Interpreting Visualization :: Visualizing Interpretation

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, navigating, calculation, etc.
* Type of content - spacial, quantitative, qualitative, temporal, etc.
* The way they structure the meaning - analogy, comparison, vectors, columns, etc.

She goes on to describe our historical reference to visualizations through:

  • Timekeeping - interesting points in this to me were 'performative' actions, visualizing the temporal vs the spiritual world.  Humans needing to have a system in which to describe and make sense of their time on earth.  Abstraction and subjectivity of Time as opposed to how we interpret and use time.
  • Space-making - why do we take space and feel the need to standardize and rectify it?  Why do we attempt to put 3-dimensions into 2-D? Navigation was important in our ideas of why we needed space to be interpretable. It was also interesting that we even try to give time a 'space' quotient, so we don't feel unmoored.
  • Record keeping - helpful, and useful for future generations, as well as documenting why and how things can work.  This is rather obvious – and useful. It also has the downside of being totally inaccurate.
  • Trees of Knowledge - static and fixed, but represents growth and amounts. Family 'trees' are interesting since we think of the bottom of 'trees' as the oldest, but also the foundation of which all new growth springs from.  
  • Knowledge Generators - seem to spin around, and reinterpret different uses of meanings taking a set group of data and apply different uses/purposes to them.  
  • Dynamic Systems - like to display process rather than product.  Sometimes using direction, and different phenomena that displays an ever changing world, and again, try to make sense of it.  Transformations, and depicting them are ways to understand how chaos is a part of our lives – The law of chance, and time allowing the organization of this data.
  • Uncertainty and Cartographic Interpretations - data as interpretive.  How do we, and why do we want to understand and interpret data as graphically as possible? This is really interesting, and as an example, the use of the Olympics was used.  Gender definition has increasingly become 'fluid', and we, or have we(?) figured out how to interpret and represent this?

Humanistic Methods - a humanistic method of looking at data is ordered subjectively.  But doesn't everything have a 'point of view'?  That is the 'fuzzy logic' of the data – and constructing the graphical interfaces cannot be standardized. ANXIETY – or an interpretation of anxiety exposes the information, rather than conceals.  Who decides how this is displayed?  Who has that authority?  

As we move onto more and more graphic depictions of data – 'WHO' represents dots, lines, columns?  Are they real people, issues, emotions?  How do we, as designers attempt to make the bridge to a more humanistic approach?

My take:  I enjoyed this chapter very much – especially as it moved towards questioning our ability to understand how to 'humanize' the data.  

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