• Probabilities of failing birth control methods

    September 15, 2014  |  Statistical Visualization

    Birth control effectiveness

    In high school health class, where I learned about contraceptives and the dangers of pre-marital sex, my teacher spouted rates to scare. He would say something like condoms are 98 percent effective but never explained what that meant. Do they break 2 percent of the time? Do couples get pregnant 2 percent of the time? STDs?

    These charts from Gregor Aisch and Bill Marsh might help. They show the probability of an unplanned pregnancy, categorized by contraceptive and over a span of ten years. The top solid lines represent probabilities with "typical use" and the dashed lines on the bottom represent probabilities with "perfect use."

    Maybe it's time for better instructions on how to use these things.

    Update: The calculation of long-term probabilities is likely on the pessimistic side and makes too many assumptions about the data and population. Andrew Whitby critiques.

  • When people work, by job category

    September 4, 2014  |  Statistical Visualization

    When people work

    In another use of data from the American Time Use Survey, Planet Money looks specifically at the hours people work, separated by twenty job categories. Each density area represents a category, and height represents the percentage of people (estimated with survey answers) who are at work at various hours of the day.

    The interesting bit is that you can select two job categories to easily compare at once. For example, the above shows transportation in yellow against protective services in blue. For the latter, you see a more spread out distribution, as it's more common for those in protective services to work at night.

    Make your own comparisons.

    The stacked area chart from the New York Times from almost six years ago (whoa, time) is still my favorite visualization of the survey data.

  • Finding small villages in big cities

    August 25, 2014  |  Statistical Visualization

    Urban Village

    Daily life in cities tends to differ from daily life in small towns, especially by who we interact with. The MIT Senseable City Lab and the Santa Fe Institute studied this social aspect — individuals' contacts by city size — through anonymized mobile phone logs. As expected, those in cities with greater populations tended to have more contacts. However, when the researchers looked at who knew who, the results were more constant.

    Surprisingly, however, group clustering (the odds that your friends mutually know one another) does not change with city size. It seems that even in large cities we tend to build tightly knit communities, or 'villages,' around ourselves. There is an important difference, though: if in a real village our connections might simply be defined by proximity, in a large city we can elect a community based on any number of factors, from affinity to interest to sexual preference.

    Read the full paper for more details.

  • When the world sleeps

    August 20, 2014  |  Statistical Visualization

    Jawbone Sleep

    An additional hour of sleep can make a huge difference in how you feel the next day (especially when you have kids). It's the ability to concentrate for long periods of time versus the ability to stare at a clock until your next break. I got the Jawbone UP24 band to try to improve on that, and I still wear it every night to better understand my sleep habits.

    So, it only seems natural for Jawbone to look closer at how people sleep as a whole in a couple of interactive graphics. Select your city to see how people sleep in your neck of the woods.

    Every now and then we see a set of graphics that shows America's sleep habits, based on data from the American Time Use Survey. The Jawbone data is likely more accurate though, which makes it more interesting. The former depends on survey participants' memories and doesn't factor out things like reading in bed. The latter is actual sleep.

  • A decade of Yelp review trends

    July 25, 2014  |  Statistical Visualization

    Yelp trends

    Yelp released an amusing tool that lets you see how the use of word in reviews has changed over the site's decade of existence.

    From food trends to popular slang to short-lived beauty fads (Brazilian blowout anyone?), Yelp Trends searches through words used in Yelp reviews to show you what's hot and reveals the trend-setting cities that kicked it all off. Our massive wealth of data and the high quality reviews contributed by the Yelp community are what allow us to surface consumer trends and behavior based on ten years of experiences shared by locals around the world.

    Just type in keywords, select your city, business category, and click the search button to see the changes. For the less used words, the data looks mostly like noise, but there are also some clear trends like in craft beer and chicken and waffles.

  • Spiky betting odds during LeBron James decision

    July 17, 2014  |  Statistical Visualization

    Cleveland betting odds

    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.

  • How much underwear to bring on a trip

    July 16, 2014  |  Statistical Visualization

    Underwear to bring on a trip

    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.

  • FoamTree: Visualize hierarchical data with a lot of groups

    July 10, 2014  |  Statistical Visualization

    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.

    FoamTree

    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.

  • xkcd: Dominant players in chess and basketball

    July 9, 2014  |  Statistical Visualization

    Dominant players by xkcd

    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.

  • Birth year and political leanings

    July 8, 2014  |  Statistical Visualization

    How Birth Year Influences Political Views

    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.

  • Drone crash database

    June 23, 2014  |  Statistical Visualization

    Drone crash database

    Based on data compiled from a combination of military records, Defense Department records, and drone manufacturers, Emily Chow, Alberto Cuadra and Craig Whitlock for the Washington Post provide a quick view into drone crashes.

    More than 400 large U.S. military drones crashed in major accidents worldwide between Sept. 11, 2001, and December 2013. By reviewing military investigative reports and other records, The Washington Post was able to identify 194 drone crashes that fell into the most severe category: Class A accidents that destroyed the aircraft or caused (under current standards) at least $2 million in damage.

    The top row represents where a drone crashed, the second row who owns it, and the third tells the type. Mouse over any of the tick marks, and you get details for the corresponding crash.

  • Percentage of degrees conferred to men, by major

    June 18, 2014  |  Statistical Visualization

    Based on estimates from the National Center for Education Statistics, Randy Olson plotted the percentage of bachelor degrees conferred to men in the United States, by major. Start your eyes at the 50% line and work your way up (more men) or down (more women).

    Percentage of bachelor degrees to men, by major

    See also the inverted version that shows the percentage of degrees conferred to women.

  • Employment-to-population ratios

    June 17, 2014  |  Statistical Visualization

    Employment-to-population ratios

    The Upshot posted an interesting chart that shows changing employment rate by state.

    It shows that the economy is improving. Employment rates have climbed above the post-recession nadir in every state, although the improvements are often quite small. In Mississippi, the employment rate is just 0.1 percent above its recent low.

    It also shows that the recovery has a long way to go. Employment rates have rebounded in some states with strong growth, like Utah, Nebraska and Montana. But only three states — Maine, Texas and Utah — have retraced more than half their losses.

    You usually see this data presented as a time series chart, but this graphic focuses on three points of interest: employment rate at the start of the recession, the lowest rate, and the current. The rate is presented on the horizontal axis, so you see a cane-like shape that represents how far each state fell and how much farther they have to go.

    I like this one. See the full graphic here.

  • A visual analysis of the Boston subway system

    June 11, 2014  |  Statistical Visualization

    Visualizing MBTA Data

    For a graduate project, Michael Barry and Brian Card explored the Boston subway system through a set of annotated interactives that show train routes, usage, and scheduling.

    Through publicly available data, we have the tools to understand the subway system better than we ever have before. We have seen how the system operates on a daily basis, how people use the system, how that affects the trains and also how this ties back to your daily commute. To see a real-time version of this data, check out mbta.meteor.com for up-to-the-minute congestion and delay information.

    I like how they keep a subway map in view throughout. It helps you efficiently figure out what each chart means and is a good common factor as you move through the facets.

  • NFL players getting bigger

    June 5, 2014  |  Statistical Visualization

    Football players are getting bigger. Noah Veltman, a developer for the WNYC Data News team, shows by how much through an animated heatmap. Scrub the slider back and forth quickly for maximum effect.

    Height and weight of NFL players

    In the beginning, the league clustered in the bottom left. No one was over 300 pounds, and everyone was 6 feet and 4 inches tall or shorter. These days, player height weight are spread out more and shifted towards the top right.

  • A decade of college degrees

    June 3, 2014  |  Statistical Visualization

    A decade of degrees

    North by Northwestern looked closer at degrees awarded by their university over the past decade. Simply enter a degree to see the trend. As the makers note, the number of degrees is a lagging indictor of major popularity, since people pick their major and graduate three years later.

    Be sure to keep scrolling past the interactive for some explainers. Also, you can download the time series data for your own perusal via the link in the footnote.

  • Distribution of letters in the English language

    June 1, 2014  |  Statistical Visualization

    Distribution of letters

    Some letters in the English language appear more often in the beginning of words. Some appear more often at the end, and others show up in the middle. Using the Brown corpus from the Natural Language Toolkit, David Taylor looked closer at letter position and usage.

    I've had many "oh, yeah" moments looking over the graphs. For example, words almost never begin with "x", but it's quite common as the second letter. There's a little hump near the beginning of "u" that's caused by its proximity to "q", which is most common at the beginning of a word. When you remove "q" from the dataset, the hump disappears. "F" occurs toward the extremes, especially in prepositions ("for", "from", "of", "off") but rarely just before the middle.

    Next step: letter proximity.

  • Tron-style dashboard shows Wikipedia and GitHub streams

    May 30, 2014  |  Statistical Visualization

    GitHub stream

    As a fun learning exercise, Rob Scanlon made a dashboard that shows GitHub and Wikipedia changes in the style of graphics in Tron: Legacy.

    Hello User. This is a reproduction of the graphics in the boardroom scene in Tron: Legacy. If you have not seen that movie, check out this background material on the making of that scene before proceeding.

    To make this a bit more fun, the boardroom is configured to visualize live updates from Github and Wikipedia, with more streams to come. Click on a stream in the window to the right to continue.

    Type "cd github" and "run github.exe" for maximum pleasure.

  • Your income versus what it feels like

    May 22, 2014  |  Statistical Visualization

    Income and cost of living

    Incomes and the cost of living vary across the country. Some areas might have high median income, but the cost of living is also high. Similarly, areas might have low median income, but the cost of living is relatively low. So what happens when you take the income from the former and then move to the latter? The Bureau of Economic Analysis released estimates that help make that comparison.

    Quoctrung Bui for NPR made that data more accessible with a slope graph. On the left is median income, and on the right is what it feels like. Enter your metro area to focus on your point of interest.

  • Alcohol consumption per drinker

    May 15, 2014  |  Statistical Visualization

    We've seen rankings for alcohol consumption per capita around the world. These tend to highlight where people drink and abstain, but what about consumption among only those who drink? The Economist looked at this sub-population. Towards the top, you see countries where much of the population abstains but those who do drink appear to drink at higher volumes.

    Drinking among drinkers

    Of course, it's better to take this with a grain of salt until you see the standard errors on these estimates.

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