I recently completed an online class, Data Visualization and Infographics with D3, taught by two great teachers: Alberto Cairo and Scott Murray. I have worked on a few D3 projects from the design side before, but this was my first real foray into doing the code myself. For class exercises, I picked a dataset to work with that I cared about: youth suicides.
Clusters of suicides among young people in our community have, understandably, caused much concern. The school my children attend is highly competitive and full of students motivated to do well. One huge concern in the community is that school pressures are a major contributor to these tragic deaths. This has led to many discussions about homework, high expectations, class schedules, parental pressure, and more, with a strong undercurrent among parents and educators of a desperate need to change something.
The message I get from my son (a junior), is that the school is not the problem and the system shouldn’t be changed as drastically as some propose. He and many classmates feel like the proposed changes diminish the educational experience and are senseless.
All of this made me want to know if there really was an alarming trend here or not. How does our community compare to others? What does the data say?
I was initially relieved to see that our state and county were below the national average. Suicide data is not reported on the city level, so I tried extrapolating from what was available anecdotally for Palo Alto (a collection of publicly known cases). I was relieved again, until I realized I was extrapolating against the population of the whole city instead of the age-specific population that relates to the data. A more accurate estimate suggests that we are definitely on the high side.
With the small sample size of a single city (or even some of the smaller states), the data gets jittery. Pretty soon you are looking at individual lives – probably helpful if you really want to understand causation, though less helpful for seeing trends. I may need to take a look at three year moving averages to smooth out some of the jitter and see if that clarifies any trends.
My son saw me working on this and encouraged me to pull in the comparisons to national and county data for a clearer picture. When he saw the graph, he said “You have to share this!” – the power of accurate data displayed clearly.
Part of the challenge for the community discussion here is that one suicide is too many, so talking about comparative data can feel cold and dehumanizing. What wouldn’t we do to save even one life? The potential problem comes when you change whole systems based on a handful of tragic cases, and then later realize that you damaged the system and didn’t solve the problem you thought you were solving. I hear echoes of this challenge in what I have been reading in Daniel Kahneman’s book Thinking Fast and Slow regarding loss aversion and the way humans respond to risk.
As with many things, it’s complicated.
This has been a valuable, if painful, discussion in our community, causing us to examine what we really value and how that gets reflected in our education system. I hope some clear data can contribute positively to the conversation.
I was reading an article in Fast Company this week called The Visible Man by J.J. McCorvey about being black in Silicon Valley. A statement on the first page caught my attention: “…of [Google's] 46,000 employees, just 2%—and just 1% of its technology workforce—are black.” Followed by: “In case you were wondering, blacks make up 13% of the U.S. population.”
Comparing 1 or 2% of the Google workforce to 13% of the U.S. population felt dramatic, but incomplete. I determined to try to get more of the story. Where does the supply chain of black tech workers get pinched? Here’s what I dug up:
I recognize that these are imperfect proxies for the workforce pipeline that leads to Google, but think they are worth exploring nonetheless.
It appears that black high school graduates are well represented in their interest in STEM subjects, but not well prepared — that’s the biggest drop-off (from 13% to 5%). The numbers hold steady for black high school graduates and those that get degrees in fields that might prepare them for a job at Google (5% in each case). And then the 5% to 2% drop from grads to Google hires. Clearly, there’s work to be done.
The Asian and Hispanic stories are also interesting. Qualified Hispanic high school grads do not appear to go on to get as many degrees in CS, etc. while the Asian representation in tech grows with each step.
As with many explorations of data, this may lead you to ask more questions than it answers. It did for me. Google is not dissimilar to other tech companies who have shared the same data, but I’d like to see actual employment data for a broader group of tech companies. That data is only just beginning to be shared with the public. Open Diversity Data is a good source. What isn’t public — and isn’t likely to become public — is data on the ethnicity of job applicants. That’s an important piece of the puzzle and one I imagine Google and others concerned with workforce diversity examine internally.
Also interesting to look at the ethnicity of the population in the four counties within a commute-friendly distance of the Googleplex. I know people relocate to work at Google, but the ethnicity of those who live nearby has to be a factor. Looking at the local population of those counties (Santa Clara, San Mateo, San Francisco, Alameda), Asians are perfectly represented at Google at 30% of the population and 30% of the workforce. And whites are far over-represented (36% of the population, 62% of the Google workforce).
Some notes on data sources
I used 2013 U.S. workforce ethnicity numbers from the U.S. Bureau of Labor Statistics — slightly different than the total U.S. population that was referenced in the Fast Company article, but more relevant in this case.
Data on high school graduate interest levels in STEM (science, technology, engineering, mathematics) by ethnicity comes from the people that administer the ACT test. The latest data is from 2014 graduates. ACT tracks both expressed and measured interest. The numbers I include are expressed, measured or both. They also track which of the students interested in STEM meet standards in math and science, by ethnicity. Ideally, I’d have numbers for those meeting standards in math or science or both, but since I couldn’t discern where any overlap might be, I chose to use the math standard — it was met at a slightly higher level across the board than science.
Data on graduates with college degrees (bachelors, masters, PhD) in Computer Science, Computer Engineering, or Information comes from the 2012-2013 Taulbee Survey. Information degrees include Information Science, Information Systems, Information Technology, Informatics and related disciplines.
The way ethnicity is recorded is (thankfully) fairly consistent across the different data sources. Although there were some inconsistencies (some included options for “Other” or “race not stated”, and others didn’t, for example), the discrepancies don’t materially affect the numbers that we are examining here.
I’m interested in your comments, here or on Twitter.
I love this stunningly clear, easy-to-use, and information-rich interactive piece that encourages exploration of VC investments. It works well on many dimensions. Looks like it’s a collaboration between Accurat Studio, Ben Willers and Visual.ly, with all the data being drawn from the CrunchBase API, so it should stay as up to date as CrunchBase is.
Some detail of the interaction.
It’s much more interesting to play with it than read about it, so go take a look.
I keep hearing about Density Design’s RAW tool and had it filed away in the back of my mind to try out sometime. As usual, necessity is the mother of actually doing something. So, I did something, and now I am a happy camper. What a great tool!
I wanted to show a client how an alluvial diagram would do a good job of expressing some relationships in their data and was not thrilled at the prospect of trying to mock it up in Illustrator. In my search for an easier way, I stumbled across RAW again and had a vector sample in minutes. Brilliant.
Here’s a sample output (not the actual client’s data):
Thanks Density Design! Sad I waited so long.
The conceptual illustration of brains has become kind of a quirky side business for me. I’ve constructed brains with Lego bricks and Lite Brite pegs, and created a DNA brain tree. And now in the style of Gustav Klimt’s Portrait of Adele Bloch-Bauer I – this one to submit as cover art for a scientific journal (the paper was on mosaic mutation patterns in the brain).
It’s true that you get to know a piece of art (and an artist) much more intimately when you try to recreate it yourself than you would by just studying it. Perhaps even more so when you try to capture the essence of a style and apply it to a new subject.
The gold background and black and white checkered stripe are Klimt’s (the joys of public domain), but the rest is my creation.
I don’t think I’ve ever found a reasonable use for a coxcomb chart, until now. It seemed to fit the bill for a recent project for The Foundation Center. With funding from the Rockefeller Foundation, they recently published a synthesis of success factors for small-scale coastal fisheries management in developing countries. The Foundation Center hired us to create an interactive visual representation of their findings.
It appears that fisheries have to balance a number (twenty, to be precise) of factors on different levels to be successful. We ended up using a coxcomb chart (or polar area diagram or rose diagram) as the base for an interactive presentation built on raphael.js. The interaction allows you to visualize how different stakeholders might prioritize the twenty factors, as well as drill down to get more detail about each of the factors.
The coxcomb overlays do a particularly nice job (I think) of showing where there might be gaps in perspective that would encourage you to bring another stakeholder to the table. Overall, I think the impression is that there is a lot to keep tabs on if you are managing a fishery.
Dr. Markus Covert’s lab commissioned Threestory Studio to create cover art again for another Cell Magazine article. This time, the article had to do with the ability to track protein activity in a single cell. To quote the article summary: “Our technology converts phosphorylation into a nucleocytoplasmic shuttling event that can be measured by epifluorescence microscopy.” Got it? Good.
Since the technology involves lighting up parts of a cell, a little light painting seemed in order. We found a nice stage with an already-lit circular cell membrane in the courtyard of the Clark Center building that houses Dr. Covert’s lab. Lab members formed the nucleus and Dr. Covert’s two young children did the running around, impersonating reporter proteins with colored LED flashlights. You can see here the version we submitted, mocked up in the Cell template (“ERK” stands for extracellular-signal-regulated kinases).
The recent discussion on DataStories about Data Art with Jer Thorp resonates with this kind of “science art”. I recommend giving the podcast a listen.
Here are a few additional shots of light painting I attempted after the lab members had left. I got a few curious looks from passersby, watching me wave flashlights around. In the middle of an empty courtyard. Late at night. Alone.
I love my job.
I’ve had my head down lately, working hard on a number of projects, so I haven’t done much posting to the blog, but this put a smile on my face so I knew I had to share it.
Alexander Chen, Creative Director at Google Creative Lab, put this together back in 2011, but I just discovered it today. It’s an interactive visualization of the Prelude from Bach’s Cello Suite No. 1 – one of my favorite pieces of music.
Here’s a video of it. You can also play with the interactive version and tangle yourself up in the strings a little.
Thanks to Jon Schwabisch for mentioning Alexander’s site and making me smile.
First impression: it’s a whole lot more windy over the oceans than it is over land. Intuitively obvious (even to the casual observer, as an old friend used to say), but the visualization drives the point home instantly. Now, what happens if we combine global wind data with ocean current data? It would be interesting to see how they interact.
Thanks to Andy Kirk at Visualising Data for pointing this out.
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.
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.
The work I enjoy most is divided pretty evenly between two things: visualizing complex data and visualizing complex systems. Both are trying to get at truth through some degree of abstraction.
Choosing the right type of chart for your data should be a thoughtful process and may, at times, requires some creative thinking, but choosing the right format for showing a system can be a lot less straightforward. There is seldom just one right way to depict a system. I find myself grappling with how position, shape, size and color might give meaning to different viewers in different contexts – not to mention line weight, arrow style, iconography, etc. Developing a consistent visual language can be a challenge, but it pays dividends, especially in a series of related diagrams.
Despite the complexity, or maybe because of it, I find great satisfaction in discovering simple solutions that are true and understandable. The best diagrams often feel simple and obvious when they are done. Which means that people who weren’t part of the process don’t look at the result and say “Wow, that’s amazing! How did you do that!”
Which makes it a little harder to show examples and have people appreciate what went into the image they are seeing, but I’ll show a few anyway. Here are some examples from work done for Palo Alto Networks.
Their large scale VPN technology:
And their Panorama technology:
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.
I recently taught an information design workshop to science graduate students at Stanford University. The idea was to give scientists some grounding in design principles and processes that could help them in presenting their work. They were hungry for examples and I found Juan Velasco’s National Infographic blog extremely useful. Especially his post about the design decisions that went into creating a graphic for a National Geographic article on Birds of Paradise.
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.
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
Palo Alto Networks launched the data visualization that we created yesterday with the release of their Application Usage & Threat Report. It’s a depiction of network traffic collected from 3,000+ organizations. The visualization gives you a sense of the applications that eat up the most bandwidth and represent the greatest risk. There are many ways to slice and filter the data, facilitated by the capabilities of the d3.js library. Many thanks to Jérôme Cukier for his coding expertise to bring the concept to life.
Another piece of the project was to create this related infographic.
I very much enjoyed delving into the world of moving spheres. What is it about us that is drawn toward playing with bubbles? Looking forward to more projects like this.
I came across this visually arresting depiction of gun murders today, thanks to a tip from my friend, Kimball. It was created by the folks at Periscopic, whose mission is to “do good with data”. The animation packs a punch, and when it’s finished, you have a number of options for diving into the data, including getting a sense of the individuals affected.
The arcs in the graphic show how long the murdered person might have lived, trying to give a sense of “stolen years”. I was a bit skeptical when I saw some of the lines showing a life expectancy in the 90s, but reading the notes on methods and sources, I see that each individual line is based on a the age distribution of deaths (not the average life expectancy). Meaning that there would logically be a few people that would make it into their 90s (and some that would die at 50).
I know this is a topic on a lot of minds right now. Not sure where I come down on gun control, but I do find that this exploration raises a lot of questions in my mind. Like a good data visualization should.
In an interesting coincidence, the original dataset was researched by Jerome Cukier, who is helping me with a project right now.
I recently discovered Data Stories, a podcast devoted to data visualization hosted by Moritz Stefaner and Enrico Bertini. I have been listening daily in the car on my way from here to there and have made it up to Episode #7 – Color. I think I’ve found my tribe.
Came across this fascinating interaction from the New York Times, doing research for a client project. It was interesting just as a static image with a few rollovers, but then I clicked some of the links up top (types of spending, changes) and things started flying.
I like how it invites interaction. The playfulness of the motion may be a little distracting from the data, but I think it does make it more “sticky”. Try clicking back to the “all spending” tab after exploring the others – interesting to see that the individual bubbles don’t exactly fall back into their original places. I guess the budgeting process is messy like that.
Thanks to Jim Vallandingham for the link.
The success of the artwork we created for a previous Cell Magazine cover spawned two additional assignments. One of those was published in today’s issue of Cell in an article about single neuron sequencing.
We worked closely with Gilad Evrony and Xuyu Cai of Christopher Walsh’s lab at Harvard University to develop an illustration that combines elements of a single neuron, DNA, and a brain, all in the shape of a tree.
Now if I could just get my cell phone to work in the office.
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.
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.
The big news is that Dr. Markus Covert and his lab at Stanford have created an integrated computational model of a cell. Their work was featured in a recent issue of Cell magazine and a New York Times article and could lead to big things.
The side news is that the artwork Dr. Covert enlisted us to create was accepted by Cell magazine and featured on the cover. You can see a larger version of the winning cover by clicking on the thumbnail image above. It’s a composite image of chalkboard diagrams and formulas relevant to what went into creating the cell model (artist: Dr. Markus Covert).
One of our favorite images that didn’t make the cut was a pseudo cell we created from a mixture of traditional lab supplies and computing accessories. Many thanks to Bernard André for the excellent photography work.
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.
I worked with MediaX at Stanford University to illustrate the process for clearing copyrighted materials developed by the Stanford Intellectual Property Exchange (SIPX). Their system removes obstacles to the proper licensing of content in a similar way that iTunes made it easier for us to download music legally.
The poster we created together won the Best of Show and People’s Choice awards at the recent New Media Consortium conference held in Boston at MIT. I have always been leery of design awards, but it is nice to receive some recognition now and then, especially if it gives my clients some helpful publicity. You can read a full article explaining the effort, along with a video showing some of the good people I worked with, on the New Media Consortium website.
Fast Company magazine recently featured this beautiful rendition of the ocean’s currents on their blog. It was put together by the visualization geniuses at NASA’s Scientific Visualization Studio and can be viewed in a variety of formats (including an iPad app).
It’s worth downloading one of the high resolution video formats from the NASA site.
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.
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.
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.
Just discovered LinkedIn InMaps today. A good example of interactive information graphics that can lead to discovery. Interesting to find the connections that bridge groups. Like the “I didn’t know Tony knew Larry!” moment.
This fact would have been discoverable just browsing through my connections on the standard LinkedIn site, but seeing the whole network mapped in one place removes a lot of barriers to this kind of discovery.
The zoomed-out view shows an accurate picture of my circles – the smaller clearly defined orange is a networking group I’ve been closely connected to for over a dozen years, the dense multicolored cluster opposite are my various church connections, with family mixed in. In between are various work and school connections that are scattered and less well-defined.
It’s not hard to create your own. Try it here. I’m curious to see what other people’s networks are shaped like.
Check out this interactive look at the scale of the universe. Puts things in perspective, from a Planck length to the observable universe:
Hans Rosling is an entertaining and compelling presenter. He uses the now-Google-owned Public Data Explorer technology (developed by his organization Gapminder), to take you on a journey testing your concept of the developing world.
I recommend watching him in this TED talk delivered to the U.S. Department of State:
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:
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):
Why not use all the senses to understand data? It would be interesting to layer in a comparison set of data and hear how they interact.
What we’d like to see next: odor graphics. A market graph that stinks when it sinks.
Reading today in Rudolf Arnheim’s Visual Thinking, I came across this delightful extract from Francis Galton (half-cousin to Charles Darwin). It appears in the middle of a discussion about the difference between static and dynamic concepts.
“It is difficult to understand why statisticians commonly limit their inquiries to Averages, and do not revel in more comprehensive views. Their souls seem as dull to the charm of variety as that of the native of one of our flat English counties, whose retrospect of Switzerland was that, if its mountains could be thrown into its lakes, two nuisances would be got rid of at once.”
As Arnheim points out in the same chapter, there is an “attractive simplicity” in static concepts which can create tension with our human desire to comprehend more deeply and completely. I feel that tension and want both the simple, distilled understanding and the deep comprehension that comes from nuanced individual experience. I want both the forest *and* the trees.
Is that too much to ask?
Stumbled across this interesting interactive work of Ben Fry’s – a great example of visualizing large amounts of data in a cohesive way. He has visually shown all the changes made by Charles Darwin to his classic On the Origin of Species over six different editions. The book went from 150,000 words to 190,000 by the sixth edition, with some interesting edits along the way (including a significant addition to the closing paragraph).
I think what I like most about it is the clear illustration of how the scientific process can lead to continual learning and refinement of ideas. Keep your mind open. Take a look.
“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.
The promised infographic résumé tool that I mentioned a few posts back has launched at Vizualize.me. It’s a customizable infographic interpretation of your LinkedIn profile, to which you can add skills and other experience. Using LinkedIn to populate the infographic gives a jumpstart to the process. Seeing work experience in a timeline makes a lot of sense, though the scale of the education timeline differs from work experience in a way that gives a distorted view. See my full infographic CV:
I’ve found Sir Ken Robinson’s addresses to be captivating and inspiring since I first was directed to one on TED a year or two ago. RSA’s animated illustration of one of Sir Ken’s discourses brings a visual dimension to some important ideas. Makes the ideas even more sticky, for me at least.
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.
Here’s a nice interactive example of making data friendly to the average human. What country would you like to live in? With the OECD Better Life Initiative, you can pick what you care about (Environment, Work-Life Balance, Health, etc.) and see which countries rise above the rest. Another interactive wonder from the brain of Moritz Stefaner. He’s the same designer who created the Notabilia Wikipedia project that I posted about a while back. Looks like it’s time for me to move to Australia.
I’m impressed with two things about the World Bank’s approach to data: first, their commitment to openness in sharing data with the world, and second, their devotion to data visualization. They have also done a nice job inviting exploration with the way they have organized their data website.
Interesting to see that the mortality rate for children under 5 in the US is about 37% higher than the average of high income OECD countries.
And the US spends way more on healthcare than almost any other country in the world. Maybe we’re not spending it on the young.
Note: The World Bank is the source of data for the sample I gave a while back using the Google Public Data Explorer.
Looks like you may soon be able to create a visual version of your resume in “one click” with the help of vizualize.me. Resumes are certainly fertile ground for visual rethinking, and what job applicant doesn’t want their resume to stand out from the pack?
We’ll see how much customizing is possible once they launch. With the diversity of individual experiences and the differences among job opportunities, it seems like customized options are a must — I know I wouldn’t send the same resume to two different potential employers. If this catches on, it may make it easier for employers to compare resumes, but that would lead us back to people wanting to differentiate. Maybe that’s where visualize.me starts up-charging for higher levels of customization. Sounds a little like Sylvester McMonkey McBean and the racket he pulled off on the Sneetches. Are there stars upon yars?
Sorting through air travel options can be mind-numbing. The timeline approach at Hipmunk makes it easy to see everything you want to know about available alternatives: price, departure and arrival, length of flight, stop-overs, airlines. They have an “agony” algorithm that brings the least painful itineraries to the top. You can also sort instantly by more traditional criteria. And a quick click gives you all the details you need to know about a flight without having to go to a new page.
Well thought out, all the way around. Only gripe is the lack of Southwest Airlines options, but that’s not Hipmunk’s fault – you won’t find them on Orbitz either.
Time to book a flight to Honolulu.
Reading Edward Tufte’s “Envisioning Information“, I came across a simple graphic, originally published in the Chinese mathematics treatise Zhou Bi Suan Jing (or Chou Pei Suan Ching) that impressed with its simplicity. It’s a visual, geometric way of proving the Pythogorean Theorem that was published around 2,000 years ago. If you compare it with Euclid’s proof, this picture is worth about 500 words.
Any mathematicians out there know that there are many ways to prove the theorem attributed to Pythogoras – I like the simple elegance of this one. Here’s my redrawing of the Chinese original.
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.
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?
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!
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.
I bookmarked the Wolfram|Alpha site almost two years ago and just recently checked back. It has become quite a sophisticated resource – they call themselves a “computational knowledge engine”.
For example, enter the term “27 people” to calculate how big a room you need to seat 27 people in 10 different ways. You’ll also get the approximate amount of heat they will generate and the probability that two people in the group will share the same birthday.
Also handy for hangman or Scrabble (try entering “_on_u_a__ou_“) or for hearing what a particular chord or scale sounds like. The D minor bebop hexatonic scale, for instance. And that’s just scratching the surface. I’ll have to try the iPhone app.
MLB.com continues to push the envelope on incorporating information graphics in a multi-faceted online experience. Baseball is probably more stats-driven than any other sport out there, and MLB’s Gameday capitalizes on that with more ways of looking at a game than you can shake a (Candle)stick at. They’ve done a fine job of incorporating color, motion, tables and graphs, along with images of the field that make you feel like a part of it all.
A stat new to me is the “nasty factor” for pitching. According to the website, it takes into account velocity, pitch sequence, location, and movement, while adjusting for the pitcher’s specific experience with the current batter. And that’s just one of many stats you can explore during a game.
I wonder if the teams are using this to help them win games. Go Giants!
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.
Richard Saul Wurman, coiner of the term “information architect” and founder of the TED Conferences (ideas worth spreading), is behind an effort to examine 19 cities of 20 million or more in the 21st century. They are calling it 19.20.21. It promises insights into the impact of the increasing urbanization of the world’s population.
There isn’t much there yet, but it did provide me with some new knowledge. Did you know that Cordova (Córdoba) Spain was the world’s largest city in the year 1000? And that we’ve gone from 3% of the world living in cities in 1800 to more than half of us being city dwellers now? I wonder how they are defining “city”. I’m interested to see how they present their findings.
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…
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?
In an age of ubiquitous Google Maps and navigation systems that will talk to you, I love the unique maps collected by the Hand Drawn Map Association. Reminds me of the maps my dad used to draw (and still does). I should start collecting those. Below is a nice example from the HDMA site.
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.
Seemed like a good day to march forth with a new blog, so here it is, Threestory Studio‘s take on the world of information design. Expect to see examples of interesting data graphics, explanatory diagrams or engaging illustrations – from Threestory Studio and from others. And I’d love to hear it if you come across any stellar examples of the design of information. Off we go…