## Our Blog

### Measures and Averages and Indexes, Oh My!

Ah… Summer in Atlanta.
Sunshine, green trees, Peachtree Road Race… and pollution. I love living in Atlanta, I really do, but one thing that most non-Atlantans don’t know is that we have a real problem with poor air quality. As someone who really enjoys biking, hiking, and running, I pay a lot of attention to the Georgia Department of Natural Resources Air Quality Index. So far this year we’ve had a handful of “orange” days, but no “red” or “purple” ones – which sounds anticlimactic, but is a real improvement over a few years ago (we haven’t registered a “code purple” day since 2002).

Anyway, as I was checking the air quality forecast this week, it occurred to me that the green/yellow/orange/red/purple/maroon categorization is based on an index. This started some thinking about measures, averages and indexes.

If you’ve never thought much about indexes, they are calculated by dividing the measured value by a base or expected value and then (usually) multiplying by 100. The result is that the target value is “100″.

The great thing about indexes is… they are super easy for casual users to interpret. This is the case because they remove the dependency on the user to understand and keep absolute values in their head. In the case of the Air Quality Index, it’s based on the national air quality standard for the pollutant measured. Since air quality in Georgia is primarily composed of 7 measures, it can get pretty confusing if you want to know what’s going on. To demonstrate, here’s a table of the national air quality standards that the Georgia Department of Natural Resources monitors:

Get the point? As an environmental layperson, it’s much easier for me to interpret current measurements if they are expressed with respect to the national index (100) as opposed to ppb, ug/m^3, etc. Most people don’t have a clue (nor do they care) about what a mg/m^3 or a ppb is, but they do care about their respiratory health. So, providing a common, simplified measure makes complex data oh so much more accessible to the populous. This is the power of indexes over absolute measures and average values. In Atlanta the use of indexes has unified nearly every Atlantan’s practical understanding of air quality.

What it means for you

So, when it comes to creating information applications and dashboards, if you’re presenting complex values, consider using indexes to reduce the barriers to entry for non-domain-expert users. Here are a few tips to keep in mind:

• Just as with any new metric, it needs to follow certain guidelines for good metrics.
• Get buy-in with your user group so they don’t feel like you’re pushing yet another meaningless value down their throat.
• Don’t fall into the temptation to swap indexes with historical average values. Averages represent historical measures; indexes represent performance compared to a group.

Oh yeah, lest I forget, there’s always a tradeoff. As with most things, when you simplify, you lose some resolution. The draw back is that for those who want to delve deeper into the meaning of the measure, they now have to do some researching (just as I did) to understand how the actual metrics are measured and how the index is calculated. Take this into consideration in your information design and make the absolute measures readily available through alternate views, mouse overs, or similar.

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### Can familiarity trump usability?

Grocery shopping at a new store is a drag, no matter how thoughtful the supermarket layout or how clear the signage or how wide the aisles. I have a mental model of my local supermarket that makes my trip efficient and helps me avoid that frustrating “double back” to search for the peanut butter.

This thought made me wonder about the importance of familiarity in dashboards. We spend a lot of time at Juice designing intuitive, simple-to-use dashboards. We want to create a logic and cohesiveness that ensures the right things are placed in the right proximity and order; sales leads should connect to prospects in the same way as the peanut butter and jelly is shelved near the bread.

If you are starting from scratch, this internal logic and consistency is paramount. But how about a dashboard that is already familiar to the target audience? Does it makes sense to redesign a dashboard for usability if it is already heavily used and understood?

For many dashboards, the purpose is simply to convey a few key nuggets of information. Without a series of interactions or tasks, the user’s only need is to locate and absorb data. In these cases, the measure of success is whether the user can find what they are looking for quickly.

I can appreciate the value of familiarity over usability. When the new Microsoft Office “menu ribbon” was introduced, it was described as a convenience to new users because it displays the most relevant features for any given context. For power-users it broke the experience; all the effort I’d put into memorizing static menus was lost.

For all our concerns about poorly-designed dashboards, it may be familiarity that explains why it can makes sense to keep the status quo.

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### Chart Makeovers

Earlier, Zach wrote a blog post on the ins and outs of chart selection. It reminded us how important it is to balance the right chart with the right visual presentation as dimensions and complexity change.

But your data presentation decisions don’t end there! Once you have a good handle on the right structure for organizing the presentation, you have to make it look good – making it function good and accomplish original goals. As promised in the previous post, here are the chosen chart structures at each stage of complexity redesigned for presentation. We’ll keep this simple with before and after shots, key design principles highlighted, and a freeform reflection on some practical design decisions. The explanations aren’t meant to be exhaustive but rather are a glimpse into design thinking.

#### Phase 1 | Sales + Calls, Aggregate Performance

Before & After

Design Principles

• Visualisation is not always the best solution
• Emphasise the interesting

Design Reflection

• For fonts, often the best choice is sans-serif, tabular fonts (like this). For this demonstration I simply used Helvetica because it gets the job done and everyone has access to it. The font size is 18pt for primary values and 12pt for secondary.
• Qualitative values (calls, sales) will often be the text that should be treated with grey (50% black will do for most situations).
• Quantitive values (559, 71,739) should be clear and easily distinguished from less immediately critcal information. Here they are bold, 80% black.
• Superscript the dollar sign since its an unchanging qualitiative value.

#### Phase 2 | Sales + Calls / Product, Aggregate Performance

Before & After

Design Principles

• Use color carefully
• Use 50% grey carefully
• Visual rhythm
• Consider text style needs for dynamic content
• Organize data visuals in a way that mimics thought process comparisons where appropriate

Design Reflection

• Stacking the calls and sales bars should only be done with the right audience in mind. Though a dollar to calls value is not comparable in and of itself, in the midst of the context of other products, this makes it easier to visually compare the proportions of these values against each other from product to product. For example, immediately one can notice ’Ceramic Smoking Baby’ is a lucrative product.
• Add consistent, distinct visual rhythm with light separation lines
• Again, color should only be used to distinguish commonly changing quantitative values: numbers and bars in this case. But sometimes carefully using color on qualitative values can be helpful. The title (’Calls’), value (’202’), and visual representation (longest bar in this case) is an example good color management. No legend is needed, because the content itself explains visual relationships. The content is the legend.
• Choose your 50% grey visuals wisely. Product names are secondary in visual weight to colored data values, because they are secondary mentally in the thought process of reading this chart.
• Placing metric values to the left of the bars overcomes problematic rendering issues when values are very small.
• Dollar signs are not superscripted because they would become unreadable.

#### Phase 3 | Sales + Calls / Time, Aggregate Performance

Before & After

Design Principles

• Minimize chart junk
• Use white space for comfortable reading
• Remove text values that can easily be interpreted with visual counterparts

Design Reflection

• Center trend values on vertical hash marks
• Measurement dimensions should be grey
• Distinguish current date with value and endpoint
• Remove extraneous date values that can be easily interpreted with well placed light hash marks
• Distinguish every 5 hash marks with length difference

#### Phase 4 | Sales + Calls / Product + Time, Aggregate Performance

Before & After

Design Principles

• Give values context
• Red is easily noticeable when used sparingly
• Allow for easy comparison

Design Reflection

• I put the sparklines first in the visual reading for two reasons: 1) the width of this graphic is always the same/dependable and 2) the context of data is often helpful to present first so subsequent values can be better understood. This little snapshot of time provides that context.
• On the sparklines, distinguish today’s value and the lowest value (red dot). Use red carefully. You don’t need much to draw attention (where color blind issues aren’t an issue)
• Be sure to provide ample space between elements, and that all graphical elements are aligned on your grid.
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### Meet Juice and Connect More Visually

One thing we really like doing here at Juice is meeting and talking with folks who are interested in the practical application of visualization techniques to make their jobs and businesses better. We know a lot of you out there feel the same. So, we’re planning meet-ups in three cities over the next few months — Atlanta, Washington, D.C. and Boston. In addition to giving those of you in these areas a chance to get together in one place at the same time, it will give us a great excuse to share some data visualization knowledge that we think will benefit you and enhance your skills.

Each Juice Tour event will start with a meet-and-greet followed by a presentation focused on some basic rules for effectively communicating data – where we will provide you with some easy-to-use principles that you will walk away with, leaving you to become far more proficient at presenting your data forward no matter who your audience.

Afterwards, you will have an opportunity to meet one-on-one with Juice in free mini-problem-solving sessions where we can talk specifically about your visualization problems and offer suggestions to help you work through them.

If you’re interested, register here and let us know your name, email and your location. We’d like to gauge your level of interest in the Juice Tour — starting with Atlanta, Washington, D.C. and Boston. If you’re not in these areas, but are interested in the Tour, please let us know that, as well. (If these go really well, who knows, maybe we’ll expand to include other cities, too.)

We look forward to hearing from you! (Oh, and did I mention, it’s free?)

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For those of you who might be interested, we’re going to start adding our significance to the twitterverse through @JuiceAnalytics. If you’re already following us (@chrisgemignani, @zachgemignani, @khilburn, etc.) you can certainly keep doing that, but if you’re a Juice fan, we’d encourage you to follow us @JuiceAnalytics as well.

And to kick it all off, on Monday May 24th we’re going to begin with a series of tweets entitled “30 days to better visualizations.” Each day we’re going to direct our followers to an online resource that you can read, watch, play, or do something (each takes only about 5 minutes) that will help you hone your visualization skills. For these tweets, we’ll be using the hash tag #30Days2Viz.

So what are you waiting for? Follow us now.

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### Better Know a Visualization: Motion Charts

With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.

#### What is a motion chart?

Motion charts are essentially animated bubble charts. A bubble chart shows data using the x-axis, y-axis, and the size and color of the bubble. A motion chart displays changes over time by showing movement within the two-dimensional space and changes in the size and color of the bubbles.

Modern-day motion charts were developed by an organization called GapMinder as part of a product called Trendalyzer. Hans Rosling, one of the founders of GapMinder, popularized the motion chart visualization in a much-admired TED Talk.

Motion charts can include a number of features, including:

1. “Trails” to trace the path of individual bubbles
2. Animation bar to control the time range and animation
3. Selectors to define the metrics shown on the axes, bubble size, and color
4. Show/hide labels

#### What problem does this solve?

Advanced visualization methods exist for three reasons:

1. To show more dimensions of data simultaneously, therefore revealing more interesting stories in the data
2. To show high level patterns as well as the individual elements that make up the pattern
3. To dazzle viewers

Motion charts accomplishes all three. First, it brings the time-dimension into a chart that would otherwise represent a snapshot in time. Motion charts can help in an analysis if you find that you are asking yourself, how did I get here?

Secondly, Hans Rosling’s talk beautifully demonstrates the ability to see big picture patterns (flows of bubbles from one quadrant to another) while also focusing on the individual components. Finally, motion charts are sexy because stuff moves around the screen.

#### What to watch out for when using motion charts?

The masterful hands of a pro like Hans Rosling make motion charts look powerful and intuitive. Tiger Wood’s Phil Mickelson’s golf clubs are only a small part of what makes his game look so good. Effective use of motion charts can be tricky:

• As an analysis tool, motion charts ask a lot of our visual pattern recognition skills. Bubble floating around in all directions, changing size and color can overwhelm many people. Hans Rosling had a clear story to tell. He also was able to narrow the data, metrics, and scope of his visualization to support his story.

• Animation isn’t ideal for showing trends. Displaying trails can help, but is still inferior to the simple readability of a line chart. Don’t take my word for it: research shows that animation is not great for showing changes over time.

• Animations also don’t transfer to static images–like that PowerPoint presentation you need to deliver to your boss.

• Resist the temptation to cram in one more layer of data. Take this blog post comment for example:

“Great bubble chart solution. I’ve been looking for a 3D bubble chart so I can move bubbles in 3D space, allowing me to track an additional dimension. Any ideas?”

I’ve got an idea: Don’t do it!

#### Motion charts in practice

GapMinder shows a variety of public data sets using motion charts

Google Analytics has built motion charts into their interface to visualize visitor and traffic patterns.

#### Do it yourself in Excel

• Anand has a very helpful blog post about Motion Charts in Excel, including a sample excel spreadsheet.
• Jon Peltier has a first and second generation spreadsheet for motion charts.

#### Do it yourself with other tools

• Google’s Public Data Explorer offers tons of data sources visualized using motion charts.
• TrendCompass is a complex Flex-based motion chart tool. It offers all the functionality of the Google Gadget (and more), but little of the usability.
• Tableau Public can create bubble charts with the ability to “scroll” through time.

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### Memorable or Actionable or Both.

Recently, I saw the largest concentration of iPad users in the world, controlled a computer screen with my eyes, and learned about our looming robotic future. No, Apple doesn’t have a technology lab on the moon, but I did attend CHI 2010 (short for Computer Human Interaction – the entire program along with papers and authors are referenced here). I left with a bit bigger toolkit and plenty of research to consider further. One such effort investigating chart junk has been reviewed by EagerEyes’ Robert Kosara. I share his enthusiasm for research in visualization, but let’s look more closely at some issues the paper raises and consider how these findings fit into the goals of visualization.

Nothing gets information visualization designers’ feathers more ruffled than the thought of junky charts being more desirable than “Tufte-compliant” charts. I was skeptical, to say the least, in attending a presentation by Scott Bateman for a paper entitled, Useful Junk? The Effects of Visual Embellishment on Comprehension and Memorability of Charts. (The title is a bit misleading in that the paper is really about embellishments and illustration – not so much traditionally poor structural graphics often considered common “chart junk.”)

(Example of embellished vs. plain chart with same data, from the paper)

The aesthetic treatment of data presentation is a long-time debate, and Scott came all the way from Canada to answer the question: Should we use chart junk? The answer is an emphatic “maybe.” The goal of the study was to look at interpretation accuracy and long-term recall, and the papers says,

our results question some of the premises of the minimalist approach to chart design.

#### Make charts Memorable.

Skipping the gritty details of the study, here are the findings of a provoking illustration with data embedded compared to an boring, “plain” chart:

• more memorable over the long-term;
• perceived as having more value and sense of chart bias; and
• most enjoyable and easiest to remember.

More memorable is better, right? The question we should be asking is, better than what. Of course, more memorable is better than less memorable, but at what cost? And what do we really want people to remember? It’s doubtful the best way to drum up interest in data is by making it light up and do a dance to feed the public’s already marketing heavy information diet.

Your data as is mostly marketing if it looks like this: GOOD.is | The Richest and Poorest Neighborhoods

 Fully embellished charts Pros Cons Graphics and illustration heavy Draws attention, memorable imagery It looks and feels glossy so people will treat it with the bias of a magazine or commercial TV ad Little data depth Little analytical thinking needed, wider audience Non conclusive, likely not actionable Endless diversity Creative exploration Few standards, wild chart organization Production costs Little research, relatively cheap Illustration / Graphic artist talent required

Perhaps one’s attention is more likely to be drawn to these embellished charts if they are engaged in an entertaining or passive ritual, like watching TV, browsing the web, or shuffling through a newspaper. Perhaps they get the same personal impact as the funny pages. We should consider a greater sense of bias or value message is introduced through this style of data presentation (as confirmed by the study), and that can be detrimental to a viewer’s trust. It isn’t that imagery doesn’t have a place in the same conversation with data, but there are better ways to go about drawing attention than applying illustrations to data points.

In the data presentation arena, we definitely want data to be memorable, but even more so we want data to be actionable; therefore, valuable data remains the attraction.

#### Make charts Actionable.

Would you say this graphic is more or less plain than the example “plain” chart taken from the research paper earlier in the post? Would you say its more or less actionable?Â

A chart is actionable if it answers enough questions of its viewer to instigate a meaningful decision or reaction to information presented. Therefore, charts are only actionable when the right information is presented to the right people with the right visual communication.Â

Edward Tufte describes the use of this graphic by the New York Times that accompanied a data dense table along with a news column on the subject. It’s a simple point: in order to present meaningful, compelling, or personally motivating information, there either needs to be exactly the right data presented, given the context of the data and person, or enough dimensions and slices of data to be meaningful to a broader range of questions and needs. Supporting textual content always helps to tell the story, which builds the viewers mental model – thereby, making the data more understandable.

 Non-embellished charts Pros Cons No non-data graphics Minimized distractions from data focus, no graphics or imagery suggesting bias, Teachable, fundamental guidelines little visual appeal unless the data density is high (which can feel overwhelming) Sufficient data-depth emphasis Actionable information Requires more patience or experience from viewer. Production costs No illustration talent required Research time and resources required, relatively expensive

The problem with embellishments as a primary style for getting the public engaged with data is that it continues to suggest that truly understanding how data impacts their world is beyond common thought or interest. The dimensions are minimal and value statements dominate.

But value statements aren’t always bad. Sometimes when you’re saying so little with an information-starved chart, its better to come out and say the point you’re trying to make with a single data point. Like this beautiful example from goingtorain.com

Its Communications 101: say what you’re going to say, say it, and say what you said. When the information is somewhat clearly target and not exploratory in nature, this frank approach is often more effective. Embellished charts commonly stand alone with no supporting, meaningful story or conclusion. If the information is valid and valuable enough to be published, there should at least be enough effort to find and integrate a reliable source with more info to answer questions where the chart data left the viewer wondering.

#### Make charts Both.

When it comes to complicated information, stop treating it as if it can be polished nicely into a single chart and that will be sufficient to create understanding, motivation, and action. Charts make data visible and play off our innate human need to create a mental image of the information story we’re presented with. We need both visual attraction / definition and concrete factual data.

Illustration, graphics, and photography trigger emotion and interest in our right brain. They give us a chance to associate ideas and create mental connections to make sense of the world. Our right brain needs “embellishment” thinking to make connections.

Meanwhile, our left brain needs values, raw facts, and the ability to measure worth. Our left brain needs “plain chart” thinking to determine the cause and effect of connections; its interested in thinking about what really matters and impacts things at this moment.

There are few visualizations that even begin to approach the balance between imagery and data.

Example 1. The Tweet Tracker visualization is at least on the right track. One may say here that illustration is used as data points, but I would suggest the technique is appropriate here because the imagery is uniquely matched, within context, as another dimension to its data category.

Winter Olympics Tweet Tracker by Stamen.

Example 2. Embellishments come in diverse forms. You may have seen this presentation Al Gore gave on global warming. Notice what happens at 9:08 in the video as Al continues his commentary while riding a lift on stage up the side of the chart. Do you hear the background laughter? This kind of laughter is good. You know you’re audience is engaged. Duarte Design designed an embellished visual here to grab people’s attention and make the point memorable – alongside the data chart. This engaging visual device makes the data more memorable because the data is still the center of attention.

Visualization is simply the best language to create meaningful connections between data, thereby making it valuable. All charts are related to visualization, whether its good design or not. The conversation of whether embellishments are good or bad depends on many things, but the real question we should be asking is whether they are making your data more or less valuable. It is a fine thing to attract interest to data, but not when that is a device to overlook the real care needed in preparing sufficient information. Plain charts are fine also, but likely only for quick personal projects in excel where a mental model of the data connections are already well understood.

I’m thankful for Scott’s work with his colleagues on this research, and for people like Robert who also promote appreciation for the much needed research in visualization. The theme of graphical embellishment is thrown around so much in the visualization community that it rarely receives careful deliberation, and this paper starts a purposeful conversation. However, there is a long way in working towards conclusive goals.

Other visualization related papers presented at CHI 2010:

• Useful Junk? The Effects of Visual Embellsihment on Comprehension and Memorability of Charts.
• ManyNets: An Interface for Multiple Network Analysis and Visualization
• Individual Models of Color Differentiation to Improve Interpretability of Information Visualization
• High-Precision Magnification Lenses
• Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design
• Integrating Text with Video and 3D Graphics: The Effects of Text Drawing Styles on Text Readability
• Animated UI Transitions and Perception of Time — a User Study on Animated Effects on a Mobile Screen
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### Better Know a Visualization: Parallel Coordinates

(With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.)

#### What is a parallel coordinates chart?

Parallel coordinates is a visualization technique used to plot individual data elements across many dimensions. Each of the dimensions corresponds to a vertical axis and each data element is displayed as a series of connected points along the dimensions/axes.

Jon Peltier’s chart of baseball players below offers a simple example.

Each line corresponds to a player with performance plotted across four characteristics. Two players have been highlighted to compared values.

Parallel coordinates was invented by Alfred Inselberg in the 1970s as a way to visualize high-dimensional data. These charts are more often found in academic and scientific communities than in business and consumer data visualizations. This isn’t too surprising as parallel coordinate charts can become very dense and difficult to comprehend. Stephen Few has a typical reaction (PDF):

The first time that I saw a parallel coordinates visualization, I almost laughed out loud. My initial impression was “How absurd!” I couldn’t imagine how anyone could make sense of the dense clutter caused by hundreds of overlapping lines. This certainly isn’t a chart that you would present to the board of directors or place on your Web site for the general public. In fact, the strength of parallel coordinates isn’t in their ability to communicate some truth in the data to others, but rather in their ability to bring meaningful multivariate patterns and comparisons to light when used interactively for analysis.

Mr. Few’s final point is right on: with the application of interactive highlighting, filtering, and roll-over detail, parallel coordinate charts can reveal interesting stories in your data.

#### What problem does this solve?

For most standard charts, there are only so many dimensions you can effectively show. A typical progression of charts by dimensions goes like this:

 Dimensions Chart type 2 Scatterplot 3 Bubble chart 4 Bubble chart with colors 5 Bubble chart with colors and animation

And now you’ve pretty much made an indecipherable graphic. That’s where parallel coordinates can help in showing many dimensions, limited only by horizontal space.

Like all good visualizations, parallel coordinates can also show both the forest and the tree. The big picture can be seen in the patterns of lines; individual lines can be highlighted to see detailed performance of specific data elements.

#### What to watch out for when using parallel coordinates?

With its power to visualize multi-dimensional data, why aren’t parallel coordinate chart more popular? Here are a few of the issues:

• Large data sets create a lot of visual clutter. More from S. Few: “Most of us who have used parallel coordinates to explore and analyze multivariate data would agree that meaningful patterns can be obscured in a clutter of lines, especially with large data sets.”
• The order of the axes impacts how the reader understands the data. Relationships between adjacent dimensions are easier to perceive than between non-adjacent dimensions.
• As the axes get closer to each other it becomes more difficult to perceive structure or clusters.
• Depending on the data, each axis can have a different scale, which is difficult to display and for the reader to absorb.
• Lines may be mistaken for trends or change in values even thought they are only used to show the connected relationship of points.
• Then there is stuff like the following that can give the visualization technique a bad name:

#### Parallel coordinates in practice

Protovis: In this example, hundreds of cars can be quickly compared by filtering along any dimension. Click and drag along the red rule for a given dimension to update the filter.

Junk Charts revised a New York Times graphic to come up with this take on a parallel coordinates chart:

Advisor Solutions’s Parabox solution goes beyond the parallel coordinate lines to also show information about the distribution of values by dimension.

#### Do it yourself with other tools

• Macrofocus uses parallel coordinate visualizations extensively in their products (InfoScope, SurveyVisualizer)
• GGobi is an open source visualization program for exploring high-dimensional data”
• FluxViz is a simple cross-platform tool that uses parallel coordinates for the visualization of high-dimensional spaces”

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### Better Know a Visualization: Small Multiples

(With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.)

#### What is a small multiple?

Small multiples are a visualization concept introduced by Edward Tufte. He described them as:

“Illustrations of postage-stamp size are indexed by category or a label, sequenced over time like the frames of a movie, or ordered by a quantitative variable not used in the single image itself.”

In other words, small multiples use the same basic graphic or chart to display difference slices of a data set. Small multiples can show rich, multi-dimensional data without trying to cram all that information into a single, overly-complex chart. Small multiples go by many names, including Trellis Chart, Lattice Chart, Grid Chart, and Panel Chart. I would even argue that sparklines are a simpler, smaller cousin of small multiples.

#### What problem does this solve?

Small multiples offer a few valuable features:

1. They allow for the display of many variables with less risk of confusing your audience. Trying to display three or more variables in a single chart is a challenge Stephen Few calls overplotting (PDF).
2. The reader can quickly learn to read an individual chart and apply this knowledge as they scan the rest of the charts. This shifts the reader’s effort from understanding how the chart works to what the data says. That’s a worthy goal in all data presentation.
3. Small multiples enable comparison across variables and reveal the range of potential patterns in the charts.

#### What to watch out for when using small multiples?

Like any visualization, there are many ways to mess up and undermine the value of the data presentation:

• Placement of the small multiples charts should reflect some logical order, e.g. dimensional matrix, geography, or time. This helps the user quickly find the charts that are interesting to them.
• Small multiples should share the same measures, scales, size, and shape. Changing one of these factors undermines ability for people to re-use their understanding of the chart.
• Simplicity of the chart is critical. Users should be able to process information across many of these charts. The following small chart from the New York Times works as an individual graphic; when shown in “postage stamp” size across 20 cities, this chart is too data-dense.

#### Small multiples in the practice

Andrew Gelman’s analysis of public support for vouchers, broken down by religion/ethnicity, income, and state.

Trilogy Meter by Dan Meth shows movie enjoyment by sequel.

Jorge Camoes’ small multiple graphic for unemployment data

#### Do it yourself in Excel

• Jon Peltier creates small multiples in Excel
• Kelly O’Day offers numerous tutorials and example workbooks on how to create “Panel charts” in Excel
• Juice provides a few Excel tricks for replicating a chart across a data set

#### Do it yours with other free tools

• Tableau Public. Nobody has small multiples more baked into their DNA than the folks at Tableau.
• Many Eyes for map-based small multiples.

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### External Interface, Internet Explorer and Time Wasted

I leave this message for you in the case that you encounter a similar obstacle to your productivity. Internet Explorers 6, 7 and 8 do not like uppercase letters (or hyphens) in the object ID when embedding a swf into the page if you then plan on using the code with ExternalInterface. They do not like it with SWFObject or with the simple old fashioned object tag. The solution was elusive, but there is some help on the ghettocooler.net Treasure Trove.

The following code will work in Firefox and Safari, but will mysteriously have a problem in Internet Explorer:

```<script language="javascript">
function submitLemon() {
var swf = document.getElementById("myContent");
swf.iCanHasParameter('lemon');
}

swfobject.embedSWF("movie.swf", "myContent", "800",
"100%", "9.0.124", null, {}, {bgcolor: '#ffffff'});
</script>
```

It will however work if you replace the upper case letter with its lowercase equivalent:

```<script language="javascript">
function submitLemon() {
var swf = document.getElementById("mycontent");
swf.iCanHasParameter('lemon');
}

swfobject.embedSWF("movie.swf", "mycontent", "800",
"100%", "9.0.124", null, {}, {bgcolor: '#ffffff'});
</script>
```
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