• Who pays for healthcare, 1960 to 2010

    August 27, 2012  |  Statistical Visualization

    Health care spending

    Josh Cothran looked at who's paid for healthcare over the past five decades, with an animated Marimekko chart.

    In 1960, almost 100% of the spending on prescription drugs came out of the consumer's pocket; by 2010, out-of-pocket spending was down to 20%. Over the past 50 years, there have been major shifts in the way hospital care, physician services, long-term care, prescription drugs, and other services and products are paid for. This interactive graphic uses data from the Centers for Medicare and Medicaid Services to show national spending trends from 1960 to 2010 for health care by payer.

    In case you're unfamiliar with the layout, there are two visual dimensions to the Marimekko. On the vertical is percentage for the main categories: hospital care, physicians and clinical services, etc. On the horizontal is a breakdown of the main categories: private insurance, Medicare, etc. The animation brings time as a third dimension for which the overall size of the chart is constant, so pay attention to the changing relative percentages.

  • Twitter Political Index measures feelings towards candidates

    August 1, 2012  |  Statistical Visualization

    Obama vs Romney

    In partnership with social analytics service Topsy, Twitter launched a Political Index that measures sentiment towards Barack Obama and Mitt Romney.

    Each day, the Index evaluates and weighs the sentiment of Tweets mentioning Obama or Romney relative to the more than 400 million Tweets sent on all other topics. For example, a score of 73 for a candidate indicates that Tweets containing their name or account name are on average more positive than 73 percent of all Tweets.

    The key is the comparison against all tweets for a sense of scale. As seen from the chart below, the index fluctuates closely with Gallup estimates.

  • Comparing Romney’s tax returns to presidential returns

    July 27, 2012  |  Statistical Visualization

    Romney taxes

    Lee Drutman, a Senior Fellow at the Sunlight Foundation, compared the tax returns of previous presidents against that of Mitt Romney.

    This scatter plot highlights two things: First, the two highest income years we observe are Romney 2011 ($21.6 million) and Romney 2010 ($20.9 million). Nobody else comes close. The next closest are Obama 2009 ($5.5 million) and Obama 2007 ($4.1 million).

    Second, the two lowest effective tax rates we observe also belong to Romney. The 2012 Republican candidate paid an effective tax rate of 13.9% in 2010 and 15.4% in 2011. Next lowest is George H.W. Bush, who paid a 15.5% rate in 1991. By contrast, in Obama’s two highest earning years, he paid a rate of 32.6% (2009) and 33.7% (2007).

    Of course the difference is there because most of Romney's income comes from investments, but wow, what a contrast.

    [Thanks, Chris]

  • Tracking the spread of AIDS

    July 26, 2012  |  Statistical Visualization

    Spread of AIDS

    Adam Cole and Nelson Hsu for NPR plotted the percentage of people, ages 15 to 49, living with HIV from 1990 to 2009.

    By 1990, the world had a pandemic on its hands. In 1997, the peak of the epidemic, more than 3 million people became newly infected with HIV.

    Then science struck back. Drugs approved for HIV treatment in the mid-1990s proved profoundly effective, transforming AIDS from a death sentence to a chronic illness. Those treatments, combined with an international commitment to manage the disease by providing access to free drug therapy, led to a steep drop in new HIV infections.

    The countries in middle, eastern, and southern Africa stand out in the chart, like Swaziland with a whopping 25.9%, but most areas cluster well below five percent. Although the drop-down filters help some with country selection, the data probably would've benefitted from a chart that had a self-updating vertical axis.

  • Olympian age distributions compared

    July 13, 2012  |  Statistical Visualization

    Olympic age boxplots

    Last week, the Washington Post compared the ages of Olympians, but it only focused on range, so you couldn't see the variation in between. For example, Dara Torres was 41 at her last Olympics so the bar was stretched to the right even though there were no other swimmers near that age. Plus the Post piece was US-only. So Gregory Matthews took the statistician's route and box plotted the age of all olympians from all countries.

    This barebones layout of course sacrifices the relatability of the first, but it's easier to see the distributions of each sport and to spot the outliers. Apparently there was an 11-year-old swimmer Yip Tsz Wa at the 2004 games in Athens. Wha?

  • Where you measure up against Olympians

    July 11, 2012  |  Statistical Visualization

    Athletes like you

    I think the theme of this year's Olympic graphics is how you relate to athletes. In this interactive by the BBC (in Spanish), height and weight of medal winners from the last Olympics in Beijing are plotted against each other. The more red, the more athletes with that weight-height combination, and you can click on a square to see the corresponding athlete(s). The twist is that you can enter your own height and weight to see where you are in the mix.

    Combine this with the recent age piece from the Washington Post, and you've got a more complete picture. Why stop there though? I want country, gender, and hair color breakdowns. [Thanks, Ben]

  • Age range of US Olympic athletes, by sport

    July 5, 2012  |  Statistical Visualization

    The Washington Post has a fun piece that compares your age to that of Olympic athletes over the past three years.

    In the past three Summer Olympics, 64 of the U.S. team’s 1,707 athletes have been age 40 and older — and they won 23 medals. As we watch 16-year olds compete in the gymnastics events, even the 20-somethings among us look back regretfully and wonder if our glory days have passed. Here, we take a look at which sports skew young and which allow for more longevity. In which events might you still have a chance this summer?

    Enter your gender and age, and the chart updates with a slider that shows the events that you still have hope for. I don't know about you, but I'm going for shooting.

    The initial view shows both male and female ranges in an overlapping bar chart (Is there a formal name for it?), which has been showing up a little more lately, instead of a clustered bar chart. It's a more compact view, which can be useful when there are a lot of categories.

  • A graphical summary of Euro 2012 on Twitter

    July 2, 2012  |  Statistical Visualization

    Euro2012 on Twitter

    Nicolas Belmonte, a data visualization scientist at Twitter, visualized the change in tweet volume during Euro 2012. It starts with a streamgraph for an overall view, and when you click on a team you get a time series for each of that team's matches. The selected team appears on top, and the team they are against is on the bottom. Goals are also marked adding context to the spikes.

    I didn't watch any of the championship and know next to nothing about soccer, but Belmonte's piece is useful and fun to use. Would come again.

  • Side-by-side comparisons for Australian Census

    June 28, 2012  |  Statistical Visualization

    Australia Census explorer

    Last week, Australia released data for their 2011 Census. Small Multiples, in collaboration with Special Broadcasting Service, put the data to use and built an interactive that compares demographics based on primary language or location. Choose a language from the dropdown menu on both the left and right, and your selections are presented side-by-side. The graphics themselves are fairly straightforward, showing estimates of things like gender and household income, but the key is in the comparison, which provides a sense of scale to what would otherwise be a bunch of percentages.

  • Chart Chooser helps you choose charts

    June 25, 2012  |  Statistical Visualization

    Chart Chooser

    There are a lot of charts to choose from, and if you pick the wrong one you'll end up communicating the wrong message or make it hard for others to read your data. Luckily, Juice Analytics has you covered with an interactive Chart Chooser, based on Andrew Abela's flowchart from a few years ago.

    There are toggle buttons on the top that let you filter based on what you're looking for, such as a trend or relationship. For example, if you select comparison, distribution, and composition, you're left with a bar chart. Don't care about distribution? You can also try a stacked bar chart.

    There is a second set of buttons that let you choose between Powerpoint or Excel. Once you find the appropriate chart type, you can download the template for the software you selected. Of course, if you're not an Office user, you can always just use it for the choice making.

  • Evolution of movie poster colors

    June 13, 2012  |  Statistical Visualization

    Movie poster colors, the evolution

    We've seen a number of looks at movie poster cliches, but this is the first time I've seen how the color of movie posters have changed over time. Vijay Pandurangan downloaded 35,000 poster thumbnails from a movie site, counted the color pixels in each image, and then grouped them by year and sorted by hue.

    Some thoughts from Pandurangan's designer friend Cheryle Cranbourne:

    The movies whose posters I analysed "cover a good range of genres. Perhaps the colors say less about how movie posters' colors as a whole and color trends, than they do about how genres of movies have evolved. For example, there are more action/thriller/sci-fi [films] than there were 50-70 years ago, which might have something to do with the increase in darker, more 'masculine' shades.”

    There's no mention of the blanked out 1924. That must've been a sad year. Oh wait, there were movies during that year, so there was either a massive ink shortage or it's just missing data.

    [via @DataPointed]

  • When the world sleeps, based on Twitter activity

    June 11, 2012  |  Statistical Visualization

    Sleeping and Twitter

    Twitter engineers Miguel Rios and Jimmy Lin explored tweet volumes in different cities and found some interesting tidbits about how people use the service.

    We see different patterns of activity between the four cities. For example, waking/sleeping times are relatively constant throughout the year in Tokyo, but the other cities exhibit seasonal variations. We see that Japanese users' activities are concentrated in the evening, whereas in the other cities there is more usage during the day. In Istanbul, nights get shorter during August; Sao Paulo shows a time interval during the afternoon when Tweet volume goes down, and also longer nights during the entire year compared to the other three cities.

    Notice the break during Ramadan in Istanbul?

  • Even simple charts can tell a story

    May 26, 2012  |  Statistical Visualization  |  Kim Rees

    ScreenShot124

    Regardless of your politics, this chart is a great example of how data can tell a story. It's a very simple graph by the Pew Forum on Religious and Public Life showing the changing attitudes about same-sex marriage. It shows that in the past couple of years, people have begun to be in favor of same-sex marriage.
    Continue Reading

  • The U.K. energy consumption guide

    May 21, 2012  |  Statistical Visualization  |  Kim Rees

    UK Energy

    I'm a sucker for anything cute and bubbly, and the U.K. Energy Consumption Guide created by Epiphany is no exception. It combines a vertical scrolling site with a lot of data visualization about different types of fuel and how they've been used historically. Most of the charts are solid and the interaction adds an even higher level of clarity and understanding.

    While I like this circle packing chart, I'm sure there will be doubters. It's very similar to McCandless' natural gas visualization that received a lot of flack. But generally speaking, anything that is engaging and welcoming garners a little extra time from the visitor to make sense of it.

  • Parallel Sets for categorical data, D3 port

    May 3, 2012  |  Statistical Visualization

    Parallel sets

    A while back, Robert Kosara and Caroline Ziemkiewicz shared their work on Parallel Sets, a way to visually explore categorical data. Software developer, Jason Davies, just ported the technique to Data-Driven Documents (D3). The interactions for sorting and rearranging are similar to the Kosara and Ziemkiewicz version, but the D3 version of course runs in the browser and has some nifty transitions. Try toggling the show curves box and the icicle plot one.

  • How recruiters look at your resume

    April 11, 2012  |  Statistical Visualization

    Recruiters looking at resumes

    In a study by TheLadders (of n equals 30), recruiters looked at resumes and make some judgments. During evaluations, eye tracking software was employed, and they found that the recruiters spent about six seconds on a resume looking for six main things: name, current company and title, previous company and title, previous position start and end dates, current position start and end dates, and education. After that, it was a crapshoot.

    Beyond these six data points, recruiters did little more than scan for keywords to match the open position, which amounted to a very cursory "pattern matching" activity. Because decisions were based mostly on the six pieces of data listed above, an individual resume’s detail and explanatory copy became filler and had little to no impact on the initial decision making. In fact, the study’s eye tracking technology shows that recruiters spent about 6 seconds on their initial "fit/no fit" decision.

    If I ever have to submit a resume, I'm just going to put those six things as bullets and then the rest will all be keywords in small, light print. It'll be like job search SEO.

    Update: I've been told that TheLadder's reputation might be less than savory, and a quick search shows some in agreement, so it might be wise to sidestep the service. Instead, go with my awesome six-bullet advice and you're gold.

    [via @alexlundry]

  • Towards a Low-carbon World

    March 20, 2012  |  Statistical Visualization

    low-carbon economy

    Carbon output. We want to reduce it, but some countries have a longer way to go than others. Pitch Interactive shows progress (or non-progress) by country in this interactive for the Climate Institute. Three indices are shown along with an overall score, which is a composite of the three, and countries are sorted by the average score from 1995 to 2008. Higher scores are better.

    The interaction makes this graphic. When you switch between indices, the countries are sorted appropriately and the time series for each country are drawn. You can also click on a country to get a closer view, which albeit is only four data points per country and index, but it's still useful.

    The lines for each country get thicker from left to right, which was to provide a sense of progress, but I wonder if it would be worthwhile to use thickness to represent an increase or decrease from the previous year. Then again, that's easy enough to see already, so maybe not.

  • Odds of losing in roulette

    March 13, 2012  |  Statistical Visualization

    Roulette single bet odds

    Jay Jacobs has some fun with roulette simulations and explores the odds of winning for different bets. Above shows a simulation of 250 spins 20,000 times. Or to put it differently, it's like simulating the play of 20,000 people, who each took 250 spins and always bet on a single number.

    I'm not sure why it doesn't start to get red until you're $500 in the hole, but bottom line: the longer you play, the higher probability you will lose all your money. That was my main takeaway from Probability 101 in undergrad. The rest is a blur.

  • Who voted for Santorum and Romney

    March 9, 2012  |  Statistical Visualization

    As a complement to Shan Carter's exit poll dancing boxes, The New York Times provides another view with an interactive triangular scatterplot.

    In the dancing boxes, you can see how states are inclined to vote based on exit poll groups. In the scatterplot, on the other hand, the groups within each state are plotted, with an added dimension towards candidates other than Santorum and Romney. The navigation bar on top and clicker on the left let you see tendencies of each state.

    Like the dancing boxes, the transitions make the chart. As you browse by state or by category, you're able to see differences between groups when shapes move across the screen.

    In somewhat related news, The New York Times graphics department is looking for summer interns. Send your interest to Steve Duenes (duenes [at] nytimes [dot] com) and Amanda Cox (coxa [at] nytimes [dot] com). I interned there a few years ago, so I can tell you first-hand that you'll learn a lot — probably more than in any class you've taken — while working with the best in the business.

    [New York Times]

  • Thomas the Tank Engine and Friends, accidental chart

    March 6, 2012  |  Statistical Visualization

    Thomas the train

    This came via Twitter from @christopferd:

    Will @flowingdata caption my 2 yr old's first [accidental] chart with his #thomas trains?

    How could I resist? You gotta get 'em while they're young. I added the labels, mostly to show off my expansive knowledge of Thomas the Train Tank Engine and Friends.

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