Visualization as a Knowledge Transfer

Visualization as a Knowledge Transfer

Anna Ursyn (University of Northern Colorado, USA)
DOI: 10.4018/978-1-5225-7659-4.ch034
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This chapter inspects visualization tools and applications enabling visualization of data, information, and knowledge. The background is provided first about visualizing information in a pictorial yet abstract rather than illustrative way. Further text discusses visual metaphors as a basic structure in metaphorical language of visualization. Selected methods and tools are introduced, and the ways visualization transfers knowledge and mediates between the user and the physical world supporting cognitive ways of learning and teaching are shown. Nature-derived metaphors serve as bio-inspired, interdisciplinary models. The importance of visual and technological literacy is discussed, along with a need of teaching visualization methods as an important part of the current educational strategies. Concluding remarks examine how metaphorical visualization may support learning and teaching, and why visual and technological literacy should be taught and trained since early childhood.
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Information is usually presented in numerical, graphic, or diagrammatic form; it may be shown as a sketch, drawing, diagram, plan, outline, image, geometric relationship, map, music and dance notation, object, interactive installation, or a story. Diagrams visualize information in a pictorial yet abstract (rather than illustrative) way: as plots, line-graphs and charts, or the engineers or architects’ blueprints. Complicated presentations of data organization and interpretation, for example governmental statistics are easier to comprehend in a graphic than in a numerical form, when they serve as explanatory tools for the data sets. Thus, visualizations change numerical data into graphs, clouds (Chen, 2010), tree visualizations (Shneiderman, 2014; Lima, 2014), network data, time-based, interactive, metaphorical visualization designs, and other formats.

Visualization means the communication of information with graphical representations. At the present time, visualization means using the computer, which transforms data into information, and then visualization converts information into picture forms. Graphic images and symbols convey and express the meaning of abstract data, which lets us comprehend data and make discoveries, decisions, or explanations about patterns or individual items (Shneiderman, 1996). Thus communication through visualization is at the same time pictorial and linguistic. It is socially and culturally conditioned, based on familiar linguistic patterns, as in a ‘pie chart’ metaphor for market shares, or a ‘starry night’ metaphor showing data in 3D (Bertschi & Bubenhofer, 2005).

Cognitive way of learning and teaching may involve cooperation with specialists in several disciplines. Computer scientists and artists apply visual way of presentation while working, for example with mathematicians, anthropologists, designers, and architects to conduct computer analysis of facades and architectural details. Professionals performing scientific presentations and researchers in fields of natural sciences, medicine, pharmacology, biology, geology, or chemistry examine and visualize symmetry and patterns in natural and human-made structures. Many artists have created masterpieces this way. Visualization serves as an efficient tool that assists practitioners creating communication media-art, installations, animated video or film, architectural projects, designing newspapers and magazines, or working on website design. Users apply visualizations to understand how data analyses and queries relate to each other. From simple charts and data graphics to 3D multi-user virtual reality environments happening in real time with human interaction possible, visualizations let us fly around the organized data, comprehend, and make decisions (Chen, 2010, 2011). Structural modeling of the relationships may involve the use of graphs, trees, or cones; detecting proximity and connectivity; clustering and classification using word search; multi-dimensional-scaling; network analysis; glyphs (single graphical units portraying many variables by adapting their properties) on charts and graphs; virtual structures; applying complex network theory, and network representations (Chen, 2010).

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