Interpreting Visualization :: Visualizing Interpretation

In this chapter, Drucker lays out the core of her argument in this book: That all forms of data representation/visualization are actually acts of interpretation, even if we don't see them as such anymore. She makes this point very compellingly by pointing out that much of our thoughts about concepts of time, mapping and mathematics come from (sometimes arbitrary) graphical conventions that are so common place we don't notice them anymore (like breaking time into discrete 'day' chunks or taking for granted the implicit chart form underlying basic arithmetic operations).

Drucker writes,

"So naturalized are the maps and bar charts generated from spread sheets that they pass as unquestioned representations of "what is."" (p. 125, emphasis added)

She then argues that we need to develop methods of visualization that acknowledge this fluid aspect of interpretation to bring to the forefront the fact that all data already includes some level of interpretation.

"Rendering observation (the act of creating a statistical, empirical, or subjective account or image) as if it were the same as the phenomena observed collapses the critical distance between the phenomenal world and its interpretation" (p. 125)

Then, to make her argument more tangible, she then asks us to imagine if Snow's famous chart of deaths from cholera could be redrawn "to express the emotional landscape" or "from the point of view of a mother of six young children." This is where I start to disagree with her position. While I do agree that it is important to keep in mind the social conventions that come pre-baked, so to speak, in our representations of data, it is also exactly this ability to abstract from an individual's viewpoint that gives strength and generalizable force to data visualizations.

She even touches on this in the section about cartography,

"We cannot "see" the land's shape, its contours, or outlines ... But abstracting this into a topographic view requires understanding the rationalization of surface and its ordered schemes" (p. 77-78)

Overall, I think Drucker's critical analysis of the interpretive nature of data is incredibly strong and compelling as she astutely points out graphical conventions that are taken for granted and add implicit meaning to representations. However, her call to action for re-examining data visualizations from a humanistic perspective goes too far and seems naive in underplaying the importance of abstraction.