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INFORMATION VISUALIZATION

Stuart CardXerox PARC

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 0Example 1:Finding Videos with the FilmFinder . . . . . . 510Example 2: Monitoring Stocks with TreeMaps . . . . . . 512Example 3: Sensemakingwith PermutationMatrices . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513What Is Information Visualization? . . . . . . . . . . . . . . . . . 5 15Why Does Visualization Work? . . . . . . . . . . . . . . . . . . ,515Historical Origins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516

The Visualization Reference ModelMapping Data to Visual FormData StructureVisual Structur

Spatial substrate . . . . . . . . . . . . . . . . . . . . . . . . . . S20Mar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521

Expressiveness and E

Composed Visual StructuresSingle-axis composition . . . . . . . . . . . . . . . . . . . . .531

Double-axis composition . . . . . . . . . . . . . . . . . . . ,532Mark composition and case composition . . . . . . . ,533Recursive composition . . . . . . . . . . . . . . . . . . . . ,533

Interactive Visual Structures . . . . . . . . . . . . . . . . . . . . 534Dynamic queries . . . . . . . . . . . . . . . . . . . . . . . . . . ,534Magic lens (movable filter) . . . . . . . . . . . . . . . . . . .534Overview 1 detail . . . . . . . . . . . . . . . . . . . . . . . . .534Linking and brushing . . . . . . . . . . . . . . . . . . . . . .534Extraction and comparison . . . . . . . . . . . . . . . . 536Attribute explorer . . . . . . . . . . . . . . . . . . . . . . . . . ,536

Focus 1 Context Attention-Reactive

Filtering . , . . . .. . . . . . . . . . . . . . . . . , . . , . . . . . .Selective aggregation . . . . . . . . . . . . . . . . . . . . . . .537

VieHighlighting . . . . . . . . . . . . . . . . . . . . . . . . . . . '537Visual transfer functions . . . . . . . . . . . . . . . . . . . .537Perspective distortion . . . . . . . . . . . . . . . . . . . . .Alternate geometric

Sensemaking With ViKnowledge Crystallization

Acquire information . . . . . . . .Make sense of it . . . . . . . . . . . . . . . . . . . . . . . . . . . .Create something newAct on it . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .540

Levels for Applying Information VisualizatioAcknowledgment . . . . . . . . , . . . . . . . . . . . . . . . . .References


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INTRODUCTIONThe working mind is greatly leveraged by interaction with theworld outside it. A conversation to share information, a grocerylist to aid memory, a pocket calculator to compute squareroots-all effectively augment a cognitive ability otherwise se-verely constrained by what is in its limited knowledge, by lim-ited attent ion, and by limitations on reasoning. But th e mostprofound leverage on cognitive ability is th e ability to inventnew representations, procedures, o r devices that augment cog-nition far beyond its unaided biological endowment-and boot-strap these into even more potent inventions.

This chapter is about one class of inventions for augmentingcognition, collectively called "information visualization." Othersenses could be employed in this pursuit-audition, for exam-ple, or a multi-modal combination of senses-the broader topicis really informationperceptualization; owever, in this chap-ter, we restrict ourselves to visualization. Visualization employsthe sense with the most information capacity; recent advances ingraphically agile computers have opened opportunities to ex-ploit this capacity, and many visualization techniques have nowbeen developed. A few examples suggest the possibilities.

Example 1 : Finding Videos with th e FilmFinderThe use of information visualization for finding things is illus-trated by the FilmFinder (Ahlberg & Shneiderman, 1994a,

1994b). Unlike typical movie-finder systems, the FilmFinder isorganized not around searching with keywords, but ratheraround rapid browsing and reacting to collections of films in thedatabase Figure 26.1 shows a scattergraph of 2000 movies, plot-ting rated quality of the movie as a function of year when it wasreleased. Color differentiates type of movies-comedy fromdrama and the like. The display provides an overview, th e en-tire universe of all the movies, and some general features of thecollection. It is visually apparent, for example, that a good shareof the movies in the collection were released after 1965,but alsothat there are movies going back as far as the 1920s. Now theviewer "drills down" into the collection by using the sliders inthe interface to show only movies with Sean Connery that arebetween 1and 4% hours in length (Fig. 26.2).As the sliders aremoved, the display zooms in to show about 20 movies. It canbe seen that these movies were made between 1960 and 1995,and all have a quality rating higher than 4. Since there is nowroom on the display, titles of the movies appear. Experimenta-tion with the slider shows that restricting maximum length to2 hours cuts out few interesting movies. The viewer chooses thehighly rated movie, "Murder on the Orient Express" by double-clicking on its marker. Up pop details in a box (Fig. 26.3) givingnames of other actors in the movie and more information. Theviewer is interested in whether two of these actors, AnthonyPerkins and Ingrid Bergman, have appeared together in anyother movies. The viewer selects the ir names in the box, andthen requests another search (Fig. 26.4). The result is a new dis-play of two movies. In addition to the movie the viewer knewabout, there is one other movie, a drama entitled "Goodbye,

FIGURE 26 1 . FilmFinder overview scattergraph Courtesy University of Maryland


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FIGURE 26 2 FilmFinder scatterg raph zoom-in. Courtesy University of Maryland.

FIGURE 26 3 FilmFinder d etails on d em an d. Courte sy University of Maryland.

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FIGURE 26.4. FilmFinder retrieval by example Courtesy University of Maryland.

Again," made around 1960. The viewer is curious about thismovie and decides to watch it.

Information visualization has allowed a movie viewer in amatter of seconds to find a movie he or she could not have spec-ified at the outset. To do this, the FilmFinder employed severaltechniques from information visualization: (a) an overview ofthe collection showing its structure; (b) dyna?ni ueries, inwhich the visualization seems t o change instantaneously withcontrol manipulations; (c) zooming in by adding restrictionsto the set of interest; (d) details on demand, in which the usercan display temporarily detail about an individual object, and(e) retrieval by example, in which selected attributes of an in-dividual object are used to specify a new retrieval set.

Example 2: Monitoring Stocks with TreeMapsAnother example of information visualization is the TreeMap vi-sualization on the SmartMoney.com website,l which is shown inFig. 26.5Ca). Using this visualization, an investor can monitor

more than 500 stocks at once, with data updated every 15min-utes. Each colored rectangle in the figure is a company The sizeof the rectangle is proportional to its market capitalization.Color of the rectangle shows movement in the stock price.Bright yellow corresponds to about a 6% increase in price,bright blue to about a 696 decrease in price. Each business sec-tor is identified with a label like "Communications." Those itemsmarked with a letter N have an associated news item.

In this example, the investor's task is to monitor the clay'smarket and notice interesting developments. In Fig. 26.5(a), theinvestor has moved the mouse over one of the bright yellowrectangles, and a box identifying i t as Erickson, with a +9.28%gain for the day, has popped up together with other informa-tion. Clicking on a box gives the investor a popup menu for se-lecting even more detail. The investor can either click to go toWorld Wide Web links on news or financials, or drill down, forexample, to the sec tor (Fig. 26.5[b]), or down further to indi-vidual companies in the software part of the technology sector(Fig. 26.5[c]). The investor is now able to immediately note in-teresting relationships. The software industry is now larger than

www.smanmoneycorn


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26 Information Visualization 5 13

( c )FIGURE 26.5 TreeMap of daily stock prices. Courtesy SmartMoney com

Example 3 - Sense maki ng with Permutation M atricesAs a final information visualization example, consider the caseproposed by Bertin (1977/1981) of a hotel manager who wantsto analyze hotel occupancy data (Table 26.1) to increase her re-turn. In order to search for meaningful patterns in her data, sherepresents it as a permutation matrix (Fig. 26.7[a].A permuta-tion matrix is a graphic rendition of a casesx variables display. InPig 26.7(a), each cell of Table 26.1 is a small bar of a bar chart.The bars for cells below the mean are white; those above the barare black. By permuting rows and columns, patterns emergethat lead to making sense of the data.

In Fig. 26.7(a), the set of months, which form the cases, arerepeated to reveal periodic patterns across the en d of the cy-cle. By visually comparing the pairs of rows, one can find rows


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FIGURE 26.6 TreeMap of year-to-date stock pnces. Courtesy SmartMoney com

TABLE 26 1. Dgta for Hotel Occupancy (Based on Bertin ( I 97711981))ID VARIABLE )AN FEE MAR APR MAY ]UNE IULY AUG S E R OCT NOV DECI % Female 26 21 26 28 20 20 20 20 20 40 I5 402 % b c a 1 6 9 70 77 7 3 7 36 39 39 55 60 68 723 %USA 7 6 3 6 23 I4 I9 I 4 9 6 8 84 % South America 0 0 0 0 8 6 6 4 2 I2 0 05 %Europe 20 I5 I4 I5 23 2 7 22 30 27 I9 I9 I76 % M EastIAfrica I 0 0 8 6 4 6 4 2 I 0 I7 %Asla 3 I0 6 0 3 13 8 9 5 2 5 28 % Bus~nessmen 78 80 85 86 85 87 70 76 87 85 87 809 %Tour~sts 22 20 15 14 I5 13 30 24 13 I5 I3 20I 0 % D irect R e s e ~ a t ~ o n s 70 70 75 74 6 9 68 74 75 68 68 64 751 I %A gency Reservations 20 I8 I9 I7 27 27 I9 19 26 27 21 I5I2 %Air Crews I0 I2 6 9 4 5 7 6 6 5 I5 I0I3 %Under 20 2 2 4 2 2 1 I 2 2 4 2 5I4 %20-35 25 27 3 7 35 25 25 27 28 24 30 24 30I5 % 35-55 48 49 42 d8 54 55 53 5 1 55 46 5 5 43I 6 % O v e r 5 5 25 22 I7 I5 I9 I9 I9 19 19 20 I9 22I7 Price of rooms I63 I67 I66 174 I52 I55 I45 I70 15 7 174 165 156t 8 k n @ h of s ta y I 7 1 .7 I 7 I 9 1 I 9 2 1 54 I 6 1 ~ 7 3 I 8 2 1 6 6 I 4 4I9 % Occupancy 67 82 70 83 74 77 56 62 90 92 78 5520 Conventions 0 0 0 I I I 0 0 I I I I


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that are s~nlilar. hese are reorclered and grouped (Fig. 26.7[b]).By this means, ~t s discovered that there seem to be two pat-terns of yearly variation. One pattern 111 Fig, 26.7(b) is seniian-nual, clividing the year into the cold months of October throughApril and the warm months of May through September. Theother pattern breaks the year into fo~ w ~s t~nc tegions. We havethus found the beginnings of a schewza-that is, a fran~eworkIn terms of which we can encode the raw data and describe it111a more compact language, Instead of talking about the eventsof the year in terms of ~ndividualmonths, we can now talk interms of two series of periods, the semiannual one , and the fourdistinct periods. As we do so , there is a residue of informationnot incl~~decls part of our clesci-iptive anguage. Sensemakingproceeds by the onzission a?zd t-ecoding o/i?zjomzation intomore co~npact,fonnsee Resnikoff, 1989). This residue of in-formation may be reduced by finding a better or more articu-lated sclleina, or ~tmay be left as noise. Beyond finding the ba-SIC patterns In the data, the hotel manager wants to make senseof the data relative to a purpose: she wants to increase the oc-cupancy of the hotel, Therefore, she has also pernluted gen-eral ~ndicators f activity in Fig. 26 7@), such as % Occ~~pancyand Length of Stay, to the top of the diagram and put the rowsthat correlate w~thhese below them. Th ~seveals that Conven-tions, B~~s ine ss~n en,nd Agency Resel-vations, all of which gen-erally have to do w ~ t honvention business, are associated w~ t hhigher occupancy This 1nsig11t comes from the match in pat-terns i??ternal o the visual~zation;t also comes from notingwhy these variables ni~ ght orrelate as a consequence of factorsextenzal to the visual~zation. he also d~scovershat marked dif-ferences ex ~st etween the winter and summer g~ ~ e s t suringthe slow periods. In winter, there are more local g~~es t s ,omen,and age differences. In summer, there are more foreign tourlstsand less variation in age.

This visualization was L I S ~ ~ L I Ior sensenialcing on hotel oc-cupancy data, but ~t s too complicated to communicate thehigh points. The hotel manager therefore creates a s~mpli fieddiagram, Fig. 26 7(c). By graymg some of the bars, the m a npoints are more 1-ezd11~raspable, while still preserving thedata relations. A December convention, for example, does notseem to have the effect of the other conventions in bringing inguests. It is shown in gray as residue in the pattern. The hotelmanager s~~ggestsoving the convention to another month,where it might have more effect on increasing the occupancy ofthe hotel.

What Is Information Visualization?The FihnFi?zde~;he PeeMap, and the pe~wzutation nat?-ix 90-tel analysis a ~ ell examples of the use of infomiation visual-ization. We can deJne in/omzation visualization as "the useof computer-supported , mteractive, vis~ial epmenta tlons ofabstract data in order to amphfy cognition'' (Card, Mackinlay,&Shneiderman, 1999).

Information vis~~alizationeeds to be disting~~ ishedromrelated areas: scientijic z~isz~alizations like information visu-alization, but it is applied t o sc ient~fic ata ancl typically is~hysica lly ased, The starting point of a natural geome t~ic alsubstrate for the data, whether the human body or earth ge-

ography, tends to emphasize fincling a way to make v~s~blelieinvis~ble sayj velocity of air flow) w~ th in n exlstmg spatialframework, The chief problen~ or ~nfornmtion isualization, Incontrast, is often findmg an effectwe nlapping between ab-s~rac tntities and a spatial repl-esentation. Both informationvisualization and scientific vis ~~a lizat ~onelong to the broadei-f~eld f data graphics, wliich is the use of abstract, 110111-epre-sentational visual representations to amplify cognition. Datagsaphics, in turn , is part of info?-mationdesigfz,which con-cerns itself w~ t hxternal repl-esentations for amplifying cog-n~tion. t the l~ igl ~es tevel, we could considel- infol-niation cle-sign a part of external cognition, the L I S ~ S f the externalworld to acco~npli sh ome cognitive process. Characterizingthe purpose of information visualization as ainplgving cog?tz'-tio~zs purposely broad. Cognition can be the process of writ-ing a scientif~c aper or shopping on the Int ernet for a cellphone. Generally, ~t re fa s to th e in tell ect~ ~alrocesses inwhich information is obtained, transformed, stored, retrieved,and used. All of these can be advanced generally by means ofexternal cognition, and In particular by means of ~nformationvis~ializat~on.

Why Does Visualization Work?Visualization aids cogn~ tio n ot beca ~ise f some myst~cal u-periority of pc tures over other forms of thought and comnlu-nication, but rather because v~sual~zationelps the user bymaking the world o~ ~ t s i d ehe mncl a resource for th o~ ~g htnspecif~cways. We list six groups of these in Table 26.2 (Cardet al., 1999): Visualization amphfies cogn~t ion y (a) increasingthe memory and processing resources available to the users,(b) reducing search for informat~on,c) using visual represen-tations to enhance the detection of patterns, (d) enabhng per-ceptual inference operations, (e) using perceptual attentionmechanisms for monitoring, and (f) by encocling mformationin a manipulable mecl~unl.The F11mFincie1-, or examplej allowsthe representation of a large amount of data in a small spacein a way that allows patterns t o be perceived visually in thedata, Most important, the method of instantly responding 111the d~splayo the dynamic movement o f ~ l ~ e11clers allowedusers to rapidly explore the multidimensional space of f11ms.The TreeMap of the stock market allows mon~toring nd explo-ration of many equities. Again, much data is represented in lit-tle space. In tlxs case, the display nlanages the user's attent ion,drawing it to those equities with unusually large changes, andsupplying the means to drill down into the data to ~~n de rs ta ndwhy these movements may be happening . In the hotel man-agement case, the visual representation makes it easier to no-tice similarities of behavior in a ni~~ltidiniensionalttributespace, then to cl~ ~s te rnd laerepresent rliese. The f1na1prod-uct is a compact (ancl sin~ plif ~ed)epresentation of the origi-nal data that supports a set of forward clecisions. In all of thesecases, visualizat~onallows the user to (a) examine a largeamount of information, (b) le e p an overview of the wholewhile p~~rs uin gletails, (c) keep track of (by using the display asan external working memory) many things, and (d) produce anabstract representation of a sit~~at ionhrough the omission 2ndrecoding of inlormation.


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26. information Visuahzat~on 5 17

TABLE 26.2. How Informat~onVisualization Amplifies Cogn~tion1. Increased ResourcesH~gh-bandw~dthierarch~calnteraction

Parallel perceptual processmgOffloadwork from cogntive to

perceptual systemExpanded work~ngmemoryExpanded storage of ~nformat~on2. Reduced Searchbcal~tyf procesmg.

High data denstySpat~ally-indexed ddressmg3. Enhanced Recognition of PatternsRecogn~t~onnstead f recall

Abstraction and aggregat~on

Vsual schemata for organlzat1onValue, relationsh~p,rend4. Perceptual InferenceVisual representat~onsmake someproblems obv~ousGraph~cal omputations

5. hrceptuai Monitoring6. Manipulable medium

Human movmg gaze system part~t~onsmted channel capacity SO that ~tcomb~nes ~gh patialresolution and w~de perture In sensmg the visual environments ( L a r k ~ n&Simon,1987).Some attr~butesf v~sual~zationsan be processed In parallel compared to text, wh~chSserialSome cognitive ~nferences one symbol~cally an be recoded into ~nferences one w~th imple

perceptual operat~onsLark~n Simon, 1987)Visual~zat~onsan expand the working memory available for solving a problem (Norman,1993)Visual~zations an be used to store masswe amounts of mformatm In a quickly access~bleorm

(eg , maps)Visual~zations roup informat~on sed together reducmg search (Lark~n S~mon, 987)Visuahzat~ons an often represent a large amount of data In a small space (T~~fte,983)By grouping data about an object, v~sual~zat~onsan avo~d ymbohc labels (Larkin& Smon, 1987)Recogn~zingnformat~on enerated by a v~sualizationS easler than recalling that ~nformat~onythe userVisuahzat~ons imphfy and organlze ~nformat~on,upplying higher centers with aggregated Forms ofmformat~onhrough abstraction and select~ve mlsslon (Card,Robertson,& Mack~nlay, 991)-(Resn~koff,989)Visually organizing data by structural relat~onsh~pse g , by t~me)nhances patternsVisualizat~onsan be constructed to enhance patterns at all three levels (Bertin, 196711983)

Visuahzat~onsan support a large number of perceptual ~nferenceshat are very easy for humans[Lark~n Simon, 1987)

Visualizations can enable complex spec~ahzed raphical computations (Hutchins,1996)Visual~zat~onsan allow for the monltormg of a large number of potential events if the display IS

organ~zed o that these stand out by appearance or mot~onUnhke statlc d~agrams, isual~zat~onsan allow explorat~onF a space of parameter values and can

amplify user operationsSource Card, M a c k l n l a ~&Shne~derman,999

many colors9Sunlight enters fl-om the window at right and isrefracted into many colors by a prism. One of these colors canbe selected (byan aperture in a screen) and further refracted byanother prisnl, but the light stays the same color, showing thatit has already been reduced to its elementary components. As inNewton's illustration, early scientific and mathematical diagramsgenerA1y had a spatial, physical basis and were used to revealthe hidden, underlying order in that world.

Surprisingly, d~agrams f abstract, nonphysical mformationare appa m~tl yather recent. Tufte (1983) dates abstract clia-grains to (Playfa~r~786) in the 18th century Figure 26.9 is oneof Playfair's earliest diagrams. The purpose was to convincereaders that English Imports were catching up with imports.Starting with Playfar, the classical methods of plotting data weredeveloped-graphs, bar charts, and the rest.

Recent advances in the visual representation of abstract in-formation derive from several strands that became intertwined.In 1967, Bertin (1967/1983, 1977/1981), a French cartographerpublished his theory of TheSemidogy of Graphics. This theoryidentified the basic elements of diagrams and their combma-tion. Tufte (1983, 1990, 1997), from the fields of visual designand data graphics, published a series of seminal books that setforth principles for the design of data graph~cs nd emphasizedmaximizing the density of useful informat~on. oth Bertin's andT~fte'sheories became well known and influential. Meanwhile,within statistics, Tukey (1977) began a movement on exploratorydata analysis. His emphasis was not on the quality of graphical

presentationl but o n the use of pictures to give rapid, statistical~nsightnto data ~da tio ns.or example, "box and whisker plots"allowed an analyst to get a rapid characier~zation f data distri-butions. Cleveland and McGill (1988) wrote an influential book,Dy~a mic ~aphics07- tatisticsj explicating new v~sualizat~onsof data with particular emphasis on the vis~~alizat~onf mu1tid1-mensional data,

In 1985, NSF launched an initiative on scientzfic visualiza-tion (McCorn~ick& DeFantil 1987). The purpose of this initia-tive was to use advances in computer graphics to create a newclass of analytical instruments for scient~fic nalys~s, speciallyasa tool for comprehending large, newly produced datasets in thegeophysical and biological sciences. Meanwhile, the computergraphics and artificial ~ntelligence ommunities were interestedin the automatic design ofvisual presentations of data. Maclunlay's(1986a, 1986b) the m APT formalized Bertin's design theory,added psychophysical data, and used these to build a system fora~itomat~callyenerating diagrams of data, ta~loredor somepurpose. Roth and Mattis (1990) budt a system to d o more com-plex visualizat~ons, uch as some of those from T~ ~f t e .asner(1991) added a representation of tasks. This community was in-terested not so much in the quality of the graphics as in the au-tomation of the match between data characteristics, presenta-tional purpose, and graphical presentation. Finally, the userinterface community saw advances in graphics hardware open-ing the pos~ibil ity f a new generatlon of user interfaces. Thefirst use of the term "information v~sualization"was probably in


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5 18 CARD

FIGURE268. Newton's optics ihs trat ion [from Robin, 1992)

Rober tson, Card, and M acklnlay (1989). Early s t~ ~c ii esn this tion for trees. Robe rtson, Ca rd, an d Mackinlay (1993) presen tedc o l n n i ~ ~ n i t yo c ~ ~ s e dn ~ 1 x 1 -nteraction with large amounts of ways o r ~ ls in g nimation and distortio n t o interact wit11 largeinformation: Felner an d Beshers (1990) pre sen ted a methocl, data sets in a system called th e Information Visualizer, whichworlds wlthin worlds, for sh owin g six-c1in-ie1is10nal inancial d ata ~ised,foczis -con tex t d~sp lays o non~ miformly r esen t la rgein an immersive virr~la11-eahty hneiderman (1992) d e ve lo pd a am oun ts of information. T he en-iphasis for thes e studies wastechn ique ca l led "dynamic q~ ~ er ie s"or interactively selecting o n t h e m ean s fo r cog n it w e am p l i f ~ca t ~o n ,a ther than o n thesubse ts of data items a nd Tk eM aps, a space-filling 1-epresenta- q~1a11tyf the graphics presentations.


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26 Information Visualization 5 19

The remainder of this chapter will concentrate on the tech-niques that have been developed for mapping abstract infor-mation to interactive visual form to aid some intellectual task.The perceptual foundations of this effort are beyond the scopeof this chapter, but are covered in Ware (2000). Further detailson information visualization techniques are addressed in a textby Spence (2000). The classic papers in information visualiza-tion are collected in Card et a1 (1999).

TH E VISUALIZATION REFERENCEMODELMap ping Data to Visual FormDespite then- seeming variability, information visualizations canbe systematically analyzed. Visualizations can be thought of asadjustable mappings from data to visual form to the human per-ceiver. In fact, we can draw a simple Visualization ReferenceModel of these mappings (Fig. 26.10). Arrows follow from RawData (data in some idiosyncratic format) on the left, though aset of Data Tramfor~nationsnto Data Tables (canonical de-scriptions of data in a variablesx cases format extended to in-clude metadata). The most important mapping is the arrowfrom Data Tables to Visual Structures (structures that combinevalues an available vocabulary of visual elements-spatial sub-strates, marks, and graphical properties). Visual Structures canbe fur ther transformed by View Trumformations, such as vi-sual distortion or 3D viewing angle, until it finally forms a Vieiuthat can be perceived by human users. Thus, Raw Data mightstart out as text represented as indexed strings or arrays. These

sfoi-med into document vectors,normalized vec-e with dimensionality as large as the number of

Data

words Document vectors, in turn, might be reduced by multi-dimensional scaling to create the analytic abstraction to be vi-sualized, expressed as a Data Table of x, y, 2 coordinates thatcould be displayed. These coordinates might be transformedinto a Visual Structure-that is, a surface on an informationlandscape-which is then viewed at a certain angle.

Similar final effects can be achieved by transformations at dif-ferent places in the model: When a point is deleted from the vi-sualization, has the point been deleted from the dataset? Or is itstill in the data merely not displayed? Chi and Riedl (1998) calledthis the uiew-value distinction,and it is an example of just oneissue where identifying the locus of a transformation using theVisualization Reference Model helps to avoid confusion.

Information visualization is about the not just creation of vi-sual images, but also the interaction with those images in theservice of some problem. In the Visualization Reference Model,another set of arrows flow back from the human at the right intothe transformations tl~enlselves, ndicating the adjustment ofthese transformations by user-operated controls. It is the rapidreciprocal reaction between the generation of images by ma-chine and the selection and parametric adjustment of those im-ages, giving rise to new images that gives rise to the attractivepower of interactive information visualization.

Data StructuresIt is convenient to express Data Tables as tables of objects andtheir attributes, as in Table 26.3. For example, in the FilmFinder,the basic objects (or "cases") are films. Each film is associatedwith a number of attributes or variables, such as title, stars, yearof release, genre type, and so forth. The vertical double blackline in the table separates data in the table to the left of the line

Visual Form

Human Interaction

Raw Data: idiosyncratic formatsData Tables: relations (cases by variables) -1- meta-dataVisual Structures: spatial substrates + marks + graphical propertiesViews: graphical parameters (position, scaling, clipping, . )

FIGURE 26 10 Reference model for visualization (Card et a1 , 1999) Visualization can bedescribed as the mapping of data t o visual form that supports human interaction in a workplacefor visual sense making.


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520 CARD

TABLE 26 3 A Data Table About FilmsFilmID 230 105 54 0Title Goldfinger Ben Hu r Ben Hu rDirector Hamilton Wyler NibloActor Connery Heston NOvarroActress Blackman Harareet McAvoyYear 1964 1959 1926Length 112 2 12 133Popularity 7 7 8 2 7 4Rating PG G GFilmType Action Action DramaSource (Card et al , 1999)

from the metada ta, expressed as variable names, to the left ofthe line. The ho rizontal black line across th e table separates in-put variables from outp ut variables-that is, the table can bethou ght of as a function,/(input variables) = out put variables.

Year (FilmID = 105) = 1959.Variables imply a scale of me asur em ent, an d it is imp ortan t tokeep these straight. The most important to distinguish areN = Nom inal (are only = or # to other values)0 = Ordinal (obeys a < relation)Q = Quantitative (can do arithmetic on them)

A nominal variable N is an uno rdere d se t, such as film titles{Go ldfinger, Ben H ur, Star Wars}. An ordinal variable 0 is a tu-ple (ordered set), such as film ratings (G, PG, PG-13, R). A quan-titative variableQ is a num eric range, su ch as film length [ O , 3601.In addition to t he th ree basic types of variables, subtyp esrepresent impo rtant properties of the world associated withspecialized visual conventions. We so metim es distinguish thesubtype Quantitative Spatial (Qg) for intrinsically spatial vari-ables comm on in scientific visualization and the subtyp e Quan-titative Geographical (Qp) for spatial variables that are sp ecifi-cally geophysical coordinates. Othe r impo rtant subtypes aresimilarity me trics Quan titative Similarity (Q,,,), and the tempo-ral variables Quantitative Time (Q) and Ordinal Time (0,).Wecan also distinguish Interval Scales (I) (like Quantitative Scales,but since there is not a natural zero poin t, it is not m eaningful totake ratios). An example would be dates. It is meaningful to su b-tract two dates (June 5, 2002 - u n e 3 , 2002 = 2 days), but itdoes not make sense to divide them (June 5 , 2 0 0 2 + June 23 ,2002 = Undefined). Finally, we can define an UnstructuredScale (4, hose only value is present or absen t (e.g., an e rrorflag). The scales are summ arized in Table 26.4.Scale types can be altered by transformations, and this prac-tice is sometim es con venient. For exam ple, quantitative variablescan be mapp ed by data transformations into ordinal variables

by dividing them into ranges. For exa mple, film leng ths [ O , 3601minutes (type Q) can be broken into the ranges (type O),[ 0 , 3 6 0 ]minutes (SHORT, EDIUM ,ONG).

This comm on transformation is called "classing,"because itmaps values o nto classes of values. It creates an accessible sum-mary of the data, although it loses information. In the o the r di-rection, nominal variables can be transform ed to ordinal values

based o n their name. For exam ple, film titles {GOLD FINGE R,ENHUR, TARWARS}an be sorte d lexicographically

Strictly speakin g, we have n ot transfornlecl thei r values, but inmany uses (e.g., building alphabetically arranged dictionaries ofwords o r sliders in th e FilmFinder), we can act as if we had.Variable scale types form an im por tant class of metadata that,as we shall see, is im portant for prop er information visualiza-tion . We can add scale type to o ur D ata Table in Table 26.3 to -geth er with cardinality or range of the data to give us essentiallya code boo k of variables as in Table 26.5.

Visual Structu resInform ation visualization map s data relations int o visual form . Atfirst, it might seem that a hopelessly op en set of visual forms canresult. Careful reflection, however, reveals what every artistknows: that visual form is subject to stro ng co nstraints. Visualform that reflects the systematic mappmg of data relations ontovisual form , as in inform ation visualization or data g raphics, issubject to even more constraints. I t is a genuinely surprisingfact, therefore, tha t m ost information visualization involves themapping data relations o nt o only a half dozen c omp onents ofvisual encoding:1. Spatialsubstrate2 . Marks3 . Connection4. Enclosure5 . Retinalproperties, or6 . Temporal encoding

Of these mappings, the most powerful is how data aremapp ed on to the spatial substrate-that is, how data aremapp ed into spatial position. In fact, on e might say that the de-sign of an information visualization consists first of decidingwhich variables are going to ge t the spatial mappings, and th enhow the rest of th e variables are going to make d o with the cod-ing mappings that a re left.Spatial substrate. As we have just said , the m ost impo r-tant choice in designing an information visualization is whichvariables are going t o map on to spatial position. This decision


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26 infoimation Visualization 52 1

TABLE 26 4 . Classes of Data and Visual ElementsData Classes Visual Classes

Class Description Example Description ExampleU Unstructured (can only distinguish ErrorFlag Unstructured (no axis, indicated merely Dotpresence or absence) whether something is present or absent)N Nom i n a l (can only distinguish whether {Goldfinger, Nomina l Grid (a region is divided into Colored circletwo values are equal) Ben Hur, subregions, n which something can beStar Wars} present or absent)0 Ordinal (can distinguish whether one (Small, Medium, Ordinal Grid (order of the subregions is Alpha slidervalue is less or greater but not Large) meaningful)difference or ratio)I Interval (can do subtraction on values, 110 Dec 1978- Interval Grid (region has a metric but no Year axisbut no natural zero and can't compute 4 lu n 19821 distinguished origin)ratios)Q Quan t i ta t ive (can do arithmetic on values) 10-1 00) kg Quantitative Grid (a region has a metric) Time sliderQS -Spatial variables 10-201 m -Spatial gridQlll -Similarity 10-1 I -Similarity space0 9 -Geographical coord 130N-500NlLa -Geographical cood01 -Time variable 11 0-201 psec -Time grid

TABLE 26 5 Data Table with Meta-DataDescribing the Types of the VariablesTitle NDirector NActor NActress NYear QILength QPopularity QRating 0Filrnvpe NSource (Card et a l 1999)

GoldfingerHamiltonConneryBlackman19641 27 7PGAction

Ben HurWylerHestonHarareet19592 128 2GAction

gives importance to spatially encoded variables at the expenseof variables encoded using other mappings. Space is perceptu-ally dominant (MacEachren, 1995); it is good for discriminatingvalues and picking out patterns. It is easier, for example, to iden-tify the difference between a sine and a tangent curve when en-coded as a sequence of spatial positions than as a sequence ofcolor hues.Empty space itself, as a container, can be treated as if it hadmetric sti-LICLUI-e.ust as we classified variables according to theirscale type, we can think of the properties of space in terms ofthe scale type of an axis of space (cf. Engelhardt, Bruin, Janssen,& Scha, 1996).Axis scale types correspond to the variable scaletypes (see Table 26.4). The most important axes areU = Unstructured (no axis, indicated merely whether some-thing is present or absent)N = Nominal Grid (a region is divided into subregions, inwhich soniething can be present or absent)0 = Ordinal Grid (the ordering of these subregions is mean-ingful), andQ = Quantitative Grid (a region has a metric).

Besides these, it is convenient to make additional distinctionsfor frequently used subtypes, such as Spatial axes (Qs)Axes can be linear or radial; essentially, hey can involve anyof the various coordinate systems for describing space. Axes arean important building block for developing Visual Structures.Based on the Data Table for the FilmFinder in Table 26.5, we rep-resent the scatterplot of as composed of two orthogonal quan-titative axes:Year -+ Qy,Popularity Q,,.

The notation states that the Year variable is mapped to aquantitative X-axis and the Popularity variable is mapped to aquantitative Y-axis. Other axes are used for the FilmFinder querywidgets. For example, an ordinal axis is used in the radio but-tons for film ratings,Ratings O,,.

and a nominal axis is used in the radio buttons for film type,

Ma r k s . Marks are the visible things that occur in space.There are four elementary types of marks (Fig. 26.11):1. P = Points (OD),2. L = Lines (ID),3. A = Areas (2D), and4. V = Volumes (3D).Area marks include surfaces in three dimensions, as well as 2D-bounded regions.Unlike their mathematical counterpart, point and line marksactually take up space (otherwise, they would be invisible) andmay have properties such as shape.


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522 CARD

PointsLinesAreasVolumes

FIGURE 26 1 1 Types of marks

Connection and enclosure. Point marks and line markscan be used to signify other sorts of topological stru cture: graphsand trees. T hese allow showing relations amo ng objects with-ou t the geometrical constraints implicit in mapp ing variableson to spatial axes. Instead, we d raw explicit lines. Hierarchies an doth er relationships can also be enco ded using en closure. En-closing lines can be drawn a roun d sub sets of items. Enclosurecan be used for trees, con tour maps, and Venn Diagrams.

Retinal properties. Other graphical pro perties werecalled retinal properties by Bertin (1967/1983), beca use theretina of th e eye is sensitive to the m in dep en den t of position.For example, the FilmFinder in Fig. 26.1 uses color to enco de in-formation in the scatterplot:

This notation says that th e FilmType attribute for any FilmIDcase is visually mappe d o nto the color of a point.Figure 26.12 shows Bertin's six "retinal variables" separatedinto spatial properties and object prope rties according to whicharea of the brain they are believed to be processe d (Kosslyn,1994). They are sorted according to whether the property isgo od for expressing the ex tent of a scale (has a natural zeropoint), or wh ether its principal use is for differentiating marks(Bertin, 1977/198 1). Spatial position, discussed earlier as basicvisual substrate, is shown in t he position it wou ld o ccupy in thisclassification.Other graphical properties have also been p roposed for en -coding information. MacEachsen (1995) has proposed (a) crisp-ness (the inverse of the am ount of distance used to blend twoareas or a line into an area), (b) resolution (grain with raster orvector da ta will be d isplayed), (c) transparen cy, an d (d) arrang e-men t (e.g., different ways of configuring dots ). He furthe r pro-pos ed dividing color into (a) va lue (essentially, th e gray level ofFig. 26 12), (b) hu e, and (c) satu ration. Graphical prop ertiesfrom th e perception li terature that can supp ort preattentiveprocessing have bee n sug gested candidates for coding variablessuch as cu rvature, lighting direction, o r direction of motion (seeHealey, Boo th, and Enns, 1995). All of th ese sugges tions requirefurth er research.

Temporal encoding. Visual Stru cture s can also tem po-rally enco de information; hum an p erception is very sensitive

Spatial Object

Extent (Position) -I-\-\ Gray ScaleSize 0

FIGURE 26 12 . Retinal properties (Card et a ]., 1999). The sixretinal properties can be grouped by whether they form a scalewith a natural zero point (exte nd) and whether they deal withspatial distance or orientation (spatial)

to changes in mark position and the mark's retinal properties.We need to distinguish between temporal data variables to bevisualizedQt -+some visual represe?ztation

and animation, tha t is, mapping a variable into time,some variable -+ Time.

Time as animatio n could enc ode any type of data (w hether itwould b e an effective encodin g is ano the r matter). Time as ani-mation, of course, can be used to visualize time as data.

Q,+ Time.This is natural, but not always the most effective encod ing. Map-ping time d ata into space allows comparisons between two pointsin time. For example, if we ma p time a nd a fun ction of time intospace (e.g., time and accum ulated rainfall),

Qi Qx [make time be theX-axis]f (Q ) -+Qy, [make accumulated rairzfall be the lkccis,then we can d irectly experience rates as visual linear slope, andwe can experience changes in rates as cu rves. This enco ding oftime into space for display allows us to make much mo re pre-cise judgments abou t rates than would be possible from encod-ing time as time. An othe r use of time as anim ation is similar tothe u nstructured axes of space. Animation can b e used to en -hance t he ability of th e user to keep track changes of view orvisualization. If the user clicks on som e structure, causing it toenlarge and other structures to b ecom e smaller, animation caneffectively convey the change and th e identity of objects acrossthe change, whereas simply viewing the two en d states is con-fusing. Another use is to enh anc e a visual effect. Rotating a com -plicated object, for example, will induc e 3D effects (hen ce, allowbette r reading of som e visual mappings).


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26 Information Visualization * 523

Expressiveness and EffectivenessVisual mappings transform Data Tables into Visual Structure andthen into a visual image. This image is not just an arbitrary im-age. It is an image that has a particular meaning it must express.That meaning is the data relation of which it is the visual trans-formation. We can think of the image as a sentence in a visuallanguage (Mackinlay, 1986b) that expresses the relations in theData Table To be a good information visualization, the map-pings must satisfy some constraints. The first constraint is thatthe mapping must be expressive.A visualization is said to beexpressive if and only if it encodes all the data relations intendedand n o other data relations. The first part of expressivenessturns ou t to be easier than the second. Suppose we plot Film-Type against Year using the data-to-visual mapping in Fig. 26.13.The problem of this mapping is that the nominal movie ratingdata are expressed by a quantitative axis. That is, we have triedto map

In so doing, we have visually expressed all the data relation,but the visualization also implies relationships that do not ex-ist. For example, the 1959 version of Ben Hur does not havea film type that is five times greater than the 1926 version ofBen Hur, as implied in the figure. Wisely, the authors of theFilmFinder chose the mapping

Of course, there are circumstances in which color could beread as ordinal, or even possibly quantitative, but the miscella-neous order of the buttons in Fig. 26.1 discourages such an in-terpretation and the relatively low effectiveness of color for thispurpose in Table 26.7 also discourages this interpretation.

Table 26.6 shows the mappings chosen by authors of theFilmFinder. The figure shows the Data Table's metadata and data

Horror

SF

War

1920 1930 1940 1950 1960 1970Year

FIGURE 26.13. Mapping from data to visual form that violatesexpressiveness criterion

and how they are mapped onto the Visual Structure. Note thatthe nominal data of the PG ratings is mapped onto a nominalvisualization technique (colors) Note also, that names of direc-tors and stars (nominal variables) are raised to ordinal variables(through alphabetization), and then mapped o nto an ordinalaxis. This is, of course, a common way to handle searchingamong a large number of nominal items.Some properties are more effective than others for encodinginformation. Position is by far the most effective all-around rep-resentation Many properties are more effective for some typesof data than for others. Table 26.7 gives an approximate evalua-tion for the relative effectiveness of some encoding techniquesbased on (MacEachren, 1995). We note that spatial position is ef-fective for all scale types of data. Shape, on the o ther hand, isonly effective for nominal data. Gray scale is most effective forordinal data. Such a chart can suggest representations to a vi-sualization designer.

Taxonomy of Information VisualizationsWe have shown that t he properties of data and visual repre-sentation generally constrain the set of mappings that form thebasis for information visualizations. Taken together, these con-straints form the basis of a taxonomy of information visualiza-tions. Such a taxonomy is given in Table 26.8. Visualizations aregrouped into four categories. First are Simple Visual Struc-tures, the static mapping of data onto multiple spatial dimen-sions, trees, or networks plus retinal variables, depicted in Fig.26.10. Here it is worth distinguishing two cases There is a per-ceptual barrier a t three (or, in special cases, four) variables, alimit of the amount of data that can be perceived as an imme-diate whole. Bertin (1977, 1981) called this elementary unit ofvisual data perception the "image". Although this limit has notbeen definitively established in information visualization by em-pirical research, there must be a limit somewhere or else peo-ple could simultaneously compreh end a thousand variables.We therefore divide visualizations into those that can be com-prehended in an elementary perceptual grasp (three, or in spe-cial cases, four variables)-let us call these direct rea din gvisualizations-and those more complex than that barrier-which we call arti cula ted read ing visualizations, in whichmultiple actions are required.

Beyond the perceptual barrier, direct composition of data re-lationships in terms of 1, 2, or 3 spatial dimensions plus re-maining retinal variables is still possible, but rapidly diminishesin effectiveness. In fact, the main problem of information visual-ization as a discipline can be seen as devising techniques for ac-celerating the comprehension of these more complex n-variabledata relations. Several classes of techniques for n-variable visu-alization, which we call Composed Visual Structures, are basedon composing Simple Visual Structures together by reusingtheir spatial axes.A third class of Visual Structures-Jnterac-tiue Visual Structures-comes from using the rapid interactioncapabilities of the computer. These visualizations invoke theparameter-controlling arrows of Fig. 26.10. Finally, a fourth classof visualizations-Anention-Reactive Visual Structures-comesfrom interactive displays where the system reacts to user actions


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TABLE 26.6. Meta-Data and Mappings of Data onto Visual Structure in the FilmFinderData VisualForm

Visual Tra n&torrnationVariable Type Range Case, Case, Casei . . Type Structure Control Aiiocted

N Pants Button All (dsta115l*sort 0+sort 0

Comedy,M u s c ~ 1 1 1 1 1ction, Was, SF.Western, Horror}

AlpnasluuAlpliasluerAlpliaslderAlpnaslwuAM5Two -sid ~d interAdsRadiobuttonsRadD buttons

SetactcasesSetactcasesSelect casesSeta rt C35B5Clip rangeClip rangeClip rangeSelectcasesSelect casas

Source (Card e t al , 1999)

TABLE 26 7 Relative Effectiveness of Position and Retinal EncodingsSpatial Q 0 N Object Q 0 N

Extent (Position) Gray Scale Q 0Size Color Q C Differential Orientation Q Q Texture 0 Q

Shape 0 0 0Source (Card et al , 1999)

by changing the display, even anticipating new displays, to lowerto cost of information access and sensemaking to the user. Tosummarize,

cases), while another was used t o encode the objects' values.Ex-amples of this notation appear in Table 26.8 and Fig. 26.21,

I Simple Visual StructuresDirect ReadingArticulated Reading11. Composed Visual StructuresSingle-Axis CompositionDouble-Axis CompositionRecursive CompositionIll Interactive Visual StructureIV Attention-Reactive Viszial Structure

These classes of techniques may be combined LOproduce vi-sualizations that are more complex. To help us keep track of thevariable mapping into visual structure, we will use a simple short-hand notation for listing the element of the Visual Structure thatthe Data Table has mapped into . We will write, for example,[xYR~]to note that variables map onto the X-axis, the Y-axis, andtwo retinal encodings. [OX] will indicate that the variables mapont o one spatial axis used to arrange the objects (that is, the

SIMPLE VISUAL STRU CTURESThe design of information visualizations begins with mappingsfrom variables of the Data Table into the Visual Structure Thebasic strategy for the visualization designer could be describedas follows:1. Determine which varzables of the Analytic Abstraction tomap into spatial position in the Visz~al tructure2 . Combine these mappings to increase dimensionality (e g,by folding).3. Use retinal variables as a n overlay to ad d more dimemions4 Add controls for interaction.5 . Consider attention-reactive features to expand space andmanage aftention.We start by considering some of the ways in which variables canbe mapped into space.


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26 Information Visualization 525

TABLE 26.8. Taxonomy of Information Visualization Techniques

Direct Reading1-Variable [XIListsID object chartsID scatterplotsPie chartsFolded DimensionsDistributionsBox Plots2-Variable [XY]2D object charts2D scatterplots3-Variable[XYRI

Retinal scatterplotKahonen diagramsRetinal topographies[ (XVZIInformation landscapesInformation surfaces[XYZI3D scatterplots4-Variable[XYZR]3D retinal scatterplots3D topographies

-Barrier of Perception-Articulated Readingn-Variable[xYR"-~]2D Retinal scatterplots[xYzR"'~]2D Retinal scatterplots

TreesNode and link treesEnclosure treesTreeMapsCone treesNetworksTime

II. COMPOSEDVISUALTRUCTURES

Singles-AxisComposition [XYn]Permutation matricesParallel coordinatesDouble-AxisComposition [XY]GraphsRecursive C omposition2 D in 2 D [ ( x Y ) ~ ~ ]Scatterplot matricesProsection matricesHierarchical axesMarks in 2D [( xY) ~]Stick figuresColor iconsShape codingKeiin spirals3D in 3D [ ( x Y z ) ~ ~ ~ ]Wor lds within worlds

One-variable visual displays may actually use mo re th an o n e vi-sual dimension. This is because th e data variable or attribute isdisplayed agains t som e set of objects us ing som e mark and be-cause t he m ark itself takes space. Or, mo re subtly, it may be be-cause on e of the dimensions is used for arranging the objectsand ano the r for encoding via position th e variable. A simple ex-ample would be wh en the data are just visually map ped into asimp le text list as in Fig. 26.14(a). The o bjects form a seq uen ceon the Y-dimens ion , and t he width of the marks ( the t ex t de -scriptor) takes space in t he X-dimension. By contrast, a one-di-mensional scat terg raph (Fig. 26.14fbI) do es n ot use a cl imen-s ion fo r the o bjects . Here, th e Y-axis is use d to display th ea t t r ibute va ri ab le ( suppose these a re d i s t ances f rom ho me ofgas s tat ions); the objects are enco ded in the mark (which takesa little bit of t he X-dimension).

IIL INTERAC TIVEVISUALTRUCTURES

Dynamic queriesMagic lensOverview+detailLinking and brushingExtraction & comparisonAttribute xxplorerIV. FOCUS+CONTEXTATTENTION-

REACTIVEVISUALBSTRACTION

Data-based MethodsFilteringSelective aggregationView-based methodsMicro-macro readingsHighlightingVisual transfer functionsPerspective distortionAlternate geometries

More g enerally, ma ny single-variable visualizations a re in t heform u =f(o),w h e r e u is a variable attribute an d o is the object.Figure 26.14(c) is of this form a nd uses the Y-axis to en co de th evariable an d th e X-axis for th e objec ts. Note th at if th e objectsare, as usual, nominal, then they are reorderab le: sorting the ob-jects o n th e variable prod uce s easily perceivable visual pattern s.For convenience, we have used rectangular coordinates, but anyothe r orthogonal coordinates could be used as the bas is of de-comp osing space. Figure 26.14(d) uses 0 from polar coordinatesto e nco de, say, percentage voting for different presidential can-didates. In Fig. 26.14(e), a transformation on th e data side hastransforme d variable o in to a variable representing the distribu-t ion , then map ped tha t on to poin t s on th e Y-axis . In F ig .26.14(f), another t ransformat ion o n the da ta s ide has m appedthis dist r ibution into 2 nd quart i les , 3rd quart i les , and out l ierpoints , which is then m appe d o n th e visual s ide into a box ploton the Y-axis. Simple as they are , thes e techn iques can be veryuseful, especially in co mbination with o th er techniques.


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bidfingericn Hu r+omRussiawith Loveh r d e r on the OrientExpress^everSay Never

a) List

c) ID obieel chartI---------- --

e ) Histogram of IDiistribution

(b1) ID scattergraph

A B C D Efb2) ID object chart

(d)Pie chart

(f) Box plot of ID distribution

FIGURE 26 14 1-vanable visual abstractions

One special, but common, problem is how to visualize verylarge dimensions. This problem occurs for single-variable visu-alizations, but may also occur for one dimension of a multi-vari-able visualization. Figure 26.15 shows several techniques forhanding the problem. In Fig. 26.15(a) (Freeman & Fertig, 1995),the visual dimension is laid out in perspective. Even thougheach object may take only one or a few pixels on the axis, theobjects are actually fairly large and selectable in the diagram. InFig, 26.15@) pick, Steffen,& Sumner, 1992), the objects (rep-resenting lines of code) are laid out on afolded'y-axis. When theY-axis reaches the bottom of the page, it continues offset at thetop. In Fig. 26.15(c) (Keim & Kriegel, 19941, the axis is wrappedin a square spiral. Each object is a single pixel, and its value iscoded as the retinal variable color hue. The objects have beensorted on anothe r variable; hence, the rings show the correla-tion of this attribute with that of the sorting attribute.

One-variable visualizations are also good parts of controls.Controls, in the form of slides, also consume considerable spaceon the display (for example, the controls in Fig. 26.1) that couldbe used for additional information communication. Figure26.15(d) shows a slider on whose surface is a distribution rep-resentation of the number of objects for each value of the in-put variable, thereby communicating information about theslider's sensitivity in different data ranges. The slider on the left

of Fig. 26.1 50) has a one-variable visualization that serves as alegend for the main visualization: it associates color hues withdates and allows the selection of date ranges.

As we increase the number of variables, it is apparent that theirmappings form a combinatorial design space. Figure 26.16schematically plots the structure of this space, leaving out theuse of multiple lower variable diagrams to plot higher variablecombinations. Two-variable visualizations can be though t ofas a composition of two elementary axes (Bertin, 1977, 1981;Mackinlay, 1986b), which use a single mark t o encode the posi-tion on both those axes. Mackinlay called this mark composi-tion,and it results in a 2D scattergraph (Fig. 26.16[g]). Note thatinstead of mapping onto two positional visual encodings, onepositional axis could be used for the objects, and the data vari-ables could be mapped onto a position encoding and a retinalencoding (size), as in Fig. 26.16(f).

3-Variables and Information Lan dsca pesBy the time we get to three data variables, a visualization ca beproduced in several ways. We can use three separate visual di-mensions to encode the three data variables in a 3D scatter-graph (Fig. 26.16[j]). We could also use two spatial dimensionsand one retinal variable in a 2D retinal scattergraph (Fig26.16[k]). Or we could use on e spatial dimension as an objectdimension, one as a data attribute dimension, and one two reti-nal encodings for the other variables, as in an object chart suchas in Fig 26.16(i). Because Fig. 26.16(i) uses multiple retinal en-coding~,owever, it may not be as effective as other techniques.Notice that because they all encode three data variables, wehave classified 2D and 3D displays together. In fact, one popular3-variable information visualization that lies between 2D and 3Dis the information landscape (Fig. 26.16[m]). This is essentiallya 2D scattergraph with one datavariable extruded into the thirdspatial dimension. Its essence is that two of th e spatial dimen-sions are more tightly coupled and often relate to a 2D visual-ization. For example, the two dimensions might form a mapwith the bars showing the GDP of each region.

Another special type of 3-variable information visualization isa 2D i?rformation opography.In an information typography,space is partly defined by reference to external structure. For ex-ample, the topography of Fig. 26.17(a) is a map of San Francisco,requiring two spatial variables. The size of blue dots indexesthe number of domain names registered to San Francisco streetaddresses. Looking at the patte rns in the visualization showsthat Internet addresses have especially concentrated in the Mis-sion and South of Mission districts. Figure 26.17(a) uses a topog-raphy derived from real geographical space. Various techniques,such as multidimensional scaling, factor analysis, or connec-tionist self-organizing algorithms, can create abstract spacesbased on the similarities among collections of documents orother objects. These abstract similarity spaces can function likea topography. An example can be seen in Fig. 26.17@), wherethe pages in a website are depict ed as regions in a similarity


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(a) Off-axis 1-variable visual abstraction:LifeLines (Freeman & Ferlig, 1995).

abstraction: VisDB (Keim & Krieeel.

space. To create this diagram2, a web crawler crawls the site andindexes all the words and pages on the site. Each page is thenturned into a document vector to represent the semantic con-tent of that page. The regions are created using a neural net-work learning algorithm (see Lin, Soesgel,& Maschionini (1991)).This algorithm organizes the set of web pages into regions. Avisualization algorithm then draws boundaries around the re-gions, colors them, and names them. The result, called aKahonendiagram after its original inventor, is a type of retinals imilari tytopograpby.

Information landscapes can also use marks that are surfaces.In Fig. 26.18(a), topics are clustered on a similarity surface, andthe strength of each topic is indicated by a 3D contour. A moreextreme case is Fig. 26.18(b), where an information landscape isestablished in spherical coordinates, and the amount of ozoneis plotted as a semitransparent overlay on the p-axis.

(b) Folded long 1-variable visualabstraction: SccSoft (Eick, Stcffen,&Sumner, 1992).

[dl. 1-variable visual abstraction used asa control. (Eick, 19931.

FIGURE 26 15. Uses of 1 -vanable visual abstractions

Beyond three variables, direct extensions of the methods wehave discussed become less effective. It is possible, of courseto make plots using two spatial variables and 72-2 retinal vari-

ables, and the possibilities for four variables are shown in Fig.26.16. These diagrams can be understood, but at the cost of pro-gressively more effort as th e number of variables increases. Itwould be very difficult to understand an [XYR20] retinal scatter-graph, for example.

TreesAn interesting alternative to showing variable values by spatialpositioning is to use explicitly drawn linkages of some kind.Trees are the simplest form of these. Trees map cases into sub-cases. One of the data variables in a Data Table (for example, thevariable ReportsTo in an organization chart) is used to definethe tree. There are two basic methods for visualizing a tree: (a)Connection and (b) Enclosures.

Connection. Connection uses lines to connect marks sig-nifying the nodes of the tree. Logically, a tree could be drawnmerely by drawing lines between objects located randomly po-sitioned o n the plane, but such a tree would be visually unread-able. Positioning in space is important. Figure 26.20(a) is a treefrom Charles Darwin's notebook (Robin, 1992) drawn to help

?his figure s produced bya program called SiteMap by Xa Lin and associates See http~//facultycis rexel edu/s~temap/inclextml


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Single-AxisComposition-*- r-n^-1A B O D E

'a ) [OX] 1DSbject chart

A B C D E

(e) [2_OX] Per-mutation matrix

A B O D E(h) [3YOXIPermutationmatrix

(0) 4YOX] Pepmutation matrix

Object ChartsIi

B C D E'b) [OX] 1DObject:hartA B C D E

W [OR] 1D Retinalabject chart

A B C D E

ffl NOXR] 2DObiect chart

A B O D E

fi) [OXR~]DRetinal object chart

A B O D E

(p) [OXR~]DRetinal object chart

Scatterplots

i4 XI 1DScattergraph

0 XYI 2Dscattergraph

(k) [XYR] 2D Reti- 0) [XYZ] 3Drial scattergraph Scattergraph

(in) [(XY)Z] Infor-mation Landscape

6)XY R ~ ]D (a) [XYZRI 3DRetinal object Retinal scatter-chart graph

ft) [(XY)ZR] Reti-nal informationlandscape

Topographies

(1) [XIYIR] 2D Reti-nal topography

(n) [(XlY,)RI Topog-raphic informationlandscape

(s) [XYZR] 3DRetinaltopography

(it) [(XYZ)R] 3DTopographicinformationlandscape

FIGURE 26.16 Simple Visual Structure s.

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(a) XIYIRRetinal topography- .... I

(b) X,Y& Retinal similarity topograp hyFIGURE 26.17. Retinal information topographies.

him work out the theory of evolution. Lines proceed from an-cestor species to new species. Note that even in this informalsetting intended for personal use that the tree uses space sys-tematically (and opportunistically). There are no crossed lines.A common way of laying out trees is to have the depth in thetree map onto one ordinal access as in Fig. 26.20@), while theothe r axis is nominal and used t o separate nodes. Of course,trees could also be mapped into other coordinate systems: forexample, there can be circular trees in which the r-axis repre-sents depth and the 9-axis is used to separate nodes as in therepresentation of the evolution species in Fig. 26 .20(~) .~t is be-cause trees have no cycles that o ne of the spatial dimensionscan be used to encode tree depth. This partial correlation of

(a) News stones based on I hemescapes(Wise el a1 .1995) C ourlesy NewsMaps coin

(b)Ozone layer s un 'ou~ ~ dinaarth. L. Trcinish.Courtesy IBMFIGURE 26 18 3D information surface topographies

tree structure and space makes trees relatively easy to lay outand interpret, compared to generalized networks. Hierarchicaldisplays are important not only because many interesting col-lections of information, such as organization charts or tax-onomies, are hierarchical data, but also because important col-lections of information, such as websites, are approximatelyhierarchical. Whereas practical methods exist for displayingtrees up to several thousand nodes, no good methods exist fordisplaying general graphs of this size. If a visualization probleminvolves the displaying of network data, a practical designheuristic is to see whether the data might not be forced into adisplay as a modified tree, such as a tree with a few non-treelinks. A significant disadvantage of trees is that as they get large,they acquire an extreme aspect ratio, because the nodes expandexponentially as a function with depth. Consequently, any suffi-ciently large tree (say, >I000 nodes) resembles a straight line.Circular trees such as Fig. 26.20Cc) are one way of trying to buymore space to mitigate this problem. Another disadvantage oftrees is the significant empty space between nodes to maketheir organization easily readable. Various tricks can be used to

figure is f rom David 1-Iillis,University of Texas


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530 CARD

(a) Tree tromDarwin's notes, m m(Robin, 1992) Courtesy Syndics ofCambndgc UniversityLibrary,

(c) Circular tree of evolution orlife.Texas.

(b)Typical link and node tree layout

(d) Tree in (a) drawn using en closure.

FIGURE 26.19. Trees

wrap parts of the tree into this empty space, but at the expenseof the tree's virtues of readability.

Enclosure. Enclosure uses lines to hierarchically enclosenested subsets of the tree. Figure 26.20cd) is an enclosure treeencoding of Darwin's tree in Fig. 26.20(a). We have already seenone attempt to use tree enclosure, TreeMaps (Fig. 26.5).TreeMaps make use of all the space and stays within prescribedspace boundaries, but they d o not represent the nonterminalnodes of the tree very well and similar leaves can have wildly dif-ferent aspect ratios. Recent variations on TreeMaps found waysto "squarify" nodes (Shneiderman & Wattenberg, 2001), miti-gating this problem.

NetworksNetworks are more general than trees and may contain cycles.Networks may have directional links. They are useful for de-scribing communication relationships among people, traffic in atelephone network, and the organization of the Internet. Con-tainment is difficult to use as a visual encoding for network re-lationships, so most networks are laid out as node and link dia-grams. Unfortunately, straightforward layouts of large node andlink diagrams tend to resemble a large wad of tangled string.

We can distinguish the same types of nodes and links in net-work Visual Structures that we did for spatial axes: (a) Unstruc-tured (unlabeled), (b) Nominal (labeled), (c) Ordinal (labeled

(c ) Line shortening ~ i & , & Wills, 1997),FIGURE 26.20. Network methods


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with an ordinal quantity), or (d) Quantitative (weighted links).Retinal properties, such as size or color, can be used to encodeinformation about links and nodes. As in the case of trees, spa-tial positioning of the nodes is extremely important. Network vi-sualizations escape from the strong spatial constraints of sim-ple Visual Structures only to encounter ano ther set of strongspatial constraints of node links crossing and routing. Networksand trees are not so much an alternative of direct of the directgraphical mappings we have discussed so far as they are anotherset of techniques that can be overlaid on these mappings. Smallnode and link diagrams can be laid out opportunistically byhand o r by using graph drawing algorithms that have been de-veloped (Battista, Eades, Tamassia,& Tollis, 1994; Cruz& Tamas-sia, 1998; Tamassia, 1996) to optimize minimal link crossing,symmetry, and other aesthetic principles.

For very large node and link diagrams, additional organizingprinciples are needed. If there is an external topographic struc-ture, it is sometimes possible to use the spatial variables associ-ated with the nodes. Figure 26.20(a) shows a network based oncall traffic between cities in the United States (Becker, Eick, &Wilks, 1995). The geographical location of the cities is used tolay out the nodes of the network. Another way to positionnodes is by associating nodes with positions in a similarityspace, such the nodes that have the strongest linkages to eachother are closest together. There are several methods for com-puting node nearness in this way. One is to use multidimen-sional scaling (MDS) (Fairchild, Poltrock, & Furnas, 1988). An-other is to use a "spring" technique, in which each link isassociated with a Hooke's Law spring weighted by strength ofassociation and the system of springs is solved to obtain nodeposition. Eick and Willis (1993) have argued that the MDS tech-nique places too much emphasis on smaller links. They have de-rived an alternative that gives clumpier (and hence, more visu-ally structured) clusters of nodes. If positioning of nodescorresponds perfectly with linkage information, then t he linksdo not add more visual information. If positioning does not cor-respond at all with linkage information, then the diagram is ran-dom and obscure. In large graphs, node positions must have apartially correlated relationship to linkage in o rder to allow theemergence of visual structure. Note that this is what happensin the te lephone traffic diagram Fig. 26.20(a). Cities are posi-tioned by geographical location. Communication might be ex-pected to be higher among closer cities, so th e fact that com-munications is heavy between coasts stands out.

A major problem in a network such as Fig. 26.20(a) is thatlinks may obscure the struc ture of the graph . One solution isto route the links so that they do not obscure each other. Thelinks could even be drawn outside the plane in the third di-mension; however, there a re limits to the effectiveness of thistechnique. Another solution is to use thresholding, as in Fig.26.20(b). Only those links representing traffic greater than a cer-tain threshold are included; the o thers a re elided allowing usto see the most important structure. Another technique is lineshortening, as in Fig. 26.20Cc). Only the portion of the line nearthe nodes is drawn. At the cost of giving up the precise linkage,it is possible to read the density of linkages for the differentnodes. Figure 26.20(d) is a technique used to find patterns in anextremely large network. Telephone subscribers are repre-sented as nodes on a hexagonal array. Frequent pairs are located

near each other on the array. Suspicious patterns are visible be-cause of the sparseness of the network.

The insightful display of large networks is difficult enoughthat many information visualization techniques depend on in-teractivity. One important technique, for example, is node ag-gregation. Nodes can be aggregated to reduce the number oflinks that have to be drawn on the screen. Which nodes are ag-gregated can depend on the portion of the network on whichthe user is drilling down. Similarly, the sets of nodes can be in-teractively restricted (e .g. , elephone calls greater than a cer-tain volume) to reduce the visualization problem to one withinthe capability of current techniques.

CO MP OS ED VISUAL STRUCTURESSo far, we have discussed simple mappings from data into spa-tial position axes, connections and enclosures, and retinal vasi-ables. These methods begin to run into a barrier around threevariables as the spatial dimensions are used up and as multipleof the less efficient retinal variables needed . Most interestingproblems involve many variables. We shall therefore look at aclass of methods that reuse precious spatial axes to encodevasi-ables. This is done by composing a compound Visual Structureout of several simple Visual Structures. We will consider five sub-classes of such composition: (a) mark composition, (b) casecomposition, (c) single-axis composition, (d) double-axis com-position, and (e) recursive composition. Schematically, we il-lustrate these possibilities in Fig. 26.21.

Single-axis composition . In single-axis composition,multiple variables that share a single axis are aligned using thataxis, as illustrated in Fig. 26.21(a). An example of single-axiscomposition is a method due to Bertin called permutationmatrices (Bertin, 1977/1981). In a permutation matrix (Fig.26.16[0], for example), one of the spatial axes is used to repre-sent the cases and the o ther a series of bar charts (or rows of cir-cles of different size or some o ther depiction of the value ofeach variable) to represent the values. In addition, bars for val-ues below average may be given a different color, as in Fig. 26.7,in order to enhance th e visual patterns. The order of the objectsand the order of the variables may both be permuted until pat-terns come into play. Permutation matrices were used in our 110-tel analysis example. They give up direct reading of the dataspace in order to handle a larger number of variables. Of course,as the number of variables (or objects) increases, manipulationof the matrices becomes more time-consuming and visual in-terpretation more complex. Still, permutation matrices or theirvariants are one of the most practical ways of representingmulti-variabledata.

If we superimpose the bar charts of the permutation matrixatop one another, and then replace the bar chart with a line link-ing together the tops of the bars, we get another method forhandling multiple variables by single-axis composition-paral-lei coordinates (Inselberg, 1997; Inselberg & Dimsdale, 1990),as shown in Fig. 26.22.A problem is analyzed in parallel coordi-nates by interactively restricting the objects displayed (the lines)in order t o look at cases with common cl~aracter istics. n Fig.


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a) Single-axis composition

Y Y%) Double-axis composition 4-

x x x

u(e) Recursive composition

+v x x

(d) Case composition

FIGURE 26 2 1. Composition types.

x l x x x y l x x x ylx xA B C A B C x

FIGURE 26 22 S~ngle-axis omposition parallel coordinates

26.22 , parallel coordinates are used to analyze the problem ofyield from a certain processor chip.X l is chip yield,X2 is qual-ity,X3 through XI2 are defects, and the rest of the variables arephysical parameters. The analysis, looking at those subsets ofdata with high yield and noticing the distribution of lines onthe other parameters, was able to solve a significant problem inchip processing.

Both permutation matrices and parallel coordinates allowanalyses in multi-dimensional space, because they are efficientin the use (and reuse) of spatial position and the plane. Actually,they also derive part of their power from being interactive. Inthe case of permutation matrices, interactivity comes in re-ordering the matrices. In the case of parallel coordinates, inter-activity comes in selecting subsets of cases to display

composition. In double-axis composition,two visual axes must be in correspondence, in which case thecases are plotted on the same axes as a multivanable graph (fig.26.21 [b]). Care must be taken that the variables are plotted on acomparable scale. For this reason, the separate scales of the vari-ables are often transformed to a common proportion changescale.An example would be change in price for various stocks.


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The cases would be the years, and the variables would be thedifferent stocks.

Mark composition and case composition. Coniposi-tion can also fuse diagrams. We discussed that each dimensionof visual space can be said to have properties as summarized inTable 26.4. The visual space of a diagram is composed from thepropert ies of its axis. In mark co?nposition Fig. 26.2 l[c]), themark on one axis can fuse with the corresponding mark on an-other axis to form a single mark in the space formed by the twoaxes. Similarly, two object charts can be fused into a single dia-gram by having a single mark for each case. We call this latterform case compositionFig. 26.2 1 d) .

Recursive composition. Recursive composition dividesthe plane (or 3D space) into regions, placing a subvisualization

(a) 2D-in-2D: Attribute Explorer (Tweedie,Spence, Dawkes,& Su, 1996).

(c) Visualization of stick figures showingweather around Lake Ontario.

in each region (Fig. 26.21[e]). We use the term somewhatloosely, since regions have different types of subvisualizations.The FilmFinder in Fig. 26.1 is a good example of a recursive vi-sualization. The screen breaks down into a series of simple Vi-sual Structures and controls: (a) a 3-variable retinal scattergraph(Year, Rating, FilmType) + (b) a 1-variable slider (Title) + (c) a1-variable slider (Actors) + (d) a 1-variable slider (Actresses) +(e) a 1-variableslider (Director) + ( f ) a 1-variable slider (Film-Length) + (g) a 1-variable radio but ton control (Rating) +(h) a 1-variablebutton-set (FilmType).

Three types of recursive conlposition deserve special men-tion: (a)2D-in-2D, @) marks-in-2D, and (c) 3D-in-3D.An exam-ple of 2D-in-2D composition is the "prosection matrix"(Tweedie, Spence, Dawkes,& Su, 1996) shown in Fig. 26.23(a).Each smaller square in the prosection matrix represents a pair ofparameters plotted against each other. The coloring shows

(b) Marks-in-2D. Composition of a stick figuremark (Pickett & Grinstein, 1988).

(d) 3D-in-3D: Worlds-within-worlds (Feiner&Beshers, 1990).FIGURE 26 23 . Recursive composition.


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which values of the plotted pair give excellent (red region) orpartly good (gray regions) performance for the design of somedevice. The arrangement of the individual matrices into a su-permatrix redefines the spatial dimensions (that is, associatesit with different variables) within each of the cells, and the cellsthemselves are arranged in an overall scheme that systematicallyuses space. In this way, the precious spatial dimension is effec-tively expanded to where all the variables can reuse it. An im-portant property of techniques similar to this one is that spaceis defined at more than onegrain size, and these levels of grainbecome the basis for a macro-micro reading.

An example of inarks-in-2D composition in the use of "stickfigure" displays. This is an unusual type of visualization in whichthe recursion is within the mark instead of within the use ofspace. Figure 26.23(b) shows a mark that is itself composed ofsubmarks. The mark is a line segment with four smaller line seg-ments protruding from the ends. Four variables are mappedon to angle of these smaller line segments and a fifth onto theangle of the main line segment. Two additional variables aremapped onto the position of this mark in a 2D display. A typi-cal result is the visualization in Fig. 26.23(c), which shows fiveweather variables around Lake Ontario, the outline of whichclearly appears in the figure.

Feiner and Bes11e1-s (1990) provided an example of the thirdrecursive composition technique, 3D-in-3D composition. Sup-pose a dependent variable is a function of six continuous vari-a b l e s , ~ ~(x, y, z,w, s). Three of these variables are mappedonto a 3D coordinate system.A position is chosen in that space,say, x l ,y l, zl At that position, a new 3D coordinate system ispresented with a surface defined by the o the r three variables(Fig, 26.23[d]).The user can thus viewy = (x l ,y l , z l , w, < s).The user can slide the seconcl-order coordinate system to anylocation in the first, causing the surface to change appropriately.Note that this technique combines a composed visual inter-action with interactivity on the composition. Multiple second-order coordinate systems can be displayed at the space simul-taneously, as long as they do not overlap by much.

INTERACTIVE VISUAL STRUCTURESIn the examples we have considered so far, we have often seenthat information visualization techniques were enhanced by be-ing interactive. Interactivity is what makes visualization a newmedium, separating it from generations of excellent work onscientific diagrams and data graphics. Interactivity means con-trolling the parameters in the visualization reference model (Fig.26.10). This naturally means that there are different types of in-teractivity, because the user could control the parameters to datatransformations, to visual mappings, or to view transformations.It also means that there are different forms of interactivity basedon the response cycle of the interaction. As an approximation,we can think of there being three time constants that govern in-teractivity, which we take to be 0.1 sec, 1sec, and 10 sec (Card,Moran,& Newell, 1986) (although the ideal value of these maybe somewhat less, say, 0.07 sec, 0.7 sec, and 7 sec). The first timeconstant is the time in which a system response must be made, ifthe user is to feel that there is a direct physical manipulation of

the visualization. If the user clicks on a button or moves a slider,the system needs to update the display in less than 0.1 sec. Ani-mation frames need to take less than 0.1 sec. The second timeconstant,1sec, is the time to complete an immediate action, forexample, an animated sequence such as zooming in to the dataor rotating a tree branch. The third time constant 10 sec (mean-ing somewhere in the 5 to 30 sec interval) is the time for com-pleting some cognitive action, for example deleting an elementfrom the display. Let us consider a few well-known techniques forinteractive information visualizations

Dynamic queries. A general paradigm for visualizationinteraction is dynamic quer ies, the interaction technique usedby the FilmFinder in Fig. 26.1. The user has a visualization of thedata and a set of controls, such as sliders, by which subsets ofthe Data Table can be selected. For example, Table 26.9 showsthe mappings of the Data Table and controls for the FilmFinder.The sliders and other controls will select which subset of thedata is going to be displayed. In the FilmFinder, the control forLength is a two-sided slider. Setting one end to 90 minutes andthe other end to 120 minutes will select for display only thosecases of the Data Table whose year variable lies between theselimits. The display needs to change within the 0.1 sec of chang-ing the slider.

Magic lens (movable ilter). Dynamic queries is onetype of interactive filter. Another type is a movable filter that canbe moved across the display, as in Fig. 26.24(a). These magiclenses are useful when it is desired to filter only some of thedisplay. For example, a magic lens could be used with a map thatshowed the population of any city it was moved over. Multiplemagic lenses can be used to cascade filters.

Overview + detail. We can think of an overview + detaildisplay (Fig. 26.24[b]) as a particular type of magic lens, one thatmagnifies th e display and has the magnified region off to theside so as not to occlude the region. Displays have informationat different grain sizes.A GIS map may have information at thelevel of a continent as well as at the level of a city. If the shapeof the continent can be seen, th e display is too coarse to seethe roadways of a city Overview + detail displays show that dataat more than one level, but they also show where the finer graindisplay fits into the larger grain clisplay. In Fig. 26.24(b), fromSeeSoft (Eick et a]., 1992), a system for visualizing large softwaresystems, the amount of magnification in the detail view is largeenough that two concatenated overview4- detail displays are re-quired. Overview+ detail displays are thus very helpful for datanavigation. Their main disadvantage is that the require coordi-nation of two visual domains.

Linking an d brushing. Overview + detail is an exam-ple of coordinating dual representations of the same data.These can be coordinated interactively with linking a n dbrush-ing. Suppose, for example, we wish to show power consump-tion on an airplane, both in terms of the physical representationof the airplane and a logical circuit diagram. The two viewscould be shown and linked by using the same color for thesame component types Interactivity itself can be used for a dy-namic form of linking called brushing. In brushing, running the


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TABLE 26 9. Visual Marks and C ontrols for FilmFinderData Visua!Form

Visual Tran~itonnationType Range Case, Case, Caseh Typo Structure Control Atiected

Alpliaslua' Select casas~ ~ pnas~ ue r sbctcases

-. Q hn-sldaualder Clip range+ Q Y-ax15 AM$ Clip range4 0 Radio buttons Select casm

N Ctllcr Radio buttons Sslectcases

(a) Magic Lens (Bier, Stone, Pier, Buxlon, &DeRose, 1993): Detail of map. Courtesy XeroxCorp.

(c) E xtract and compare: SDM (Roth, Chuah, &Mattis, 1995).

(b) Cascading overview + detail: SeeSoft (Eickct &I., 1992)..

(d) Attribute Explorer: (Tweed ie el a l., 1996).Courtesy Robert Spence..

FIGURE 26 2 4 Interaction technique s.

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t view and in the o ther view.Extraction and comparison. We can also use interac-

subset of the data to compare with another sub-An example of this is in the SDM system (Chuah, Roth, Mat-& Kolojejchick, 1995) in Fig. 26.24(c). The data are displayedh the ability to compare it. Inforn~ations therefore extracted

compared using 2D.le these manipulations, while keeping them coor-

Attribute explorer. Several of these interactive tech-e combined in the Attribute Explorer (Tweedie et al.,

Each attribute is displayed by a histogram, where eache. The user selects a range of some attribute, say price.s making up the histogram on price have their cor-

pixels linked representing houses highlighted onothe r attributes. Those houses meeting all the criteria are

ne color; those houses meeting, say, all but onein another color. In this way, the user can tell

the "near misses." If the users were to relax one of the cri-(say, reducing price by $loo), then the user

gain more on another criterion (say, reducingmute by 20 miles).

FOCUS + CONTEXT ATTENTION-REACTIVEABSTRACTIONS

r, we have considered visualizations that are stat ic map-

e user. We now consider visualizations in which theew are altered by the computer according to the

f the user's degree of interest. We can, in principle,

on the Orient Express" are accessible at low cost inpresently visible on the screen.

"Goldfinger," a movie with only a mark on the dis-y, take more time to find. Details of "Last Year at Marienbad,"

e with no mark on the display, would take much morethat with a model for predicting users' changes

st, the system can adjust its displays to make costs lowerr example, if the user wants some de-

l about a movie, such as the director, the system can antici-the user is more likely to want other details about thewell and therefore display them all at the same time:

have execute a separate command; the cost

Focusi-context views are based on several premises: First,the user needs both overview (context) and detail information(focus) during information access, and providing these in sepa-rate screens or separate displays is likely to cost more in usertime. Second, information needed in the overview may be dif-ferent from that needed in the detail. The information of theoverview needs to provide enough information the user to de-cide where to examine next or to give a context to the detailedinformation rather than the detailed information itself.As Fur-nas (1981) has argued, the user's interest in detail seems to fallaway in a systematic way with distance as information objectsbecome farther from current interest. Third, these two types ofinformation can be combined within a single dynamic display,much as human vision uses a two-level focus and context strat-egy. Information broken into nlultiple displays (separate leg-ends for a graph, for example) seem to degrade performancedue to reasons of visual search and working memory.

Furnas (1981) was the first to articulate these ideas system-atically in his theory offisheye views. The essence of focusi-con-text displays is that the average cost of accessing information isreduced by placing the most likely needed information for nav-igation and detail where it is fastest to access. This can be ac-complished by working on either the data side or the visual sideof the visual reference model, Fig 26.10. We now consider thesetechniques in more detail.

Data-Based MethodsFiltering. On the data side, focus+context effects can be

achieved by filtering out which items from the Data Table are ac-tually displayed on the screen. Suppose we have a tree of cate-gories taken from Roget's Thesaurus, and we ar e interactingwith one of these, "Hardness."

MatterORGANICVitalityVitalityingene&Specific vitalitySensationSensation ingeneralSpecific sensationINORGANICSolidHardnes

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