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Posts tagged with data visualisation




afrographique:

An infographic celebrating African Nobel Prize winners from across the continent.

afrographique:

An infographic celebrating African Nobel Prize winners from across the continent.


hi-res




The classic red/green colouring scheme for trading screens seems too alarmist.

http://media.dailyfx.com/illustrations/2012/04/30/AUDUSD_Trading_the_Reserve_Bank_of_Australia_Interest_Rate_Decision_body_ScreenShot100.png

http://graphics.moneyshow.com/traders/TipsCharts/March2012/daytraders07_1_med.gif

http://i.istockimg.com/file_thumbview_approve/7204532/2/stock-photo-7204532-stock-market-financial-trading-screen-in-green-and-red.jpg

http://accuratestocktrading.com/wp-content/uploads/2010/01/screenshot-when-email-alert5.jpg
http://media.dailyfx.com/illustrations/2012/04/30/AUDUSD_Trading_the_Reserve_Bank_of_Australia_Interest_Rate_Decision_body_ScreenShot100.png
http://4xlounge.com/wp-content/uploads/2011/07/tbconsolelive.png

Conceptually, the red/green distinction makes sense as corresponding to stop/go in traffic signals. But traffic signals need to be neon and striking in a hectic 3-D environment where it’s paramount for everyone to definitely not-miss the stop command.

But in a sheltered 2-D environment where goals commonly include to master emotion, to control passive reactivity, to keep a long-term head in the middle of short-term volatility, and to digest (calmly) massive amounts of information en simultáneo, neon red/green seems too grating.

 

One theory of the evolution of trichromacy in primates says that

  • red/green dichotomy tells us whether meat or fruit is rotten or ripe (especially in dappled light)
  • blue/yellow dichotomy tells us how cool/warm something is
  • light/dark (value) is the most basic kind of vision.

If we take that as a starting point, a less alarmist colour scheme for trading software could use the blue/yellow dichotomy to indicate whether a security price went up or down. Use a neutral chroma for “small” moves (this depends upon one’s time-frame, but properly the definition of “big move” should be calibrated to an exponential moving average with some width depending on one’s market telescope). Intensity of the move could be signalled with lightness, so that most figures on a screen are a readable lightness of a neutral colour, but “big moves” are tinged with convexly more chroma and very-convexly more lightness.

XSTRATA

The definition of “up/down” might be refigured as whether the trader is short/long the security in question, or perhaps redness/greenness could be used in conjunction with the “market view” of cold/hot, to indicate whether a security is moving for/against one’s strategy. That too could be seen as overly alarming, but a (pseudo)convex coding of red-ness might again solve the problem again, only invoking the “panic mode” when there’s really something to worry about.

(Source: twitter.com)




This is a beautiful and terrible data graphic—what Edward Tufte calls “chartjunk” or “design over communication”.
I’ll note some of the flaws for later reference in a longer piece I’m working on where I try to hit the highlights of numeracy / practical data literacy for non-statisticians.
The total spending size is irrelevant. The major determinant should be spending per pupil; country size is a confounding factor. Bubbles should be sized to the second set of numbers. (Theme: sensibly transformed data are better than raw data.)
The reordering effect and coloured strings look great and are helpful in tracking how orderings change across variables.
But, the scales are chosen so you can’t tell anything from the length of the ribbon! Look at the literacy rates. If it’s so undifferentiated why even include it? If you want to show some differences it’s acceptable to use an odds-ratio scale or a flog scale. (Theme: transformations are good.)
Literacy rates and schooling attainment are, I guess, “ok” measures of how educated your population is. But it’s circular reasoning. Spend more on education per pupil because more of them stayed in school for longer. Duh. But the question is, did they learn more per [dollar|ruble|euro|peso]?
It could be interesting to look at years in school versus test scores and spending but the literacy numbers get in the way. I would put literacy last as no one wants to compare it with any of the other neighbours. You could even put it before the bubbles (main graphic) as if to say “Look, things aren’t all that bad in education. Let’s start with something nice” whilst comparing literacy to spending but not to science scores or years in school or what-all else.
The bubbles overlap like a Venn diagram. Not a huge problem since it’s fairly apparent that this is just to make things pretty but it could be confusing to someone who expects visual overlap to indicate conceptual overlap.
It would be reallynice to bring out the number of crossings between spending and per-cap spending. I can’t think of one obvious best way to do this but for one thing you could put science and math on a horizontal comparison rather than vertical. Then maybe change the lightness/value of crossing points to somehow draw the eye to it? The crossing points (differences between per-cap spending and measured outcomes) are what’s really interesting in this inquiry.

This is a beautiful and terrible data graphic—what Edward Tufte calls “chartjunk” or “design over communication”.

I’ll note some of the flaws for later reference in a longer piece I’m working on where I try to hit the highlights of numeracy / practical data literacy for non-statisticians.

  • The total spending size is irrelevant. The major determinant should be spending per pupil; country size is a confounding factor. Bubbles should be sized to the second set of numbers. (Theme: sensibly transformed data are better than raw data.)
  • The reordering effect and coloured strings look great and are helpful in tracking how orderings change across variables.
  • But, the scales are chosen so you can’t tell anything from the length of the ribbon! Look at the literacy rates. If it’s so undifferentiated why even include it? If you want to show some differences it’s acceptable to use an odds-ratio scale or a flog scale. (Theme: transformations are good.)
  • Literacy rates and schooling attainment are, I guess, “ok” measures of how educated your population is. But it’s circular reasoning. Spend more on education per pupil because more of them stayed in school for longer. Duh. But the question is, did they learn more per [dollar|ruble|euro|peso]?
  • It could be interesting to look at years in school versus test scores and spending but the literacy numbers get in the way. I would put literacy last as no one wants to compare it with any of the other neighbours. You could even put it before the bubbles (main graphic) as if to say “Look, things aren’t all that bad in education. Let’s start with something nice” whilst comparing literacy to spending but not to science scores or years in school or what-all else.
  • The bubbles overlap like a Venn diagram. Not a huge problem since it’s fairly apparent that this is just to make things pretty but it could be confusing to someone who expects visual overlap to indicate conceptual overlap.
  • It would be reallynice to bring out the number of crossings between spending and per-cap spending. I can’t think of one obvious best way to do this but for one thing you could put science and math on a horizontal comparison rather than vertical. Then maybe change the lightness/value of crossing points to somehow draw the eye to it? The crossing points (differences between per-cap spending and measured outcomes) are what’s really interesting in this inquiry.

(Source: mat.usc.edu)


hi-res




the Good People and the misguided

HT @jaredwoodard (supervenes)

the Good People and the misguided

HT @jaredwoodard (supervenes)


hi-res




Components of internet traffic 1995-2005
via proofmathisbeautiful, un, infoneer-pulse, byWired:

“the center of gravity of … media … is moving to a post-HTML environment,” we promised nearly a decade and half ago. The examples of the time were a bit silly — a “3-D furry-muckers VR space” and “headlines sent to a pager”…

Look how much DNS requests used to take up!
Also surprised email isn’t more of the traffic now. I guess this is measured in terms of bytes rather than in ℓ₀ terms: number of
I remember how in the late 90’s people would speculate that everyone would become a co-creator (in fact big-money books were written to this theme). But maybe the lesson is that there are a relatively small number of passionate artists and artisans trying to get the word out about their stuff, and well-organised corps are very good at getting us to pay attention to certain art and not other art—although “viral” is a fairly chaotic a Wild West, certainly more so than three-channel broadcast. The “peer-to-peer” category I interpret as people trading albums and movies by the top artists. The picture doesn’t go up until 2010 but I think big corps have made inroads into the fuchsia video band by now.
One more mathematical observation about this chart: the total amount of traffic obviously exploded during 1995-2005 but we see a constant height on the graph. So that’s like “modulo size changes”aka the familiar.

Components of internet traffic 1995-2005

via proofmathisbeautiful, un, infoneer-pulse, byWired:

“the center of gravity of … media … is moving to a post-HTML environment,” we promised nearly a decade and half ago. The examples of the time were a bit silly — a “3-D furry-muckers VR space” and “headlines sent to a pager”…

  • Look how much DNS requests used to take up!
  • Also surprised email isn’t more of the traffic now. I guess this is measured in terms of bytes rather than in ℓ₀ terms: number of

I remember how in the late 90’s people would speculate that everyone would become a co-creator (in fact big-money books were written to this theme). But maybe the lesson is that there are a relatively small number of passionate artists and artisans trying to get the word out about their stuff, and well-organised corps are very good at getting us to pay attention to certain art and not other art—although “viral” is a fairly chaotic a Wild West, certainly more so than three-channel broadcast. The “peer-to-peer” category I interpret as people trading albums and movies by the top artists. The picture doesn’t go up until 2010 but I think big corps have made inroads into the fuchsia video band by now.

One more mathematical observation about this chart: the total amount of traffic obviously exploded during 1995-2005 but we see a constant height on the graph. So that’s like “modulo size changes”aka the familiar
.


hi-res