The Design of Information

We appear to be past the point of needing to name new babies in our family, but I still like the interactivity of the Baby Name Voyager. You could consider this a more specialized cousin to the Google Ngram Viewer. In fact, it’s interesting to compare results from the two. You would expect the increased usage of a name to correspond to an increase in print, perhaps with a time lag to give the babies time to grow up before they become newsworthy. Does it play out?

It appears that I had a good run in the 70’s and 80’s but have dropped sharply since then. Those were good years for me – hmm…

We developed all the data graphics (about 70 individual graphs and charts) for a report on Gender Diversity in company leadership that was published this week by Fenwick & West. The report compares the top Silicon Valley companies with the S&P 100. According to the survey, Silicon Valley companies overall have less female representation in company leadership than large public companies nationwide, though both seem to be increasing in representation over time.



This bubble chart gives you an overall sense of female representation on boards of different sizes. You can see that the S&P 100 companies tend to have more directors overall, and also more women directors. How long will Silicon Valley companies stay clustered at the bottom?

The Wall Street Journal picked up the story. You can download the full report from Fenwick & West’s website: Gender Diversity in Silicon Valley: A Comparison of Silicon Valley Public Companies and Large Public Companies.

I keep meaning to post about some recent data visualization work from Threestory Studio that formed an integral part of Santa Clara University’s President’s Report. The design firm Cuttriss & Hambleton did a great job with the overall design of the report while we focused on the infographic components.

The spread featured here highlights SCU’s global reach, showing the inflow of international students who study at the university and the outward reach of students who leave to study abroad during their time at SCU. A third layer of global connectivity shows the affiliated Jesuit institutions scattered across the world. It all serves to give you the sense that this is a place that is anything but provincial.

You can see the whole report in a handy PDF viewer (here’s a screenshot). Clicking on this image will show you a large scale version of the illustration itself.

I met someone recently who pointed me to an interesting tool – apparently an outgrowth of Google’s efforts to digitize anything ever printed. You can track the frequency of any words that have appeared in print over the last many years.  Try it.  If you are competitive, like I am, you’ll find it hard to stop. Tell me your favorite comparisons.

I’m adding a few more graph samples from the Gender Diversity study, pertinent to an interesting discussion of appropriate line graph scales on Alberto Cairo’s The Functional Art blog and a discussion of slopegraphs on Andy Kirk’s Visualising Data blog.

For the Gender Diversity study, we chose to use a 50% maximum for the scale of the line graphs, with the idea that 50% represents parity. I suppose that sends the subtle message that parity is what we are shooting for, so you can visually see how far the line is from parity. For female representation at the largest U.S. companies, those lines are mostly still quite far below the 50% line, which makes it a little difficult to get a good sense of recent change. For some of the data, we included the average number of women in a position over time, using a scale which comes closer to Cleveland’s 45 degree optimum

I think I was first introduced to slopegraphs in Alberto Cairo’s book, The Functional Art (a book I recommend to anyone wanting to do a better job of presenting data). We used them to show the change of women in key positions from the beginning to the end of the survey period. “GC” is for General Counsel – the other abbreviations are probably more familiar.

A data-heavy project I’ve been working intensely on the last week or so was released yesterday. It’s a statistical review of corporate governance practices since Sarbanes-Oxley, done by the law firm Fenwick & West.

I enjoyed wrestling with Excel and Illustrator to create histograms, box and whisker plots and a few original creations. And the client was great to work with – detail-oriented, appreciative of good design, understanding of complexity. You can download the full report here: Corporate Governance Practices and Trends

It’s amazing how much more understanding you get out of a well-designed visualization than a spreadsheet of numbers. We went from something like this:

To these:

Is it strange that I love graphs so much?

Here’s one person’s list of the “12 Great Visualizations that Made History“. I’m in general agreement, although I think #11 (the gold plaque on the Pioneer 11 spacecraft) can’t count until we hear back from the aliens.

Reading the raw data, or even a well-written description, doesn’t have the same impact on understanding as an effective visualization. Like in this famous image of how to pack a slave ship (#2 on the list):

Source

Several people have pointed me to a well-crafted data visualization by Pitch Interactive showing data for U.S. drone attacks in Pakistan. Like a good visualization should, it answered some questions and sparked a few more. To answer some of the questions that came to me, I put together this graphic that gives another angle on the same data.

The bands of color represent total deaths by victim category. From the notes on Pitch Interactive’s site, it sounds like the victim categories can be a little fuzzy, with the definition of “other” depending on who is doing the defining. The Obama administration calls an able-bodied adult male a military combatant if it has not been proven that they are a civilian – here those are classified as “other”. The death totals are approximations too, as many reports include a range (e.g. “6-10 people were killed in a drone attack…”).

Assuming the primary targets of the drones are the so-called “high-profile” combatants, with other combatants as secondary targets, I calculated percentages comparing all civilian deaths (children + civilians) to non-civilian deaths (high-profile + other) to see how often the drones were on target. Those are the dotted lines. This data is up to date through a few days ago (March 22, 2013). I encourage you to check out the interactive visualization here: drones.pitchinteractive.com

Fenwick & West continues to keep me busy creating data graphics for a series of surveys they publish. Here’s one from their Technology and Life Sciences IPO Survey, plotting each deal by number of shares (log scale) and share price at the time of the offering. The bubble size represents the overall deal size. The data visualization software company Tableau made the chart this time – looks like a pretty big deal. We’ll see if these bubbles burst.

This week, my sister tipped me off about The Atavist, a new take on multi-layered storytelling via iPhone or iPad apps (also available on Kindle and Nook). Threestory Studio got its name in part because of my interest in telling stories visually, so I was intrigued to see what The Atavist had to offer.

One of the first things I discovered was a rich infographic showing the events leading up to the fall of the regime in Egypt. It combines a timeline of events with web traffic data and social media engagement in Egypt.

It wasn’t immediately clear what the black bars rising from the bottom were – they appear to indicate numbers of people involved in protests or revolutionary activities. Otherwise, this graphic receives high marks.

The full graphic can be downloaded here. It was created by Erik Steiner of the Spatial History Project at Stanford University.

We just returned from a family trip to Europe. I first visited London in 1999. I was impressed then, and again on this visit, by the well integrated, and cohesively branded, transportation system.

This was the first time I had seen the shared bicycle system in action. We wanted to try it out, but the bikes were too big for our 7 year old, so we contented ourselves with the double-decker buses, the overground, the river boats, and the tube instead.

I found myself wondering about the flow of the bicycles around town, wishing I could get my hands on the data to see what that looked like. Do they get stacked up in one location and require redistribution? Imagine my delight when I stumbled across this nice graphic created by Álvaro Valiño for National Geographic this morning.

And for an encore, I discovered that Sr. Valiño also created the graphic that accompanied the article on whaling that I read just this morning (also in National Geographic). Nice work Álvaro.

It may seem like a bit of navel gazing, but Ivan Cash, an art director based in Amsterdam, has created an interesting infographic of infographics. The sample size is a bit low – he examined only 49 individual infographics to draw his conclusions – but I’m guessing his goal was more self-promotional than academic. He seems to have gained some notoriety from it. Mission accomplished.

Maybe most interesting to me is that “health” was the most common theme. I wonder if that would hold up with a larger sampling.

The EPA has announced a new fuel efficiency label for cars – mandatory in 2013. It’s a nice effort toward providing some information design, but it makes my head hurt a little more than it should. Too much cramped type, overbearing borders and a confused information hierarchy.

The apparently committee-designed version adds a figure for what you will save (or spend) compared to the average vehicle over 5 years. Seems like that could have been integrated with the Annual Fuel cost more tightly to save space and increase clarity.

The original proposed design (below) had a little more breathing room and the useful feature (in my opinion) of ranking fuel economy within the vehicle class as well as compared to the overall average. For those who need a large car (to carry 8 kids safely through the mountains, for example), it’s just stating the obvious to tell them that an SUV is not going to have the fuel economy of the average car.

It took 30 years to decide to do this redesign. Look for a new and improved label in 2040.

Moritz Stefaner is a freelance designer in Europe creating some beautiful and data-rich visualizations. I came across his Notabilia project yesterday, after following a lead from someone at The Leonardo.

It maps the collective editing process for Wikipedia articles up for deletion. Right-leaning red segments are votes to delete; left-leaning green ones are votes to keep. The shape of each branch is an excellent mapping of the shape of the discussion. And the collection of 100 branches makes a lively, energetic whole that begs to be explored.

Projects like this excite me about the power of information design to bring things to light that aren’t easily discernible any other way.

Thanks to my nephew Christopher for bringing this infographic to my attention. According to the calculations done by the creators of the graphic (degreesearch.org), a stay-at-home mom should be paid $115,432 for her troubles, including 56.6 hours of overtime. Still a gross underestimation of the value of a full-time mother.

I like the inventive use of the circular graphic to show salary earned vs. time spent, though I needed the accompanying tables to help me figure out what was going on.  Like most good infographics, this one starts with a compelling idea and interesting data. That was enough to make me want to spend the time to understand it.

I’ve only shown part of it here. It’s worth clicking through to see the whole thing.

Happy Mother’s Day!

“Things change. Now what?”
That’s the tagline for a new website examining statistical indicators of how life in the U.S. has changed over the last few decades: startlingstats.com. Threestory Studio created all the graphics and designed the site. The aim of the site is to wake people up and promote dialogue about life “as we know it”. I use quotes there because who is “we” and what do “we” really know are all part of the debate. I hope you’ll take a look at the site and leave a comment or two.

Threestory Studio’s second data visualization project with Silicon Valley law firm Fenwick & West was released to the public this week. This report looks at trends in venture-funded deals in the life sciences.

Though not as extensive or complex statistically as the first one (Corporate Governance Practices and Trends), this one presented some interesting challenges in presenting data clearly, accurately and concisely. I’m happy with the results.

Thanks to my brother for pointing me to this simple but telling graphic showing the flow of talent among the top tech companies. It was created by TopProspect and was featured recently on TechCrunch.

I like the simplicity of this, but it seemed like there was an opportunity to go a little further. Here’s my take on the same data expressing the ratios visually:

I can think of several other factors pertinent to talent flow that would be nice to show: company size (Microsoft=89,000+, LinkedIn=~1,000); revenues, age of company, geographic location, average age of employee, some gauge of talent (productivity per employee? IQ?). Then, of course, I’d like to see this all animated over time to see how the flow shifts and size changes. Anyone game?

Thanks to my watchful nephew Christopher P. for pointing me back to my Alma Mater to see this nice interactive visualization linking college majors to career choices.

It was put together by Williams College math students and their professor using CIRCOS visualization software. Rolling over the thumbnails allows you to isolate the paths from individual majors to careers. Nice use of colors to organize majors within larger groupings. I majored in Music (Composition) – not sure if I’m represented by the path to arts/entertainment, writing/communication or other. I’ve always preferred to be uncategorizable.

I’m guessing Williams, as a liberal arts school, may have a more evenly distributed set of careers than some other schools. I’d be interested to see how this same distribution looks for other schools of different types and sizes.

I’m working on a project aimed at illustrating dramatic changes in the way we live now compared to the not-so-distant past. One startling statistic is the change in outstanding consumer credit in the United States. That’s the amount of money people owe on credit cards, car payments, etc. (not counting home mortgages – that’s another story).

Here’s one attempt to illustrate the change over the past 30 years. I don’t think it’s getting the point across. The gut reaction seems to be excitement that she gets to take home so much stuff. I acknowledge that representing debt with shopping bags is a step removed from most people’s perceptions. But if they were more closely connected in our minds (shopping and the cost of buying on credit), maybe we’d be healthier consumers.

The truth is, debt is a crippling burden for many in America today. Maybe she needs to be crawling in tattered clothes, dragging the heavy bags along behind her. Uphill. In the snow. Back to the drawing board.

With the presidential election fast approaching, interest in the predicted outcome is high. I’m impressed by detailed data graphics on Nate Silver’s FiveThirtyEight blog for the New York Times, not only for their clarity but for his thoroughness in examining the data.

I guess we’ll see about the accuracy of all these predictions after November 6.

Trust the Olympics to inspire some innovative data visualization. Thanks to friend Peter F. for the tip on a nice series of visualizations from The New York Times that compare today’s winning sprinters, swimmers and jumpers with past medalists.

Interesting to see the steady march forward over the years in swimming and sprinting.

Curious that there’s not a similar progression in jumping. Is that because the long jump is a more complicated venture than a pure sprint? Or just because there was only one Bob Beamon?

I was thinking about the visual display of uncertainty today and came across this nice example from a weather site in Norway. It shows the probable range of future temperature and precipitation levels for the city of Oslo. This is a good solution for something I’ve puzzled about for a while: When I hear that there’s a 30% chance of rain, I’m always asking myself “a 30% chance of how much rain?” A 30% chance of a light sprinkle is a much different forecast than a 30% chance of a deluge.

It would be interesting to know what factors go into the variability of the forecast. I imagine that the further out in time the forecast is, the more uncertain it would be, but there are obviously other factors that affect probability as well.

I also like their “detailed meteogram” with an hour-by-hour view of precipitation, temperature, and pressure, enhanced by an elegant indication of wind speed and direction, and topped off with an artful visualization of cloud cover.

Makes me proud to be 1/8 Norwegian.

Doing research for a project today, I stumbled across a powerful data visualization tool – the Public Data Explorer from Google. The power is in the stories that are told as you scan through the data over time. Two dramatic stories involving Rwanda and China come to light in the World Development Indicators data for fertility rate and life expectancy at birth. And these are just two of the parameters available in this data set – there are many others. I could spend all day…