telling compelling stories with numbers

Perceptual Edge 11/21/2006

Copyright © 2006 Stephen Few 1

Telling Compelling Stories with Numbers

Stephen Few, Perceptual Edge

Data visualization for enlightening communication.

Stephen Few, Principal, Perceptual [email protected]

(510) 558-7400

I’m going to talk about communication—a particular type of communication that tells the stories that are contained in numbers. Much of the information that the CDC deals with to do its job is quantitative in nature. It measures what’s going on in the country regarding health and threats to health. This is important information that needs to be expressed and understood. Unfortunately, it is not always expressed as clearly as it could be—as clearly as it should be.I have good news for you. The skills required to tell the important stories contained in your numbers are easy to learn, but learn them you must or the stories will wither. We have a communication problem, and here’s the story of how we arrived here.



In 1786, an iconoclastic Scot – William Playfair –published a small atlas that introduced or greatly improved most of the quantitative graphs that we use today.

Prior to this, graphs of quantitative data were little known.



Today, 220 years later, partly due to the arrival of the PC, graphs are commonplace, fully integrated into the fabric of modern communications.

Surprisingly, however, Playfair’s innovative efforts – sprung from meager precedent – are still superior to most of the graphs produced today.



A powerful language,with such promise,

is largely being wasted!



Despite a recent explosion in available data, most lies stagnant in ever-expanding pools.

Data is useless until we understand what it means and can clearly communicate that meaning to those who need it, those whose decisions affect our world.



We are awash in data.

“Just show me the numbers!”

We live in the so-called “information age.” So much information is available, without proper care and skill we can easily drown in it.

People need information to make decisions. They don’t need reams of data; they need straightforward answers to their questions. They just want to see the numbers right now!



We’re getting better at handling numbers –

Right?

Wrong! We’re getting worse. Despite great progress in our ability to gather and warehouse data, we’re still missing the boat if we don’t communicate the numbers effectively. Contrary to popular wisdom, information cannot always speak for itself.



Quantitative information is primarily communicated through tables and graphs.

But few communicate effectively. Why?

Why? Few people are trained.Why? Few people recognize the need.Why? Few examples of good design exist to expose the problem.

“Poor documents are so commonplace that deciphering bad writing and bad visual design have become part of the coping skills needed to navigate in the so-called information age.” Karen A. Schriver, Dynamics in Document Design, John Wiley & Sons, Inc., 1997.

“The public is more familiar with bad design than good design. It is, in effect, conditioned to prefer bad design, because that is what it lives with. The new becomes threatening, the old reassuring.” (Kevin Mullet and Darrel Sano, Designing Visual Interfaces, Sun Microsystems, Inc., 1995 – quoting Paul Rand, Design, Form, and Chaos)

Effective communication is not always intuitive – it must be learned.



Intentional deceit is no longer our biggest problem

In 1954, Darrell Huff wrote his best-selling book about how people often intentionally use graphs to spread misinformation, especially in favor of their own products or causes. Today, vastly more misinformation is disseminated unintentionally because people don’t know how to use charts to communicate what they intend.



Example #1

I found this table on the Web site for Bill Moyers’ public television show “Now”. I felt that it provided important information that deserved a better form of presentation. In this case the story could be told much better in visual form.



Example #1 - Improved

This series of related graphs tells the story in vivid terms and brings facts to light that might not ever be noticed in the table.



Example #2

Here’s an example that I pulled from one of your reports. This is typical of many graphs today—all dressed up, but overdressed to the point of distraction.




Here’s the same information, presented in a way that tells the story plainly and clearly.



Example #3

Here’s another public health example from the state of Maine. This graph contains important patterns that are difficult to discern due to clutter.




But in this graph the pattern is crystal clear.



Example #4

If you were asked to tell the story contained in this display, it would take you some time to put it together before you could even begin to explain it to others.




In this display of the same information, however, the story is clear and aspects of the story that weren’t apparent in the pie charts jump right out.



What do tables and graphs help us do?

“Above all else show the data.”Edward Tufte

Thinkand

Communicate

This Edward R. Tufte quote is from his milestone work, The Visual Display of Quantitative Information, published by Graphics Press in 1983.



The two fundamental challenges of data presentation1. Determining the medium

that tells the story best2. Designing the visual components

to tell the story clearly

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EastWestNorth

From

toRevenue

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20

40

60

80

100

1st Qtr 2nd Qtr 3rd Qtr 4th QtrYear 2003

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1,592,36777,211Total

845,98442,374Beverage

746,38334,837Food

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or

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Marketing

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andwhich kind?

Either

1. You begin by determining the best medium for your data and the message you wish to emphasize. Does it require a table or a graph? Which kind of table or graph?

2. Once you’ve decided, you must then design the individual components of that display to present the data and your message as clearly and efficiently as possible.



Why use graphs?

Graphs show relationships between values by giving them shape.

The old saying, “A picture is worth a thousand words,” applies quite literally to quantitative graphs. By displaying quantitative information in visual form, graphs efficiently reveal information that would otherwise require a thousand words or more to adequately describe.

“[When] we visualize the data effectively and suddenly, there is what Joseph Berkson called ‘interocular traumatic impact’: a conclusion that hits us between the eyes.” William S. Cleveland, Visualizing Data, Hobart Press, 1993.

Take a moment to identify the various types of information that are revealed by the shape of the data in this graph.



Can you see trends, patterns and exceptions in this table of numbers?

Tables work great when you need to look up individual facts, but they don’t reveal trends, patterns, and exceptions very well. This particular table could be improved through some simple formatting changes to make it easier to connect the data from column to column,…



Graphs make trends, patterns and exceptions visible

Now, however, by expressing this same information visually, thereby giving shape to the data, the trends come alive.



But graphs must be properly designed to tell the story effectively

Faster rate of increase

Normalscale

Logscale

Not just any graph will do, however. The graph must be designed properly to display the intended message. In this case, because there is such a big difference between the total population and that portion of the population that fall into the highest age groups, a normal scale does not allow us to compare rates of change. A logarithmic scale, however, supports this nicely. With a log scale, the same rate of change equals the same slope of the line. Now we can see that the oldest portion of the population has grown at a faster rate than the population as a whole through the year 2000 and will resume this faster rate from 2004 through 2040.



Quantitative messages always involve relationships.

The stories contained in numbers all revolve around relationships. The stories contained in the numbers that measure public health, in fact, involve six fundamental types of relationships. If you know the relationship that you’re trying to communicate graphically and you know the best ways to graphically encode that relationship, you possess a simple vocabulary that anyone can learn to communicate numbers effectively.Allow me to introduce the six relationships that you should get to know.



Relationship? Time Series

Time

A time-series graph has a categorical scale that represents time, subdivided into a particular unit of time, such as years, quarters, months, days, or even hours. These graphs provide a powerful means to see patterns in the values as they change through time.



Could this pattern of change be displayed more clearly?

Here’s an attempt to display a time-series relationship regarding HIV diagnoses, but the trend and patterns could be much more clearly displayed.



Bars and lines tell time differently

Here’s the same exact data presented in two ways: one using bars and one using a line. If you want to show trends and patterns of change through time, lines do this job much clearer than bars.



We don’t picture time as vertical

Here’s a graph that shows change through time arranged vertically from top to bottom as a sequence of bars,…



Always show time horizontally, left to right

…but in western culture we don’t think of time as vertically, but as horizontal, marching from left to right.



RankingRelationship?

1

2

3

4

5

6

7

8

Ranking graphs show the sequence of a series of categorical subdivisions, based on the measures associated with them. When values are ranked, they are much easier to compare, because those that are closest in size are near one another.



Unsorted values are difficult to compare

In this display of trauma registry injuries by county, notice how much more difficult it is to compare the values and to get a sense of rank when they aren’t sequenced to reveal the ranking.



A simple sort adds meaning and clarityUnsorted Sorted

Here’s the same data, with the counties arranged alphabetically on the left and by number of injuries on the right. If the purpose of the display is to look up individual values, which is the only thing that alphabetical order supports, a table would work much better. The ranking display on the right, however, tells a story.



Part-to-WholeRelationship?

+ + + =100%

A part-to-whole graph shows how the values associated with the individual items in a full set of items relate to the whole and to one another.



Pie chart displays of part-to-whole relationships are difficult to read

Part-to-whole relationships are typically displayed as pie charts, but they don’t communicate very effectively. If you want to see the order of items and to compare the size of one to another, with this display you would struggle,…



A bar graph tells the story clearly

…but with this simple bar graph the story is told simply and clearly.



DeviationRelationship?

A deviation graph shows how one or more sets of values differ from a reference set of values, such as the deviation between expected and actual cases of flu shown here.



Don’t force people to calculate differences in their heads

When people need to see the differences between things, show them the difference directly, rather than showing them the two sets of values and forcing them to build a new picture in their heads of how they differ. The difference between the median annual household income in Utah and in the U.S. as a whole isn’t as easy to see in this graph,…



Display deviations directly

…as it is in this one, which directly expresses how household income in Utah differs from the U.S. as a whole in positive and negative dollars.



DistributionRelationship?

This type of distribution graph, called a frequency distribution, shows the number of times something occurs across consecutive intervals of a larger quantitative range. In a frequency distribution, a quantitative scale (in this case the range of people’s ages) is converted to a categorical scale by subdividing the range and giving each of the subdivisions a categorical label (“< 15”, and so on).



Histograms with multiple data sets can look cluttered

Here’s a graph that attempts to show the distribution of overweight children by grade separately for boys and girls, but doing it in this way results in clutter that makes the patterns difficult to segregate and compare.



But clutter can be easily eliminated

or

This pair of histograms—one for boys and one for girls—are arranged in a way that makes the patterns of each easy to see, yet still easy to compare.

Even better, by using lines rather than bars, the separate patterns can be shown in the same graph in a way that features the shape of the patterns and how they differ.



CorrelationRelationship?

A correlation graph shows whether two paired sets of measures vary in relation to one another, and if so, in which direction (positive or negative) and to what degree (strong or weak). If the trend line moves upwards, the correlation is positive; if it moves downwards, it is negative. A positive correlation indicates that as the values in one data set increase, so do the values in the other data set. A negative correlation indicates that as the values in one data set increase, the values in the other data set decrease. In a scatter plot like this, the more tightly the data points are grouped around the trend line, the stronger the correlation.



Interesting correlations abound

Exceptfor

Yemen

As literacy increases fertility decreases

I didn’t see many displays of correlations in the public health data that I reviewed before the conference, but many interesting correlations live in your data. In this example, I’m using WHO data to explore the correlation between adult literacy and fertility rate by country. A correlation clearly exists: higher literacy corresponds to lower rates of fertility. It is also clear from this display that the highest rates of fertility all occur in Africa (the blue circles), which the one exception of Yemen (the one green circle at the high end of fertility).



• Time-series• Ranking• Part-to-whole• Deviation• Distribution• Correlation

Common relationships in graphs

Almost every quantitative story involves one or more of these fundamental relationships, these comparisons, between numbers. The skills required to tell these stories clearly are easy to learn.



Multiple relationships can be displayed together

Sometimes the stories that numbers tell involve multiple relationships that must be shown together. Although these pie charts don’t do a good job of showing the changing relationship between these age groups from year to year, the line graph below works like a charm. It is now easy to see that in 2004 the number of people 75 years and older surpassed in population those from 65-74, and that it is projected that before 2050 those 75 and older will surpass the 55-64 year old age group as well.



Colors should not differ arbitrarily

To this simple vocabulary of quantitative communication, you can add a few simple expressions, such as proper use of color. You shouldn’t differ colors except to indicate differences in the underlying data. These five reasons for taking mental health days are meant to be compared, but the different colors in the top graph visually segregates them, discouraging comparisons, while the graph below visually encourages the comparison by using the same color for each of the bars.



Color intensity should not vary arbitrarily

Are males more important than females or the total?

It all makes perfect sense if you think about it. Here, the use of varying intensities of color along a gray scale, from black for the rate of asthma diagnoses among males, medium gray for rate among females, to light gray for the combined rate of both genders, visually suggests a ranking of importance, the darker the greater, which isn’t appropriate. Variations in intensity work wonderfully when you want to communicate a ranked relationship, but shouldn’t be used arbitrarily. Entirely different hues would do this job better.



Avoid clutter through simple visual design

Simple guidelines exist for keeping clutter to a minimum so people can clearly see and think about the data. Follow the advice of Thoreau when he wrote “Simplify, simplify, simplify.”



Clutter can sometimes be reduced by separating data sets into individual graphs

Here’s an example of a clutter reduction technique that I found in one of your data displays. Trying to tell this entire story with a single graph would have been cluttered beyond comprehension, but by separating it into two graphs that share the same quantitative scale and arranging them close together as you see here, the information is clear and though separated, the percentages of asthma attacks among children three to ten years of age in the top graph and children 11-17 years of age in the bottom graph can still be compared quite easily.



Animations can be used to show change in through time

Once you have the basics down, you can begin to tell more complex stories using more advanced techniques, such as this example from GapMinder, which uses an animated display to tell the story of how the correlation between the number of births per woman and mortality among young children throughout the world has changed through time.



You have a choice to make!

To communicate

or not to communicate,

that is the question!

This important health information relies on you to give it a clear voice.

The good news is, although the skills required to present data effectively are not all intuitive, they are easy to learn. This won’t happen, however, until you recognize the seriousness of the problem and commit yourself to solving it. It is up to you. It’s worth the effort. If the data is important enough to communicate, it is worth communicating well.

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