Quantcast

Posts tagged with Russia

jkottke:

This video is 13 minutes of traffic accidents in Russia and totally amazing.

  1. Show this to your teenagers before they take the wheel. If it doesn’t scare the p*ss out of them—or even worse, if it excites them—no more Grand Theft Auto and hide the car keys.
  2. Next time you complain about public services, boring orderliness and “safety first”, the desireability of risk, Panglossian everything-optimal economics, or forget how relatively safe you are on your German freeways, …. watch this.

    As someone else remarked (can’t remember the source), the difference between Somalia and the USA is the stuff everybody in the US completely forgets is even possible.
  3. Notice how many of the accidents are caused by people trying to zoom ahead of everyone else—off the side of the road, cutting down a tree without noticing it will land on somebody else, trying to pass on the left or on the right or across the lane. Is your time really that important relative to everyone else’s, people?
  4. Assumptions. You think you can make assumptions, like that someone won’t fell a tree on your head, or a military jet won’t fly over your head, that someone won’t spill military equipment near you, or that people from the other lane (or off the road) won’t drive completely orthogonal and attack your car. Sometimes those assumptions are wrong.
  5. How many of these people do you think actually accepted the blame on themselves for their reckless actions?

via @Alea_, @felixsalmon




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