Martin Theus

High-dimensional Data Visualization

One of the biggest challenges in data visualization is to find general representations of data that can display the multivariate structure of more than two variables. Several graphic types like mosaicplots, parallel coordinate plots, trellis displays, and the grand tour have been developed over the course of the last three decades. Each of these plots is introduced in a specific chapter of this handbook. This chapter will concentrate on investigating the strengths and weaknesses of these plots and techniques and contrast them in the light of data analysis problems. One very important issue is the aspect of interactivity. Except for trellis displays, all the above plots need interactive features to rise to their full power. Some, like the grand tour, are only defined by using dynamic graphics.

Creating Instruments for Ideation: Software Approaches to Geovisualization

Publisher Summary Ideation relates to the formation of ideas and concepts—the end goal of geovisualization. There are many tools and techniques for creating instruments for ideation—sophisticated hardware, advanced programming languages, graphics libraries, visual programming systems, and complex GUIs. In each, the developer or visualizer wishes to generate effective interactive graphic realizations of their data that are useful to them and/or their users. This chapter expands upon these ideas and considers the way each of these issues influences the uses and development of software instruments that support the exploratory process. Some examples of software approaches are also documented. Current technology has an important enabling and limiting impact upon the available range of instruments for ideation, which changes significantly over time. A major benefit of contemporary computer technology is the possibility to rapidly generate various graphical displays from data. This gives an opportunity to try alternative transient realizations of data, to discard those deemed ineffectual but when necessary reproduce them again, and to look at several displays simultaneously to provide multiple views of data. It is not only the increase of computer power that offers new opportunities to create more sophisticated instruments, but also the progress in software environments such as the development of programming tools that are high level and/or cross platform and the availability of libraries and reusable software components.

Statistical Data Exploration and Geographical Information Visualization

Publisher Summary This chapter focuses on the analysis of the data of a geographical referenced data set and the integration of these analyses within the geographical context. The geographical reference is the dominant aspect of geographical data; these data can draw upon statistical principles and methodologies to be investigated thoroughly. Exploratory data analysis (EDA) has been widely adopted by many disciplines beyond statistics. For these methods to be applied appropriately, the statistical background of EDA should be considered and drawn upon. Much of statistical data analysis has its roots in mathematics, whereas most of geovisualization draws directly from cartography. Currently, both fields make heavy use of computing power and computer graphics and take advantage of highly interactive interfaces. Several software packages have been developed to either incorporate geographical information into interactive statistical software, or vice versa to incorporate statistical graphics into Geographical Information Systems (GIS) and geovisualization environments. This chapter investigates the overlap and differences between the two areas and identifies a possible synergy between statisticians and geographers in the light of geovisualization.

Exploratory Graphics of a Financial Dataset

The first stages of any data analysis are to get to know the aims of the study and to get to know the data. In this study the main goal is to predict a company’s chances of going bankrupt based on its recent financial returns. In another chapter of the Handbook, some sophisticated prediction models based on support vector machines are discussed for a similar dataset. Here, visualization methods are used to explore the large dataset of American company accounts that was made available for predicting bankruptcy in order to get to know the data and to assess the quality of the dataset. This is an initial exploratory analysis that does not use any expert accounting knowledge.

Creating Instruments for Ideation

Publisher Summary Ideation relates to the formation of ideas and concepts—the end goal of geovisualization. There are many tools and techniques for creating instruments for ideation—sophisticated hardware, advanced programming languages, graphics libraries, visual programming systems, and complex GUIs. In each, the developer or visualizer wishes to generate effective interactive graphic realizations of their data that are useful to them and/or their users. This chapter expands upon these ideas and considers the way each of these issues influences the uses and development of software instruments that support the exploratory process. Some examples of software approaches are also documented. Current technology has an important enabling and limiting impact upon the available range of instruments for ideation, which changes significantly over time. A major benefit of contemporary computer technology is the possibility to rapidly generate various graphical displays from data. This gives an opportunity to try alternative transient realizations of data, to discard those deemed ineffectual but when necessary reproduce them again, and to look at several displays simultaneously to provide multiple views of data. It is not only the increase of computer power that offers new opportunities to create more sophisticated instruments, but also the progress in software environments such as the development of programming tools that are high level and/or cross platform and the availability of libraries and reusable software components.