Perception

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(136.152.168.242 - Feb 09, 2011 01:56:43 pm: new section)
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Regarding Figure 29 in the Cleveland & McGill paper, I would argue that this graph and a more traditional heat or patch map serve two different purposes.  Although in this map it is easier to compare the murder rates from one state to another it does not take advantage of the preattentive processing described in the Healey paper.  A traditional heat map gives a good overview of quantitative variables but it allows for only minimal comparison of similar values.  This type of map gives a poor overview of quantitative variables and requires postattentive processing for finding anomolies, but allows for much more nuanced comparison of individual states' values even when the difference between them is minimal.
Regarding Figure 29 in the Cleveland & McGill paper, I would argue that this graph and a more traditional heat or patch map serve two different purposes.  Although in this map it is easier to compare the murder rates from one state to another it does not take advantage of the preattentive processing described in the Healey paper.  A traditional heat map gives a good overview of quantitative variables but it allows for only minimal comparison of similar values.  This type of map gives a poor overview of quantitative variables and requires postattentive processing for finding anomolies, but allows for much more nuanced comparison of individual states' values even when the difference between them is minimal.
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== Michael Cohen - Feb 09, 2011 05:03:53 pm ==
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I was frustrated by the "perceptually-motivated" visualizations featured in the Healey article, particularly the electoral map at the top and the weather history map (Fig. 17).  I think this frustration spilled over into our discussion of distinguishing orientation and length in class and led me to question whether using both together would be a good idea in practice.  I think Healey's visualizations are impressive in that they demonstrate that you ''can'' cram so many dimensions of data into a single visualization and have it all be fundamentally perceptible.  However, in practice I think both visualizations are difficult to read and draw useful conclusions from.  In the electoral map, the depth of each state corresponds roughly to its importance in the election, but since depths obscure other depths they are very difficult to compare except between geographically adjacent states.  The depth dimension also causes some states to obscure others (e.g., California & Oregon).  In the weather map, the use of size and density of strokes to encode some information means that the Southeast has significantly lower data density than the North on dimensions encoded by color and orientation.  It's also difficult to disentangle density and size visually.  I think the weather map, in particular, could have been better handled with small multiples.
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To me, these visualizations illustrate that more information in a single display isn't always better, even if your perceptual theory argues that all of the information is available.  The interference between dimensions is an crucial factor.

Revision as of 23:28, 9 February 2011

Lecture on Feb 2, 2011

Slides

Contents

Readings

  • Perception in visualization. Healey. (html)
  • Graphical perception. Cleveland & McGill. (jstor)(Google Scholar)
  • Chapter 3: Layering and Separation, In Envisioning Information. Tufte.

Optional Readings

  • Gestalt and composition. In Course #13, SIGGRAPH 2002. Durand. (1-up pdf) (6-up pdf)
  • The psychophysics of sensory function. Stevens. (pdf)
  • Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. Heer & Bostock. ACM CHI 2010. (html)

136.152.168.242 - Feb 09, 2011 01:56:43 pm

Regarding Figure 29 in the Cleveland & McGill paper, I would argue that this graph and a more traditional heat or patch map serve two different purposes. Although in this map it is easier to compare the murder rates from one state to another it does not take advantage of the preattentive processing described in the Healey paper. A traditional heat map gives a good overview of quantitative variables but it allows for only minimal comparison of similar values. This type of map gives a poor overview of quantitative variables and requires postattentive processing for finding anomolies, but allows for much more nuanced comparison of individual states' values even when the difference between them is minimal.

Michael Cohen - Feb 09, 2011 05:03:53 pm

I was frustrated by the "perceptually-motivated" visualizations featured in the Healey article, particularly the electoral map at the top and the weather history map (Fig. 17). I think this frustration spilled over into our discussion of distinguishing orientation and length in class and led me to question whether using both together would be a good idea in practice. I think Healey's visualizations are impressive in that they demonstrate that you can cram so many dimensions of data into a single visualization and have it all be fundamentally perceptible. However, in practice I think both visualizations are difficult to read and draw useful conclusions from. In the electoral map, the depth of each state corresponds roughly to its importance in the election, but since depths obscure other depths they are very difficult to compare except between geographically adjacent states. The depth dimension also causes some states to obscure others (e.g., California & Oregon). In the weather map, the use of size and density of strokes to encode some information means that the Southeast has significantly lower data density than the North on dimensions encoded by color and orientation. It's also difficult to disentangle density and size visually. I think the weather map, in particular, could have been better handled with small multiples.

To me, these visualizations illustrate that more information in a single display isn't always better, even if your perceptual theory argues that all of the information is available. The interference between dimensions is an crucial factor.

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