vizdat

In less than a week, more than 125 million people will be watching the 2012 edition of the Eurovision Song Contest, the annual competition held among active member countries of the European Broadcasting Union (EBU).

An important part of the televised contest is the whole voting process, during which each participating country makes their favorites votes public in about 3 different languages.

For those nerdy people who like to know what country voted when in some particular way, there is now the following information graphic. Eurovizion [site50.net], designed by graphic designer Ben Willers, provides a detailed overview of all voting patterns that occurred during the last 10 years of Eurovision Song Contests.

The top bar graph shows the votes received by each country. The bottom, dot-plot kind of graph reveals all individual votes, where the horizontal axis denotes the 'giving' countries, and the vertical axis the 'receiving' ones.

One can detect some remarkable anomalies that show how the perceived quality of musical songs is quite a cultural affair. Or how can one explain the relationships between Greece and Cyprus (or Albania), and Germany and Israel?

See also: . Eurovision 2009 Results . Visualising the Eurovision

The OECD's Better Life Index which debuted last year to much fanfare has been updated with some great new features by Moritz Stefaner.

The concept and beauty of the original piece remain intact. However, the experience is made better by the ability to compare to different demographics. For instance, after I adjust my Better Life settings, I can see how my settings compare to other women my age in the US, or to French men. It's fun to compare to different people around the world and watch the flowers readjust themselves to the various comparisons. It invokes a sense of global community and humanity.

It also has better sharing. It offers the usual suspects, plus you can embed your index on your site. Equality between men and women is always a big issue, so that's addressed in the new version as well. You can select to see the split, and it also shows both gender and social inequality per indicator when you drill down to the specific country level.

This is an excellent update to an already great tool. I'm glad the OECD sees the value and continues to invest in it.

[via @jcukier]

Those of you who have watched the video footage from the recent SEE conference may have seen Manuel Lima’s talk on the ‘The Power of Networks: Knowledge in an age of infinite interconnectedness’. Manuel is a Senior UX Design Lead at Microsoft Bing, founder of VisualComplexity.com and author of the Visual Complexity book.

However, this isn’t the first time he has delivered this talk, having previously presented at the Royal Society of Arts (London) in December 2011. Today, Manuel has shared a wonderful animated version of this December presentation which has been chosen to be part of the RSA Animated series, where the contents of 14 key speeches and books have converted into beautifully hand-drawn 10-minute animations. The illustrations have been created by the talented people from Cognitive Media.

Here is the background to the talk:

[Manuel Lima] visits the RSA to explore a critical paradigm shift in various areas of knowledge, as we stop relying on hierarchical tree structures and turn instead to networks in order to properly map the inherent complexities of our modern world. The talk will showcase a variety of captivating examples of visualization and also introduce the network topology as a new cultural meme.

If you want prefer to look through original slideshow, visit slideshare.

Related posts:

1. Slides from my Malofiej talk

Visual.ly analyzed the top 30 infographics posted on their site and determined that data visualization doesn't matter:

Data visualization certainly matters when it comes to conveying information effectively, but when it comes to sharing, the answer is no: having data to represent is not a critical ingredient in infographics. More than half, or 53%, of the top 30 graphics do not contain data visualization. And by data visualization, we mean visual objects that are sized, colored, or positioned to represent numerical values.

I think what they actually mean is that data visualization is not the sole factor of a successful visualization. Since they are only analyzing the top 30 infographics, the minority 47% that had data visualization are still very successful. It would be a different story if the 53% of infographics without dataviz were the top successes and the 47% with dataviz were the bottom losers.

My hunch is that the successful infographics posted on Visual.ly are popular because, like other viral content, they strike a nerve, are of the moment, are humorous yet relevant, or have some other je ne sais quoi.

If you want to get a quick numerical summary of a data set, the summary function gives a nice overview for data frames:

> require(ggplot2)
Loading required package: ggplot2
> data(diamonds)
> summary(diamonds)
     carat               cut        color        clarity          depth           table      
 Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065   Min.   :43.00   Min.   :43.00  
 1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258   1st Qu.:61.00   1st Qu.:56.00  
 Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194   Median :61.80   Median :57.00  
 Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171   Mean   :61.75   Mean   :57.46  
 3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066   3rd Qu.:62.50   3rd Qu.:59.00  
 Max.   :5.0100                     I: 5422   VVS1   : 3655   Max.   :79.00   Max.   :95.00  
                                    J: 2808   (Other): 2531                                  
     price             x                y                z         
 Min.   :  326   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
 1st Qu.:  950   1st Qu.: 4.710   1st Qu.: 4.720   1st Qu.: 2.910  
 Median : 2401   Median : 5.700   Median : 5.710   Median : 3.530  
 Mean   : 3933   Mean   : 5.731   Mean   : 5.735   Mean   : 3.539  
 3rd Qu.: 5324   3rd Qu.: 6.540   3rd Qu.: 6.540   3rd Qu.: 4.040  
 Max.   :18823   Max.   :10.740   Max.   :58.900   Max.   :31.800  

But if you'd prefer a visual overview of your data, Andrew Barr suggests the tableplot function (included in the tabplot package) for a graphical version:

tableplot(diamonds, cex = 1.8)

Andrew explains how to use the tabplot function in the post linked below.

W. Andrew Barr's Paleoecology Blog: Quickly Visualize Your Whole Dataset (via @JacquelynGill)

One does not require flashy Powerpoint slides or state exact statistical numbers to be able to convey an educational yet compelling presentation. Nor should the presentation need to take that long.

Hans Rosling, who is already known to use physical props during his presentations, ranging from IKEA storage boxes, over washing machines to a real sword, demonstrates again how physical objects can be used to focus human attention to important statistical trends. He also shows how stones can convincingly form bar graphs.

The following 'talk', taking less than 50 seconds, 'happened' during a spontaneous interview after the TEDxSummit 2012 in Doha, Qatar and discusses the impact of global population growth.

Watch the presentation below. The presentation also reminds me to always show up with clean shoes.

See also: . The Joy of Stats: Combining Hans Rosling with Holographic Infographics . Swine Flu News versus Death Ratio . Joy of Stats . Hans Rosling TED Talk 2007 . Hans Rosling TED Talk 2006

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.

I warned Nathan that I was going to drop a pie bomb on Flowing Data. Well, here it is; it's labeled by its creator as a "feather chart." I really hate to pick on people. I truly think Jon made a valiant attempt to use pie charts innovatively. However, this chart is not effective.

The chart uses 11 million ACT records (for international readers, that's a standardized test in the US). It's trying to show the relationship between ethnicity and test score and income and test score.

I created the y-axis as the ACT composite score, and then used self-reported income bands as the x-axis. Both are discrete, categorical values, even though ACT is numeric. ACT increases bottom-to-top, and income bands increase left-to-right. At the intersection of each variable is a pie chart, sized by the number of students in that group, and colored by ethnicity

The only problem is that the overlapping pie charts occlude one another. Unless one section of the pie chart dominates and allows the other sections to peek out over the top of the previous pie, then the chart is useless. For instance, in the first feather, there's no way to know if the orange section is 40% or 60% for most of the chart.

This chart has really good intentions, but the data would be better served with a bean or violin plot. If you're a subscriber, you can check out Nathan's great tutorial from last week about visualizing distributions.

Great stuff from Simon Rogers (Guardian Datablog), Mirko Lorenz (Deutsche Welle) and Dan Nguyen (ProPublica).

• 
• Source: Datadrivenjournalism
• Via: @ddjournalism

Video: School of Data Journalism – Making Data Pretty workshop An abundance of mapping and data visualisation tools, such as Tableau, Google Fusion Tables, Google Maps and Datawrapper, is available for journalists to start using visualisations and maps to enhance their news stories.

Read more at: datadrivenjournalism.net

Content has its own importance on a website however; mostly people judge an entire website over images and graphics used. So, the images used on a website should convey your basic message and purpose of the website.

If there is a complex piece of information which should be delivered to a viewer in a simpler manner, Infographics are a way to do so. Used by technical writers, statisticians and many others to simplify the process of conveying a complex message. At times, presenting too much of information in written form can confuse the viewer and also it gets time taking. So, in such cases Infographics assist in understanding...

A few elements are required for Infographics. First of all you should have a clear knowledge about the message you want to deliver. Once you are sure of that, you would require color coding, graphics, reference icons, time frames, statistics and off course references...

Gundega Strautmane, a Latvian textile artist and designer, visualizes social and physical networks in a show called Relational Ornaments. The networks are created using various sized pins to depict nodes and threads connecting them to show relationships. Bringing visualization into the tactile world lends it a weight not able to be achieved on a computer screen. It allows the viewer to pause, spend time with the information, feel it, sense it in a more holistic way. The placement of pins and threads is imprecise because they are placed by hand giving the work a very natural, organic feel rather than the rigidity of the exact calculations of programming.

[via The Network Thinkers]

This Wall Street Journal graphic shows who's selling (or sold) a percentage of their Facebook stocks and who's holding steady.

This graphic is the perfect example of why I'm a proponent of the pie chart. First, they stuck to two values per pie chart. That makes it easy to read. Next, they used the size of the pie to denote the number of shares. Finally, they used small multiples to easily compare both the shares owned by each entity as well as change in percentage of shares being sold.

I'm sure bar charts would be fine too, but WSJ really used all aspects of the pie chart very effectively.

[via Barry Ritholtz]

Last weekend’s birthday heatmap post has been hugely popular by The Daily Viz standards, drawing in more than 100,000 readers and tons of social media attention. While I’m excited about the traffic, I’m also worried that the graphic may have misled some readers.

Some people read the map assuming that darker shades represented higher numbers of actual births, even though I tried to explain in the post that the colors were shaded by birthday rank, from 1 to 366, in popularity. Or I thought I did. Because of that, Sept. 16 — the most popular birthday — seems wildly more common than January 1, among the least popular. Both may be relatively close in the raw number of births, even though their ranks are far apart.

Unfortunately, I haven’t yet been able to acquire a list of all dates and total births for each. But last night I compiled a decade’s worth of nationwide birth data by month. Those data show that August, in fact, saw the most births during the 10-year period. Each month is over 3.1 million births, however:

August, of course, has an extra day for potential births, so I created an average births by month field. Viewed that way, September did have more births relative to its size. But notice there isn’t much difference between months in the distribution of the births. Alas, all our birthdays are probably pretty normal:

I should note that this blog is a place for me to experiment with visualization techniques in my own time, and I will occasionally make bad design choices or produce work that is less useful to some. This is one of those times, I suppose. Thanks to Dan DeFelippi, Waldo Jaquith and several others who prompted this post. Download the data if you want to create your own visualizations.

Data source: Centers for Disease Control, National Vital Statistics Reports

Game Theory is the mathematical study of how agents in a system make choices for their actions, in light of the fact that other agents are also making competitive choices of their actions. As the name suggests, the "system" is often some kind of game and the "agents" are players, but game theory is also used to explain the behaviour of crowd motion, business dealings, foreign relations, and even the evolution of altruism. (There's a excellent chapter involving game theory in The Selfish Gene.)

The textbook example of game theory is the Prisoner's Dilemma. The UK game show "Golden Balls" includes a form of the Prisoner's Dilemma where the stake is prizemoney instead of freedom. Each player may independently choose to "split" or "steal" the prize, but if both steal each goes away empty-handed. You might think there aren't many strategic options to guarantee a win in a game like this, but one player found a way:

It's an elegant strategy. Nick is apparently altruistic (or at least risk-averse), and wants to share the prizemoney. But altruistic actions are open to subversion by a selfish opponent. Ibraham, unable to discern Nick's apparent "mutually assured destruction" strategy, has no option but to behave altruistically. I wonder if this strategy has ever been attempted again on the show; but now that this strategy is in the "gene pool" of strategic options, it would likely be less effective the next time around. And that's one of the subtle beauties of Game Theory.

via Business Insider: British Gameshow Contestant Puts On Badass Display Of Game Theory

An interview with Revolution Analytics CEO Dave Rich was published this week by BeyeNetwork. During the interview, Dace was asked about how the statistical modeling platforms have changed over the decades:

People have been doing statistical modeling and predictive analytics for 50 years now, SAS and SPSS have been around since the early ‘70s. What’s different now -- what’s making this move toward other statistical and “big data” areas? David Rich: Well, I think obviously SAS and SPSS have been around, as you pointed out, for decades. We call that sort of the first generation of analytics and insight-driven solutions. In my perspective, having been in the business for more than three decades, it reminds me a bit of what COBOL was back in the day relative to business software. I see R as the more modern language. In this analogy, R would represent Java or C++. What happened in the middle of the nineties when the shift occurred is very similar to where we are now with R. Open source is a worldwide collaboration innovation. It’s a way to tap into that channel for research, and I think the role that Revolution Analytics can play – very similar to what Red Hat did back in the Linux days – is to be the conduit between the community and enterprise deployment.

The conversation also touched on the future of big data analytics, impact of advanced analytics on business, and the benefits of R and Revolution R Enteprise to reduce costs and expand the scope of possibility with big data analytics. For the complete interview, follow the link below.

BeyeNetwork: Advanced Analytics, Big Data and the Power of R: A Q&A Spotlight with David Rich, CEO of Revolution Analytics

Two distinct approaches to presenting data graphically exist today—data visualization and data art—and rarely do the twain meet. They differ in purpose and in design. When we fail to distinguish them from one another, we not only create confusion, but do great harm as well.

There are as many definitions of data visualization as there are definers, but at the root of this term that has been around for many years is the goal that data be visualized in a way that leads to understanding. Whatever else it does, it must inform. If we accept this as fundamental to the definition of data visualization, we can judge the merits of any example above all else on how clearly, thoroughly, and accurately it enlightens.

By data art, I’m referring to visualizations of data that seek primarily to entertain or produce an aesthetic experience. It is art that is based on data. As such, we can judge its merits as we do art in general.

Either one, done well, is worthwhile, assuming that it fits the task at hand. If the task is to help a particular group of people understand something, then data art is not appropriate, no matter how well it is executed. If the task is to entertain or engage an audience in a particular emotional experience, then data visualization probably isn’t appropriate. If the situation requires that both objectives are achieved, then a deeply informing and aesthetically beautiful visualization would be in order. Although it is quite easy to make any data visualization aesthetically pleasing, it takes a great deal of skill as a visual designer and information communicator to make one beautiful.

People make better decisions when they’re based on understanding. For information to be understood, it must often be presented in visual form. This is because patterns, trends, outliers, and a sense of the whole as opposed to its parts require a picture for the human brain to see and comprehend. Data visualization is essential. Visualizing data effectively is vital. Anything less is frivolous, costly, and harmful.

How in particular is data art—visualizations that strive to entertain or to create aesthetic experiences with little concern for informing—harmful when it masquerades as data visualization?

1. It suggests that data cannot be visualized without training in the graphic arts. As such, it works against the democratization of data. In fact, anyone of reasonable intelligence and a little training can present data effectively. It’s vital that this ability spreads more broadly across the population, because it can play a role in making a better world.
2. It features ineffective practices as exemplars of data visualization. It encourages people to present data in ways that are difficult to perceive and understand simply because they are prettier or more entertaining, which is rarely relevant to the task.
3. It keeps the practice of data visualization spinning its wheels, never able to progress beyond the mistakes of the past. Best practices of data visualization have emerged through many years of research and experience. “Those who cannot remember the past are condemned to repeat it” (Santayana).

I am personally and painfully acquainted with each of these problems. For this reason, I try to differentiate data art from data visualization and encourage others to do so as well.

Take care,

R user Diego Valle analyzed the rate of divorces in Mexican marriage since 1993 (the earliest date for which data are available) and found that not only have more marriages ended in divorce over time, but marriages that do end are ending sooner:

This chart is a bit complicated, but it bears close inspection. Each line you see is a cohort of all of the marriages in a given year: 1993, 1994, all the way up to 2009. The vertical height of each line is proportional to the total number of divorces in each subsequent year within each cohort (expressed as a fraction of all marriages in the cohort year). Cleverly, the cohort lines are all arranged not by calendar time, but by years since marriage: the leftmost point represents divorces in the first year (relatively few), then divorces in the second year, and so on. 

More residents of Mexico married in 1993 saw their 10th wedding anniversary than those married in 1998. Overall, the trend is clear: more weddings that take place now will end than those from previous years, and they're likely to end sooner as well. Although there's not much historical data for recent marriage, the steady progression of divorce rates over time allows Diego to create a forecast (using a linear mixed-effects model in the R language) of the outcomes of recent marriages. He predicts, for example, that 11% of marriages registered in 2007 will have ended in divorce by 2022. By contrast though, that's about the same rate as US marriages from the fifties.

If you want to do a similar analysis, Diego provides R code in his post linked below, and at his github.

Diego Valle-Jones: Proportion of marriages ending in divorce

Washington Post's Ezra Klein busts on the filibuster. Gone are the days of Mr. Smith when invoking the filibuster was seen to serve a greater purpose. The filibuster has its roots in Ancient Rome, and apparently even then it had its critics.

This chart is a great example of providing a lot of information in a concise area. All of these data points are relevant to the topic and helps us inform our opinion about the matter.

[via @hfairfield]

Even if information is highly out of ordinary and attention-grabbing, if your content contains lengthy plain text without illustration or images whatsoever, the entire page becomes dull and unimaginative.

To sort this out, infographics help in routing information in a creative manner and in a style making your information easier to understand.

In a nutshell, infographics are visual representations involving data with applied design and style aspects to display written content. In forms of images plus text, some charts and other friendly resources, they extend the content of articles, usually of statistical data, and increase familiarity of readers in a way that elevates their comprehension. In this article, we will tackle how you can design effective infographics for your blog...

The New York Times does it again with this succinct look at tech IPOs. It begins with looking at everything through the lens of when Google's IPO in 2004, which, at the time, was considered huge. The next screen adds Facebook to the mix which dwarfs everything prior. It continues on to show the first day of trading pop and where things landed long term (3 years post-IPO).

It's a very interesting view of IPOs and could actually be a good financial analysis tool with a few more features.