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 has been 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.

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.

The primary goal for collecting and analyzing temporal data differs between individuals and their domain of expertise e.g., forecasting might be the goal in meteorology, anomaly detection might be the goal in finance. While the goal differs, one common denominator is the need for exploratory analysis of the temporal data, as this can aid the search for useful information. However, as temporal data can be challenging to understand and visualize, selecting appropriate visual representations for the domain and data at hand becomes a challenge. Moreover, many visual representations can show a single variable that changes over time, displaying multiple variables in a clear and easily accessible way is much harder, and inference-making and pattern recognition often require visualization of multiple variables. Additionally, as visualization aims to gain insight, it becomes crucial to investigate whether the representations used help users gain this insight. Furthermore, to create effective and efficient visual analysis tools, it is vital to understand the structure of the data, how this data can be represented, and have a clear understanding of the user needs. Developing useful visual representations can be challenging, but through close collaboration and involvement of end-users in the entire process, useful results can be accomplished. This thesis aims to investigate the usability of different visual representations for different types of multivariate temporal data, users, and tasks. Five user studies have been conducted to investigate different representation spaces, layouts, and interaction methods for investigating representations’ ability to facilitate users when analyzing and exploring such temporal datasets. The first study investigated and evaluated the experience of different radial design ideas for finding and comparison tasks when presenting hourly data based on an analog clock metaphor. The second study investigated 2D and 3D parallel coordinates for pattern finding. In the third study, the usability of three linear visual representations for presenting indoor climate data was investigated with domain experts. The fourth study continued on the third study and developed and evaluated a visual analytics tool with different visual representations and interaction techniques with domain experts. Finally, in the fifth study, another visual analytics tool presenting visual representations of temporal data was developed and evaluated with domain experts working and conducting experiments in Antarctica. The research conducted within the scope of this thesis concludes that it is vital to understand the characteristics of the temporal data and user needs for selecting the optimal representations. Without this knowledge, it becomes much harder to choose visual representations to help users gain insight from the data. It is also crucial to evaluate the perception and usability of the chosen visual representations.

In the era of big data, healthcare practitioners are increasingly generating and analyzing data. The huge volume of data provides opportunities for evidence based medicine to answer research and practical questions, and for individuals to make better informed, smarter decision. Yet, making sense of massive healthcare datasets remains a fundamental challenge. In order to make inferences on the data, an effective visual representations of the data is needed. We propose visualization as a means to derive inferences on healthcare data. Design of visual analytics tools, therefore, becomes important in the healthcare domain. Healthcare is a broad area involving users with high variation in roles, expertise, and background. The diverse characteristics of users and their shifting contexts makes it a challenge to process and to present healthcare data in appropriate visual representations that is directly relevant to the analytics tasks.We advocate using the User-Centered Design (UCD) approach in designing and developing visual analytics tools in healthcare. The core research question is how a user centered approach should be adopted in designing visual analytics tools. We address the question by bringing the user centered design process and visual analytics process together, and by applying interdisciplinary methods, such as data mining, information visualization and network analytics. In particular, the dissertation looks to three studies in the healthcare domain where practitioners follow UCD to design visual analytics in healthcare domain. We emphasis the role of user involvement in the entire iterative design lifecycle. The users spanning these studies include patients, physicians, nanomaterial scientists, biomedical professionals, and healthcare policy makers. The three studies are:1. Developing a visual web-based tool called VisOSA to interpret and explore medical records of patients with sleep apnea. For physicians, the ClinicView provides an interactive tool to have an overview of the entire medical record of patients including anomalies, and therefore help with generating research hypothesis. For patients, the PatientView allows the lay individual - without any medical background - to understand their health conditions under treatment at a glance. We also defined medical dashboard in this study. We conducted a supporting quantitative behavioral research to examine the effect of multiple monitors on people's performance. Results suggested that the medical dashboard needs to be arranged as that the most important information is in a single screen. 2. Studying the nanomaterial environmental impact (NEI) is a critical task in nano-health and safety. The information visualization module for NEIMiner is a visual analytic tool that can efficiently query and present large-scale bibliography meta-data, NEI characterizations and nanomaterial toxicity. It supports nanomaterial scientists and analysts to explore the concepts and relationships in studying nanomaterial toxicity and its impact on health. 3. Translating research findings into effective clinical care is another overwhelming task. VATS is a scalable multi-scale visual analytical tool for advancing translational sciences. The system integrates the data of National Center for Advancing Translational Science (NCATS) funded projects and publications from NIH Research Portfolio Online Reporting Tools (RePORTER) and PubMed, and helps healthcare policy makers to understand the big picture of translational science and to evaluate the impact of NCATS funded projects through publicly available data sources.Through these studies, we discuss how innovative visual analytics techniques and capabilities with help users understand and act on various type of data. The results and applications demonstrate what purposes visualizations are used for each problem, how the design and development team make decisions around datasets, and the different visual representations healthcare practitioners use to explore and gain insight of data. The research contributions are (1) a joint model of UCD and visual analytics process that overcomes the design challenges, (2) a justification of how existing data mining and visualization techniques can be usefully combined to support analytics task in healthcare, (3) the approaches to integrate and enrich the dataset, and turn the data into reliable and provable information, (4) the visual encoding and interaction mechanisms to present complex information, (5) three visual analytics system built are beneficial to healthcare community and society. The design guidelines and recommendations generated from these studies are applicable to various domains. We hope this work inspire more innovations and conversations at the intersection of user centered design and visual analytics.

The field of computer graphics combines display hardware, software, and interactive techniques in order to display and interact with data generated by applications. Visualization is concerned with exploring data and information graphically in such a way as to gain information from the data and determine significance. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. Expanding the Frontiers of Visual Analytics and Visualization provides a review of the state of the art in computer graphics, visualization, and visual analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in visualization and visual analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of visual, multimedia, and multimodal user interfaces, visual analysis at extreme scale, and adaptive visualization.

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. The multilayer network model recognizes that the complexity of relationships between entities in real-world systems is better embraced as several interdependent subsystems (or layers) rather than a simple graph approach. Despite only recently being formalized and defined, this model can be applied to problems in the domains of life sciences, sociology, digital humanities, and more. Within the domain of network visualization there already are many existing systems, which visualize data sets having many characteristics of multilayer networks, and many techniques, which are applicable to their visualization. In this Synthesis Lecture, we provide an overview and structured analysis of contemporary multilayer network visualization. This is not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those solving problems within application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer network visualization, as well as tools, tasks, and analytic techniques from within application domains. We also identify the research opportunities and examine outstanding challenges for multilayer network visualization along with potential solutions and future research directions for addressing them.

Data science addresses the need to extract knowledge and information from data volumes, often from real-time sources in a wide variety of disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, business, and the humanities. The range and volume of data sources has increased enormously over time, particularly those generating real-time data. This has posed additional challenges for data management and data analysis of the data and effective representation and display. A wide range of application areas are able to benefit from the latest visual tools and facilities. Rapid analysis is needed in areas where immediate decisions need to be made. Such areas include weather forecasting, the stock exchange, and security threats. In areas where the volume of data being produced far exceeds the current capacity to analyze all of it, attention is being focussed how best to address these challenges. Optimum ways of addressing large data sets across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. Being driven by national priority, they are able to attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. Visual computing is a set of tools and methodologies that utilize 2D and 3D images to extract information from data. Such methods include data analysis, simulation, and interactive exploration. These are analyzed and discussed.