discovery, and others. Drawing from research in multiple disciplines, we introduce novel concepts and terms that can positively contribute to visualization design practice and education, and will hopefully stimulate further research in this area.

In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk For more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.

This book designates Visualization Psychology as an interdisciplinary subject. The book contains literature reviews and experimental works that exemplify a range of open questions at this critical intersection. It also includes discourses that envision how the subject may be developed in the coming years and decades. The field of visualization is a rich playground for discovering new knowledge in both visualization and psychology. As visualization techniques augment human cognition, these techniques must be developed and improved by building on theoretical, empirical and methodological knowledge from psychology. At the same time, visualization processes surface numerous phenomena about interactions between the human mind and digital entities, such as data, visual imagery, algorithms, and computer-generated predictions and recommendations. Visualization psychology is a new type of science in the making.

The four-volume set LNCS 14442 -14445 constitutes the proceedings of the 19th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2023, held in York, UK, in August/September 2023. The 71 full papers and 58 short papers included in this book were carefully reviewed and selected from 406 submissions. They were organized in topical sections as follows: 3D Interaction; Accessibility; Accessibility and Aging; Accessibility for Auditory/Hearing Disabilities; Co-Design; Cybersecurity and Trust; Data Physicalisation and Cross-device; Eye-Free, Gesture Interaction and Sign Language; Haptic interaction and Healthcare applications; Self-Monitoring; Human-Robot Interaction; Information Visualization; Information Visualization and 3D Interaction; Interacting with Children; Interaction with Conversational Agents; Methodologies for HCI; Model-Based UI Design and Testing; Montion Sickness, Stress and Risk perception in 3D Environments and Multisensory interaction; VR experiences; Natural Language Processing and AI Explainability; Online Collaboration and Cooperative work; Recommendation Systems and AI Explainability; Social AI; Social and Ubiquitous Computing; Social Media and Digital Learning; Understanding Users and Privacy Issues; User movement and 3D Environments; User Self-Report; User Studies; User Studies, Eye-Tracking, and Physiological Data; Virtual Reality; Virtual Reality and Training; Courses; Industrial Experiences; Interactive Demonstrations; Keynotes; Panels; Posters; and Workshops.

Prevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. Particle data typically originates from measurements and simulations in various fields, such as life sciences or physics. The particles are often visualized directly, that is, by simple representants like spheres. Interactive rendering facilitates the exploration and visual analysis of the data. With increasing data set sizes in terms of particle numbers, interactive high-quality visualization is a challenging task. This is especially true for dynamic data or abstract representations that are based on the raw particle data. This book covers direct particle visualization using simple glyphs as well as abstractions that are application-driven such as clustering and aggregation. It targets visualization researchers and developers who are interested in visualization techniques for large, dynamic particle-based data. Its explanations focus on GPU-accelerated algorithms for high-performance rendering and data processing that run in real-time on modern desktop hardware. Consequently, the implementation of said algorithms and the required data structures to make use of the capabilities of modern graphics APIs are discussed in detail. Furthermore, it covers GPU-accelerated methods for the generation of application-dependent abstract representations. This includes various representations commonly used in application areas such as structural biology, systems biology, thermodynamics, and astrophysics.

At the 2016 IEEE VIS Conference in Baltimore, Maryland, a panel of experts from the Scientific Visualization (SciVis) community gathered to discuss why the SciVis component of the conference had been shrinking significantly for over a decade. As the panelists concluded and opened the session to questions from the audience, Annie Preston, a Ph.D. student at the University of California, Davis, asked whether the panelists thought diversity or, more specifically, the lack of diversity was a factor. This comment ignited a lively discussion of diversity: not only its impact on Scientific Visualization, but also its role in the visualization community at large. The goal of this book is to expand and organize the conversation. In particular, this book seeks to frame the diversity and inclusion topic within the Visualization community, illuminate the issues, and serve as a starting point to address how to make this community more diverse and inclusive. This book acknowledges that diversity is a broad topic with many possible meanings. Expanded definitions of diversity that are relevant to the Visualization community and to computing at large are considered. The broader conversation of inclusion and diversity is framed within the broader sociological context in which it must be considered. Solutions to recruit and retain a diverse research community and strategies for supporting inclusion efforts are presented. Additionally, community members present short stories detailing their ""non-inclusive"" experiences in an effort to facilitate a community-wide conversation surrounding very difficult situations. It is important to note that this is by no means intended to be a comprehensive, authoritative statement on the topic. Rather, this book is intended to open the conversation and begin to build a framework for diversity and inclusion in this specific research community. While intended for the Visualization community, ideally, this book will provide guidance for any computing community struggling with similar issues and looking for solutions.

There is ample evidence in the visualization community that individual differences matter. These prior works highlight various personality traits and cognitive abilities that can modulate the use of the visualization systems and demonstrate a measurable influence on speed, accuracy, process, and attention. Perhaps the most important implication of this body of work is that we can use individual differences as a mechanism for estimating when a design is effective or to identify when people may struggle with visualization designs. These effects can have a critical impact on consequential decision-making processes. One study that appears in this book investigated the impact of visualization on medical decision-making showed that visual aides tended to be most beneficial for people with high spatial ability, a metric that measures a person’s ability to represent and manipulate two- or three-dimensional representations of objects mentally. The results showed that participants with low spatial ability had difficulty interpreting and analyzing the underlying medical data when they use visual aids. Overall, approximately 50% of the studied population were unsupported by the visualization tools when making a potentially life-critical decision. As data fluency continues to become an essential skill for our everyday lives, we must embrace the growing need to understand the factors that may render our tools ineffective and identify concrete steps for improvement. This book presents my current understanding of how individual differences in personality interact with visualization use and draws from recent research in the Visualization, Human-Computer Interaction, and Psychology communities. We focus on the specific designs and tasks for which there is concrete evidence of performance divergence due to personality. Additionally, we highlight an exciting research agenda that is centered around creating tailored visualization systems that are aligned with people’s abilities. The purpose of this book is to underscore the need to consider individual differences when designing and evaluating visualization systems and to call attention to this critical research direction.

When you picture human-data interactions (HDI), what comes to mind? The datafication of modern life, along with open data initiatives advocating for transparency and access to current and historical datasets, has fundamentally transformed when, where, and how people encounter data. People now rely on data to make decisions, understand current events, and interpret the world. We frequently employ graphs, maps, and other spatialized forms to aid data interpretation, yet the familiarity of these displays causes us to forget that even basic representations are complex, challenging inscriptions and are not neutral; they are based on representational choices that impact how and what they communicate. This book draws on frameworks from the learning sciences, visualization, and human-computer interaction to explore embodied HDI. This exciting sub-field of interaction design is based on the premise that every day we produce and have access to quintillions of bytes of data, the exploration and analysis of which are no longer confined within the walls of research laboratories. This volume examines how humans interact with these data in informal (not work or school) environments, paritcularly in museums. The first half of the book provides an overview of the multi-disciplinary, theoretical foundations of HDI (in particular, embodied cognition, conceptual metaphor theory, embodied interaction, and embodied learning) and reviews socio-technical theories relevant for designing HDI installations to support informal learning. The second half of the book describes strategies for engaging museum visitors with interactive data visualizations, presents methodologies that can inform the design of hand gestures and body movements for embodied installations, and discusses how HDI can facilitate people's sensemaking about data. This cross-disciplinary book is intended as a resource for students and early-career researchers in human-computer interaction and the learning sciences, as well as for more senior researchers and museum practitioners who want to quickly familiarize themselves with HDI.

Visual analytics has come a long way since its inception in 2005. The amount of data in the world today has increased significantly and experts in many domains are struggling to make sense of their data. Visual analytics is helping them conduct their analyses. While software developers have worked for many years to develop software that helps users do their tasks, this task is becoming more and more onerous, as understanding the needs and data used by expert users requires more than some simple usability testing during the development process. The need for a user-centered evaluation process was envisioned in Illuminating the Path, the seminal work on visual analytics by James Thomas and Kristin Cook in 2005. We have learned over the intervening years that not only will user-centered evaluation help software developers to turn out products that have more utility, the evaluation efforts can also help point out the direction for future research efforts. This book describes the efforts that go into analysis, including critical thinking, sensemaking, and various analytics techniques learned from the intelligence community. Support for these components is needed in order to provide the most utility for the expert users. There are a good number of techniques for evaluating software that hasbeen developed within the human-computer interaction (HCI) community. While some of these techniques can be used as is, others require modifications. These too are described in the book. An essential point to stress is that the users of the domains for which visual analytics tools are being designed need to be involved in the process. The work they do and the obstacles in their current processes need to be understood in order to determine both the types of evaluations needed and the metrics to use in these evaluations. At this point in time, very few published efforts describe more than informal evaluations. The purpose of this book is to help readers understand the need for more user-centered evaluations to drive both better-designed products and to define areas for future research. Hopefully readers will view this work as an exciting and creative effort and will join the community involved in these efforts.