Reality Prose has an excellent analysis on the changing price of LEGO bricks over the years and a misconception that cost has gone up. According to the chart above, based on data from BrickSet and adjusted for inflation, the average cost per brick has come down.
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David Brooks for The New York Times on the philosophy of data and what the future holds:

If you asked me to describe the rising philosophy of the day, I’d say it is data-ism. We now have the ability to gather huge amounts of data. This ability seems to carry with it certain cultural assumptions — that everything that can be measured should be measured; that data is a transparent and reliable lens that allows us to filter out emotionalism and ideology; that data will help us do remarkable things — like foretell the future.

Be sure to read the comments. There’s actually quite a bit of anti-data talk.

From Reddit user ddurrr while visiting London. Pretty much my current status and mental capacity. [Thanks, Tom]

Information visualization firm Periscopic just published a thoughtful interactive piece on gun murders in the United States, in 2010. It starts with the individuals: when they were killed, coupled with the years they potentially lost. Each arc represents a person, with lived years in orange and the difference in potential years in white. A mouseover on each arc shows more details about that person.
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In Waters Re~ artist Xárene Eskandar placed video of the same landscape at different times of day in parallel.

They capture the subjective and perceptual qualities of time expressed as events, moments, memory and landscape. The goal is to break the linear experience of time, allowing viewers to perceive multiple times within a single viewpoint. As a result insignificant moments become significant events, heightening one’s experience of the landscape and one’s existence in that particular moment in time and space.

The results are beautiful. [via FastCo]

Ritchie King for Quartz compared money spent on Super Bowl ads — now about $3.75 million for a 30-second spot — to how much the companies make on average in 3 and a half hours (the average length of a game).

It’s impossible to say exactly how much a successful Super Bowl ad ultimately earns a company. Surely the Wassup commercials were a huge boon for the Budweiser brand—but how huge?

One thing is clear though: for the biggest advertisers, that $3.75 million is truly a pittance. In fact, some of them make almost as much in profits in an average 3.5 hours—roughly the time it takes to air the Super Bowl itself.

Note that spending (on the bottom) is total between 2002 and 2011, and the vertical scales are different (so it probably would’ve been good to give more visual separation between the two charts), but still, kind of an interesting perspective.

Biostatistics PhD candidate Hilary Parker dived into the most poisoned names in US history. Her own name topped the list. There were several fad names such as Deneen, Catina, and Farrah that saw a quick spike and then a plummet, but the trend for Hilary is different.

“Hilary”, though, was clearly different than these flash-in-the-pan names. The name was growing in popularity (albeit not monotonically) for years. So to remove all of the fad names from the list, I chose only the names that were in the top 1000 for over 20 years, and updated the graph (note that I changed the range on the y-axis).

I think it’s pretty safe to say that, among the names that were once stable and then had a sudden drop, “Hilary” is clearly the most poisoned.

There it is minding its own business, enjoying a steady rise in popularity over a few decades, and then boom, Bill Clinton is elected, and the name dies a quick death.

Be sure to check out the rest of the analysis. Good stuff. [Thanks, @hspter]

The Mercator projection can be useful for giving directions, but when it comes to world maps, the projection doesn’t hold up well as you move far north and south. By how much? Give this puzzle game a try and match the red boundaries to their respective countries.

Carlos Scheidegger and Kenny Shirley, along with Chris Volinsky, visualized Major League Baseball Hall of Fame voting, from the first class in 1936 (which included Babe Ruth) up to present.

All a fan can do is accept that Baseball Hall of Fame voting, conducted by the Baseball Writers Association of America (BBWAA), is a phenomenon unto itself. If we can’t understand baseball Hall of Fame voting, though, maybe the next best thing is visualizing the data behind it. The set of interactive plots on this webpage is our attempt to do that. We were especially interested in two things: (1) viewing the trajectories of BBWAA vote percentage by year for different players throughout history, and (2) simultaneously viewing the career statistics of these players, to help find patterns and explain their trajectories (or to reassure ourselves that the writers really are crazy).

The interactive is on the analysis side of the spectrum, so you might be a bit lost if you don’t know a lick about baseball. However, if your’re a baseball fan, there’s a lot to play around with and dimensions to poke around at, as you can filter on pretty much all player stats such as home run count, batting average, and innings played. At the very least, you’re getting a peek at how statisticians pick and prod at their data.

Start at the examples section for quick direction. I eventually found myself looking for downward trajectories. Poor Mark McGwire. [Thanks, Chris]

As the Super Bowl draws near, Facebook took a look at football fandom across the country.

The National Football League is one of the most popular sports in America with some incredibly devoted fans. At Facebook we have about 35 million account holders in the United States who have Liked a page for one of the 32 teams in the league, representing one of the most comprehensive samples of sports fanship ever collected. Put another way, more than 1 in 10 Americans have declared their support for an NFL team on Facebook.

It’s a fairly straightforward geographic breakdown based on the most liked team in each county, as shown above. So you can kind of see where rivalries come from.
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We’ve all seen rain maps for a sliver of time. Screw that. I want to see the total amount of rainfall over a ten-year period. Bill Wheaton did just that in the video above, showing cumulative rainfall between 1960 and 1970. The cool part is that you see mountains appear, but they’re not actually mapped.

The hillshaded terrain (the growing hills and mountains) is based on the rainfall data, not on actual physical topography. In other words, hills and mountains are formed by the rainfall distribution itself and grow as the accumulated precipitation grows. High mountains and sharp edges occur where the distribution of precipitation varies substantially across short distances. Wide, broad plains and low hills are formed when the distribution of rainfall is relatively even across the landscape.

See also Wheaton’s video that shows four years of rain straight up.

Is there more recent data? It could be an interesting complement to the drought maps we saw a few months ago. [Thanks, Bill]

I’m almost certain this relationship is significant. Side note: Is there a meaningless-correlations tumblr yet? [via]

Arthur Buxton plotted the most common colors of Penguin Publishing science fiction colors and arranged them over time. Also available in print.

I wonder if there’s a good way to show connections between the titles or the different covers for each title.