Synthesis Lectures on Visualization – serie

Displaying multiple levels of data visually has been proposed to address the challenge of limited screen space. / Decision 3: Simultaneous or Temporal Displays of the Multiple Visual Levels / Decision 4: How to Spatially Arrange the Visual Levels, Embedded or Separate?

This chapter provides an introduction to young researchers as an overview of common visual representations and statistical analysis methods utilized in a variety of visual analytics systems. Basic analytical methods are explored as a means of enhancing the visual analysis process, moving from visual analysis to visual analytics.

This includes a critical discussion of interaction, the identification of links between the key aspects of interaction, and the formulation of research topics for future work with a focus on interaction.

As specific examples, this book details GPGPU usages to produce fast enough visualization to be interactive with improved brushing techniques, fast animations between different data representations, and view simplifications (i.e. We will investigate innovative data processing techniques: while classical algorithms are expressed in data space (e.g.

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.

This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation.

Prevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. This is especially true for dynamic data or abstract representations that are based on the raw particle data.

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.

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.

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 thatis 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.

The emergence of multilayer networks as a concept from the field of complex systems provides many new opportunities for the visualization of network complexity, and has also raised many new exciting challenges.

Internal labels are placed inside or in the direct neighborhood of features, while external labels, which form the topic of this book, are placed in the margins outside the illustration, where they do not occlude the illustration itself.

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 book provides an overview of the many visualization strategies that have been proposed in recent decades for solving problems within the disciplines of medicine and biology. It also evaluates which visualization techniques applied to various areas of biomedicine have been the most impactful and which challenges can be considered solved using visualization. The topics covered in this book include visualization research in omics, interaction networks and pathways, biological structures, tumor diagnosis and treatment, vasculature, brain, surgery, educational contexts, therapy and rehabilitation, electronic health records, and public health. One chapter is dedicated to general visualization techniques commonly used for biomedical data, such as surface and volume rendering, as well as abstract and illustrative approaches. For each of these areas, the past and present research trends are discussed, highlighting the influential works. Furthermore, the book explains how research is affected by developments in technology, data availability, and domain practice. Individual sections also summarize the typical target users, the nature of the data, and the typical tasks addressed in the given domain.With a uniquely broad scope, the book identifies research trends in biomedical visualization using a manually curated and searchable repository of more than 3,800 publications. The resultant trends are further categorized according to the application field and using natural language processing. The book also utilizes 16 interviews conducted with researchers in the field of biomedical visualization for the purpose of soliciting their opinions regarding the evolution and trends in the domain.The book handles these topics in a concise manner to help readers orient themselves in the already mature and diverse field of biomedical visualization without overwhelming them with technical details. As such, the book can help young researchers to become familiar with past challenges and identify future ones. For more senior researchers, it can serve as an insightful overview of the research field and the direction in which it is heading.

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering;

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models. It shows how visualization can bridge the gap between complex models or algorithms and human understanding while also facilitating data curation and model refinement. Therefore, visualization for artificial intelligence (VIS4AI) has become an emerging area that combines interactive visualization with machine learning techniques to maximize their values. VIS4AI techniques focus on every phase of the machine learning life cycle, from data preprocessing to model development and deployment. These techniques are closely aligned with the well-established data and model pipelines in machine learning. In the data pipeline, they contribute to improving data quality and feature quality, including training data cleaning and feature engineering. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering; and (2) model deployment by enabling decision explanation, model performance monitoring, and model maintenance. This book provides a framework of VIS4AI and introduces the associated techniques in the two pipelines. It emphasizes the importance of interactive visualization in AI and presents various visualization techniques for different purposes. It also discusses the challenges and opportunities of VIS4AI and proposes several promising research topics for future work, such as improving training data using complementary modalities, online training diagnosis, fitting the dynamic nature of AI systems, and interactively pre-training and adapting foundation models. Overall, this book aims to serve as a resource for researchers and practitioners interested in both visualization and artificial intelligence.