CHAPTER 30

Information Visualization

Because it is now easier to create visually pleasing graphics, because there is so much data to manage and understand, and because tools such as spreadsheets have finite limits in their ability to convey meaning, information visualization is a rich area, particularly linked as it usually is with ideas of information analytics. The ultimate challenge is less technical than cognitive: What is the creator trying to say, and what can he or she assume the reader will bring to the task of understanding both the data and its representation?

Over the past 30 years or so, the field of information visualization has evolved rapidly. Several factors help explain this development: supply, demand, and the audience. Today, each of these elements is changing rapidly, with broad consequences.

Supply

Both the quantity of information that needs to be processed (at both human and organizational levels) and the quality of the tools for managing and displaying it are increasing. To process this information, humanity makes increasing use of larger and higher-resolution displays as well as faster computing in devices not technically called computers: Sony PlayStations are wonders of graphics processing, for example.

Demand

More and more processes are being driven by digital information. Text and numbers gave way to pictures and sounds, then to video, and now to three dimensions. Where sailors once relied on stars and sun to navigate, differential GPS deliver resolution to a few meters nearly any-where on earth.

Audience

With the numbers of people accessing Internet and other information growing rapidly, translation into countless languages is often difficult. Visual displays, while not culturally universal, can help bridge across audiences that may be divided by language. Audiences are also expecting information in visual forms. In the United States, such expectations were traditionally set with election-night reporting; network news organizations often revealed new quantitative tricks to help hold audiences.

Definition and Purpose

For our purposes, a simple definition of visualization should suffice: “the use of computer-supported, interactive visual representations of data to amplify cognition.”¹ “Cognition” is a word with its own definitional issues: Do I want information to enlighten me about something I did not know (much in the manner of USA Today's random daily graphics), to answer a question I already have, or to provide context for some future action? The need is acute: According to a 2006 Harris survey, 75% of respondents said they had made a flawed business decision because of flawed data.²

Different visualizations serve different purposes. In the 1980s and 1990s, for example, graphics workstations were widely deployed in television studios as an arms race of weather-casting helped advance the state of the field. The weather people can boast results: Many more people can understand a Doppler radar image than can grasp binomial distributions, bid-ask spreads, or genetic mutations. As we shall see, geospatial information can bring with it built-in tools for understanding: Many people can find north on a map, while other cultures have yet to invent the concept of a map at all.

Historically, many visualizations have resulted from individuals who wanted to change their world: Florence Nightingale's striking maps relating conditions in military hospitals after the Crimean War helped persuade Queen Victoria to initiate broad reforms in public sanitation. More recently, the tools built by Hans Rosling and his colleagues at Gapminder are clearly aimed at increasing public awareness of international economics and other social issues. His visualizations have made this Swedish public health expert an Internet celebrity based on his TED* videos.³

Implicitly or explicitly, all visualizations answer roughly the same set of questions:

• How are similarity and difference conveyed?
• Is time static, as in a pie chart, or dynamic, as in many line graphs or slider-bar tools?
• How much granularity is sacrificed for “glanceability,” and how much does comprehension require investments of time and skill to deliver details?
• Is causality intended to be conveyed? Can it be unreasonably inferred?
• How does space function in the representation? Do proportions relate to some ground truth?
• What do the colors convey? Are colors used in standard (green = proceed safely; red = danger) or nonstandard ways?
• How are the reliability, timeliness, accuracy, precision, and other attributes of the underlying data represented in the visualization?⁴
• Computer visualizations, unlike paper ones, can be interactive. How easily can users learn to reposition, zoom in, reset a baseline, and otherwise get the visualization to respond to their actions?
• How likely is ambiguity? Why might the display be read three different ways by three different people?
• Independent of the graphical tool(s) chosen, does the visualization ask appropriate questions of the data?

Current State

There's no shortage of activity in the field of information visualization, samples of which can be experienced at Flex.org, VisualComplexity.com, or IBM's Many Eyes. Some work is truly stunning, and global centers of design leadership are emerging. Even so, the fundamental tension quickly becomes evident: Words like “galleries” suggest that we are viewing works of art, and in many instances the work should be in museums. But art by definition is unique; visualization has yet to be brought to the masses of managers, citizens, and students who have something to say but lack the tools, grammar, and training to create the beautiful. In short, the task of helping high levels of information visualization migrate from the artist to the worker remains unaccomplished. Three categories of work can be broadly identified: business intelligence tools, physical data visualization, and nonspatial data.

Business Intelligence Tools

As part of an ongoing trend toward consolidation of the enterprise application software market, visualization vendors have been incorporated into broader package or service offerings. In 2007, for example, Oracle bought Hyperion, IBM acquired Cognos, and SAP bought Business Objects. Combined with a strong offering from Microsoft, these players dominate the market, leaving only SAS and MicroStrategy as leading standalone contenders. Business intelligence (BI) software, while not particularly expensive or difficult to use, sometimes requires extensive cleanup work on the data to be analyzed. Between standardization on format and nomenclature, explication of assumptions across operating units, and shared storage and access at the enterprise level, the data warehousing and data mining components of a BI effort can run into millions of dollars before a single report is generated.

Executive dashboards have become common, but as noted visualization pioneer Edward Tufte notes, the metaphor itself introduces issues, as do words like “cockpit” or “command center.” As Tufte wrote in 2003, three basic questions must be addressed:

1. What are the intellectual problems that the displays are supposed to help with? The point of information displays is to assist thinking; therefore, ask first of all: What are the thinking tasks that the displays are supposed to help with?
2. How much can I trust the underlying information? It is essential to build systematic checks of data quality into the display and analysis system. For example, good checks of the data on revenue recognition must be made, given the strong incentives for premature recognition. Beware, in management data, of what statisticians call “sampling to please”—selecting, sorting, fudging, choosing data so as to please management.
3. What is the underlying business or other process I am managing? For information displays for management, avoid heavy- breathing metaphors, such as the mission control center, the strategic air command, the cockpit, the dashboard, or Star Trek. As Peter Drucker once said, good management is boring. If you want excitement, don't go to a good management information system.⁵

Physical Data Visualization

In 1931, Alfred Korzybski, a little-known philosopher, stated that “the map is not the territory.”⁶ Visualizations of physical phenomena are by definition abstractions, which means that interpretation, selection, and representation issues confront both makers and viewers of these visualizations. Compared to nonphysical phenomena, such as beliefs and risk, physical data is more straightforward, but significant considerations still inform the craft of visualization in the physical domain.

A tangible example is provided by Harry Beck's classical visualization of the London Underground first completed in 1931 and revised thereafter.⁷ Compared to the predecessor map, which is unremarkable, Beck's elegant abstraction was much more readable. It does suffer, however, from factual errors: Stations on different lines that are only a few hundred meters apart are shown as far away, leading riders to make two transfers and walk down long tunnels when, had they been aboveground, they could have seen how physically close the stations were.

Beck was an electrical draftsman by training, so the convention of a grid, allowing only 90- and 45-degree angles, was familiar to him from circuit diagrams. This artifice largely eliminated topography from the design brief, enabling him to concentrate instead on relative location along a given line: Distances between stations on different lines are often vastly out of proportion. Other key elements of the map's success—use of white space, color-based simplification, and modern design cues, including typography and limited symbolism—came from Frank Pick, the design-minded head of publicity for the London Underground. Weighing the importance of usability for the task at hand against the physical reality being represented is a key step, and the importance of Beck's contribution, as well as its limitations, is reflected in London Transport's decision to call the aid not a map but a Journey Planner.

Nonspatial Data

The task of conceptual data visualization is particularly difficult because good displays must create spatial representations of nonspatial data. While the concept of geographic information displays is relatively straightforward (using one or more variations of a map and presenting overlaid information on it), representation of nonspatial data can be more challenging. This is not new: Linear representations have conveyed time for millennia, and pie charts have become handy shorthand for subsets of a whole. Good maps remain the gold standard but enjoy the advantage of being a spatial representation of space rather than something less tangible. Consult a UK Ordnance Survey map or a fine nineteenth-century sample from any number of countries, and compare the quality to the nonspatial representations we encounter every day: USA Today visuals, executive dashboards, or owner's manuals. In most cases, the antique remains superior to the modern.

A powerful visualization known as a tree map has proven very useful for nonphysical data. Smart Money's Map of the Market,⁸ which visualizes daily stock market performance, is probably the best-known tree map. Information domains are formed of rectangles, each of which includes component entities, sized proportionately to population, market capitalization, risk, or other variables and available for inspection by mouse rollover. The visualization provides at-a-glance awareness of the state of the entire domain, a given sector, or individual components.

As the field evolves, information architects are challenged to create readable, repeatable conventions for such abstractions as risk, intellectual property (patents are a poor proxy for human capital, for example), and attitudinal information, such as customer satisfaction or confidence in government. Search and visualization have much to offer each other: Semiarbitrary lists of text-string matches remain hard to make visual. (Concepts are notoriously difficult to map spatially, in contrast to the elegance of the periodic table of the elements, to take a classic example.) Static social network maps, especially those of large social graphs, such as Facebook, quickly grow useless at their enormous scale. Attempting to show a dynamic social network using a graph (e.g., showing individuals as nodes in a two-dimensional graph and social interconnections as links between nodes) is a common representation but may not be satisfying because of limitations in showing the character of the social links (e.g., type of connection, strength, duration, interactions among multiple people, etc.) and other factors.

Looking Ahead

Some precedents may be useful. The history of sailing and shipping is rich with examples of various parties agreeing on conventions (port and starboard do not vary in different countries the way rules for automobiles do) and solving problems of conveying information. Shipping containers interlock regardless of carrier while being handled at countless global ports.⁹ The Beaufort wind scale arose from the need for agreed-on metrics for measuring wind aboard a ship, a matter of great practical importance. Even today, with satellites and computerized navigation systems, a Beaufort 0 (“Calm; smoke rises vertically”) is the same around the world, while a 12 (“Air filled with foam; sea completely white with driving spray; visibility greatly reduced”) spells disaster no matter how fast the hurricane winds are actually blowing.¹⁰

Musical notation presents another relevant example. Easily transportable, relatively impervious to language, and yet a representation (rather than a reproduction) of a performance, scores have the kinds of conventions that information visualization for the most part still lacks. At this point, good visualizations are featured in “galleries”—as befit works of art. They are created by artists and artisans, not by people who merely have something to say. At the risk of a strained analogy, we are at the stage where latter-day monks painstakingly hand-letter sacred texts, still awaiting both Gutenberg and the typewriter.

In his book Envisioning Information, Tufte suggests five tactics for increasing information density and “escaping flatland”—conveying more than two dimensions of meaning on paper. These are:

1. Micro/macro readings (relating both wholes and parts as distinct entities)
2. Layering and separation (often by use of color and graphic weight, as in a technical drawing)
3. Small multiples (to show often-subtle differences within elements of a system: A good lunar chart is an example)
4. Color and information (sensitivity to the palette as color labels, measures, represents reality, and enlivens)
5. Narratives of space and time (compressing the most powerful human dimensions onto flatland).¹¹

For all of the wisdom in these suggestions and the beauty of Tufte's examples—it's no accident that he's both a statistician and a working artist—good information visualizations remain rare. For information to convey meaning in standard, predictable ways, we need tools: “tools” as in grammars and lexicons rather than more widgets. Somewhat paradoxically, the path to better visualizations will be paved not with software but with words.

Notes

1. S. K. Card, J. Mackinley, and B. Schneiderman (eds.), Readings in Information Visualization—Using Vision to Think (San Francisco: Morgan Kaufmann, 1998).

2. Business Objects press release, “Information Workers Beware: Your Business Data Can't Be Trusted,” June 26, 2006, www.sap.com/about/newsroom/businessobjects/20060625_005028.epx.

3. Hans Rosling, www.ted.com/speakers/hans_rosling.html TED talks; www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html.

4. S. Deitrick and R. Edsall, “Making Uncertainty Usable: Approaches for Visualizing Uncertainty Information,” in M. Dodge, M. McDerby, and M. Turner, Geographic Visualization: Concepts, Tools and Applications (Hoboken, NJ: John Wiley & Sons, 2008), pp. 277–291.

5. Edward Tufte, “Ideas for Monitoring Businesses and Other Processes,” July 11, 2003 blog post at edwardtufte.com; www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0000bx.

6. Alfred Korzybski, “A Non-Aristotelian System and Its Necessity for Rigour in Mathematics and Physics,” paper presented at the American Mathematical Society at the New Orleans, Louisiana, meeting of the American Association for the Advancement of Science, December 28, 1931. Reprinted in Science and Sanity (1933): 747–761.

7. K. Garland, Mr. Beck's Underground Map (London: Capital Transport Publishing, 1994).

8. www.smartmoney.com/map-of-the-market/.

9. M. Levinson, The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger (Princeton, NJ: Princeton University Press, 2006).

10. S. Huler, Defining the Wind: The Beaufort Scale, and How a 19th-Century Admiral Turned Science into Poetry (New York: Three Rivers Press, 2004).

11. Edward Tufte, Envisioning Information (Cheshire, CT: Graphics Press, 1990).

*Originally founded by Richard Saul Wurman and now under the direction of Chris Anderson, TED (Technology Entertainment and Design) is a global set of conferences intended to disseminate “ideas worth spreading.” Free online videos of the conference talks are available at YouTube.