The New York Times is covering Malaysia Airlines Flight 17 with a series of maps. The ones above show a sample of recent flights in the area. Some airlines, such as British Airways and Air France show a clear path around Ukraine, whereas others take a more direct route.

The USGS released a more detailed geologic map of Mars, not just renderings based on rough models.
Read More

LeBron James decided to head back to Cleveland, so naturally the odds that they win the championship went up. Todd Schneider charted the betting odds as the announcement happened to see how much they went up.

Of course that 10% already had built in some likelihood that James would choose to play for the Cavaliers next season. Before Cleveland was considered a threat to land LeBron, their championship odds were around 2%, so the 10% Cleveland odds immediately before LeBron’s decision perhaps reflected market expectations that LeBron had a 50% chance of choosing Cleveland: 0.5 * 0.18 + 0.5 * 0.02 = 0.1

Houston, who was expected to pick up Chris Bosh if James went to Cleveland, also saw a spike during the announcement, but the odds quickly came back down once Bosh decided to re-sign with Miami.

Packing underwear for a short trip is easy. You just pack a pair for each day you’re away. However, longer trips require extra planning. Pack a pair for every day, and you get a bag that’s too heavy. Pack too few and you have to launder your dirties more often.

Reed Kennedy and Carrie Smith gave this problem some extra thought, in search for the ideal underwear count, given the number of days you leave. The result is the chart above.

Simply select your trip length on the top, and then move down to find your ideal underwear count. The numbers inside the grid cells indicate how many times you have to launder. Gold numbers indicate a perfect remainder of zero pairs of clean underwear by the time you get home.

Note: This chart assumes you do not turn your underwear inside out for another wearing. Not that’d I’ve ever done that.

See the full post for further dirty underwear details.

It’s around that time of year when more people than usual ask for advice about degrees in statistics, career paths in visualization, and how to get started with something that looks awesome.

The high of graduation from high school, undergrad, and grad school has settled, and it’s time to think about the future. Maybe summer brought more idle time at work to imagine what else you could do every day. I know the feeling.

I’ll try to answer the more common questions. However, keep in mind that I’m nowhere near the best person to ask about these things. I didn’t grow interested in statistics until late in college, I studied remotely for most of my graduate student life, and although I consult occasionally, I run FlowingData for a living.

So there’s your salt. Now some Q & A.

Read More

Shan Carter and Kevin Quealy for the Upshot have a look at sports fandom once again using Facebook usage as a proxy. This time they examined shifting fan support during the World Cup.

A new analysis by Facebook’s data science team analyzed migrations of fan support from one country to another throughout the tournament, stage by stage. It’s based partly on the contents of people’s posts, which means it is largely a reflection of the views of people who follow the World Cup at least to some degree. In the chart above showing global opinion, Brazil, the U.S. and Mexico have a strong influence on the results, because of their size, Facebook population and high interest in the World Cup.

Keep in mind World Cup posts for a specific country aren’t counted once that team dropped from the tournament. So it’s not so much shifting fandom as it is who people rooted for during each round.

Be sure to check out the whole article to see how fandom shifted by country. (Congrats, Germany.)

This graphic from the Gates Foundation is from a few months ago, but it was just National Mosquito Control Awareness Week. The small illustrations in this case make the graphic. Although I’m interested in seeing those “wide error margins.”

For small-ish amounts of hierarchical data, most JavaScript libraries can handle the load. However, it gets tricky when you get into hundreds and thousands of levels and groups. FoamTree is a library that helps you with this problem.

It’s a Voronoi Treemap, which sure, looks kind of neat, but the nice part is how well it handles large amounts of groups. It’s puts off computation and rendering until it’s needed, so it cuts down on load and run times. Just check out the Tree of Life demo and select “Homo sapiens” in the ride sidebar to see how it works.

The library is free to download, but you have to pay a license fee to get rid of the branding.

I’m pretty sure xkcd is the only one who gets away with showing player ratings for both basketball and chess players in the same frame, without the y-axis labels. And somehow it seems logical.

NPR, the Robert Wood Johnson Foundation and the Harvard School of Public Health conducted a survey about peoples’ stress levels and factors contributing to the stress. It took place for about a month. NPR started a summary of their findings, of what will be a two-week segment on the air and online.

The above shows the percent of respondents in the age brackets who said the factors (the rows in this case) contributed to their current stress. It looks like I might be in a less stressful stage of my life, between the age of 30 and 39.

It’s just an early summary of poll responses right now, so I’m hoping they go into more detail about statistically significant differences between demographics and how the 2,500-person sample correlates to the the US population.

A statistical model, from Yair Ghitza of Catalist and Andrew Gelman of Columbia University, estimates when people form their political preferences. The analysis uses presidential approval ratings from Gallup to approximate political events “that estimates when people form their political preferences.”

Amanda Cox for the Upshot demonstrates the model in an interactive. Simply drag the slider to see how the political leanings of you and your birth cohort changed over time. The takeaway: Events between the ages of 18 to 24 are far more influential than those that occur at an older age.

It seems like the model might apply to a lot of things in life.

After a ruling by the United Nations International Court of Justice, Japan was ordered to stop whaling in parts of Antarctica. However, Japan’s Prime Minister Shinzo Abe recently sees the whaling practice differently. Adolfo Arranz for the South China Morning Post explains in a detailed graphic. Above is only about a third of it, so be sure to click through for the full version.

Smarty Pins is a simple, fun map game by Google. You get a trivia clue about some location, and the goal is to drop the pin as close as you can to the correct place. You start with 1,000 miles, and you get docked each time for how far your choice was from the actual location.

For more on how little you know about where stuff is, see also the state matching game and the Mercator map puzzle.

We produce data all the time, everywhere we go, and this process implies something about how we live. Jer Thorp explains in this short explainer video animated by Erica Gorochow.

Jaap de Maat, a graduate student at the Royal College of Art, rigged a filing cabinet to follow people around for his final project. It reminds people of the data traces we leave behind. It’s called I know what you did last summer.

It is physically impossible for the human brain to remember every event from our past in full detail. The default setting is to forget and our memories are constructed based on our current values. In the digital age it has become easier to look back with great accuracy. But this development contains hidden dangers, as those stored recollections can easily be misinterpreted and manipulated. That sobering thought should rule our online behaviour, because the traces we leave behind now will follow us around for ever.

See more details on Wired.

Terry Speed, a emeritus professor in statistics at University of California at Berkeley, gave an excellent talk on how statisticians can play nice with big data and data science. Usually these talks go in the direction of saying data science is statistics. This one is more on the useful, non-snarky side.

Ditch the computer screen for your data. It’s all about the food. Moritz Stefaner and prozessagenten, process by art and design ran a second round of the Data Cuisine workshop to explore how food can be used as a medium to communicate data. Naturally, you’ve got your basic visual cues, but when you introduce food, you open lots more possibilities.

[W]e have all kinds of sculptural 3D possibilities. We can work with taste — from the basic tastes of sweet, sour, salty, bitter, umami to complex combinations or hotness. There is texture — immensely important in cooking! Then we have all the cultural connotations of ingredients and dishes (potatoes, caviar, …). We can work with cooking parameters (e.g. baking temperature or duration). Or the temperature of the dish itself, when served!

The above shows piece of bread shows youth unemployment in Spain. See more data dishes here.

Julia Angwin and Jeff Larson for ProPublica made a chart of NSA programs revealed in the past year. Programs were plotted subjectively from foreign to domestic surveillance on the horizontal axis and targeted to bulk surveillance on the vertical. So you get more controversial the further you move up towards the top right corner.

Interesting stuff.

The best part though is the goofy program names, as illustrated by Alberto Cairo. ParanoidSmurf and his siblings Nosey, Tracker, and Dreamy; EgotisticalGoat and EgotisticalGiraffe; WillowVixen. First off, who names these programs? And second, how do I get in on the naming action (without becoming creepy)?