The trouble with pies

by Mark Harrower on November 13, 2008

I made mention of 3D pie charts in an earlier post and thought I’d outline exactly why they are such a bad idea. As both a teacher and designer I campaign hard against “chart junk” and the needless and confusing eye candy tricks that software companies create to clutter-up our lives. I know these companies need to offer something to try and convince us to upgrade to the new version, but let’s be clear: drop-shadowing every element on the page, or adding an outer glow to the text isn’t going to make your message any clearer, and will most likely distract from the very thing you’re trying to show. My design philosophy can be summed up as:

In cartography, aim to be clear, not cool.

Anything that doesn’t contribute to the message, or worse, distracts from it, probably doesn’t need to be on the page. Since maps are small and the world is large, every inch on the page and every pixel on the screen has to count and we can’t afford to waste any of them. Draw what you need, and no more. Fans of Edward Tufte, Presentation Zen (recent post on ‘chart junk’), or old-school cartographer’s like J. K. Wright and Arthur Robinson will recognize all of this – this is hardly a new message. But it is one that still needs to be heard, apparently. For a quick overview of many of these arguments I’d strongly recommend reading John Krygier‘s excellent post “How useful is Tufte for making maps” (his 20 Tufte-isms is a great crash-course in Tufte).

Consider this graph (from here):

This pie chart commits a half dozen design mistakes and is a grrat example of chart junk

Pure chart junk: This pie chart commits a half dozen major design mistakes rendered it as little more than visual junk food (looks tasty at first, but isn't that good for you).

As an information graphic, let’s step back and think about what a pie chart is suppose to do and how it works at a perceptual level: Pie charts are used to tell us (1) how much of something exists, and (2) how much that is compared to the other categories. ‘How much’ is encoded by the size of the pie (or segment) and ‘relatively how much’ by the internal angles of the pies and/or their relative sizes. To extract data from the graphic you have to be able to quickly visually compute both areas and angles.

The bad news: Years of testing has shown that most of us are really bad at estimating the areas of even simple shapes – just try visually estimating how much carpet you’ll need for a room, especially if the room isn’t square if you don’t believe me – and we’re pretty bad at eyeballing and comparing angles too.

Not convinced, you say? Looking at the pie chart above:

  • What percent of the total does DP Tech have?
  • Is that more or less than IBM?
  • How much more/less?

Now think about presenting those data as a boring old table: DP Tech 4%, IBM 5%. Done. Simple. Think about the difference in mental workload, and the confidence you have in your answer when the data are presented as a 3D oblique pie chart and when they’re numbers sitting in front of you. This problem is so commonplace (and yet ignored) that most folks resort to putting numbers on pie charts because the graphic itself is not sufficient, which is waste of ink, their time and mine. If you have that little confidence in your charts, just give me the numbers!

Here’s a rule of thumb I like to use:

If a map/graphic needs ‘crutches’ like number labels and can’t stand on its own, don’t use it. It’s the difference between “A Tale of Two Cities” and “A Tale of Two Cities: A Novel.”

People read the simple 2D pie faster, with greater accuracy AND greater confidence.

People read the simple 2D pie faster, with greater accuracy AND greater confidence.

The same data is presented three different ways above, and each change made to “enhance” the simple 2D pie chart makes it worse because the two basic perceptual tasks – ‘how big is something’ and ‘what are the angles’ – are much harder to perform when the pie is lying down. This is exactly what happens when design decisions are made in a vacuum and based simply on “it looks cooler this way” rather than an understanding what we need from a graphic to make it readable/work.

Problem 1: Adding the 3rd dimension adds no new information to the graphic. That’s bad because it is wasted ink (that could be doing real work) and it requires the tilting of the pie so the designer can show off the 3D effect. If the height (z-dimension) added some additional data (i.e., a second data variable), it might be worth adding, although I would caution against that since we’re even worse at estimating volumes than we are at estimating areas (which is why “how many jelly beans in the jar” contests or “how big a moving van do I need?” continue to challenge us – we’re terrible at numerically estimating volumes beyond the crude level of “bigger / smaller”).

Problem 2: On both oblique pies the scale is not consistent across the graphic. In other words, the same pie segment will look larger or smaller to you the observer simply based on where it lands in the circle…things closer to us will look larger, even if they’re not. This couldn’t be more counter-productive when we’re simultaneously asking the viewer to estimate areas. This is an absolute rule: If you expect people to judge the size of things, don’t change the scale of the objects on them.

Problem 3: Splitting the pie apart makes matters worse because the further objects are from each other, the harder it is to compare them (which is why we like to hold things side-by-side if we want to carefully compare them). Why? The extra time and effort it takes for your eyes to search for and acquire the now-separated segments uses-up your precious (visual) working memory and requires more eye trips back-and-forth to make the same estimation. Cognitive scientists call those back-and-forth trips extraneous cognitive load which cut the available brainpower (working memory) that can be devoted to the real task of comparing segments (intrinsic and germane cognitive load).

Solution? Simple: Stop using oblique, exploded, jazzed-up 3D pie charts. 2D work better, are easy to read, faster to read, and easier to make. Importantly, they also can be drawn much smaller and remain legible – as cartographers we’re always looking for ways to do more with less ink. If your powerpoint slides feel naked without fancy transitions and giant 3D graphics, you’d do better to work on the substance of the talk, rather than bury your good ideas in a pile of chart junk.

I’ll leave with one of my all-time favorite spoofs – the Gettysburg Address as Powerpoint.

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A new kind of election map

by Andy Woodruff on November 8, 2008

Update, Dec. 22: A few variations of the map technique are posted here.

2008 election results with population

We spent some of our spare time last week exploring data from the 2008 presidential election and thinking of some interesting ways to visualize it. Above is one map we put together.

One thing we sought to do was present an alternative to cartograms, which are becoming increasingly popular as post-election maps. Cartograms are typically offered as an alternative to the common red and blue maps showing which states or counties were won by each candidate, wherein one color (presently, red) dominates the map because of the more expansive—but less populated—area won by one candidate. Election cartograms such as the popular set by Mark Newman distort areas to reflect population and give a more accurate picture of the actual distribution of votes. A drawback of cartograms that we’re very aware of, however, is that in distorting sizes, shapes and positions are necessarily distorted, sometimes to the point of making the geography virtually unrecognizable.

Our map is one suggestion of a different way to weight election results on the map while maintaining correct geography. What we’ve done is start with a simple red and blue map showing which candidate (Republican and Democrat, respectively) won each county in the lower 48 states. Then, to account for the population of those counties (or, the approximate distribution of votes), we’ve adjusted opacity. High-population counties are fully opaque while those with the lowest population are nearly invisible. Against the black background, the highest concentrations of votes stand out as the brightest.

We’ll let viewers be the judge of its cartographic effectiveness, but we hope you’ll at least agree that it looks pretty cool!

Click on the image at the top of the post to view a larger version, or see it in a Zoomify viewer, or download the full size (suitable for printing).

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ColorBrewer 2.0

by David Heyman on November 4, 2008

I love ColorBrewer. All of us here at Axis rely on it almost daily and it’s helped us to make nice looking maps quickly; and that’s what good tools do, they make their users look really good at their jobs.

7+ years later, ColorBrewer is due for some changes and Cindy Brewer has been kind enough to ask us to hold the scalpel. Nothing major. Same great color schemes (of course), but a new interface and some new functionality to help ColorBrewer’s 2000 visitors per week get the most out of the experience.

We’re in the early stages of planning this project but we though we would open this up for some discussion amongst the ColorBrewer-using, Axis Maps Blog-reading masses.

QUESTION: What would you like to see in the new version? What should remain untouched? What do you love? What do you wish was done better?

Let us know your thoughts in the comments. Thanks!

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The geography of presidential campaign rhetoric

by Andy Woodruff on October 29, 2008

A few months ago I started on a little side project to visualize presidential campaign speeches spatially. My idea was to collect speeches by the 2008 US presidential candidates, generate a word cloud of the most common words in each, and each word cloud on a map in the location where the speech was given.  We’ve seen a number of text visualizations and analyses, sometimes in-depth, during this campaign, but so far not by geography that I can recall.  (See those from Martin Krzywinski, and The New York Times with help from Many Eyes, for just a few examples.)  Are the candidates speaking to different issues in different parts of the country?  Are they talking about jobs in Michigan and immigration in New Mexico?  Are they pandering to everyone, everywhere they go?  (Can we call this project PanderViz?)  Visualizing campaign words on a map might answer such questions.

Campaign speeches by John McCain and Barack Obama as word clouds. (Click for a larger map)

We hoped to develop this idea into a sophisticated interactive map in which a user could search for words, filter speeches by date, and so on.  Other work has kept us from doing that before the election next week, but it seems worth showing some screenshots from what I did manage to get done originally.

I went to the official websites of the Obama and McCain campaigns, where the text of speeches is transcribed, and ran the speeches through a simple PHP script to count words and record the location of the speech.  This week I revisited the sites to catch up on speeches since the summer.  These sources have their drawbacks, of course.  For one, although as prepared speeches they contain perhaps the most carefully chosen words for a particular audiences, they do not represent the complete vocabulary used on the campaign trail.  Also, Obama’s team has been more diligent in posting speeches, it seems, providing close to 80 speeches since May, compared to about 30 for McCain, a disparity that makes comparison between the two candidates a bit difficult.

As far as I got with the capabilities of this map was generating scale-dependent word clouds (I’ve written more about those on my personal site) and searching for individual words to display proportional symbols representing the frequency of use.  With less than a week until election day, we might as well get out of it what we can, so I’ve generated a series of maps of word clouds and individual word frequencies.

Use of the word war by John McCain

Use of the word war by Barack Obama

The whole series is long—obnoxiously long for a blog page—so it’s at a separate page, linked below.  Enjoy, and please comment if there’s an interesting word to look up that I didn’t think of!

See the full article: The Geography of Presidential Campaign Speeches

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The Golden Age of Cartography is Now

by Mark Harrower on October 27, 2008

This is an exciting time to be a cartographer. Cartography has changed more in the past 5 years than in the previous 50, and the field is in the midst of an unprecedented revolution that has forever altered what maps can do, and how and why we use maps. How far have we come? I now see teenagers using on-demand, customizable maps rendered in real time from multiple, distributed data sources on their cell phones that automatically geotag and upload photos to their blogs while they sit on the bus. Five years ago, heck, one year ago this would have been science fiction, now it’s just a collection of geoservices on a $200 phone. As a result, mapping technology has quickly outpaced mapping theory and practice.

While much attention has (rightly) been focused on the technology that is enabling these amazing advances  (Google Earth, mash-ups), I think the equally significant change is why people are making maps and the role maps now play in our everyday lives.

Take “pocketcasting” for example, the next step in social networking, where folks geo-broadcast their locations so they can see where their friends are at any given moment allowing unplanned meetings (“I’m at this cafe!” as a kind of mass, voluntary, geo-voyeurism). This adds a degree of instantaneous spatial awareness to our social lives that would have been impossible without the serendipitous convergence of technologies like GPS, wireless networks, and customizable on-demand maps. Other new ways the public is using maps include monitoring traffic conditions in real-time or using Google’s wonderful streetview to check-out a potential new home virtually. One thing is clear: Maps have become fully integrated into the fabric of our lives in ways we couldn’t have imagined a few years ago.

Beyond the popularity of these maps, however, has been the complete blurring of the distinctions between map maker and map reader, data provider and data user. It is precisely this tectonic shift in the world of cartography that underlies the philosophy of GeoCommons Maker!, the product we’ve been jointly developing with the powerhouse team at FortiusOne, described by the O’Reilly Radar as “a Flickr/Swivel/YouTube/Scribd of geodata.” Maker is at the vanguard of the democratization of cartography and the promise of Web 2.0 services that eliminate the need for expensive software/data for most casual ‘citizen cartographers’ and allows people to make great looking maps quickly while guiding them through the process. We here at Axis Maps feel strongly that powerful tools (e.g., desktop GIS) aren’t much good if they don’t provide guidance – it’s like giving the keys of an F-16 to someone who doesn’t know how to fly. Furthermore, while an F-16 is amazing, few folks actually need one. Same with $30,000 mapping software.

One of the reasons we like Maker! is that it empowers people – who otherwise would never be able to participate – to make their own maps and start publishing, sharing, and commenting on geographic data and the things we learn from those data. High-end, professional cartography is not going to disappear, and the world will always need premium map products (such as National Geographic Atlases or legally-binding land surveys). The same is true of professional authors and photographers; neither blogging nor Flickr have eliminated the need for these professionals, rather they have opened up these activities to a much larger group and drawn people into the process, rather than relegate them merely to being spectators to the process.

One thing is clear: As the GeoWeb/Web2.0 revolution continues, we need to move beyond paper map thinking and starting seeing maps much more broadly as services that can be integrated with other services. As a professional cartographer this means to me that the “rules” of cartography established through a century of study and practice are now up for grabs at the very moment mapping finds itself in a multi-billion dollar spotlight from both the private and public sectors. Some of the biggest companies in the world (Google, Microsoft, Yahoo!) are betting a big chunk of their digital future on maps and the central role they want cartography to play in their digital empires. With the backing of these companies, digital, on-demand maps have gone from technological curiosities to everyday tools worth billions of dollars. This begs the question: Where is mapping headed and what might our maps do for us in 10 years?

Further questions we need to think carefully about (these are the sorts of questions that keep us up at night!!)…

  • How much of what we have learned about static maps—both in practice and theory—holds true when these maps become animated, interactive, and customizable?
  • What are the relative merits of 2D versus 3D?
  • How do we keep users from becoming disoriented and lost in 3D immersive maps?
  • What are the perceptual limits of animation and for what kinds of map reading tasks (e.g., rate estimation, change detection) are animated maps especially well-suited (and how could those tasks be better supported)?
  • How can we reduce the problem of “split attention” in immersive and visually-rich environments like Google Earth?
  • How can we create intelligent Web-based software that is both easy to use and powerful? To what degree can the map-design process be automated to further the democratization of map-making? How can we help novices to think like experts?
  • What should our map interfaces look like and why? How does the map interface structure the user’s experience? How do we know if our map interfaces work?
  • Who benefits from these billions being invested in mapping?
  • How does this technology change the way we do business and the way we interact with each other?
  • What are the limitations and liabilities of decentralized data structures and technologies that run on volunteered geographic data?
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I’m not here to make your data look pretty.

by Mark Harrower on October 21, 2008

“Good design is clear thinking made visible” -Edward Tufte

Geographic Visualization, the Artist Formerly Known as Cartography, derives much of its power to speak because it is visual. We humans are voracious abstract visual thinkers: just try not seeing the characters in front of you as words that denote meaning. Or eevn wehn the wrods are sellped wonrg, our bainrs jsut power thugroh fnie, in part because we don’t just see letters, we mentally ‘chunk’ information into high-level structures shaped in large part by a clever bit of programming called prior experience. In fact, we can only read as fast as we do because we don’t read individual letters but groups of them called words, and beyond that at the highest level, because languages have understood rules that make certain combinations of letters and words impossible allowing our brains to filter-out the ridiculous and focus on the likely. This, however, is both a blessing and a curse since we can process information very, very quickly (hitting a 95 mp/h fastball), but we often only see what our brains tells us we should expect to see (why trick pitches work). As a result, words, maps and other graphic representations have an expressway into our consciousness, often imparting vast amounts of data in mere glance. We can’t help it – it’s literally how we’re wired.

While the eye-brain system is a masterpiece of evolution, it also has these well-known limitations and pitfalls. Optical illusions are one such example, including the one below by MIT professor Edward Adelson which is one of the best I’ve ever seen: It beautifully illustrates how our brains automagically discount the actual gray of the squares (their real lightness) in order to keep the logic of the checker-floor true (it discounts the shadow cast by the cylinder, our own built-in ‘image correction software’). Here’s the proof.

The brain sees what it expects to see

The brain sees what it expects to see: Squares A and B are exactly the same shade of gray. For real.

Within visualization we worry about both Type 1 and Type 2 errors; seeing things that aren’t there, and missing things that are. Given both the power of graphics to speak so clearly to us and the very real limitations of ‘visual thinking’, it behooves us to not only use such power wisely, but to also understand as Alan MacEachren notes, how and why maps work.

Maps, Schmaps

When some visiting speakers come to my department and learn I’m ‘the cartographer’ it’s amazing how many times their next comment is “I know my maps are bad” and smile or chuckle, but with the unspoken “but that doesn’t really matter to my message/findings/purpose.” Can you imagine if I confessed that I was ‘the statistician’ and they said “I know my stats are totally wrong” and brushed it off with a smile? This is especially disturbing when these maps are so often the central piece of evidence offered up by these speakers (“as you can see here on the map, there is a clear correlation between…”). It’s not so much that this is bad graphic design that worries me; It’s that this is bad science.

I’ve seen many researchers take years to painstakingly collect and verify their data. Science is by design a very slow and thorough process and it has to be to ensure that our knowledge claims are correct. But after taking sometimes years to collect the data, I’m astonished when I see brilliant scientists content to present their findings using clunky maps and graphics that showcase how bad software defaults are and little else (don’t get me started on 3D pie charts!). They look as if they were slapped together in 20 minutes and that saddens me because their work deserves better than this, especially when one considers that these images so often become the public face of this data. Indeed, many famous maps and graphs are produced and reproduced for years, long after the original paper they were attached to have been forgotten. As Edward Tufte has demonstrated time and time again, better designed graphics would make their arguments clearer, more convincing, the data richer and more nuanced.

Why Design Matters

To be clear: Good cartography is more than making data pretty. It’s a recognition that the best data in the world can be diminished–or worse, distorted–if the map is clumsily executed. It’s a recognition that the map is the intuitive and flexible interface between our data and the knowledge we seek to gleam from those data. We may live in a glorious digital age, but let’s face it, those 1’s and 0’s we’re so good at collecting don’t really come alive until we translate them into images and maps and graphs that are representations of data, those data themselves being representations of the real thing.  Maps should not, thus, be confused with reality (although they are often assumed to be perfect mirrors of reality).

Most importantly, good design and good map-making is an understanding that the graphic choices we make fundamentally change what our data say, and thus, what we think we know about the world. If we’re sloppy about how we choose to represent our data (and by proxy, the world), then we’re being sloppy about the knowledge those images create inside our heads. This is why relying on software defaults, the one-size-fits-all-needs approach to design, is something we at Axis Maps have worked so hard to fight.

When maps are offered-up in the dual role of both ‘evidence’ of our knowledge claims, and the means by which we explain those knowledge claims to others, should they not be subject to at least the same standards that would be applied to any other part of the scientific process (e.g., data quality, statistical significance)? Maps are the ultimate executive summary: caveat emptor.

I’ll leave with a quote from the delightful blog Presentation Zen (August 30th, 2006):

To many business people, design is something you spread on the surface, it’s like icing on a cake. It’s nice, but not mission-critical. But this is not design to me, this is more akin to “decoration.” Decoration, for better or worse, is noticeable, for example — sometimes enjoyable, sometimes irritating — but it is unmistakably *there.* However, sometimes the best designs are so well done that “the design” of it is never even noticed consciously by the observer/user, such as the design of a book or signage in an airport (i.e., we take conscious note of the messages which the design helped make utterly clear, but not the color palette, typography, concept, etc.). One thing is for sure, design is not something that’s merely on the surface, superficial and lacking depth. Rather it is something which goes “soul deep.”

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