Friday, July 29, 2011

porn & cake

Get your attention?

The following data cake pic was posted over at Chart Porn a couple of weeks ago (originally from Epic. graphic). I couldn't help but share.


While all are important, one might guess that my favorite step is presentation (yes, I like to make things pretty, cakes included). But that's not the case. My favorite is the final step to knowledge: information is worthless if we don't learn something and act on (eat!) it.

Tuesday, July 26, 2011

lessons in innovation












Earlier this week, Google published Think Quarterly, an online magazine of sorts that provides "a snapshot of what Google and other industry leaders are thinking about and inspired by today." The topic of the current issue is innovation.

While the focus isn't data visualization, many of the lessons shared can be applied in this space. For example:
  • In The 8 Pillars of Innovation, SVP of advertising Susan Wojcicki discusses iteration as the way to strive for consistent innovation, not instant perfection, and looking for ideas everywhere. I appreciate the concept she introduces of "sparking with imagination, fueling with data."
  • Head of Americas Sales, Dennis Woodside, talks about how audiences today want and expect "something more sophisticated, more considerate" than they have in the past. In Route to 2015, he's talking about marketing and advertising, but I would argue the same trend is happening when it comes to information visualization. His 4 B's are also applicable: be found, be engaging, be relevant, be accountable.
  • "The most original innovations come from mucking about, not from thinking hard" (Russell Davies, Practical Magic). It's often that sort of mucking about with a dataset that leads to new insights you wouldn't have found with a hypothesis-driven approach.
  • In Next Gen Innovators, Sarah Ohrvall calls out data aggregation as the trend driving the most exciting innovations in digital media in her opinion. She says: "information can be used to improve your daily life and improve the world around you" and calls out that the more people know about the impact their behavior has, the more they will change their behavior based on this knowledge. 
These are just some highlights intended to pique your interest; I highly recommend checking out the full publication. See where you can apply the innovation lessons presented.

I'll wrap this up with some words from Susan Wojcicki: never fail to fail. In data visualization (as in life), learn from the things that don't work, adjust accordingly, and try again.

Friday, July 22, 2011

vacation stress: visualized

Love this. Reminds me of the clever Facebook breakup visualization that David McCandless did last year. Though my personal sample size (1) is small, this is an accurate reflection based on my empirical evidence...

Wednesday, July 20, 2011

death to pie charts

I hate pie charts. 

I mean, really hate them.

Those who have heard me speak on data visualization will have learned that the only thing I hate more than a pie chart is a 3D, exploding pie chart - they are the absolute worst - but the plain vanilla pie charts are pretty bad, too. Here's a recent one from TechCrunch, which is intended to show how much they cover start-ups versus big companies (full article):


I'll start with the lesser evil of the above visual: meaningless color. The pie above is what happens if you put the data in Excel and say "chart data". I've said this before and I'll say it again: graphing your data with a tool like Excel should be the first step in your design process, not your last! In TechCrunch's pie, the color itself doesn't represent anything, it's simply used as a categorical differentiator. One unintended side effect is the optical illusion you get with a darker colored slice appearing larger than a same-size slice of a ligher color.

My strong opinion is that color should always be an explicit choice and should be used strategically to draw the audience's eye. This preattentive power is being wasted here. If you must use a pie chart, at least make the slices the same color and highlight only the one or two you want to draw attention to. Or if you don't want to highlight a particular slice, but rather are intending the visual to aid in information discovery, you may consider something like the following:

Hopefully you can see that this still isn't a very good visualization. The labels are messy. Only a few things are immediately apparent: General Consumer Web is the biggest piece, there are a lot of small slices.

My main beef with pie charts like the one above (and in general) is this: our eyes aren't good at attributing quantitative value to two dimensional spaces. In English: pie charts are really hard for people to read! When segments are close in size, it'd difficult (if not impossible) to tell which is bigger. When they aren't close in size, the best you can do is determine that one is bigger than the other, but you can't judge by how much. To get over this, you can add data labels, as they've done in the TechCrunch version. But I'd still argue the visual isn't worth the space it takes up.

What should you do instead? My typical advice would be to replace a pie chart with a horizontal bar chart, organized from greatest to least or vice versa (unless there is some intrinsic value in the categories, in which case that should be followed). With bar charts, our eyes compare the end points. Because they are aligned at a common baseline, it’s very easy to assess relative size. This makes it easy to see not only which segment is the largest (for example), but also how incrementally larger it is than the other segments. Here's what this looks like with the TechCrunch data:


One might argue that you lose something in the transition from pie to bar. The unique thing you get with a pie chart that is absent in a bar chart is the concept of there being a whole, and thus, parts of a whole. But if the visual is difficult to read, is it worth it? Ultimately, it's up to the designer of the visual. My advice is as follows:
  1. Don't use pie charts.
  2. If you find yourself unable to follow #1, keep in mind the challenges with pie charts: if relative sizes are important, you'll need to include data labels. Also be aware of impact of color on 2D space (darker looks larger); don't let your tool decide your color scheme. 
Personally, I will continue to avoid pie charts.

Sunday, July 17, 2011

what makes good data visualization?

Here is David McCandless' take: a balance of interestingness, function, form, and integrity.


My personal view is similar, but I articulate it differently (and I've found that exactly how I articulate it changes over time as I continue to learn and iterate). Lately, I've been reading up on general principles of design to expand how I think about data visualization. In design language, I would say that effective data visualization should leverage the following:

  • Affordances: In the field of design, experts speak of things having affordances - characteristics that reveal how they're to be used. A teapot has a handle. A door that you push has a push plate. The design of an object should, in and of itself, suggest how the object should be used. The same is true of your graphs, tables, and slides. Lead your audience through your visual – make it easy on them! Provide a visual hierarchy of information, these are visual cues for your audience so they know where to direct their attention.
  • Accessibility: Designs should be usable by people of diverse abilities. Example of good design by this measure are Apple products: my mother can barely send an email, but put her iPhone or iPad in her hand and it's so intuitive that she doesn't feel overwhelmed by the technology. Work to make your data visualizations similarly straightforward and easy to use. Don't overcomplicate. Use text to label, introduce, explain, reinforce, highlight, recommend, and tell a story.
  • Aesthetics: People perceive more aesthetic designs as easier to use than less aesthetic designs whether they are or not. Specifically, studies have shown that more aesthetic designs are perceived as easier to use, more readily accepted and used over time, promote creative thinking and problem solving, and foster positive relationships, making people more tolerant of problems with design (this is crazy, right? leverage it!). Use a pleasant color palette (personally, I tend to do everything in shades of grey with strategic, explicit use of bright blue to draw my audience's eye). Bring a sense of visual organization to your design (preserve margins, align things visually), showing attention to detail and a general respect for your work and for your audience.

What do you think of these descriptions of effective information design? What makes good data visualization from your perspective? Leave a comment with your thoughts.

Friday, July 15, 2011

interesting

I just came across this graphic over at Chart Porn. What story would you tell with this data?


Wednesday, July 13, 2011

visual.ly is live

A few months ago, I came across the visual.ly site, which at that point was a temporary landing page with a lot of sexy looking graphics where you could input your email to be notified when the full site launches. I received that notification this morning, and it's certainly creating a lot of buzz: I've had a number of friends and colleagues forward me the announcement and ask for a review. 

Visual.ly says it is the world's largest community for exploring, sharing, creating, and promoting data visualizations. I have mixed feelings so far based on the detail I've perused. It seems like describing the graphics there as "data visualizations" might be somewhat of a misnomer; perhaps "information graphics" would be a better description? A number of the visuals I've looked at contained no data at all (example).

One thing the images do seem to mostly have in common is their visual bling - they look exciting at first glance due in many cases to color and complexity. I worry about this, as sexy can be good for grabbing an audience's attention, but to maintain it, the visual needs to be clear and straightforward: I'm not sure all of the content there meets the mark on this latter piece. If it works as it appears is planned, this should self-correct over time, with popular visuals rising to the top and vice versa through the wisdom of crowds. I just hope the crowd is wise enough to value utility over sexy.

There are some stellar graphics there for sure. I've included a few of my most and least favorites from what I've looked at so far at the bottom of this post.

There seem to be some technical difficulties (I've had a lot of instances of pages timing out, visuals not loading, and buttons not following through on what they claim they will do for me), but expect that these are painpoints that the crew at visual.ly is actively working to fix.

I'm interested to see whether this site will take off. Take a look. Leave a comment with your thoughts!


cole's faves (based on what I've looked at so far):
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going to give cole nightmares (notice a theme?):
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Tuesday, July 12, 2011

I like this chart

David McCandless does some beautiful work (if you aren't familiar, check out his website here or TED Talk here). His latest post is on sunscreen and features a massive infographic titled The Suncream SmokeScreen.

As is the case with many infographics (and here, I use infographic in what I consider the true sense of the word - when many different aspects on a single topic are shown through multiple visualizations and compiled together to form a single master infographic), you have to have the desire to spend some time with it to really understand what's going on, because there's a lot going on. But that's kind of the point. What I like about it is that each segment within the infographic is really straightforward: it demonstrates good use of preattentive attributes (e.g. color, size) and is very clean - no clutter to distract from the data.

Here's a segment I particularly like:


Attention is drawn to the data through the preattentive attribute of color (my only gripe is that I wouldn't have gone with an orange/red color scheme, which is not so colorblind friendly, but I imagine this choice was made to be in keeping with the topic and reminiscent of the sun). There is no presence of unnecessary gridlines or tickmarks. The rest of the stuff (chart axes, labels, sources) is pushed to the background by making it grey. This simultaneous emphasis of the important stuff, elimination of the unnecessary, and de-emphasis of the other stuff that needs to be there but doesn't need to compete for attention really makes the data sing. And it sings beautifully.

Until you start to think about what you're looking at. Cancer is clearly the antithesis of beautiful. And the instances of it in Australia dwarf the US and UK. Capping the y-axes on the US and UK charts and allowing Australia's to continue upward is really clever and helps to emphasize just how much higher the melanoma incidence rates are in Australia.

Showing each trendline in its own graph prompts a different sort of data discovery than it would if all were shown on a single chart. I have to think this was a very explicit choice. Because we read left to right, top to bottom, placing all three lines on the same chart would mean you'd encounter the Australia line first. Instead, with the three broken out, our eye looks first at the US, then to the UK (hm... lower than the US, but overall less sunny so makes sense), then to (holy sh**!) Australia, where the trend is not only much higher, but also following a steeper trajectory than observed in the other locations.

This visual tells a clear story because of all of these explicit choices made on the part of the designer. This information is beautiful (even if the underlying story is not).

Interested in the full infographic? You can find it here.

Saturday, July 9, 2011

breathtaking data art


It doesn't matter how many times I look at this image: I still find it stunning. I have a 3-foot by 3-foot version printed on canvas that hangs on my wall.

What exactly is it, you may ask? It's San Francisco, mapped by photo locations from Flickr and Picasa search APIs and then plotted on OpenStreetMap. Those posting photos in a given city for more than a month are considered local (blue); those posting in the given city for less than a month who appear to be residents of another city based on their posting are considered tourists (red). Yellow designates those not fitting into either category (likely but not conclusively tourists). Lines connect places where the same person took two pictures within ten minutes of each other.

It's simultaneous art and information discovery. The expected places are red: Golden Gate, Alcatrez, Fisherman's Wharf. But there are some places I hadn't realized were so frequented by tourists until I studied this infographic: twin peaks, AT&T stadium, Berkeley. Makes sense, just not something I would have known before seeing the data.

This piece is part of a collection (currently made up of 135 cities) called Locals and Tourists by Eric Fischer. Take a look, find your favorite city, and see what you can learn!

Thursday, July 7, 2011

how we use the mobile web

One of the perks of writing this blog is that friends and colleagues send me all sorts of examples of data visualization that they come across in their daily lives. This is helping me to amass quite the collection of good and not-so-good infographics.

A recent forwarded email from a friend had examples that fall into both of these categories. The email highlighted 10 recent infographics on the topic of how people use the mobile web. I've included my favorite and least favorite (aka favorite example of what not to do) below.

Thanks, Danny, for sharing!


Favorite
Why I like it: it's clean and easy to read. I think the use of pics vs. words to label the chart axes is clever (and manages to be straightforward without being obnoxious). It allows for some interesting info discovery, for example, high tablet use while watching television.

I would like a little more information on exactly what data is being depicted, though. Is it the percent who say they ever access the web on the given device in the given location/occasion, or do so with some specific level of frequency?


Least favorite
Why I think it's bad in a nutshell:
  • It's glitzy and includes a lot of noise that distracts and doesn't add informative value: background figures, shadowing, bizarre shapes and fonts. The Christmas color scheme, in addition to being obnoxious, is not color-blind-friendly.
  • The data visuals are hard to read (visual comparisons between the number of little phones or - even better - little phones with little bows on them - are not straightforward for our eyes, which have a hard time attributing quantitative value to 2D space).

Sunday, July 3, 2011

food & data viz

As those who know me are aware, in addition to opining on visual representations of information, I also cook (and blog about cooking at cole's kitch). I've joked in the past that those sharing the intersection of my personal passions - data visualization and cooking - are likely few in number. But every so often, I am reminded that there are some of them out there. The following is a snapshot of some cool things in this space I've come across recently.


Two years of food consumption...visualized
As part of her PhD thesis, Lauren Manning documented everything she ate over the course of a two year period. She turned this dataset into 40 visual representations of her food consumption. Crazy, or cool? I vote supercool. In the matrix below, the various visuals are arranged along an x-axis that ranges from straightforward (left) to complex (right) and a y-axis that ranges from literal (top) to abstract (bottom).


One thing I'm unsure of is the order in which the food groups appear in the various visuals. It's consistent across most of the visuals, which is helpful, but there isn't a clear meaningful order. If there isn't an intrinsic order in categories, how they are ordered should be an explicit decision on the part of the designer, as it has important implications on what stands out and what gets compared within the visual. The easiest comparisons are those next to each other. So if we were to group all of the starches, for example, it would become immediately clear that the majority were consumed in the form of pasta. Or you could order the categories by food consumption (from greatest to least or vice versa), which would better highlight the relative differences between neighboring categories.

One visualization that I didn't see in Lauren's set that I would be tempted to try with this data: spider graphs.


Our dwindling food supply
National Geographic Magazine recently published an interesting visual showing the relative varieties of different fruit and vegetables a century ago vs. today. In the visual, the width represents the number of varieties of the given food. Above ground are the varieties that existed in 1903; below ground is 1983.

The conclusion is a sad one: 93% of the varieties that existed in 1903 have gone extinct.

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A complete guide to kitchen tools
The following poster by Brooklyn-based Pop Chart Lab arranges kitchen apparati into a massive flow chart. The tools are divided into categories according to function (e.g. those that divide, those that protect).

I find the "meat manipulation" category a little frightening (looks like a bad mob-murder-tool-kit). But happy to see it's neighboring category, "tongs", which I've been told are perhaps the most important tool in any kitchen.

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If you happen to come across other interesting food related data visualizations, be sure to send them my way!