Johanna Drucker starts off by clearly making a distinction between Visualizations that represent information, and those that are knowledge generators - which creates new interpretations and information from their use.She breaks down the visualizations by: * Graphical Formats - bar charts, line graphs, etc* Intellectual purpose or Function - mapping,…
FrontmatterJohanna Drucker begins with discussing 2 visuals. One by Kircher in 1669 – a system of the mystic Ramon Llull’s ‘Great art of knowing’ – as PRODUCING the knowledge it draws (which looks like and idea of how humans make connections(?) and Kircher’s computer generated image, although looking quite similar,…
It is important to give color its due worth of being able to intuitively tell the stories of data. Lightness (Black, white, Grayscales) , Hue (Color) and Saturation (sometimes known as Chroma, which is the saturation of color, using either color or grays to create these colors).Connecting color to meaning…
Misleading Axis on GraphsGood data visualization seems to be utilitarian in a way, perhaps devoid of taste and style. But after reading the intro to this essay, and after our discussion today, I feel that we all come equipped with a ‘point of view’ – as a designer and a viewer.…
created in 1973 by Herman Chernoff - an applied mathematicianThe Chernoff 'faces' (called GLYPHs - a symbol or pictograph) are probably the easiest and possibly the most humanistic way of visualizing data. Its called a 'MULTIVARIATE' (or more than 2 variables ) ANALYSIS. These faces are universal, quick and easy. All…