Design

• Analysis versus storytelling

  April 23, 2012 to Design  •  Nathan Yau  •  Share on Twitter

  Robert Kosara contrasts my version of the pay gap graphic with the NYT original and notes how small changes make a big difference in how a graphic reads.

  But what Nathan's version is missing is the story. The additional data mostly adds confusion: move your mouse over the year in the lower right, and what do you see? Lots of points are moving around, but there doesn’t appear to be a clear trend. The additional categories are interesting, but what do they add?

  Not much. When I was putting together the graphic, I was hoping for a clear trend — something so obvious that didn't have to be explained. Instead I got fuzzy results. And that's where I stopped. On the other hand, the NYT version explains those fuzzy results, namely the outliers, such as women CEOs who work for non-profits or the greater percentage of men in medical specialties like surgery.

  In analysis, assuming the users are experts of their data, annotation is less important. It's about allowing them to stay nimble and ask/answer a lot of questions. Graphics that tell stories with data, however, already have something interesting to say.

• Too many axes

  April 11, 2012 to Design  •  Nathan Yau  •  Share on Twitter

  Kaiser Fung talks about the suck of overlaying plots to show a relationship.

  When the designer places two series on the same chart, he or she is implicitly saying: there is an interesting relationship between these two data sets.

  But this is not always the case. Two data sets may have little to do with each other. This is especially true if each data set shows high variability over time as in here.

  This seems to happen a lot when people take the data-to-ink ratio too literally or they're trying too hard to be clever within a given space. Overlays work on occasion, but I can't think of any that did off the top of my head. Most of the time it's better to split up the layers into multiple charts.

• How businesses approach infographics

  April 10, 2012 to Design  •  Nathan Yau  •  Share on Twitter

  The Washington Post asked three "young entrepreneurs" how their company uses infographics. They responded with similar sentiments. The first one said:

  Infographics can be great as part of presentations, newsletters or other research content. It keeps people's interest by lending a storytelling and visual element to what can be sterile research.

  The second said:

  Infographics are outstanding for bringing life to content that would otherwise be dry, uninteresting or unshareable.

  And the last one, who to be fair, seems to know more than the first two, said:

  At the end of the day, the main use for infographics is to create content that can potentially go viral and drive traffic, links and exposure to a Web site and the brand.

  If I were new to these infographic things, my main takeaway here would be that they're used to make boring material interesting. Shouldn't it be the other way around though? Information graphics are interesting because their foundations of data and um, information are worth looking at in the first place. Don't fall into the trap of trying to make something "visually compelling" without anything to compel with.

• Fast and slow visualization

  March 8, 2012 to Design  •  Nathan Yau  •  Share on Twitter

  James Cheshire ponders the difference between fast and slow thinking maps, and the dying breed of the latter.

  So do the renowned folks at the NY Times Graphics Dept. prefer fast or slow thinking visualisations? I asked them what they think makes a successful map. Archie Tse said what I hoped he would: the best maps readable, or interpretable, at a number of levels. They grab interest from across the room and offer the headlines before drawing the viewer ever closer to reveal intricate detail. I think of these as rare visualisations for fast and slow thinking. The impact of such excellent maps is manifest by the popularity of atlases and why they inspire so many to become cartographers and/or travel the world.

  A graphic that takes a little while to understand doesn't always mean it was a failure in design. It might mean that the underlying data is hard to understand. Likewise, a graphic that isn't what you expect might let you answer different questions than from the usual standby.

  [Spatial Analysis]

• Van Gogh for the colorblind

  December 22, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  

  After a chat with his color deficient friends about how Vincent van Gogh's paintings seem to appeal to all eyes, Kazunori Asada used visual filters to see how the paintings looked to the colorblind. The experiment produced some interesting results and musings:

  Was van Gogh partially color vision deficiency (anomalous trichromat)? Perhaps using a strong color vision deficiency (dichromat) simulation was the wrong approach. How about carrying out the simulation by removing the middle portion of normal color vision, maybe then I could see van Gogh’s pictures in a better light?

  The color choices for van Gogh's popular paintings seem less out there with the filters. The greens in the sky of Starry Night, for example turn to yellows.

  A colorblind van Gogh though? Probably not. Either way, don't forget to pick your colors wisely. Asada has an easy-to-use tool to see what your own images look like to others.

  [Asada's memorandum]

• Substratum: A series of interviews with smart people

  December 8, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  It's always nice to hear from the people who are the best at what they do. Data visualization studio, Interactive Things has an interview series going, Substratum, that asks designers and artists the same set of questions. The most recent issue is with Amanda Cox from The New York Times and Nicholas Felton, who you know from his annual Feltron reports and now at Facebook.

  Amanda Cox, the chart marker, on how her work and goals have changed over the years:

  At one point — I call it my impressionist phase — I was really interested in making things abstract but interesting and beautiful. And then I had a "curves are fun" phase for a while where I was really into curved things. And then I had an "intentional simplicity" phase for a while, like, how stripped down can you make something and have it still be interesting? I don’t know what my current phase is, but it's kind of an "aspirational reporting" phase. I'm not that great of a reporter yet, but I'm thinking a lot about how we can stop using the same information that's already on the Internet and just remix that. I want to start working with more, deeper information, information that's harder to surface.

  This is coming from someone who has won an international award for being the best. So much to learn, I have.

  [Substratum]
• 

  On low-quality infographics

  This has been sitting in my drafts folder for a few months. Figured I'd just hit publish and throw it...

• Significant digits and relevance

  November 8, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  Lulu Pinney goes over the subtle art of working with significant digits:

  When we say on the phone "I'll be there in half an hour" it’s quite likely we'll arrive sometime in the next 25 to 35 minutes. But for the context of meeting up with a friend "half an hour" will do. If you said "see you in 27 minutes" that would raise a laugh being an odd level of precision for the given context. The same ideas apply to numbers in journalism.

  Important in both accurate representation of data and readability.

• The Don’ts of Infographic Design

  October 19, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  

  Written by Amy Balliett of Killer Infographics, the post in question is basically tips for how to create linkbait that doesn't work. Or at least I hope it doesn't.
  Continue Reading

• 5 misconceptions about visualization

  September 23, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  Last month, I had the pleasure of spending a week at the Census Bureau as a "visiting scholar." They're looking to boost their visualization efforts across all departments, and I put in my two cents on how to go about doing it. For being a place where there is so much data, the visual side of things is still in the early stages, generally speaking.

  During all the meetings, there were recurring themes about what visualization is and what it is used for. Some people really got it, but others were new to the subject, and we ran into a few misconceptions that I think are worth repeating.

  Here we go, in no particular order.
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• Why learning code for data is worthwhile

  July 12, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  There are lots of tools that have come out in the past couple of years that make data easier to handle, analyze, and visualize. Maybe you've used them. I use them all the time. However, no matter what software you use, there is always going to be a limitation in what you can do with it.

  Have you ever been using an application (not just for data) and wished it could do something else? If you want a new feature, you have to wait for someone else to develop it, but if you program, you could implement your own features.

  With a little bit of coding know-how, you gain more flexibility — and a little goes a long way.
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• Wow vs. Ah-ha for data graphics

  July 8, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  After attending the Eyeo Festival, Zach Gemignani of Juice Analytics noticed a difference in the approach of artists and his own practices with business-related data:

  The artists are looking for an emotional “wow” moment; our goal is the “ah ha” moment when a user learns something that can lead to productive action. The question that we so often ask: “what can you do about it?” wasn’t a top priority within the Eyeo crowd.

  One group is telling a specific story and the other is searching for one. That's not to say that one way or the other is bad, however. Each group can benefit from the other:

  Ultimately this art vs. practice dichotomy is natural and healthy. In our work, we are inspired by the fun and energy expressed in artistic visualizations. Data visualization is a tool that can and should be used differently depending on the purpose and the audience. The skill in using the tool can be appreciated equally across these different contexts.

  There'd be a lot less ruffled feathers if we could all remember that.

  [Juice Analytics]

• Approaching data, a UX perspective

  June 8, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  UX designer and consultant, Hunter Whitney, describes a good mindset as you start digging into data, with the end target of visualization. "Why might you want to collect data about something and are you sure you know what you really need? ... How are the data stored? ... How are they summarized (statistically) and modified? ... How are the charts displayed, formatted, and presented in the context of the full interface?"

  [UX Magazine | Thanks, Elise]

• Chart doesn’t work for colorblind

  February 2, 2011 to Design  •  Nathan Yau  •  Share on Twitter

  

  In regards to a performance chart posted by Netflix, Andy Baio, who along with around 7 percent of men, is colorblind, explains why it's so hard to read the chart. "When doing the right thing is this easy, it's really disturbing when it's dismissed as a waste of time."

  [Waxy]

• Target for charting excellence

  November 19, 2010 to Design  •  Nathan Yau  •  Share on Twitter

  

  The scales for what qualifies as pretty and useful change depending on the application and purpose, but you always aim for the same quadrant. The best data graphics come from those who are able to find the right balance between aesthetics and utility.

• Learn the rules of data graphics so you can bend them

  November 15, 2010 to Design  •  Nathan Yau  •  Share on Twitter

  If you take away anything from The Visual Display of Quantitative Information, make it the epilogue. This is the most important part:

  Design is choice. The theory of the visual display of quantitative information consists of principles that generate design options and that guide choices among options. The principles should not be applied rigidly or in a peevish spirit; they are not logically or mathematically certain; and it is better to violate any principle than to place graceless or inelegant marks on paper. Most principles of design should be greeted with some skepticism, for word authority can dominate our vision, and we may come to see only through the lenses of word authority rather than with our own eyes.

  When we first start out with data graphics, it is easy to read a list of rules about ratios, flourishes, and sizes, and then trick ourselves into believing that is all there is to it. But like cooking, writing, programming, painting, speaking, designing, sporting and numerous other things, you learn the basics first. The principles. And then you figure out what rules can bend and how far.

• Graph Design Rule #2: Explain your encodings

  August 26, 2010 to Design  •  Nathan Yau  •  Share on Twitter

  

  This is part two in a seven-part series on basic rules for graph design. Rule #1 was to check your data. Today we cover rule #2: explain your encodings.

  The design of every graph follows a familiar flow. You get the data, you encode the data with circles, bars, or colors, and then you let others read it. The readers have to decode your encodings at this point. What do those circles, bars, or colors represent?

  William S. Cleveland and Robert McGill have written about encodings in detail. Some encodings work better than others. But it won't matter what you choose if readers don't know what the encodings represent. If they can't decode, the time you spent designing your graphic goes to waste.
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• Graph Design Rule #1: Check the data

  August 17, 2010 to Design  •  Nathan Yau  •  Share on Twitter

  

  Now that we've covered the 7 basic rules to graph design, it's time to go deeper, starting with the first: check the data.

  I have to admit. Data checking is definitely my least favorite part of graph-making. I mean, when someone, a group, or a service provides you with a bunch of data, it should be up to them to make sure all of their data is legit, goshdarnit. But this is what good graph-makers do. After all, reliable builders don't use shoddy cement for a house's foundation. You don't use shoddy data to build your data graphic.

  Data-checking and verification is one of the most important—if not the most important—part of graph design.
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• Redesign of the Federal IT Dashboard

  August 2, 2010 to Design, Statistical Visualization  •  Nathan Yau  •  Share on Twitter

  About a year after the launch of the Federal IT Dashboard, business intelligence consultancy Juice Analytics focuses on five areas — message, flow, charts, context, and design fundamentals — where the dashboard could use some improvement.

  The first tip on message:

  The information designer is responsible for presenting the data in a way that the message is delivered in a clear and understandable way. If the data is left to speak for itself, users can be left confused or frustrated. And in all likelihood they won't to [sp] see the full value of the data. That's particularly tough for this Federal IT Dashboard where a huge amount of effort has been put into gathering consistent data across agencies.

  Totally agree with this, but to avoid confusion, let's clarify. You should always let the data speak for itself. It's just that what the data says often seems like a foreign language to non-professionals. It's up to you, the information designer, to translate. The better you can translate, the better the information designer you are.

  See the rest of the redesign on Juice Analytics (who is hiring, by the way).

• Process: Mapping War Logs for the Guardian

  July 28, 2010 to Design  •  Nathan Yau  •  Share on Twitter

  This is a guest post by Alastair Dant, interactive lead at the Guardian. He describes the efforts that went into designing the recently published war logs map of incidents revealed by Wikileaks.

  Our site editor approached me with a serious challenge: could I visualize six years worth of military reports? Up in their makeshift war room, our team introduced me to Julian Assange. While reporters from the New York Times and Der Speigel took photos and video, the director of Wikileaks booted his encrypted netbook and showed me a page from the war logs. I may have looked a little distressed. The gravity of this material was stark and, having never dealt with such documents before, I was uncertain if I wanted to start.

  After several days feeling like I'd walked into the Bourne trilogy, David Leigh and Rob Evans put my mind at rest. We wouldn't be publishing any material that might put anyone at risk and my work could focus on charting the rise in explosive devices from 2004 – 2009.
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