This is an eye-opening visualization of “cognitive surplus” from www.informationisbeautiful.net:
Thus, in the amount of time U.S. adults spend watching TV in one year, roughly 2000 Wikipedia-style projects could be created.
I once heard someone suggest a wikipedia as a unit of measure of collective human cognitive output. So every year, U.S. adults spend 2000 wikipedias watching TV. How can we more efficiently harness this cognitive surplus?
An interesting article in the New York Times (with a cool interactive graphic) digs down into the data of “on-time performance” for the three major NYC-area railways–LIRR, Metro North, and NJT.
http://www.nytimes.com/2010/07/27/nyregion/27ontime.html
The official figure is that 96% of trains arrive on time. This may be hard for the average commuter to believe, but it is technically true. What accounts for this discrepancy between perception and performance?
As the data shows, trains were far more likely to run behind schedule during peak times (rush hours) than during non-peak times. This makes intuitive sense–more people on the trains, more trains running simultaneously, and so more potential problems and delays. But this also means that a delay during rush hour will affect more people, and thus have a greater impact on the perception of the train’s timeliness than, say, a late-night lateness.
Furthermore, there are only a couple of “rush” hours, so the total number of rush-hour trains is less than the total number of non-rush-hour trains. The latter are more likely to run on time, and so their aggregate impact on overall performance dominates the calculation.
To simplify, if 5 out of 10 rush hour trains are late (50%), and 5 out of 50 non-rush hour trains are late (10%), then the overall lateness ratio is 10 out of 60, or about 17%. But a lot more people are riding the rush hour trains, and to them, it seems like the trains are late half the time. This is a example of the Inspection Paradox.
There are also some interesting psychological principles at work here: for example, people will generally think more about the one time they are late than, say, the 10 times the train ran on-time. It will be even more significant if that lateness ends up costing you something–like a good impression on a client or a boss.
This is a lovely three-minute visual homage to the Fibonacci sequence and its relationship to various natural phenomena.
I found this attempt to quantify the decline in quality of M. Night Shyamalan’s movies very amusing:
Using the rotten tomatoes rankings is a very interesting idea. I wonder what other kinds of analyses could incorporate that data.
There seemed to be a lot of geometry involved in my grilling this weekend. The circular grill, the cylindrical chimney starter, the pyramid of coals. The charcoal briquettes themselves look like solids with mirrored parabolas as vertical cross-sections and squares as horizontal cross-sections.
As I cooked a few times over different coal arrangements, I wondered about the heat distribution on the grill over the perfect pyramid of charcoal. Obviously the hottest point is the center (nearest the peak of the pyramid), and the temperature drops as you move toward the edge along a radius. Does it drop linearly? Like a parabola? Like a log function?
My guess is it looks like the image below. I don’t have any evidence for this speculation, but this is a representation of a Gaussian distribution, and when in doubt, go with Gauss. Gauss seems to have a hand in everything. Maybe I’ll bring the meat thermometer next time and take some readings.
