Catálogo de publicaciones - libros

In this chapter, we introduced geographical visualization as the starting point of our journey, emphasizing that the goal of information visualization is to represent abstract information spaces intuitively and naturally. We also pointed out that the power of information visualization will only be fully understood when information visualization becomes an integral part of users’ activity. Optimal foraging theory and cognitive maps were introduced, to provide a wider context in which to shape the requirements for information visualization. In Chapter 2, we focus on techniques to extract salient structures from a complex information system, for the purpose of information visualization. In subsequent chapters, we will demonstrate the visualization techniques available to deal with these structures.

In this chapter, we have introduced several major aspects of information visualization: structural modeling, in particular, the use of the vector space model and its variants, multidimensional scaling and trajectory mapping. There was also an introduction to the generalized similarity analysis (GSA) framework, giving several examples to illustrate its extensibility and flexibility. More examples are cited in subsequent chapters. The next chapter focuses on graphic representation, another fundamental aspect of information visualization, introducing some of the most popular and advanced spatial layout algorithms.

In this chapter, we have discussed a variety of empirical studies concerning various aspects of information visualization, ranging from the usage of classic information visualization designs such as cone trees and hyperbolic view browsers to individual differences in terms of spatial ability, associative memory, and visual memory. Much of the existing empirical studies can be divided into ones that deal with lower-level elementary perceptual tasks or higher-level application-related tasks. Although information visualization can no doubt benefit from all empirical studies, the crucial and profound understanding at this stage is more likely to come from the study of lower-level tasks. Many application-oriented empirical studies have generated valuable insights into the complex relationship between tasks and visual cues. On the other hand, the empirical link between visual attributes and perceptual tasks is still missing in many areas of information visualization.

In this chapter, we started with a number of examples outside the mainstream information visualization to illustrate the complexity of detecting abrupt changes in phenomena such as the collapse of a civilization, detecting thematic changes, monitoring business activities in recessions, and identifying intellectual turning points. The second part of the chapter introduces the CiteSpace system and its application to the visualization of superstring revolutions. We have emphasized the significant role of knowledge discovery and data mining techniques in the second generation of information visualization. The success of the first generation of information visualization is largely related to the structure- centric focus. We argue that the second generation of information visualization needs to go beyond the structure-centric mindset. One of the crucial components of the second generation is a dynamics-centric focus, which is an emphasis of increasing challenges to visualize the profound dynamics that govern rapid as well as gradual changes in so many practical issues. In this context, we also call for more substantial integration between knowledge discovery and data mining and the mainstream of information visualization. As information visualization extends to a wider range of practical domains, it is inevitable to incorporate technologies that can efficiently detect changes and emerging patterns.