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Posts tagged with data visualization

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.

yellow and blue trading screen (GVZ)

I made the above picture with R of course, like this:

    require(quantmod)
    getSymbols("^GVZ")
    chartSeries(GVZ)
    reChart(up.col="light blue", dn.col="yellow")

(GVZ is the gold volatility index.)

It’s not a perfect colour scheme—I would use Lab to do better—but it already improves on #FF0000 versus #00FF00.

 

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)




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
image.


hi-res







The ratio of US jobseekers to US jobs stands at 4:1.via John Irons (of argmax.com fame)
 
The jobs-to-seekers ratio rises immediately during a recession, but does not decrease as quickly after the recession ends. (Is this true in general?)

The 2011 jobs-to-seekers ratio, broken down by sector.

The ratio of US jobseekers to US jobs stands at 4:1.
via John Irons (of argmax.com fame)

 

The jobs-to-seekers ratio rises immediately during a recession, but does not decrease as quickly after the recession ends. (Is this true in general?)

The 2011 jobs-to-seekers ratio, broken down by sector.


hi-res




The last decade’s debt record for several rich countries.
3-month Bond Yields owed by some of them:      (SOURCE: Bloomberg)
Japan   .10%
UK      .41%
Germany .28%
US      .04%
And here’s one of the yield curves (US’):
 
(Remember, higher yield means the debt costs more to service for the country that’s borrowing.)

The last decade’s debt record for several rich countries.

3-month Bond Yields owed by some of them:      (SOURCE: Bloomberg)

Japan   .10%
UK      .41%
Germany .28%
US      .04%

And here’s one of the yield curves (US’):

image 


(Remember, higher yield means the debt costs more to service for the country that’s borrowing.)


hi-res




US presidential voting record, 2008
The map has been transformed (via a diffeomorphism) to make its graphical area proportional to the number of voters. Each county was then coloured according to the proportion of votes for Obama:McCain.
SOURCE

US presidential voting record, 2008

The map has been transformed (via a diffeomorphism) to make its graphical area proportional to the number of voters. Each county was then coloured according to the proportion of votes for Obama:McCain.

SOURCE


hi-res




Look at just the first digit and the number of digits.

  • science: 32914, 11566, 4989, 3743, 968, 814, 673, 482, 286, 2811
  • black and white: 1694, 1167, 1108, 988, 919, 639, 596, 591, 580, 544
  • lol: 22627, 18100, 17688, 14374, 13459, 12045, 4711, 3779, 3670, 3393
  • fashion: 955, 581, 486, 435, 402, 303, 279, 279, 278, 275
  • architecture: 1426, 461, 433, 251, 230, 219, 194, 194, 175, 167
  • art: 7492, 2965, 2761, 1316, 544, 435, 413, 331, 307, 296

Snapshots taken between 9:30-10:30 on 5 April 2011.

distribution of likes on tumblr

To the victors, go the spoils. (popularity?)

R code:

> science <- c( 32914, 11566, 4989, 3743, 968, 814, 673, 482, 286, 281 )
> bw <- c( 1694, 1167, 1108, 988, 919, 639, 596, 591, 580, 544 )
> lol <- c( 22627, 18100, 17688, 14374, 13459, 12045, 4711, 3779, 3670, 3393 )
> fashion <- c( 955, 581, 486, 435, 402, 303, 279, 279, 278, 275 )
> architecture <- c( 1426, 461, 433, 251, 230, 219, 194, 194, 175, 167 )
> art <- c( 7492, 2965, 2761, 1316, 544, 435, 413, 331, 307, 296 )
> require(RColorBrewer) > accent = brewer.pal(8, "Accent")
> leg.txt <- c("science", "black & white", "lol", "fashion", "architecture", "art") > leg.col <- c(accent[1], accent[2], accent[3], accent[4], accent[5], accent[6])
> par(bg="#fafaff")
> plot(science, type="s", log="y", lwd=2, col=accent[1], xlab="x-th most popular blog post", ylab="# likes", main="distribution of LIKES on tumblr", cex.axis=.8, col.main="#444444", col.axis="#333333", fg="#332211")
> points(bw, type="s", lwd=2, col=accent[2])
> points(lol, type="s", lwd=2, col=accent[3])
> points(fashion, type="s", lwd=3, col=accent[4])
> points(architecture, type="s", lwd=2, col=accent[5])
> points(art, type="s", lwd=2, col=accent[6])> legend("topright", leg.txt, fill=leg.col, title="TAG", text.col="#393939", title.col="#222222", border="#f0ffff", box.col="#666666")

Question: is there lag-1 autocorrelation in the likes per tag over time? If I were scrolling down from the top, I’d be more likely to quit (or skip to the bottom, not AR[1] or AR[3]) if the last few pictures were boring.

I wonder how this distribution of Likes compares under the Explore vs Directory régimes. I would guess Likes are more focused under the new régime.




Here is how to improve your charts, graphs, maps, and plots:
Erase non-data ink.
Erase redundant data ink.
Maximize the ratio of data to ink.
Show data variation, not design variation.
The surface area of graphical elements should be directly proportional to the numerical quantities represented.  (Don&#8217;t use 3-D bar charts, for example.)
Don&#8217;t lie.
Get as much data as you can in the first place.
Apply the right transformations to the data (adjust for inflation, divide to per-capita numbers, take the square root of naturally squared quantities).
Then, you can shrink the graphics way down.
Increase data density and data resolution.
Maximize the amount of information per unit of space.
Maximize the amount of information per unit of ink.
Above all else show the data.
For example, here&#8217;s how he would use the eraser, not the pen to improve on the typical bar chart or histogram.  (3-D bar charts are right out.)

Additionally, Tufte wants news publications to use sophisticated graphics that let the data tell their intricate story, rather than simplistic graphics that attempt to &#8220;dazzle&#8221; the viewer.
Like good writing, good graphical displays of data communicate ideas with clarity, precision, and efficiency.
Like poor writing, bad graphical displays distort or obscure the data, make it harder to understand or compare, or otherwise thwart the communicative effect which the graph should convey. 
Lastly, regarding wide versus tall graphics:
If the data suggest a shape to the chart, follow that suggestion.
Otherwise, move toward graphics about 50 percent wider than tall.

Here is how to improve your charts, graphs, maps, and plots:

  • Erase non-data ink.
  • Erase redundant data ink.
  • Maximize the ratio of data to ink.
  • Show data variation, not design variation.
  • The surface area of graphical elements should be directly proportional to the numerical quantities represented.  (Don’t use 3-D bar charts, for example.)
  • Don’t lie.
  • Get as much data as you can in the first place.
  • Apply the right transformations to the data (adjust for inflation, divide to per-capita numbers, take the square root of naturally squared quantities).
  • Then, you can shrink the graphics way down.
  • Increase data density and data resolution.
  • Maximize the amount of information per unit of space.
  • Maximize the amount of information per unit of ink.
  • Above all else show the data.

For example, here’s how he would use the eraser, not the pen to improve on the typical bar chart or histogram.  (3-D bar charts are right out.)

Tufte histogram

Additionally, Tufte wants news publications to use sophisticated graphics that let the data tell their intricate story, rather than simplistic graphics that attempt to “dazzle” the viewer.

  • Like good writing, good graphical displays of data communicate ideas with clarity, precision, and efficiency.
  • Like poor writing, bad graphical displays distort or obscure the data, make it harder to understand or compare, or otherwise thwart the communicative effect which the graph should convey.

Lastly, regarding wide versus tall graphics:

  • If the data suggest a shape to the chart, follow that suggestion.
  • Otherwise, move toward graphics about 50 percent wider than tall.

hi-res