One of the things Ken wanted to depict was the population fluctuations of Canada geese (Branta canadensis) and black swans (Cygnus atratus) on the lake. He had several years of historical data, though fewer for swans than geese, and wanted to show how goose numbers had dropped below their optimum population level.This is the goose graph; the swan one looked much the same but with fewer bars.

The first thing I decided to do was place both sets of data on the same axes. Because the population data were continuous, it made more sense to link them in a line graph, which I created in Ken’s Excel file and copied into Illustrator. Normally I spend five minutes with the direct selection tool deleting all the crap that Excel adds to its graphs; in this case it was simpler to select the trend lines alone and pull them into a new layer, the create axes and numbers from scratch, using the original graph in the background layer as a guide.

(By the way, not many people seem to know that you can drag whatever’s selected from one layer to another by dragging that little colored square near the layer’s name.)

To color the goose trend line, I used the eyedropper to sample some brown from a photo of a Canada goose; I just made the swan line black. Those colors were applied directly to the text in the title, as I didn’t want to have a key or legend. Working from Googled photos of each bird, I created silhouettes to label each line, again to avoid a key. It was easy enough to annotate the graph with a level line.

When a graph isn’t working, our temptation is to jazz it up. Excel has any number of ways of making graphs fancier: WordArt, shadows, gradients, 3D, backgrounds and so forth. But this usually makes things worse. Ken had used some of these in his lake level graph, but I thought it needed some simplifying.

I used the same color palette, stroke thicknesses, and font as in the swan and goose graph, so they looked like they belonged together on the poster. It was important, I felt, to translate the rather cryptic numbering scheme for lake openings into English, and to annotate the graph with lines to show the duration of lake openings, rather than just listing dates.

If I were to do this again from scratch, I might use a series of little horizontal lines, one at each sampling time, to represent the lake level, rather than a continuous line.

There’s more information on Ken Hughey’s Waihora research at his group’s web site. Thanks to Ken and to EOS Ecology, for whom I did the design work, for permission to reproduce these graphics. I especially appreciate it when a scientist is brave and altruistic enough to let me post their “before” versions in a forum like this.

My posting frequency has taken a hit since I started working full-time as an information designer, but when the ukulele book is finished I’ll be posting more regularly to Numberpix. The project for 2008 is to finish Pictures of Numbers, my book on data presentation. If readers of this blog have any suggestions for content, you’re welcome to email me: I’m mike, at numberpix.com.

This is a perfectly sensible, reasonable book, but it irritated me. First, it’s not that original: there’s extensive quoting from Cleveland’s books, including reproduction of many examples, while Playfair and Minard make their usual appearances, second-hand from Tufte. Recycling of previous authors’ examples seems to be endemic in this literature; Tufte even recycles himself. Perhaps recycling examples fights global warming. Anyway, Robbins’ debt to Cleveland is notable in the use of dot charts, and the proliferation of boxes around elements. I wouldn’t say the figures are particularly innovative or well-designed, and most of the text is just common sense, like “large markers are easier to read than small ones”, and “overlapping things are harder to read” (I’m paraphrasing, or course—the style is not this pithy.) There’s actually surprisingly little content; often a short paragraph will float in the middle of a page, padded with ornamental margin art, and a page or two after the graph it’s discussing. The whole thing could have easily been a quarter of the length. Plus it’s over US$40 and contains no color, which, given the fundamental importance and frequent misuse of color in amateur graph-making, seems unforgivable. So not a recommended purchase, particularly to the readers of this blog who may well have come across all this advice already.

Information Dashboard Design
Stephen Few
O’Reilly, 2006 • ISBN 0596100167

Few has a seemingly quite specific aim: producing a series of small charts and tables that fit onto a single screen—an information dashboard. It’s aimed at, but not limited to, businesses who need to summarize performance indicators at a glance—many of the principles are quite general, and in fact scientists presenting data would do well to think in terms of a cluster of small figures that present related information, rather than a few big independent ones. There is plenty of good sensible advice on toning down garish colors, reducing chartjunk, trimming away superfluous labels and gridlines and so forth, in much the same vein as Tufte. I wouldn’t say it’s an essential book, particularly if you already have a work or two by the usual suspects. Nor does it add much that’s new. It does, though, recontextualize good practices, differing from Tufte in that the writing is very clear and straightforward, and so far more likely to be tolerated by a down-to-earth managerial or administrative type. The best strategy would be to show them the chapter on simplifying charts, then Few’s elegant high-density redesigns at the end, so they leave you alone and stop asking for colorful banners and animated logos.

If you said Iran and Bahrain, you are so, so wrong. Pakistan actually has 60 times as many Shiites as Bahrain, a tiny country with far fewer Shiites than Iraq. What the graphic is showing is percentages, not numbers; percentages of a total population we’re not told. The designers probably decided that percentages were most important when you’re discussing elections, but the convention in these types of graphics is that each little person stands for an actual amount (say, a million people), rather than 1%.

If we go the the Foreign Affairs article cited, we find the following data.

Note that Catalogtree chose just a few of these countries to graph; Bahrain rather than, say, Afghanistan or Syria. Why? Well, it rather looks like they read no further than paragraph 2 of the multi-page article (their choices are the bold ones):

“Shiites account for about 90 percent of Iranians, some 70 percent of the people living in the Persian Gulf region, and approximately 50 percent of those in the arc from Lebanon to Pakistan — some 140 million people in all.… Recent events in Iraq have already mobilized the Shiites of Saudi Arabia (about 10 percent of the population); during the 2005 Saudi municipal elections, turnout in Shiite-dominated regions was twice as high as it was elsewhere. … The Shiites of Lebanon (who amount to about 45 percent of the country’s population) have touted the formula, as have the Shiites in Bahrain (who represent about 75 percent of the population there), who will cast their ballots in parliamentary elections in the fall.”

The main problem with including all the countries in the table is obvious when you graph it: India dwarfs everybody else. In other circumstances, one might log-transform the x axis, but that’s just silly when you’re trying to compare numbers and percentages.

One solution, and it’s the one used in the Foreign Affairs article, is to indicate percentages on a map rather than on a bar graph. The size of countries is very roughly proportional to their population anyway, and by cropping the map one keeps India from dominating it.

This map has a few problems, though. Because it uses no colors, it relies on rather odd patterns to convey the percentage of Shiites. It’s also a bit of a tangle of coastlines, borders, and pointers. I tried a redesign with a more intuitive color palette, scaling back boundaries as much as possible. Of course you’d want to label countries as well.

We still don’t know the actual numbers of Shiites, though. That could be done by overlaying little person-markers proportional to numbers, the way the Catalogtree graph seemed to be doing but wasn’t. I intentionally didn’t arrange the Shiites in serried ranks, like an army on a parade ground presumably about to march on the effete West.

Of course, it ends up looking like a certain board game. But I guess when you want to show hordes of figures camped on countries, you just have to run that Risk.

Reference: Vali Nasr, When the Shiites Rise, Foreign Affairs, July/August 2006

The problem with this graphic is that it separates different individuals of the same species, and it’s a little confusing to see how the tank acts as a treatment and the ray color is the result. I suggested he rearrange the photos into a matrix, which is often a good way to show the interaction between factors (in this case, ray color and tank color).

I’d suggest some pretty standard changes: removing boxes around things wherever possible, paying attention to the typeface, and showing rather than labeling the tank color.

I’ve chosen here to make a swatch of the ray color, which can be an excellent way to show color change, but the sacrifice is the distinctive silhouettes of the two species, which make it much easier for us to follow what’s happened. The ideal situation would be better-quality photos of each ray, showing the whole fish without reflections to distract the reader. The rays should be photographed on a neutral background, or using each of the actual tanks as a background (the photos currently all use the same gray background, which just confuses things). The advantage of using the actual tanks is that you’d no longer need to label the colors—they’d be self-evident. The only text necessary would be the species names. Show, not tell.

Your map may start off in PostScript or Illustrator format. PostScript is a language used to define the lines and curves that make up vector graphics; a PostScript file looks like an ordinary text file, and indeed you can open it with a word processor, although it’s scarcely enthralling reading. Once you open it in Illustrator or any other vector graphics program that speaks PostScript, it will display as a series of shapes with lines and fills. It’s also possible to buy maps already saved in Illustrator format, which allows them to have complex layering and makes them easier for you to edit. Older Illustrator maps will need to have their fonts updated when you open them in Illustrator CS; this is an option you’re given when you first open them, and there’s no going back. Use Save As to make a working copy of the original. Of course, you should be using Save As all the time anyway to make multiple working versions of any graphic you’re editing. Go on, you’ll thank me one day.

If the graphic has layers, turn off or delete the ones you don’t want. If it doesn’t have layers, similar objects (land, sea) are probably arranged in groups, so you can select them all with a single click. Sometimes, though, there are just too many points on a complex path (for example, a detailed coastline) for it to be a single Illustrator object. If that’s the case, and the things you want to change are tiled or split into several groups, you have two choices. You can shift-click to select multiple objects, or you can do a Select > Same (for example, Same > Fill Color) to catch everything sharing the properties of what’s already selected. This is useful for catching every last one of a cloud of things like rivers or placenames if they’re not handily grouped.

When you have a group of objects selected, I’d recommend converting them to grayscale, since most maps you make will have to be reproduced in black and white at some point. You can pick a color model from the popup menu in the Color palette. (You know those little triangles are popup menus, don’t you? Not everyone does.) Make sure the shades of gray are far enough apart to remain distinctive after printing or photocopying (perhaps at least 20%), and avoid less than 15% or greater than 85%, as these will degrade to solid white or black after a couple of rounds of photocopying. In this map, the pale blue ocean converted to 9% Black when I switched to grayscale, so I bumped it up to 15%.

If you don’t need all the layers in an object, you can delete them, but sometimes you’ll just want to hide them in case they’re needed one day. These invisible layers are still included when you send something to the printer or export it as an EPS, which can have a big effect on file size. To keep an invisible layer available but not have it included when you print or export, uncheck the Print checkbox in the Layer Options dialog, which is in the Layers popup menu.

Many of the shapes will have a line and a fill, and you almost never need both. Remove unnecessary lines; I set the lines of latitude and longitude to white, and eliminated the black box around the whole map. I selected the lakes and made their line and fill the same 15% Black as the ocean, then changed the rivers to make them blend in with the land (so they don’t show up, but don’t leave gaps). The countries now have a 60% stroke and fill; turning off the stroke left some gaps along state borders, so I just set it to the same color as the fill. I deleted the placenames, and changed the color of the coastlines to 60% Black again.

It’s perfectly possible to simplify things further, of course. Feel free to eliminate lines and labels and fade back land and sea as required to make your point. To crop away parts of the map you don’t want, draw a box over the desired portion (this needs to be the topmost object), select everything, then choose Crop from the Pathfinder palette. This will trim away everything outside the boundaries of the topmost object. (Remember to save a backup version!) For the map below, I’ve added a colored shape with a Gaussian blur, and used the Brush tool with the Arrows brush library to draw the currents. Clicking on an arrow with the text tool turns it into a path for labels to march along.

You can now use Save As to create an EPS for print publication, or Save For Web if you want a GIF or JPEG (the former is probably a better choice with maps) for PowerPoint or the Web. Remember, if you’re using color make sure it’s CMYK if the map’s for print, RGB if it’s for a computer screen (another reason why we save multiple versions…) There’s plenty more we could say about map editing, so feel free to mail me tips or queries, or comment below.

Most vector maps commercially available will be EPS files, though PDF is becoming more common; Illustrator can read them both. There are any number of map art vendors on the web; Map Resources and Digital Vector Maps are typical. Expect to pay $50 or more for a single map, and several hundred dollars for a CD with a selection of them. It’s worthwhile for a whole department or lab to invest in a full set of countries of the world, plus globes in a variety of projections, but this is often too expensive for an individual. There are very few places where you can get inexpensive quality vector maps online; here are two.

National Geographic’s website gives away world and country map, with placenames or just plain vanilla, in PDF format. Illustrator can convert these and open them, though you probably don’t own the fancy fonts that National Geographic uses, so it will substitute Arial or somesuch. It’s not too difficult to select and edit the fonts, though. The maps are plainly marked as copyrighted, though modifying them for non-commercial purposes would seem to be allowed under Fair Use provisions. They’re being given away explicitly for educational use, after all.

iStockPhoto has an enormous amount of artwork available very cheaply; a basic cartography search brings up a bunch of globes, and you can search more specifically for Mollweide and so on. Most cost around a buck. This is an excellent site for quickly finding quality images of any kind, especially generic advertising photography useful for illustrating brochures and blog postings; it’s worth bookmarking and buying a bunch of credits just in case you need a cheap photo in a hurry.

The next big decision is choosing an appropriate map projection. The Earth is a sphere and paper is flat, so any representation of the planet has to distort something. Distances, shapes, or areas: what gets distorted depends on what you want to use the map for.

Regardless, rectangular world maps are generally bad. The traditional Mercator projection (which radically distorts sizes), and the Peters projection (which distorts shapes and distances) should both be avoided. The Mercator is designed for sailors plotting a compass bearing, but isn’t much good for anything else. Because it overemphasizes polar area, it has to be cropped to not look silly, and the Southern hemisphere usually comes off worse in the cropping because, well, there’s less stuff down there (it’s really not an insidious plot by European mapmakers). The Peters projection is a political rebuttal to the Mercator, but in keeping relative land area equal it sacrifices everything else. Peters didn’t invent equal-area maps; there are many different one, going back to the 19th century, and the Peters version is by no means the best, though it’s certainly the most controversial; see the discussion in Monmonier (2004).

If you allow the poles to contract a little, and depart from a rectangle, you distort relative sizes far less. The Robinson projection was adopted by the National Geographic as a good general-purpose world map, and later replaced by the Winkel Tripel projection. For most of us, as long as you avoid rectangles, it doesn’t make much difference which projection you use (there are plenty more available, all different solutions to fitting a sphere on a page).

Here’s another one. The Mollweide is one of a number of projections that preserves relative area, but in the process distorts shape. One way to reduce the distortion is to interrupt it at the oceans (assuming, of course, you’re not interested in oceans). These orange-peel interrupted maps come in many different variations, and should be used more widely. I’ve never seen one for marine biologists, carving up land masses so as to accurately portray the oceans, but it must exist.

If instead of portraying the whole planet you only want to focus on one area, a planar projection might be better. The usual ones we see are the round maps of the North or South poles, but they can be centered on any part of the world; they’re most accurate in the middle and become progressively more distorted as you reach the edges. In the past I’ve used Antarctic planar projections to depict the breakup of Gondwana and the circumpolar distribution of the ratites. If you’re buying a set of maps, make sure it comes with a good variety of planar projections (usually just categorized as “globes”), including one centered on your area of interest.

References: Map projections are of course well covered in Wikipedia and a little more accessibly on this page. Also check out Flattening the Earth: Two Thousand Years of Map Projections by John P. Snyder (University of Chicago Press, 1997 new ed.) and Rhumb Lines and Map Wars: a Social History of the Mercator Projection by Mark Monmonier (University of Chicago Press, 2004).

You’ll start in Illustrator, Freehand, or any other drawing program that has a pen tool. If you’re new to Illustrator, learning how to use the pen tool to create and edit paths is very strongly recommended: there are pen tool tutorials here, here, here, and here. Here’s a cheat sheet.

Start with a photo. Google’s Image search is good; large images are best. You’re looking for a view from the side with good strong edges. If you’re working from a fuzzy sketch or indistinct picture, print it large, color it in with a magic marker, and scan it back in. It’s also possible to find a few images and composite them together in Photoshop, as I’ve done below. (I was just at the University of Queensland, where these big ugly megapodes, called Brush Turkeys (Alectura lathami) steal food from cafeteria tables.)

File > Place the photo or scan in an Illustrator document. You may want to double-click the layer, lock it, and set its opacity to 50% if you’re planning to trace round it. If you choose the Template option when placing it, Illustrator will do exactly that for you. Create a new layer to draw on.

Choose the Pen tool, set the stroke to solid and the fill to none, and start clicking your way around the outline. Press down to drag out Bézier handles when you want a curve; you can adjust the exact curvature later. Most of your points will be curves; for the few acute or obtuse angles you can just click to make a corner point. If you make a mistake, choose Undo. When you get all the way round, click on your starting point to close the curve. It took me about five minutes to work my way around the Brush Turkey, and that was using a laptop trackpad. A mouse is far easier, and a graphics tablet better still—even a small graphics tablet, like the Wacom Graphire (look for a used older model) is a worthwhile investment if you plan on making a lot of information graphics.

Once you’ve finished the outline, switch to the Direct Selection tool, zoom in, and start adjusting the points and curves. Reposition the points, and lengthen/shorten/reposition the Bézier handles until the outline matches the photo. You don’t have to get it perfect, as the silhouette will usually be printed quite small. Use the cheat sheet or tutorials above to learn how to add and remove points and change them from curve to corner.

Hide the background layer, set the Fill to black, and check the overall look. You may find some weirdness where you’ve accidentally clicked too many times or dragged the wrong way; you can usually fix this by zooming in very close and removing unwanted points (by clicking on them with the Pen tool).

If you’re happy, save a just-in-case backup Illustrator file (with the .AI extension), delete the background layer, and use Save As to make an Illustrator EPS (probably best to make it CMYK with a black-and-white TIFF preview; if this is Greek to you, check out my handout on graphics formats). You can still edit the EPS in Illustrator, but it’s a common file type that can be understood and imported by most page layout and image editing programs. Because it’s a vector graphic, you can also resize it without changing the resolution, and the file size is a bit smaller: mine is 368K, a third the size of the original photo. Once you’ve made a few silhouettes, you’ll find a myriad of uses for them, from labelling phylogenies to tagging graphs to sprucing up your grant proposals and letterheads. Enjoy.

Many thanks to David Booth and Yvonne Eiby for chatting with me about Brush Turkeys, and to the University of Queensland’s Department of Integrative Biology (particularly Cynthia Riginos and Lyn Cook) for sponsoring my visit.

Of all Tufte’s books, I felt this was most like a curate’s egg. Good in parts, but quite a bit of stuff we’ve heard before, and some less-palatable portions.

It’s a bit of a grab-bag. One chapter, rather grandiosely titled Fundamental Principles of Analytical Design, is yet another discussion of the Minard graphic showing the retreat from Moscow. The fundamental principles, if you’re interested, are: use multivariate data, documented, integrated with explanation and text, showing causality and explanatory comparisons. There’s a chapter famously and largely justifiably condemning PowerPoint, already available for years as a separate pamphlet. There’s a nice explanation of sparklines, tiny trend lines that can be integrated into text, also posted online. There are photographs of Tufte’s sculptures, for some reason. And there’s a discussion of figure/text integration in the Hypnerotomachia Poliphili. Now, I confess to being a big typography nerd, so will happily read all about the Hypnerotomachia. But I wonder what most of the scientists I know with Tufte tucked dutifully on their shelves will make of it.

What I found most bothersome was the discussion of cladograms, (evolutionary trees inferred from attributes of living organisms). Tufte initially posted a much more critical analysis of a fairly typical cladogram, with jabs at the use of well-understood technical terms like “strict consensus” (Tufte wrote, “Sounds like marketing, not science.”) There was a subsequent pile-on by some very reputable systematists, and Tufte backpedaled, claiming he was just a curious interloper in their field. Nevertheless, the watered-down version of the critique that made it into Beautiful Evidence still accuses biologists of using feel-good pitch words with “broad cheerleading meanings” to cloak their dodgy “editorial judgements” and questionable “statistical crunching”. Yes, cladograms are information-poor, and convey undue certainty to those who aren’t familiar with them, so Tufte’s skepticism is perhaps excusable, if not really justified. Skepticism vanishes a few pages later when lauding Feynman diagrams, which to me seem to contain far more dodgy handwaving than a good cladogram.

If you already have the first three books, this is by no means necessary. There seems to be a trend towards increasing length, cost, use of color, and hodgepodgery in Tufte’s books; perhaps the fifth in the series will buck this trend when it comes out it about (I predict) 2015.

• Sketch out ideas on paper first, before you turn on the computer. All graphics used to be drawn by hand. Software reduces creativity; good graphics are created despite your software.
• People will look at your pictures before they read your text, if they read it at all. Graphics have to be self-contained. Put your conclusion right there in the caption.
• The graphic has to tell a story (if it doesn’t, don’t use it) and your job is to keep redesigning it until the story is as clear as possible.
• Show the actual data, as much as you can. People can deal with much greater information density than you think. Your job is to help them see the patterns in the data, but…
• Show as little non-data stuff as you can. Remove boxes, lines, colored backgrounds, grids, shadows, and other decoration, except where it’s essential to understanding the data. If you can’t remove it, fade it out or make it smaller, thinner, or dotted.
• Minimize the number of steps required to interpret your graphic. Don’t put required information in the text that could go in the caption, or in the caption if it could go in a key, or in a key if you could just label the points or lines directly.
• Avoid color; it disappears on photocopying or printing. Use contrasting thicknesses, tints, line styles or shapes first, then color. Your graphic must work in black and white.
• If you use color, use an intuitive scale that relates sensibly to your data, not all the colors of the rainbow. Make sure colors vary in intensity, not just hue, and remember some of your readers will be color-blind.
• Provide context. Always use a scale and give sources. Six small, related graphs juxtaposed in the space we’d usually use for just one provide more than six times as much content.
• Learn some basic typography, Illustrator, and Photoshop. It’s not hard to find tutorials, and they’re wonderful transferable skills.
• Never print out your slides. Give people a handout with your contact details, a couple of graphics or tables (including any too detailed for a PowerPoint slide), your conclusions, and a bibliography.
• Don’t make lists of bullet points, like this one. Show, not tell.

Manifestos are great for removing nuance and blurring away contention, aren’t they? That’s why they’re so satisfying to write.

Regardless of the shape chosen, because we have a hard time judging areas, it’s vitally important that sizes are calculated accurately: namely, proportional to the value they represent. Otherwise the designer is telling lies.

From the Sunday New York Times magazine for February 25, here’s another mess from Catalogtree. There are plenty of poor choices made here—note the particularly ugly way that circles have been doodled on top of the text, making the chart look like a printing error. Leave aside also the fact that half the wording consists of dull qualifiers that could have been easily turned into footnotes, and that the designer could think of no better way to arrange the text than just dumping it on the page in a block, as if they didn’t actually want anyone to read it. These pale into insignificance beside the circles being the wrong size.

Note the largest value (892) and the fourth largest (436). One is just over twice the size of the other, and a circle twice the size of another should have a diameter √2 as big: about 1.4 times as wide. The larger circle in the graphic is actually about twice as wide, and it’s about four times as wide as the 204–225 circles. To see this amount of distortion this creates, compare the original proportions of the two largest circles, (right, top), with the corrected ones (right, below). I bet the designer just halved or doubled the circle diameters rather than actually calculated the areas required, which is pretty inexcusable.

To see the the way it should be done, check out the circles from an earlier New York Times article reporting frequency of the word “Iraq” in different presidential State of the Nation addresses. The first circle is twice the area of the second, not twice the width. Note the elegant overlapping too.

It’s understandable that some designers might mess up circular areas, as it takes a little algebra to work back from the desired area to calculate the right diameter to use; namely, 2√(area/π). But even simple squares can defeat a designer’s math abilities. Below is another example from a much earlier NYT magazine—the design firm 5W infographic, discussed in a previous posting, is no longer used by the Times.

How do we know if there might be a problem with an information graphic? One clue is weird instructions on how one should interpret it; a good data graphic doesn’t need instructions in how to read it. Note, for example, a key that points out that area only 25% of the original somehow equals 50% of the value. Again, this looks like a math-phobic designer at work. Illustrator’s Info palette works in widths and heights, not areas, so it’s easy for a designer to drag to draw a square where one pixel of linear distance, not area, equals 1% in value. Actually calculating the correct widths, from √area, was obviously too difficult.

The result is a graphic that makes large values too large and small ones too small. In this case, the designer chose to use only some of the data in the survey, mostly the pro-US results, so the graphic is not only inaccurate but biased.

It’s really not even necessary to use a data graphic at all when there are just a few data points, if you arrange them carefully. I tracked down the original report and summarized the questions in tabular form, arranging the columns in a different and more intuitive order. The margin of error was such that I wasn’t happy pulling out trends with anything more than some boldface type; in fact, that 41 probably shouldn’t be highlighted at all, since it’s not really different from the 37. And I had room to add a couple of extra questions. The whole thing could I’m sure be squeezed into the area the original graphic occupied, but I’m getting tired of fixing the work of highly-paid, supposedly-professional designers, so will spend the next couple of posts looking at something else and give Catalogtree a break.

Well, it does look exciting and retro. One problem is there’s no room to label the bars directly, so we have to laboriously go back and forth to puzzle out which is which, distinguishing 9 from upside-down 6 (if your chart requires upside-down reading to interpret, you’re probably doing something wrong), making pairwise mental rotations of clumps of bars, and so forth. Quite a bit of work for nine data points; a simple table would be clearer.

But it gets worse. Again the designer helpfully annotated the bars with actual data. The first thing I noticed was that 92% looks rather more than four-and-a-bit times as high as 21%. So I traced over one member of each clump in Illustrator, and measured their lengths. (The easiest way to measure the lengths of angled lines in Illustrator is Window > Document Info > Objects. If I wasn’t intending to reconstruct the graph, I could have just used the Measure tool, of course.) Sure enough, the bars weren’t even remotely to scale. I rotated them all to vertical, turning on the invisible grid to help, then typed the actual data into Excel and produced a quick bar chart, and juxtaposed the two (the Excel bars are flipped to make comparison easier).

We can see straight away that the shorter bars are disproportionately small. This has a pretty serious effect on the political slant of the chart, as it minimizes the amount of time the clergy seem to spend holding forth on immigration and stem-cell research, and overemphasizes their sermonizing on hunger and poverty. (I’d be interested in knowing if clergy really only talked about nine things, and if this is a subset what was left out and why—but another time). What’s caused this distortion? It’s not from stretching the 92% bar too much; there’s progressive distortion of all the other bars. After playing around with scaling—see the inset—it seemed more like an arbitrary fixed chunk has been lopped off each bar… Then I realized that the bars originally ran all the way to the center of the circle. That’s where we’re supposed to be mentally measuring to! Go check the original chart—was this obvious to you? Or were you fooled by the virtual baseline, and the numbers, into thinking the bars stopped there? (Me, I have enough trouble comparing ink I can see without factoring in imaginary ink that I can’t.) The designer felt they had to fill the middle with white so they could arrange their numerical labels there, and the numbers were only required because the chart’s groovy circularity left no room for anything better. So the path to the resulting mess seems clear.

But there’s another problem. If there were just one bar for each value, we’d at least all agree we should be comparing their heights. But using multiple bars creates a sort of exploded pie chart, with a wedge for each datum. Pie charts, clunky as they are, are a type of chart most people recognize, and we’re used to comparing areas, even if we don’t do it very accurately. But look at the exaggeration caused by mistaking the wedges for pie slices. I traced over the largest and smallest wedges and compared their areas; the larger has what Tufte calls a Lie Factor of 6.9 (doesn’t that sound imposing?), meaning it’s nearly seven times as large as it should be.

(An aside for the geeky: there is in fact a way to measure the area of closed paths in Illustrator, though it seems so s00per-seekret that I’m reluctant to share it. Briefly, open the debug window (on a Mac, command-option-shift-F12), click on Objects and Object Tree to expand then, select your closed path and see it become bold in the object tree, click its bold underlined name, and lots of terrifying numbers will appear in the Objects section, including the area—in points, I think, but it scarcely matters.)

So a cascade of bad design choices has led to a needless distortion of the relevant data, for those readers who weren’t so rebuffed by this wacky graphic that they just skipped it. At least the designer left the numbers on there, or we’d never even have known. The whole thing could have been avoided with a simple bar graph—not as sexy, but comprehensible, no larger, and (most importantly) not a big lie.

What’s so bad about it? Well, there are some pedestrian faults, the sort of things we find in a lot of graphs. The lines are labeled indirectly, with a key, so one has to jump back and forth to interpret them. It’s possible the designer felt there wasn’t room to label the lines directly, yet there’s a lot of wasted horizontal space between each year—he or she could have easily fitted them in. (And what are those lines anyway? What’s the difference between “Automotive Factory and Dealer Associations” and plain old “Auto Dealerships”? Surely one of the designer’s jobs is to communicate, not just recite corporate spin that calls fast-food vendors “Quick-Service Restaurants”.) The colors are pretty, but don’t signify anything, serving only to distinguish the lines. And it’s strange that every point has its value labeled; why not just use a y-axis scale? After all, with a general newspaper readership surely it’s the trends and relative magnitudes that matter here, not the exact values.

A good thing the designer did label the points, though, or we wouldn’t be able to see how misleading the graphic is. Absolute height doesn’t correspond to value, for example (see A). I’m guessing he or she did this to stop lines 2 and 3 from crossing—they did cross in inconvenient old reality, but that messes up the pretty pattern. Note that line 1 should be about three times the height of line 2, but I suppose that would create an ugly gap.

Change in height doesn’t match change in value either. The two lines in B correspond to the two changes in line 5. Since both are 0.3 billion dollars, the lines should be the same height, and they obviously aren’t. Look at the magnitude of change in line 1 as well. It looks like every line is using a different scale, and the designer just made the ends all join up so it looked nice, like a subway map.

And what the heck’s going on with C? 1.2 should equal 1.2! Perhaps it’s 1.25, and the digit was left off so it would match the others, and incidentally make the graph absurd. Doesn’t anyone at the Times proofread these things?

To redo this, I first generated a basic chart in Excel. I pasted this into a background layer in Illustrator, locked it, and just traced over all the components in a new layer (the chart is so simple it’s hardly worth ungrouping and deleting all the junk that Excel puts in its graphs). I came up with category names that were a bit more meaningful, and created a y-axis, which really only needs to be anchored by a few values.

One thing very obvious now is the dominance of auto advertising. I’m sure I’ve oversimplified the two auto categories; I’d want to see to what extent they overlap or could be lumped. Another thing to note is how wildly the original graph overemphasized changes; increases and decreases now look much more modest (although I wish we had ten or twenty more years of data; it would easily fit in the same space.) The color-coding is still meaningless; perhaps rising and declining categories could be colored differently, or color could encode information, like predominantly-print vs. TV advertising. The main thing, though, is that the graph’s no longer telling lies. (And we got to keep the groovy rounded orange lines.)

The opposite of this approach is a graphic that uses every color in the visible spectrum for no apparent reason. One finds this so often in computer-generated charts in scientific publications that I call it The Scientist’s Rainbow. Once identified, you’ll see it everywhere. Here’s a good example.

The original had no key to the colors, and they seem to correspond only to the scale on the y-axis. One way of testing whether color is conveying any information is to convert the graphic to grayscale.

So this color was just for decoration—yes, rainbows are pretty. But sometimes the Scientist’s Rainbow is actually impeding communication. The visual spectrum isn’t arranged in an intuitive order, and if there’s a scattering of colors it can be pretty hard to extract values for the mess.

We sould start by extracting the meaningful stuff from the empty space. I expanded the vertical scale until the pixels were squares, not rectangles, converted everything to grayscale, blurred away the sharp edges, and messed with the contrast a little.

Note that the lightest areas are not the highest intensity, but correspond to yellow and green in the middle of the spectrum. The highest values on the colored graph are reds, which are as dark as the blues at the other end (so the original would be meaningless as soon as it was photocopied). We’d be best to plot the raw data again using a black-to-white scale, although some clever color substitution in Photoshop could also convert a rainbow to shades of gray. That sort of work is best left to interns though, with their love of drudgery.

Quick, what’s the message of the table? That most people think we should use diplomacy against Iran? If so, then why show three surveys? There must be some kind of trend here. Wait, why is the timeline going from right to left? OK, there seems to have been a dip in support for military action back in May, or was that a rise in the number of people who don’t think Iran is a threat? But there’s a 4% margin of error, isn’t there? Does that matter?

Let’s try a graphical depiction. To reflect the 4% margin of error (presumably a 95% confidence interval 4 percentile points either side of the reported figure) I used an 8 pt line, where 1 pt = 1 pixel = 1 per cent when I originally constructed the graph in Illustrator. It would have been better to apply a linear gradient to each line that fades from the midpoint to the edges, but Illustrator can’t do that very easily.

I went into the public records and added the poll results for February, which are almost exactly the same as June. For some reason the Times left them out, though they had plenty of room. So is the dip in May significant? Did something happen then that the Times should be telling us about? Who knows?

But before we start over-analyzing the data, here are two different polls, both taken in February 2006. Note the difference in responses to essentially the same questions.

So the biggest differences are caused by pollsters; any supposed “trend” in the Times data is no greater than the difference between two simultaneous polls with slightly different questions. If the media just put a few polls side by side, maybe we’d lose our ill-founded confidence in them.

Why are we polling random Americans about what to do in Iran, anyway? Here’s the opinion poll I’d like to administer:

1. Should the United States take military action against Iran?
2. Who’s the President of Iran, anyway? Starts with A. No googling!
3. Name two cities in Iran, and two countries that border it.
4. Out of France, China, Israel, and India, which has the most nuclear weapons, and which the fewest?

Scoring note: No correct answers for questions 2, 3, or 4 scores an automatic “N/A” for question 1.

(Answers: 2, 3, 4)

Doesn’t this lay out their argument well? I couldn’t resist making a few changes, of course, because I’m fussy:

• Fading back the coastline and pointers so the data stood out
• Choosing different and related colors for the mouse ranges
• Making the color of the pointers match the ranges
• Changing the circle fills to a dark brown
• Extending the coastline and range into adjacent states, and labelling the states
• Making the state border a little different from the coastline
• And, in a bit of typographic pickiness, raising the baseline of all the “=” by half a point and putting a thin space either side.

(I notice it all looks very Tufte now, with his patented Tufte beige, but that wasn’t the intention.) If one were to do a serious redesign, my first suggestion would be to move the Oldfield mouse up into Georgia, so that it’s physically separated from the beach mice, and on land while they’re in the ocean. Adding a key to the two allele colors would be nice, and would pretty much remove the need for an explanatory caption. But I think the graphic works fine as is.

Reference: There’s a nice popular article on the findings, and the original paper is:
Hopi E. Hoekstra, Rachel J. Hirschmann, Richard A. Bundey, Paul A. Insel, Janet P. Crossland. 2006. A Single Amino Acid Mutation Contributes to Adaptive Beach Mouse Color Pattern. Science, 313(5783): 101–104. 7 July 2006, DOI: 10.1126/science.1126121. (PDF)

Note the pretty good color choice: red for rejection, something wishy-washy for “don’t know.” I would have gone with a dark green for acceptance, if only because the red/blue contrast is so over-used. The white spaces between the bars are a little obtrusive. Here’s what the New York Times did with it in the August 15th issue. They dropped the numbers, and left the “don’t know” category the same color as the white space between bars, which breaks up those obtrusive horizontal lines. Fading back the colors also helped.

How would the graph have been different if the bars had been sorted by increasing rejection of evolution? Greece would bump lower, Japan higher, and there’d be some shuffling in the middle, but the key point I think the authors wanted to make—that the USA is way down with Turkey—would have been preserved. But perusing the list a little raises all sorts of questions. Why are the Czechs and Slovaks more likely to reject evolution than the Bulgarians? Why are the Finns twice as likely as the Swedes and Danes? Poland and Ireland are two of the most religious countries in Europe, but they’re near the middle of this list. The graphic raises more questions than it answers. Part of the problem is that it’s only depicting three variables (not four; one category is just the remainder of the other two, as the Times recognized). And two of those are not really independent, having a roughly inverse relationship. So we don’t have enough information to do any analysis ourselves.

Here’s another presentation of similar data (I’ve added a key).

Now, this is not a slick graphic. I would certainly have played with the spacing, font size, alignment, ALL CAPS labels, and colors. But by choosing a scatter plot instead of a bar chart, and making the country name into the marker, seven variables are being depicted in the same space the previous graph could only manage two-and-a-bit. Moreover, we’re now equipped to evaluate trends ourselves, look at outliers (the churchgoing Irish, the oddly evolution-rejecting Dutch), and examine our preconceptions (there may be very few atheists in the USA, but that alone doesn’t account for the lack of acceptance of evolution). And note that the simple take-home message of the first graph is not being sacrificed, either: the USA stands out each time.

(The site itself has another 8 sets of charts, each with a different variable on the y-axis, and you can click through to see them appear one at a time in the same space, for easier comparison.)

We sometimes forget just how much information can be depicted in a data graphic. In a previous post I noted that exploratory and educational graphics are the extremes of a single axis. But surely a worthy goal is to do both: make your point, and include enough information for the reader to do their own hypothesis testing. Adding some variables is a good way to start.

References:
Jon D. Miller, Eugenie Scott, and Shinji Okomoto. Public Acceptance of Evolution. Science, August 11. 765–766. PDF online.
Paul, Gregory S. 2005. Cross-National Correlations of Quantifiable Societal Health with Popular Religiosity and Secularism in the Prosperous Democracies. Journal of Religion and Society, vol. 7. Online.

Here’s Sala’s key to the tags:

It’s easy enough to spot out the three book reviews and their covers, the lists of links down the side, and the flurry of forms, text, and image in the colophon.

I know what I’d like: a label beside each node giving the tag class or ID. That would make untangling endless nested DIV tags so much easier. Why doesn’t Dreamweaver have a graphical interface to its code?

Some interesting patterns can be seen on Sala’s site when large sites are compared, and design choices (such as tables vs divs) are revealed—the results reminded me of the haplotype diagrams my officemate generates as part of her research. If you’re interested in other graphical depictions of networks, check out the Visual Complexity site.

The Visual Display of Quantitative Information, 2nd ed.
Edward R. Tufte
Graphics Press, 2001 (orig. 1983) • ISBN: 0961392142

This is the most essential Tufte book, published over 20 years ago but so far ahead of its time it looks absolutely contemporary. (Compare it with some of the other data graphics books from the mid-1980s and you’ll see). Tufte here introduces his recurring themes: maximizing the data-ink ratio, stripping away unnecessary furniture and “chartjunk”, showing all the data, and graying out what’s less important. There’s elegant discussion of how readers perceive area changes, why we shouldn’t think of data as boring stuff that needs livening up, why pie charts suck, and when tables are better than graphs. Some of my favorite examples from here are Minard’s famous chart of the retreat from Moscow, the step-by-step erasure of needless ink from a bar chart, and the use of one of his own early graphs as an example of poor design. And the whole book is beautifully typeset and produced, with restrained use of color and plenty of white space.

Envisioning Information
Edward R. Tufte
Graphics Press, 1990 • 0961392118

Similar in subject and tone to the first book, though there’s more discussion of mapmaking. The most important concept discussed here is small multiples, the fruitful idea that six small graphs instead of one large one can add a new layer of information and reveal large patterns in the data. I’ve personally found this the most enlightening technique to share with newcomers to information design, and it’s often the best way to break out of the prison of an unsatisfactory design solution. Tufte also discusses how to use color effectively and with restraint, and how to successively reveal a process in a linked sequence of diagrams (such as dance notation or calligraphy). I particularly liked his coverage of tabular data, from train and bus timetables to the Vietnam War Memorial. Another fine book.

Visual Explanations
Edward R. Tufte
Graphics Press, 1997• 0961392126

This book wanders further out of strict chart-and-graph territory to cover depictions of processes, exemplified by the diagramming of magic tricks; if we understand how misdirection works, we can turn it around to draw the reader’s attention to what really matters. There are nice examples of the practical consequences of information graphics, discussing their role in the Challenger disaster and John Snow’s investigation of the London cholera epidemic. The most valuable concept to me was the notion of the smallest effective difference, with a useful discussion of just how little emphasis is needed to make a point clear in a graphic, and the power of using gray shades and faint lines. (There’s a fun genealogy of rock ’n’ roll too, illustrating parallelism as a graphical tool.) Lovely, but a less immediately practical book than the two preceding it.

Tufte’s newest, Beautiful Evidence, has just been published, and a review will appear here as soon as my copy arrives.

Label directly

If you can, put units right there on the axis, not on the axis label. In general, getting information out of the label and caption and putting it on the graph where people can see it a good idea.

(By the way, that’s a real degrees sign above; all fonts have one. See Robin Williams for tips on finding it and other special characters. Never try to fake it with a superscripted o!)

Rotate for readability

All the graphing software I know generates vertical labels on the y-axis by default, but these are really almost unreadable. It’s a good idea to make them horizontal wherever possible, moving them to the top if there’s no space to the left.

I’m also a fan of getting axes out of log form. Real units are what we’re used to reading, and forcing people to calculate antilogs in their head increases the risk they’ll misread your numbers. We’re pretty terrible at comparing logarithmic values as it is, so it’s almost deceptive to hide them behind a linear axis.

When you expand units to make them comprehensible, they do take up more room. (For example, see below. But who doesn’t understand what Ma means, I hear you ask? Well, your mother, administrators, your congressman, journalists, and the voting taxpayers or undergraduates who pay your salary perhaps.) One solution is to rotate them a little: -30° in this case. It’s also possible to make the axis smarter; why not show geological periods, for example?

Use sensible units

If you’re using non-decimal units for some reason don’t use a decimal scale, even if it seems more “scientific”. Why make needless work for the reader?

(The foot and inch marks used here, by the way, are the prime marks, not the typewriter quotes next to the semicolon key—see Robin Williams again.)

When we count days, we think in months and years, not base 10. If you turn the scale into a calendar, it no longer needs a silly axis label like “day of year”. Of course, always identify months with a word or roman numeral, because 01/05/06 can mean Jan 5 or May 1. We’re all used to reading calendars, so a detailed scale is fine—note that a sufficiently-detailed one doesn’t need an axis line.

You Don’t Have to Start with Zero

Honestly. In some cases, it’s just meaningless, as there is no “value zero” to graph, such as with the days to the right. And having the y-axis pass through 1 means a data point might get tangled up with the tick marks on the axis, so there’s no reason not to leave a small gap.

There are also cases where beginning at zero would add pointless empty space to the graph; consider how little trend we’d be able to see if the graph on the right’s y-axis went from 0 to 110. So the answer is to eliminate empty space from the axis as much as possible without being actively deceptive.

William Cleveland, in The Elements of Graphing Data, often allows the scale to continue below zero, to “avoid interference” between the perpendicular axis and any zero-value data; he uses a dotted reference line to stand in for the dropped axis. Unfortunately, in the following graph this implies that ozone could exist in a concentration of less than zero parts per billion! The space below the reference line is in fact a misleading and uninhabitable no-man’s land.

Leaving a gap so points don't hit the axis is OK, but extending the scale implies data values also continue. These days, with a better color palette, data on the axis no longer has to be a problem. Giving data points a thin white stroke allows them to intersect lines (and each other) while remaining visible; a little better than the jittered circles Cleveland was forced to use. (In the above makeover, you can see I also added units to the axes, rescued the labels from that horrible ALL-CAPS computer font, and condensed the empty space.)

Comments and suggestions welcomed, as always.

We normally just assume the novice–expert distinction is a function of how qualitative or educational a graphic is, so I strongly support putting it on an independent axis. Both novices and experts need exploratory graphics, that let them find patterns in the data, and educational ones, intended to make a point. These could equally be quantitative, where every data value could be read off, or qualitative, showing general patterns.

Three variables make a three-dimensional space divided into eight cubical sectors; technically, octants (though everyone at the meeting, shamefully, was calling them quadrants).

Which octant is your graphic in? Now what would the other seven versions of it look like? This is a good exercise, because it makes you realize there isn’t one “best” solution to a design problem, something we can forget if we read too many makeovers (including the ones on this site). It’s a good mental exercise, for you and your client, to try shifting graphics along just one axis, so you can put your finger on exactly which elements are doing which job.

Of course, heuristics like these octants are just mental tools, not the Revealed Truth. For example, are we sure these axes necessarily represent mutually exclusive goals? In the best graphics, it’s possible to display all your data quantitatively, yet still discern overall qualitative trends. Perhaps we could challenge ourselves to make graphics that are comprehensible to lay people (which includes almost everybody, even experts from disciplines just outside yours) while containing levels of richness for the informed; avoiding needlessly technical language and conventions would be a start. And a sufficiently well-crafted graphic would let people find more patterns than just the ones we want them to see, perhaps ones we hadn’t even noticed. How about as a goal for the year trying to span multiple octants? (Now that would make a befuddling bumper sticker.)

Practically every pixel is data, and even the x axis could be grayed out or dropped entirely, giving an unsurpassable data/ink ratio (which would also help Pluto show up).

The problem element here is the sun, which is so gigantic it swamps everything else. If we were to eliminate it, or just show a chunk, the graphic would become much more concise. I’ve done this below; note the two scales, implicit in the original but I think necessary here—one is five thousand times the size of the other.
 

That version’s OK for print, but for the web one needs slightly more contrast and more solid typefaces.
 

The other solution for dealing with scale is breaking the graphic into chunks and recalibrating the scale by a sensible amount in each. These comparisons do this (note the common reference object in each successive picture), and use lovely 3D depictions as well, letting us go beyond the solar system to compare our sun with other stars.

References: Wikipedia has Brian0918’s chart: if anyone has more information on its origins, please leave a comment. The models were found via BoingBoing at Rense.com, which goes to show there are jewels in dross on the Web, but I don’t know the site they were taken from originally. And a practical activity that communicates the scale of the solar system to kids is the Earth is a Peppercorn.

• Note the strong contrast between headings (in bold caps), graph title (upper and lower case) and series labels (caps).
• The same font is used throughout, the same size in axis labels as in graph titles. This means the labels are larger and the graphs smaller than Excel's defaults.
• Only two shades of black are used throughout, and no color or patterns.
• The shades and labels used here is repeated in the next two graphs (see below), consistently referring to the same series. The choice of shades intuitively corresponds to specific (Ohio) and general (U.S.).
• The bars here sensibly overlap instead of being offset as is usual for bar charts.
• Direct labelling of sample values is used instead of the little labelled swatches that Excel produces—one less interpretative task for the reader. Note how the sample data is offset, so it can't be confused with real numbers.
• Explanations are right on the chart: what's a WIN is noted by the data, instead of in a caption or title.

• All these graphs are as small as they reasonably can be, yet the bar and especially the line chart contain quite a bit of data.
• No frame around the charts, and no background colors or decoration.
• The bars have no stroke, just a fill.
• No y axis line.
• Lightly dotted y gridlines.
• Directly labelling one of the values on the y axis with the unit (in this case, percentage, but it could be dollars or kg or anything else) rather than using a rotated axis label (which is usually hard to read and a waste of space).
• Abbreviating years as '01 rather than needlessly writing them in full.
• No tickmarks on the x axis; instead, dividers are used between years.
• The Ohio series is thinner where it has to be (overlapping bars), but the same width where it doesn't (overlapping lines). All differences should be for a reason; otherwise, keep things the same.
• Unobtrusive gaps are used for the U.S. series in the bar chart, just thick enough to tell the bars apart, but not so think the series can't be viewed as a single shape.

And note some nice little touches: the % symbol in the y axis label extending into the chart a little, so the numbers align properly; the way the line for the label "OHIO" knocks out the gridline so they don't clash; and how one of the 2004 values is allowed to dip below the x axis.

A very close reading of a well-designed information graphic can yield all sorts of good ideas. Good design should require a close reading to unpack; anything immediately obvious is probably too flashy and getting in the way of the data. These examples show how a chart can be simple, beautiful, and functional at the same time.

Here’s a data set showing the male and female body masses in kilograms of the ratite birds. Let’s graph it as a scatter plot. (You can download the Excel file and follow along if you like).

There are a few things we can fix while in the Chart Wizard.

• Type in sensible axis labels and title.
• Turn off the gridlines (or, if they’re absolutely necessary, make them pale gray and dotted.)
• Turn off the legend, since we only have one data series.

Once the chart is placed on the page, this is what you get. Still a lot of work to do, isn’t there? Time to start double-clicking on various elements and altering them.

• Give the plot area no fill and no line, instead of default gray.
• Format line color and thickness of each axis; paler and thinner never hurts.
• Manually choose the best minimum and maximum numbers for each axis scale to minimize waste space. I made the maximum x-axis value 120.
• Change the font for each axis to something readable, turning off auto-scale, that very annoying feature that makes lettering unreadable if you shrink the chart..
• Adjust the font and size of all the labels, again turning off auto-scale. I made the heading in Hoefler text plain—bold tends to be over-used in non-professional design, and in fact plain text is often more striking for headings. Georgia is a good alternative to Hoefler Text on Windows machines. Trebuchet in two sizes was used for the axes, since it’s narrow and contrasts well with the heading. Pick the labels up and move them where you need them.
• Select the y-axis title and change the alignment so it’s rotated 0°. Insert line breaks as needed to make it narrower.
• Select the data points, turn off the default shadow, and use a foreground or background color (the Excel equivalent of stroke and fill); you’d rarely need both. Adjust size or shape as needed.
• Start tweaking. Here I resized the plot area so both axes used the same scale (I needed to turn off the x-axis's auto-incrementing). Drawing a square with the drawing tools (shift-rectangle) tu use as a temporary guideline helps get the intervals on each axis the same. I also added a y=x line and text label with the drawing tools.
• The next step would be to shift the units to the axes, by floating a “kg” text box next to each one, and then label some of the interesting outliers, such as ostrich and giant moa, with more text boxes. While it’s certainly possible to do all this in Excel, it would be more sensible at this stage to shift to a vector graphics program like Illustrator, and use it to make a self-contained EPS graphic.

So that’s a checklist of steps that can easily improve an Excel chart. Below is the cleaned-up version and the Excel default (note what auto-scale did to the labels when I resized the default chart!)

Too much work to do to every graph? I agree. A future posting will provide some already-cleaned-up templates you can download and install.

The units are not very friendly, either. Albedo is just the percentage of electromagnetic radiation reflected by a planet, and AU are units equal to the Earth's distance from the sun. Why temperature was chosen as a third variable is unclear (and why use degrees Kelvin?). Sure, it varies linearly with distance from the sun on a log–log scale, but that’s no surprise. The only exception is Venus, whose temperature is a consequence of a carbon dioxide atmosphere, not albedo; if anything, its cloud layer lowers the temperature by reflecting sunlight. So the graph is not really telling a coherent story.

What could be improved? We can start by just plotting albedo against distance, using more intuitive units for both. Now some of the variation that was masked by the bubble plot begins to emerge:

This is a fairly basic chart, but it still isn’t telling a story. To flesh it out, I added relative sizes, changed the brightness of the planets to match albedo, and annotated some of the outliers. The graph has become a little too cluttered, because the pattern of data points is being swamped by supplementary information, but at least now there’s some sort of narrative going on. And it prompts questions, like why does the Earth have such a high albedo when it’s mostly ocean?

A little more research, and I realized Earth’s albedo is mostly determined by cloud cover. That was the key to understanding why Venus and the gas giants were so reflective, and dry balls of rock like Mercury and Mars weren’t. So I resimplified the chart to make that point, stripping off some of the irrelevant information.

Now the graph has a point to make, and you can just tell it’s happier.

References: The original chart is from Leland Wilkinson’s The Grammar of Graphics (Springer 1999). What piqued my interest was a fascinating discussion of what color the planets really are. Planetary albedo data are taken from part of NASA’s site, Wikipedia supplied the Earth albedo data, which merely lists “Edward Walker” as its reference (as does every other site on the internet, blithely copying Wikipedia of course.) The old Wikipedia page cites, gulp, “Walker, E., 1987: Pictures of Preschoolers Out in the Snow. Dishwasher Picture Publishing, Volume 26, 151–1103.” So you may want to take those figures with a grain of salt.

Apart from the odd terminology (extrastate vs. interstate? And "war between states" means something quite different here in North Carolina...), I was puzzled by the color choice. Was this a stacked area graph, or one where the areas were superimposed, as the color scheme seemed to suggest? I went to the Human Security Centre website and found the original:

It is indeed a stacked area graph. The second paragraph of the caption reads:

Figure 1.1 is a ‘stacked graph’, meaning that the number of conflicts in each category is indicated by the depth of the band of colour. The top line indicates the total number of conflicts of all types in each year. Thus in 1946 there were five extrastate conflicts, two interstate conflicts, ten intrastate conflicts, and 17 conflicts in total.

A good rule of thumb is that if you need to spell out how to read the graph, there’s a design problem. In this case, we're being misled by the “mountain illusion.” The human eye is used to distant objects being lighter in color, so sees the area chart as a mountain range, and hence layered.

The New York Times designers, 5W Infographic, exacerbated this illusion by changing the color palette to different percentages of magenta. On the plus side, they simplified the x-axis by only numbering decades, labelled each area directly instead of with a legend, removed the frame, and replaced the x-axis tick marks with pale vertical gridlines.

How to get rid of the mountain illusion? The most obvious solution is to change the color scheme by reversing the light-to-dark direction, but this doesn't seem to help much. Better is a color scheme that flattens out the illusory layering. Making the contrast color as similar as possible to the main color seems to work best.

Another option is to change the stacking order, so the smaller series sits on top of the larger, which also fights the mountain illusion. Unfortunately this makes it much harder to discern trends in the smaller series; they're swamped by the larger. Another complication here is that one series disappears, so the trick is to distinguish between the two that remain. I've used a solid line, but a dotted line would be better, and subtle changes in shade best. The final and probably superior alternative is to just unstack everything in Illustrator, by making three graphs and combining them.

While unstacking this area graph makes it unambiguous, it’s much harder to see the total conflicts for a year. Does this matter? As always, it depends on the point one is trying to make; in this case, that world conflict has declined. The trend is pretty easy to see, as the majority of the data are in just one series. If total conflicts were important, one could add a line:

References: The accompanying article was Wonderful World, by James Traub, NYT Magazine 3/19/2006. The lovely Smoky Mountains photo is by someone called Melissa, who unfortunately has no contact details, or I'd have asked. The folks at the Human Security Center at the University of British Columbia helpfully supplied the original data from their Human Security Report 2005.

(Update: charts by 5W Infographics no longer appear in the NYT magazine...)]]> http://www.numberpix.com/2006/03/stacking_world_conflict.html http://www.numberpix.com/2006/03/stacking_world_conflict.html Makeovers Tue, 21 Mar 2006 13:20:50 +1100 Every year, the Journey North Project tracks sightings of the first Monarch butterflies (Danaus plexippus) of spring, and produces maps like this one.

The legend shows the first sightings in given two-week periods, but uses overlaid dots, which become a little confusing. The choice of colors is not the best; I had to get very close to my monitor to tell early and late May apart. It could do with some improvement, and Leland Wilkinson in The Grammar of Graphics produces a far superior version, though for an earlier year.

Could this be further improved? Wilkinson uses the “Scientist’s Rainbow” in his legend. But since the data are associated with increasing spring temperatures, and the lines are in a definite order, a legend using increasingly warm colors (different saturations of orange, for example) would work better; the original graph does this to some extent. Wilkinson uses colder colors as summer arrives, which seems counter-intuitive. The lines could easily be labelled directly, since the legend is sitting right beside them. Since most of the sightings are Eastern, half the map is blank and could be cropped.

Instead of ranges, I used a date for the advancing front. Wilkinson’s lines are nonparametrically smoothed contours through the concentration of points in each date range, rather than across the leading edge, because of “random error in the dataset”. The effect is to retard the contour, and it implies that all the points in advance of it are mistakes. Remember, these points are not a population sample from which we’re discerning a mean value, but the edge of a range.

This raises a bigger issue: what’s the purpose of this graphic, anyway? If the goal is to answer the question “When will the Monarchs appear in my state?” then averaging the lines for several successive years would be best—instead of two-week ranges, a less precise time interval would be better like “early April” and “late April”. The data are all online, and the graph is left as an exercise for the reader. Think how useful such a graph will be to our grandchildren, when they’re jaded from seeing Monarchs flitting around in February.

There was a very good article in the October 3rd New York Times science section on Monarch migration. They use a color palette similar to one I suggested above (though these butterflies are heading south), and dots for sightings like the Journey North project. The dotted line is not the wave front of butterflies, as you might think, but a 60°F isotherm—Monarchs can’t fly when the temperature drops below 55°F. The isotherm is labeled a little way offscreen, and I missed the label on my first reading of the map, so that would be my only criticism.

References. The Journey North Project archives can be found at at Annenberg/CPB Learner Online (www.learner.org). Wilkinson’s thorough and analytical book The Grammar of Graphics (Springer, 1999) is also worth a look if you’re interested in programming graph-generating applications.

The Mac is Not a Typewriter (There's a PC version too)
Robin Williams
Peachpit Press, 2003 • ISBN: 0201782634 (PC: 0938151495)

This wonderful little book is very short, but so good it should be given out with every computer sold. Get three and pass them around. Williams covers not just basic typography (like why you shouldn’t type two spaces after periods) but also some more arcane topics, like hanging punctuation and the line spacing of capitals. What I think anyone who works with text for a living should know about type.

The Non-Designer’s Design Book
Robin Williams
Peachpit Press, 2003 • 0321193857

I used this as a standard text for a course on desktop publishing and design. The students with pretensions to being graphic designers pooh-poohed it at first, since it was short and approachable, but were won over at the end. Williams walks you through half a dozen different kinds of typeface, so you’re able to mix them confidently on the page. Then she lays out the immortal CRAP principles: contrast, repetition, alignment, and proximity. Just these are enough to improve most people’s layouts about 300%.

The Non-Designer’s Scan and Print Book
Robin Williams and Sandee Cohen
Peachpit Press, 1999 • 0201353946

When creating your own graphics for publication, you always have to deal with file format issues (EPS or PDF? Vector or raster?), color models (CMYK or RGB?), resolution, scanning, paper size, embedding fonts, and so forth. Most people muddle through by following a list of arcane demands from their publisher, but wouldn’t it be great if you actually understood all this stuff and could chat with the printer ahead of time about spot colors and line screens? That’s what this book is for.

If you enjoy these, I recommend you check out:

How to Boss Your Fonts Around
The Robin Williams Design Workshop
The Non-Designer’s Web Book
The Non-Designer’s Type Book

All are easy reads, and full of good stuff. Get the most up-to-date edition of each of you can, as this field dates fast. By the way, I don’t get a cut from Amazon, and recommend you try a price comparison search on Addall or BestWebBuys.

The chart shows the geological timescale up the left (from the Maastrichtian in the late Cretaceous through to the Holocene and the present day), and when different groups of birds occur in the fossil record (the vertical lines). The timescale implies the different geological stages are all the same length, which is certainly not the case, but there is no scale in years to make this clear. The bird fossils only actually occupy half the timescale, though the gridlines and boxes continue all the way to the bottom of the page. The legend takes up almost as much room as the data, and actually reading information back off the chart requires constantly referring to the key, making comparison fairly difficult.

How could this be improved? For its size, the chart contains very little information; most of it is furniture. For each group, there are only three data points: first appearance, last appearance, and how much of the fossil record is patchy or continuous. I was interested in showing just the ratites (roughly groups 18 to 29), and some of these have no fossil record to speak of, so there was plenty of opportunity to add more information. I wanted to show actual names, localities, a picture of the birds concerned indicating relative sizes, flying ability, whether they were actually ratites or not, and some indication of how many bones had been discovered from each. So I started sketching.

I realized I could combine the bone information with the outlines, and indicate flying ability by showing the wings of flying ratites. At this point I put together a geological timeline, to scale, in Illustrator. I decided to show the actual boundary dates of geological periods as well as a linear timescale.

I roughly sketched each ratite (in some cases reconstructing them from partial skeletons, in other cases working from Googled photographs). I then photocopied them up to full-page size and traced the outline with magic marker. Each ratite was then scanned into Photoshop, the outlines filled in, and the contrast adjusted so only the black silhouette remained. This was then placed into Illustrator as a template. Illustrator has autotrace tools for converting a scanned image into an outline, but I found it much easier to click and drag my way around the silhouette with the pen tool. The nice thing about working large like this is that when you shrink the graphic back down it looks pretty sharp.

Here’s a version of the final graphic. I simplified the crowd of ratites clustered around the Holocene by noting in the text which groups did not have a significant fossil record. The shades of gray indicate how closely-related each species is to modern ratites.

Where only partial skeletons are known, bones were added to the silhouettes. Where the fossil record becomes continuous, the line for each group becomes solid. I hoped these conventions would be intuitive enough that the chart would pretty much speak for itself.

One side effect is that I now have a bunch of scalable silhouettes I can use to label other diagrams, phylogenetic trees, and the like, so it was worth the effort.

Reference: Unwin, D.M., 1993. Aves. In Benton, M.J. The Fossil Record II. London: Benton & Hall. Criticisms of this chart should in no way be seen as a reflection on the expertise of Dave Unwin, a splendid fellow, and not just because he let me hold the Berlin Archaeopteryx.

The biggest problem is that the arrangement of the bars doesn't correspond with the parts of the bone in question. Nor are the colors of the bars meaningful; they're only there to make the legend work. The cryptic y-axis label is just number of osteocytes per square millimeter. The three cases where there are significantly more osteocytes are indicated on the graph with an asterisk (*), the conventional indicator of statistical significance. Note that the asterisks are in a different position in each case; one might even think the the three black bars are being referred to, but in young fawns it's in fact the white bar.

It's so confusing that the caption takes up nearly half as much space as the graph itself.

How to make this more comprehensible? A good goal would be to reduce the amount of interpretation we were requiring of the reader. I noticed the article had a diagram showing cross-sections of the bone in question, from young fawn to adult.

I scanned them, placed them as a template in Illustrator, then traced around them with the pen tool. For each bone I made a compound path of the inner and outer circles, and sliced it like a pizza with the knife tool into four chunks, each of which could take a different fill.

I noted significant differences right on the bones themselves, and added a color scale, where shade (40% to 80% black) corresponds intuitively to osteocyte density.

The revised graph takes up less room and is pretty much self explanatory. Once the scales and labels had been given, one could even show multiple small versions of the same four bones side by side, each showing osteocyte density under different conditions.

Reference: J. G. Skedros, K. J. Hunt, and R. D. Bloebaum (1995), in the Journal of Morphology 265(2):244-247.