Abstract
This article focuses on the visual analysis of human health data that describe the number of cases of different diagnoses in a spatial and temporal frame of reference. To build a common basis for the later description of different visualization methods, basic concepts of visualization as well as an abstract data model are illustrated in Section 2. In the main part of this article we describe the visualization of human health data at different levels (see Section 3). Whereas basic visual methods for representing human health data abstractly are presented only briefly, the visualization of data with respect to space and time is described in more detail. This article concludes with remarks on future work and trends in Section 4 and a brief summary of the key issues described in this article (see Section 5).
Key Terms in this Chapter
Multivariate Data: Data that contain multiple attributes measured in some observations space are denoted multivariate data.
Visualization Pipeline: Describes data analysis, filtering, mapping, and rendering as steps of visualization.
Visualization: A visual approach to gaining insight into data.
Expressiveness: A visual representation is expressive if it communicates all information that is relevant with respect to the current analysis task.
Interaction: Denotes the use of specific techniques to adjust visual representations according to the task at hand.
Data Cube: The dimensions of a dataset can be modeled as a hyper-cube, which allows an easy selection of relevant parts of the data.
Appropriateness: A visual representation is appropriate if its benefit and creation effort are balanced.
Effectiveness: A visual representation is effective if it has been generated in accordance with the capabilities of the human visual system.