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KBeach

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Reply with quote  #1 
While reading about bricks, I was beginning to think if there is some way to include trending into bricks, while retaining the original quantitative value .  For example by using gradient shading of the bricks (e.g. bottom row green, middle row yellow  upwards into red) representing a downward trend, the inverse true for upward trends.  Or perhaps using gradients in the bins which is programmatically more difficult.  Obviously, by trying to implement trending into bricks,  this would negate the ability to use diverging scales of positive and negative values. But if you were using all positive values – would recommend this approach of trying to implement trending values into bricks?  Obviously your point #2 in What’s the Downside about border color would need to be taken into consideration.    Wanted to know your thoughts on this. 
sfew

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Reply with quote  #2 
KBeach,

I like the idea of encoding the direction of change in a set of bricks, but think this could be done more simply. Rather than using gradients of multiple hues in a single set of bricks, what if sets of bricks appeared in one color, such as blue, for increases, another color, such as red, for decreases, and another color, such as gray for little or no change? Gradients of multiple hues involves a degree of visual complexity that doesn't seem necessary, and it probably wouldn't be practical when a set of bricks consists of a single row, or even worse, a single small brick for a low outlier value.

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Stephen Few
ClaudeHenri_MELEDO

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Reply with quote  #3 
I use "bricks" for many years... Because it's almost always more efficient than bubbles.

This knowledge is obvious when you remember what said some dataviz experts:
  • Otto Neurath (born in 19th century) said that it's better to reproduce several times the same symbol (rather than enlarging it).
  • .
  • Including the scale inside the chart is a known cleverness to increase the "data ink ratio" (the popular Tufte's motto)
  • .
  • Without a scale, the human eye makes large mistake when estimating surfaces. This mistake ratio (0.7) is even known as a famous law: "Steven's Power Law" for visual area... superseding the wide Weber–Fechner law 

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sfew

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Reply with quote  #4 
Claude Henri,

I admire Neurath's work a great deal, but I don't agree that it is better to reproduce a symbol many times rather than enlarging it, at least not in data visualization. By keeping the bricks within a number that can be subitized (i.e., can be perceived preattentively), I am striving to keep the process of visual comparison efficient.

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Stephen Few
nixnut

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Reply with quote  #5 
It's certainly an interesting idea worth exploring. I am wondering in which cases using 'bricks' work better than simply using a number (or perhaps a letter) to indicate a score, a percentile or a rank for a geographic area. I suspect that in a lot of cases a label like 73% is processed faster than the eight-brick shape. Wouldn't the bricks still need to be translated to the interval in which the value for the region falls?
sfew

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Reply with quote  #6 
Nixnut,

Numbers represented textually must be read. This is a slow process. Bricks and other forms of graphical display transfer the work from the verbal processing part of the brain to the visual processing part of the brain to make the perceptual process much more efficient and to reveal patterns that can't be seen in text.

To decode values represented by bricks, you must refer to the key. Most of what we do when viewing values on a map, however, involves magnitude comparisons and pattern detection, not precise decoding of values. If the primary purpose of the display is to provide precise values, a table of numbers would do the job best.

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Stephen Few
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