Varieties of Data Visualization

Information visualization is the broadest term that could be taken to subsume all the developments described here. At this level, almost anything, if sufficiently organized, is information of a sort. Tables, graphs, maps and even text, whether static or dynamic, provide some means to see what lies within, determine the answer to a question, find relations, and perhaps apprehend things which could not be seen so readily in other forms.

In this sense, information visualization takes us back to the earliest scratches of forms on rocks, to the development of pictoria as mnemonic devices in illuminated manuscripts, and to the earliest use of diagrams in the history of science and mathematics.

But, as used today, the term information visualization is generally applied to the visual representation of large-scale collections of non-numerical information, such as files and lines of code in software systems (Eick:1994), library and bibliographic databases, networks of relations on the internet, and so forth. In this document we avoid both the earliest, and most of the latest uses in this sense.

Another present field, called scientific visualization, is also under-represented here, but for reasons of lack of expertise rather than interest. This area is primarily concerned with the visualization of 3-D+ phenomena (architectural, meterological, medical, biological, etc.), where the emphasis is on realistic renderings of volumes, surfaces, illumination sources, and so forth, perhaps with a dynamic (time) component. Finally, the areas of visual design and information graphics both draw on, and contribute to, the content presented here, but are also under-represented.

Instead, we focus on the slightly narrower domain of data visualization, the science of visual representation of "data", defined as information which has been abstracted in some schematic form, including attributes or variables for the units of information. This topic could be taken to subsume the two main focii: statistical graphics, and thematic cartography.

Both of these are concerned with the visual representation of quantitative and categorical data, but driven by different representational goals. Cartographic visualization is primarily concerned with representation constrained to a spatial domain; statistical graphics applies to any domain in which graphical methods are employed in the service of statistical analysis. There is a lot of overlap, but more importantly, they share common historical themes of intellectual, scientific, and technological development.

In addition, cartography and statistical graphics share the common goals of visual representation for exploration and discovery. These range from the simple mapping of locations (land mass, rivers, terrain), to spatial distributions of geographic characteristics (species, disease, ecosystems), to the wide variety of graphic methods used to portray patterns, trends, and indications.