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The title of this book—Human-Centered Visualization Environments (HCVE)—is composed of two key components: “human-centered” and “visualization environments”. While the first part refers to the research area of (HCI), the second one refers to the design of systems. Thus, the focus of this textbook is on the intersection of both fields. These two terms can be defined as follows:

Humans have remarkable perceptual capabilities. These capabilities are heavily underestimated in current visualizations [759]. Often, this is due to the lack of an in-depth user study to set the requirements for optimal visualizations. The designer does not understand what kind of information should be visualized, how it should be presented or what kind of interactions should be supported. The key elements of successful information visualization are the correct data using the best visualization technique and the best interaction techniques with respect to users. If one of these elements is ignored, people might interpret the data in the wrong way and thus might not understand the underlying information or a pattern.

In order to design effective interactive visualizations, it is important to take into account the limitations of human perception, context of use, and the goals and activities that are to be performed to reach these goals. In order to obtain a application, developers have to pay attention to the user’s working environment and tasks; this idea is comprised in the human-centered concept.

The next section discusses usability (the property of being usable) from the human-centered point of view. Usability has application in many areas, but our focus is on the approach to design of interactive systems, also called , in order to inform the reader on how to design visualizations according to human cognitive and perceptual abilities, specific to the context of use and goals of potential users. Then, the usability concept is explained in the “Usability in Human-Centered Design” section. The next Section “User Aims and Requirements” discusses how to define a user group, establish user goals and requirements. Finally, an overview of the different evaluation methods and current evaluation practices, including the practical issues of experiment design that can help to improve the effectiveness of visualizations is presented in the “Evaluation of Visualizations Environments” and “User Studies and a Science of Visualization” sections.

In human-media interactions, visualizations occur in a large variety of forms. However, they remain but a single form of possible feedback towards a user. In this chapter it is argued that human-centered visualization is a fundamental part of human-media interaction and vice versa. To that end, the focus in this chapter lies first on the more general topic of interaction research, thus providing a solid literary ground for the rest of this chapter (Section 3.1). In Section 3.2 the focus shifts towards the question how a display technology influences the way(s) in which interaction with visualization takes place. This path is then broadened in Section 3.3 by focusing on approaches towards interacting multimodally with visualizations. To that end a chronological overview of developments in that field is given, thus providing an insight of trends and required steps in realizing multimodal interactions. Also, future work in this field is deducted from literature and those development trends. Fourthly, Section 3.4 describes the issues at hand for effectively applying visualizations in group-based collaborative and distributed environments. The aim in this chapter is to provide an overview of developments and the current state-of-the-art of approaches in which visualization supports the human-machine interaction process and vice versa. For that, this chapter is finalized with a short summary of issues to deal with while designing interaction for visualization. In addition current and future challenges in interacting with visualizations will be discussed.

Visualization can help in the process of extracting insight from data during decision-making. Its advantages are based on the ability to rapidly interpret large quantities of data. The challenge in this context consists in constructing visualization systems which enable the user to understand and perceive the data effectively by providing transparent interaction methods for effective communication between the user and the data. Ware [872] states a number of advantages of visualization, namely:

Visual representations range from simple maps and schematic diagrams to computer generated 2D and 3D visualizations. They differ themselves from other kinds of representations, e.g., sentential representations, in the fact that they make use of to convey the information to the user. In contrast, sentential representations use the semantics of their constituent units, words, and letters, to achieve the same thing. Thus, the meaning of a word is dependent on its context and its letters but not on the spatial position of the word.

The aim of visual representations is dependent on the context of their usage. However, their advantages are usually focused on these three categories [85]: Whereas the first two advantages are valid also for other kinds of representations, the last one is specific for visual representations.

This chapter is concerned with visual representations in the context of human-centered visualization environments. Considering the variety of issues that relate to visual representations, it will focus on computer-generated visual representations in different contexts. Before introducing the various techniques which are used to visualize specific data types, a short introduction to perceptual issues is given in Section 4.1. Furthermore, different issues, such as taxonomies for data, visual variables and their ability to express different data types, are discussed in Section 4.1. Section 4.2 focuses on general criteria used in information visualization and the use of metaphors. Section 4.3 presents a survey of different visualization techniques, mainly in the context of multivariate data, by discussing properties of different techniques related to their comprehensive and interaction properties, advantages, and drawbacks with illustrations given for different data sets. Section 4.4 provides an overview of existing evaluations on visualization techniques designed for graphs and trees. The survey mainly focuses on the effect of graph layout algorithms which compute the position of nodes and edges, as well as on user’s understanding of graphs for different tasks. Section 4.5 covers several issues concerning multiple view visualizations. Apart from a classification of multiple view instances, design-issues are discussed. The section is concluded with a comparison based on evaluation studies between multiple views and integrated views.

Self-criticism, evaluation, solved and unsolved problems, and future directions are wide-spread themes pervading the visualization community today. The top unsolved problems in both scientific and information visualization was the subject of an IEEE Visualization Conference panel in 2004 [706]. The future of graphics hardware was another important topic of discussion the same year [414]. A critical evaluation of usability and utility of visualization software was also the focus of a recent panel discussion [307]. The topic of how to evaluate visualization came up again two years later [370, 852]. Chris Johnson recently published his list of top problems in scientific visualization research [409]. This was followed up by report of both past achievements and future challenges in visualization research as well as financial support recommendations to the National Science Foundation (NSF) and National Institute of Health (NIH) [410]. That report is the result of two workshops that took place in the Fall of 2004 and Spring of 2005 on visualization research challenges and also includes input from the larger visualization community. C. Chen recently published the first list of top unsolved information visualization problems [154]. Future research directions of topology-based visualization was also a major theme of a workshop on topology-based methods [341, 736].

Geographic visualizations always played an important role in human history, especially in the earth sciences, long before computer visualizations became popular. The earliest examples of geographic visualization even date back to the stone age with map-like wall paintings depicting the surroundings of our ancestors. Since then cartography, the art and science of map-making, has evolved continuously until today. This is why computer-based geographic visualization can build upon a large base of established cartographic knowledge. Well-known examples of static visualizations beyond geographic maps are thematic maps that display the spatial pattern of a theme such as climate characteristics or population density. Moreover, the use of modern visualization technology offers many new possibilities for geographical visualization tasks. These visualizations may help to explore, understand, and communicate spatial phenomena.

Many readers will already have some preconceived ideas of what geographic visualization is about. Nonetheless, to avoid misconceptions, the most common definitions of the term (short for geographic visualization) will be given. The following explanation according to the 2001 research agenda of the International Cartographic Association (ICA) Commission on Visualization and Virtual Environments is most widely accepted today:  [528]. Others take a more human-centered view and describe geovisualization as  [513] or as  [473]. There are a few immediate observations from these definitions. It is clear that geovisualization research is a multidisciplinary task. Since it is the human who uses visualizations to explore data and construct knowledge, effective geovisualization techniques must above all take the user needs into account.

The chapter is structured as follows. First, the range of possible goals of geovisualization and its driving forces are described in Sections 6.1 and 6.2, respectively. Then, Section 6.3 looks at some perceptual issues and theoretical results in geovisualization. The main part of the survey, Section 6.4, covers a variety of suitable visualization methods from map-based and abstract techniques to animation. It also deals with interaction techniques and combined visual and computational mining of geospatial data. A number of existing geovisualization environments and tools are described in Section 6.5. Usability issues in the context of geovisualization, namely user-centered design, results from user studies, and geovisualization that supports group work, are reported in Section 6.6. The final part of this chapter looks at current and future challenges in geovisualization.

In the past years, has become an important area in computer science. The ever growing software system complexity is the main reason for this phenomenon. Many software visualization tools are developed to help to understand, improve, test and debug programs. Several extensive anthologies on Software Visualization providing overviews of the field were published, cp. [230, 789, 197]. In general, Software Visualization is used for

This chapter is dedicated to and , which are two of the subfields in Software Visualization that focus primarily on education. Both of them convey information about programs and algorithms through the use of animated text and images that helps students in creating mental models how programs and algorithms work. consist of direct representations of programming constructs, e.g., object instances or method frames, whose values or positions change over time. deals with the representation of the program at a more abstract level, see for example [440]. Algorithms use advanced data structures to solve problems that contain clear semantic values and follow certain rules. Such data structures can be graphically represented in different ways, and operations on these structures require special animations. In spite of the differences, we are going to consider both approaches as the same in this section and name them as .

The chapter starts with a short overview of the field, and in Section 7.2, we distinguish the two groups of users of algorithm animation. The field of Software Visualization, and specially algorithm animation, has been subject of several classifications and taxonomies. Section 7.3 summarizes the most important taxonomies from a human-centered point of view. In Section 7.4, a set of algorithm animation tools are reviewed. These reviews summarize the way users, either teachers or students, can work with the tools, how these tools have been evaluated, and which results have been reported.

The ongoing developments in the fields of molecular biology, genetics, and medical sciences have recently generated an explosion of information collected from living organisms. For the scientist who analyzes these huge amounts of data, information visualization can be a very useful tool.

Because an exhaustive survey of the use of information visualization in the molecular biology, genetics and medical science is beyond the scope or space of this chapter, the chapter will present several highlights on the use of information visualization in the aforementioned domains.

The reader is not expected to be an expert in any of the analyzed fields, in fact, the material is organized in such a way that it does not make any assumptions on reader’s domain knowledge. This means that for the domain expert, the level of discourse might be superficial, but one can always refer to the original papers for more details. The assumption that the reader is not an expert has also determined our organization of the material: the analyzed tools are grouped according to problem domains, and each section begins by providing a brief introduction to the domain before presenting the tools and techniques. The problem domains that are addressed are the following:

The analysis of each tool described in this chapter is based on several of the following information visualization issues which are partially inspired by the work of Card et al. [145]: