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`The Visual Display
`
`of Quantitative Information
`
`EDWARD R. TUFTE
`
`0001
`0001
`
`TDA 1016
`TDA 1016
`CBM of U.S. Pat. No. 7,533,056
`CBM of US. Pat. No. 7,533,056
`
`

`

`Tufte
`Tufte
`
`
`
`
`
`138<wmsmLmeE3Nom0.55.3998Emofibmmos
`
`0002
`
`0002
`
`

`

`((A landmark book,
`a wonderful book))
`
`Frederick Mosteller,
`Harvard University
`
`· u A tour de force))
`
`John W. Tukey,
`Bell Laboratories and Princeton University
`
`The cover graphic, drawn by Minoru Niijima,
`is based on E. J. Marey's train schedule from
`Paris to Lyon in his book La Methode Graph(cid:173)
`ique (Paris, 1878).
`
`Design & Typography by Howard I. Gralla
`
`0003
`
`

`

`Edward R. Tufte
`
`The Visual Display
`of Quantitative Information
`
`Graphics Press
`
`· Cheshire) Connecticut
`
`0004
`
`

`

`Copyright© 1983 by Edward RolfTufte
`PUBLISHED BY GRAPHICS PRESS
`
`PosT OFFICE Box 430, CHESHIRE, CoNNECTICUT 06410
`
`All rights to illustrations and text reserved by Edward Rolf Tufte. This work may not be copied, reproduced, or translated in whole or in
`part without written permission of the publisher, except for brief excerpts in connection with reviews or scholarly analysis. Use with any form
`of information storage and retrieval, electronic adaptation or whatever, computer software, or by similar or dissimilar methods now known
`or developed in the future is also strictly forbidden without written permission of the publisher. A number of illustrations are reproduced by
`permission; those copyright-holders are credited on page 197.
`
`Printed in the United States of America
`
`Sixteenth printing, January 1998
`
`0005
`
`

`

`Contents
`
`PART I GRAPHICAL PRACTICE
`
`1 Graphical Excellence
`2 Graphical Integrity
`53
`3 Sources of Graphical Integrity and Sophistication
`
`13
`
`79
`
`PART II THEORY OF DATA GRAPHICS
`
`4 Data-Ink and Graphical Redesign
`5 Charijunk: Vibrations, Grids, and Ducks
`107
`6 Data-Ink Maximization and Graphical Design
`7 Multifunctioning Graphical Elements
`8 Data Density and Small Multiples
`9 Aesthetics and Technique in Data Graphical Design
`
`123
`
`177
`
`91
`
`13 9
`
`161
`
`Epilogue: Designs for the Display of Information
`
`191
`
`0006
`
`

`

`For my parents
`Edward E. Tufte and Virginia James Tufte
`
`0007
`
`

`

`Acknowledgments
`
`I am indebted to many for their advice and assistance with this book.
`For leave and research support during several academic years,
`the Center for Advanced Study in the Behavioral Sciences, the
`John Simon Guggenheim Foundation, the Woodrow Wilson School
`ofPrinceton University, and Yale University.
`For providing access to their superb collections, the Bibliotheque
`Nationale and the Bibliotheque de l'Ecole Nationale des Pouts et
`Chaussees in Paris, and, at Yale University, the Historical Medical
`Library and the Beinecke Rare Book and Manuscript Library.
`For helping me appreciate the practicalities in the production
`of statistical graphics, several members of the art department at the
`New York Times and my students in Graphic Design and in the
`Department of Statistics at Yale.
`For assistance in establishing Graphics Press, Peter B. Cooper,
`Earle E. Jacobs, Jr., and Trudy Putsche.
`For design and artwork, Howard I. Gralla and Minoru Niijima.
`For their help and hospitality in Paris during my work on the
`Minard drawings, Michel Balinski, Jean Dubout, Andre Jammes,
`and Claudine Kleb.
`For providing examples and for suggesting improvements in the
`manuscript, James Beniger, Inge Druckrey, Timothy Gregoire,
`Joanna Hitchcock, Joseph LaPalombara, Kathryn Scholle, Stephen
`Stigler, Howard Wainer, and Ellen Woodbury.
`For their reviews of the manuscript and for their inspiration and
`encouragement through all the years of this enterprise, Frederick
`Mosteller and John W. Tukey.
`
`June 1982
`Cheshire, Connecticut
`
`0008
`
`

`

`Introduction
`
`Data graphics visually display measured quantities by means of
`the combined use of points, lines, a coordinate system, numbers,
`symbols, words, shading, and color.
`The use of abstract, non-representational pictures to show numbers
`is a surprisingly recent invention, perhaps because of the diversity
`of skills required- the visual-artistic, empirical-statistical, and
`mathematical. It was not until1750-18oo that statistical graphics(cid:173)
`length and area to show quantity, time-series, scatterplots, and
`multivariate displays-were invented, long after such triumphs of
`mathematical ingenuity as logarithms, Cartesian coordinates, the
`calculus, and the basics of probability theory. The remarkable
`William Playfair (1759-1823) developed or improved upon nearly
`all the fundamental graphical designs, seeking to replace conven(cid:173)
`tional tables of numbers with the systematic visual representations
`of his "linear arithmetic."
`Modern data graphics can do much more than simply substitute
`for small statistical tables. At their best, graphics are instruments
`for reasoning about quantitative information. Often the most effec(cid:173)
`tive way to describe, explore, and summarize a set of numbers(cid:173)
`even a very large set-is to look at pictures of those numbers.
`Furthermore, of all methods for analyzing and communicating
`statistical information, well-designed data graphics are usually the
`simplest and at the same time the most powerful.
`
`The first part of this book reviews the graphical practice of the
`two centuries since Playfair. The reader will, I hope, rejoice in the
`graphical glories shown in Chapter 1 and then condemn the lapses
`and lost opportunities exhibited in Chapter 2. Chapter 3, on graph(cid:173)
`ical integrity and sophistication, seeks to account for these differ(cid:173)
`ences in quality of graphical design.
`
`0009
`
`

`

`The second part of the book provides a language for discussing
`graphics and a practical theory of data graphics. Applying to most
`visual displays of quantitative information, the theory leads to
`changes and improvements in design, suggests why some graphics
`might be better than others, and generates new types of graphics.
`The emphasis is on maximizing principles, empirical measures of
`graphical performance, and the sequential improvement of graphics
`through revision and editing. Insights into graphical design are to
`be gained, I believe, from theories of what makes for excellence
`in art, architecture, and prose.
`
`This is a book about the design of statistical graphics and, as such,
`it is concerned both with design and with statistics. But it is also
`about how to communicate information through the simultaneous
`presentation of words, numbers, and pictures. The design of statis(cid:173)
`tical graphics is a universal matter-like mathematics-and is not
`tied to the unique features of a particular language. The descriptive
`concepts (a vocabulary for graphics) and the principles advanced
`apply to most designs. I have at times provided evidence about the
`scope of these ideas, by showing how frequently a principle applies
`to (a random sample of) news and scientific graphics.
`Each year, the world over, somewhere between 900 billion
`(9 X 1011 ) and 2 trillion (2 X 1012) images of statistical graphics are
`printed. The principles of this book apply to most of those graphics.
`Some of the suggested changes are small, but others are substantial,
`with consequences for hundreds of billions of printed pages.
`But I hope also that the book has consequences for the viewers and
`makers of those images-that they will never view or create statis(cid:173)
`tical graphics the same way again. That is in part because we are
`about to see, collected here, so many wonderful drawings, those
`ofPlayfair, of Minard, of Marey, and, nowadays, of the computer.
`Most of all, then, this book is a celebration of data graphics.
`
`0010
`
`

`

`PART I
`Graphical Practice
`
`0011
`
`

`

`r Graphical Excellence
`
`Excellence in statistical graphics consists of complex ideas
`communicated with clarity, precision, and efficiency. Graphical
`displays should
`
`• show the data
`
`• induce the viewer to think about the substance rather than about
`methodology, graphic design, the technology of graphic pro(cid:173)
`duction, or something else
`
`• avoid distorting what the data have to say
`
`• present many numbers in a small space
`
`• make large data sets coherent
`
`• encourage the eye to compare different pieces of data
`
`• reveal the data at several levels of detail, from a broad overview
`to the fine structure
`
`• serve a reasonably clear purpose: description, exploration,
`tabulation, or decoration
`
`• be closely integrated with the statistical and verbal descriptions
`of a data set.
`
`Graphics reveal data. Indeed graphics can be more precise and
`revealing than conventional statistical computations. Consider
`Anscombe's quartet: all four of these data sets are described by
`exactly the same linear model (at least until the residuals are ex(cid:173)
`amined).
`
`X
`
`y
`
`X
`
`y
`
`II
`
`III
`
`X
`
`y
`
`IV
`
`X
`
`y
`
`8.04
`10.0
`8.0
`6.95
`7.58
`13.0
`9.0
`8.81
`8.33
`11.0
`14.0
`9.96
`7.24
`6.0
`4.26
`4.0
`12.0 10.84
`7.0
`4.82
`5.0
`5.68
`
`10.0
`8.0
`13.0
`9.0
`11.0
`14.0
`6.0
`4.0
`12.0
`7.0
`5.0
`
`9.14
`8.14
`8.74
`8.77
`9.26
`8.10
`6.13
`3.10
`9.13
`7.26
`4.74
`
`7.46
`10.0
`6.77
`8.0
`13.0 12.74
`9.0
`7.11
`11.0
`7.81
`14.0
`8.84
`6.0
`6.08
`4.0
`5.39
`12.0
`8.15
`7.0
`6.42
`5.0
`5.73
`
`6.58
`8.0
`5.76
`8.0
`7.71
`8.0
`8.84
`8.0
`8.0
`8.47
`7.04
`8.0
`8.0
`5.25
`19.0 12.50
`8.0
`5.56
`8.0
`7.91
`6.89
`8.0
`
`N = 11
`mean ofX's = 9.0
`mean of Y's = 7.5
`equation of regression line: Y = 3 + 0.5X
`standard error of estimate of slope = 0.118
`t = 4.24
`sum of squares X- X = 110.0
`regression sum of squares = 27.50
`residual sum of squares of Y = 13.75
`correlation coefficient = .82
`r2 = .67
`
`0012
`
`

`

`14 GRAPHICAL PRACTICE
`
`And yet how they differ, as the graphical display of the data
`makes vividly clear:
`
`F.]. Anscombe, "Graphs in Statistical
`Analysis," American Statistician, 27
`(February 1973), 17-21.
`
`I
`
`10
`
`5
`
`•
`
`•
`
`10
`
`20
`
`III
`
`•
`
`••
`••
`••
`• ••
`
`II
`
`IV
`
`•••••
`•
`•
`
`• • I • I
`
`And likewise a graphic easily reveals point A, a wildshot obser(cid:173)
`vation that will dominate standard statistical calculations. Note that
`point A hides in the marginal distribution but shows up as clearly
`exceptional in the bivariate scatter.
`
`-
`
`• •
`•
`•
`
`Cll
`
`•
`
`•
`
`•
`
`•
`
`•
`
`•
`•
`• •
`•• • •
`• •
`•• • • •
`•
`•
`
`• A
`
`t
`
`Stephen S. Brier and Stephen E. Fien(cid:173)
`berg, "Recent Econometric Modelling
`of Crime and Punishment: Support for
`the Deterrence Hypothesis?" in Stephen
`E. Fienberg and Albert]. Reiss, Jr., eds.,
`Indicators of Crime and Criminal Justice:
`Quatztitative Studies (Washington, D,C.,
`1980), p. 89.
`
`0013
`
`

`

`Of course, statistical graphics, just like statistical calculations, are
`only as good as what goes into them. An ill-specified. or prepos(cid:173)
`terous model or a puny data set cannot be rescued by a graphic
`(or by calculation), no matter how clever or fancy. A silly theory
`means a silly graphic:
`
`GRAPHICAL EXCELLENCE 15
`
`Edward R. Dewey and Edwin F. Dakin,
`Cycles: The Sciwce of Predictio11 (New
`York; 1947), p. 144.
`
`New York Stock Prices
`
`_,...~,
`
`\
`
`. /
`
`-~
`.
`'~
`......___
`'~
`London Stock Prices
`
`/
`/'
`
`o-
`::: -.004
`Q) c.
`-~ -.002
`0 ;;;
`0
`Normal
`
`168
`
`164
`
`152
`
`\
`
`\
`'
`\
`\
`\
`
`SoLAR RADIATION AND STOCK PRICES
`A. New York stock prices (Barron's average). B. Solar Radiation, inverted,
`and C. London stock prices, all by months, 1929 (after Garcia-Mata and
`Shaffner).
`
`Let us turn to the practice of graphical excellence, the efficient
`communication of complex quantitative ideas. Excellence, nearly
`always of a multivariate sort, is illustrated here for fundamental
`graphical designs: data maps, time-series, space-time narrative
`designs, and relational graphics. These examples serve several
`purposes, providing a set of high-quality graphics that can be
`discussed (and sometimes even redrawn) in constructing a theory
`of data graphics, helping to demonstrate a descriptive terminology,
`and telling in brief about the history of graphical development.
`Most of all, we will be able to see just how good statistical
`graphics can be.
`
`0014
`
`

`

`16 GRAPHICAL PRACTICE
`
`Data Maps
`
`These six maps report the age-adjusted death rate from various
`types of cancer for the 3,056 counties of the United States. Each
`map portrays some 21,000 numbers. 1 Only a picture can carry such
`a volume of data in such a small space. Furthermore, all that data,
`thanks to the graphic, can be thought about in many different
`ways at many different levels of analysis-ranging from the con(cid:173)
`templation of general overall patterns to the detection of very
`fine county-by-county detail. To take just a few examples, look
`at the
`
`• high death rates from cancer in the northeast part of the country
`and around the Great Lakes
`
`• low rates in an east-west band across the middle of the country
`
`• higher rates for men than for women in the south, particularly
`Louisiana (cancers probably caused by occupational exposure,
`from working with asbestos in shipyards)
`
`• unusual hot spots, including northern Minnesota and a few
`counties in Iowa and Nebraska along the Missouri River
`
`• differences in types of cancer by region (for example, the high
`rates of stomach cancer in the north-central part of the country
`-probably the result of the consumption of smoked fish by
`Scandinavians)
`
`• rates in areas where you have lived.
`
`The maps provide many leads into the causes-and avoidance-
`of cancer. For example, the authors report:
`
`In certain situations ... the unusual experience of a county
`warrants further investigation. For example, Salem County,
`New Jersey, leads the nation in bladder cancer mortality
`among white men. We attribute this excess risk to occupational
`exposures, since about 25 percent of the employed persons in
`this county work in the chemical industry, particularly the
`manufacturing of organic chemicals, which may cause bladder
`tumors. After the finding was communicated to New Jersey
`health officials, a company in the area reported that at least 330
`workers in a single plant had developed bladder cancer during
`the last 50 years. It is urgent that surveys of cancer risk and
`programs in cancer control be initiated among workers and
`former workers in this area.2
`
`tEach county's rate is located in two
`dimensions and, further, at least four
`numbers would be necessary to recon(cid:173)
`struct the size and shape of each county.
`This yields 7X 3,056 entries in a data
`matrix sufficient to reproduce a map.
`
`..
`
`In highest decile,
`statistically significant
`
`Significantly high, but
`not in highest decile
`
`In highest decile, but not
`statistically significant
`
`Not significantly different
`from U.S. as a whole
`
`Significantly lower than
`U.S. as a whole
`
`2 Robert Hoover, Thomas]. Mason,
`Frank W. McKay, and Joseph F. Frau(cid:173)
`meni, Jr., "Cancer by County: New
`Resource for Etiologic Clues," Science,
`189 (September 19, 1975), 1006.
`
`Maps from Atlas of Cancer Mortality for
`U.S. Counties: 1950-1969, by Thomas].
`Mason, Frank W. McKay, Robert
`Hoover, WilliamJ. Blot, and Joseph F.
`Fraumeni,Jr. (Washington, D.C.: Public
`Health Service, National Institutes of
`Health, 1975). The six maps shown here
`were redesigned and redrawn by
`Lawrence Fahey and Edward Tufte.
`
`0015
`
`

`

`All types of cancer, white females;
`age-adjusted rate by county, 1950-1969
`
`All types of cancer, white males;
`age-adjusted rate by county, 1950-1969
`
`0016
`
`

`

`Trachea, bronchus, and lung cancer;
`white females; age-adjusted rate
`by county, 1950-1969
`
`Trachea, bronchus, and lung cancer;
`white males; age-adjusted rate
`by county, 1950-1969
`
`.. ··
`
`0017
`
`

`

`Stomach cancer, white females;
`age-adjusted rate by county, 1950-1969
`
`Stomach cancer, white males;
`age-adjusted rate by county, 1950-1969
`
`0018
`
`

`

`20 GRAPHICAL PRACTICE
`
`The maps repay careful study. Notice how quickly and naturally
`our attention has been directed toward exploring the substantive
`content of the data rather than toward questions of methodology
`and technique. Nonetheless the maps do have their flaws. They
`wrongly equate the visual importance of each county with its
`geographic area rather than with the number of people living in
`the county (or the number of cancer deaths). Our visual impres(cid:173)
`sion of the data is entangled with the circumstance of geographic
`boundaries, shapes, and areas- the chronic problem afflicting shaded(cid:173)
`in-area designs of such "blot maps" or "patch maps."
`A further shortcoming, a defect of data rather than graphical
`composition, is that the maps are founded on a suspect data source,
`death certificate reports on the cause of death. These reports fall
`under the influence of diagnostic fashions prevailing among doc(cid:173)
`tors and coroners in particular places and times, a troublesome
`adulterant of the evidence purporting to describe the already some(cid:173)
`times ambiguous matter of the exact bodily site of the primary
`cancer. Thus part of the regional clustering seen on the maps, as
`well as some of the hot spots, may reflect varying diagnostic
`customs and fads along with the actual differences in cancer rates
`between areas.
`
`Data maps have a curious history. It was not until the seventeenth
`century that the combination of cartographic and statistical skills
`required to construct the data map came together, fully 5,000 years
`after the first geographic maps were drawn on clay tablets. And
`many highly sophisticated geographic maps were produced cen(cid:173)
`turies before the first map containing any statistical material was
`drawn.3 For example, a detailed map with a full grid was engraved
`during the eleventh century A.D. in China. The Yii Chi Thu (Map
`of the Tracks ofYii the Great) shown here is described by Joseph
`Needham as the
`
`. . . most remarkable cartographic work of its age in any
`culture, carved in stone in + 113 7 but probably dating from
`before + 1100. The scale of the grid is 100 li to the division.
`The coastal outline is relatively firm and the precision of the
`network of river systems extraordinary. The size of the original,
`which is now in the Pei Lin Museum at Sian, is about 3 feet
`square. The name of the geographer is not known .... Anyone
`who compares this map with the contemporary productions
`of European religious cosmography cannot but be amazed at
`the extent to which Chinese geography was at that time ahead
`of the West .... There was nothing like it in Europe till the
`Escorial MS. map of about +1550 •... 4
`
`3 Data maps are usually described as
`"thematic maps" in cartography. For a
`thorough account, see Arthur H. Rob(cid:173)
`inson, Early Thematic Mapping in the
`History of Cartography (Chicago, 1982) .
`On the history of statistical graphics, see
`H. Gray Funkhouser, "Historical Devel(cid:173)
`opment of the Graphical Representation
`of Statistical Data," Osiris, 3 (November
`1937), 269-404; and James R. Beniger
`and Dorothy L. Robyn, "Quantitative
`Graphics in Statistics: A Brief History,"
`American Statistician, 32 (February 1978),
`1-11.
`
`4Joseph Needham, Science and Cillilisa(cid:173)
`tion i11 China (Cambridge, 1959), vol. 3,
`546-547·
`
`0019
`
`

`

`GRAPHICAL EXCELLENCE 21
`
`1£
`
`"'
`
`.
`
`~·
`
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`
`..
`
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`
`11.
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`
`E. Chavannes, "Les Deux Plus Anciens
`Specimens de la Cartographie Chinoise,"
`B!llletin de l' Ecole Fran~aise de l' Extreme
`Orient, 3 (1903), 1-35, Carte B.
`
`0020
`
`

`

`22 GRAPHICAL PRACTICE
`
`~ Ecceformulam,vfum,arque
`
`ftruBuram Tabularum Prolomxi,cum quibufdam locis,m
`qui bus ftudiofus Geographix fe fa tis exercere pore!t.
`
`S E P TENT R I 0.
`pars fupcrior.
`
`37
`
`pars inferior.
`MER I DIES.
`
`The 1546 edition of Cosmographia by Petrus Apianus contained
`examples of map design that show how very close European car(cid:173)
`tography by that time had come to achieving statistical graphicacy,
`even approaching the bivariate scatterplot. But, according to the
`historical record, no one had yet made the quantitative abstraction
`of placing a measured quantity on the map's surface at the inter(cid:173)
`section of the two threads instead of the name of a city, let alone
`the more difficult abstraction of replacing latitude and longitude
`with some other dimensions, such as time and money. Indeed, it
`was not until1786 that the first economic time-series was plotted ..
`
`0021
`
`

`

`GRAPHICAL EXCELLENCE 23
`
`~~#---tt----P~~~~~~~~~--~~M-~~~~~~~~~~+-~~~~
`
`NO~:--~--d
`
`One of the first data maps was Edmond Halley's 1686 chart
`showing trade winds and monsoons on a world map. 5 The detailed
`section below shows the cartographic symbolization; with, as
`Halley wrote, " ... the sharp end of each little stroak pointing out
`that part of the Horizon, from whence the wind continually comes;
`and where there are Monsoons the rows of stroaks run alternately
`backwards and forwards, by which means they are thicker [denser]
`than elsewhere."
`
`SNormanJ. W. Thrower, "Edmond
`Halley as a Thematic Ceo-Cartogra(cid:173)
`pher," Annals of the Association of Amer(cid:173)
`ican Geographers, 59 (December 1969),
`652-676.
`
`Edmond Halley, "An Historical Ac(cid:173)
`count of the Trade Winds, and Mon(cid:173)
`soons, Observable in the Seas Between
`and Near the Tropicks; With an At(cid:173)
`tempt to Assign the Phisical Cause of
`Said Winds," Philosophical Transactions,
`183 (1686), 153-168.
`
`0022
`
`

`

`24 GRAPHICAL PRACTICE
`
`An early and most worthy use of a map to chart patterns of
`disease was the famous dot map of Dr. John Snow, who plotted
`the location of deaths from cholera in central London for Sep(cid:173)
`tember 1854. Deaths were marked by dots and, in addition, the
`area's eleven water pumps were located by crosses. Examining the
`scatter over the surface of the map, Snow observed that cholera
`occurred almost entirely among those who lived near (and drank
`from) the Broad Street water pump. He had the handle of the
`contaminated pump removed, ending the neighborhood epidemic
`which had taken more than 500 lives.6 The pump is located at the
`center of the map, just to the right of the Din BROAD STREET. Of
`course the link between the pump and the disease might have been
`revealed by computation and analysis without graphics, with some
`good luck and hard work. But, here at least, graphical analysis
`testifies about the data far more efficiently than calculation.
`
`50
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`• Deaths from cholua
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`6 E. W. Gilbert, "Pioneer Maps of Health
`and Disease in England," Geographical
`]oumal, 124 (1958), 172-183.
`
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`Charles Joseph Minard gave quantity as well as direction to the
`data measures located on the world map in his portrayal of the
`1864 exports of French wine:
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`0024
`
`

`

`26 GRAPHICAL PRACTICE
`
`Computerized cartography and modern photographic techniques
`have increased the density of information some s,ooo-fold in the
`best of current data maps compared to Halley's pioneering effort.
`This map shows the distribution of 1.3 million galaxies (including
`some overlaps). in the northern galactic hemisphere. The map
`divides the sky into 1,024- X 2,222 rectangles. The number of gal(cid:173)
`axies counted in each of the 2,275,328 rectangles is represented by
`ten gray tones; the darker the tone, the greater the number of
`galaxies counted. The north galactic pole is at the center. The
`sharp edge on the left results from the earth blocking the view
`from the observatory. In the area near the perimeter of the map,
`the view is obscured by the interstellar dust of the galaxy in which
`we live (the Milky Way) as the line of sight passes through the
`flattened disk of our galaxy. The curious texture of local clusters
`of galaxies seen in this truly new view of the universe was not
`anticipated by students of galaxies, who had, of course, micro(cid:173)
`scopically examined millions of photographs of galaxies before
`seeing this macroscopic view. Although the clusters are clearly
`evident (and accounted for by a theory of galactic origins), the
`seemingly random filaments may be happenstance. The producers
`of the map note the "strong temptation to conclude that the gal(cid:173)
`axies are arranged in a remarkable filamentary pattern on scales
`of approximately 5° to 15°, but we caution that this visual impres(cid:173)
`sion may be misleading because the eye tends to pick out linear
`patterns even in random noise. Indeed, roughly similar patterns
`are seen on maps constructed from simulated catalogs where no
`linear structure has been built in .... " 7
`
`The most extensive data maps, such as the cancer atlas and the
`count of the galaxies, place millions of bits of information on a
`single page before our eyes. No other method for the display of
`statistical information is so powerful.
`
`7 Michael Seidner, B. H. Siebers, Edward
`]. Groth and P. James E. Peebles,
`"New Reduction of the Lick Catalog of
`Galaxies," Astronomical Journal, 82
`. (April1977), 249-314.
`
`0025
`
`

`

`GRAPHICAL EXCELLENCE 27
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`0026
`
`

`

`28 GRAPHICAL PRACTICE
`
`Time-Series
`
`The time-series plot is the most frequently used form of graphic
`design.8 With one dimension marching along to the regular rhythm
`of seconds, minutes, hours, days, weeks, months, years, centuries, or
`millennia, the natural ordering of the time scale gives this design a
`strength and efficiency of interpretation found in no other graphic
`arrangement.
`This reputed tenth- (or possibly eleventh-) century illustration
`of the inclinations of the planetary orbits as a function of time,
`apparently part of a text for monastery schools, is the oldest known
`example of an attempt to show changing values graphically. It
`appears as a mysterious and isolated wonder in the history of data
`graphics, since the next extant graphic of a plotted time-series
`shows up some 8oo years later. According to Funkhouser, the
`astronomical content is confused and there are difficulties in recon(cid:173)
`ciling the graph and its accompanying text with the actual move(cid:173)
`ments of the planets. Particularly disconcerting is the wavy path
`ascribed to the sun. 9 An erasure and correction of a curve occur
`near the middle of the graph.
`
`sA random sample of 4,000 graphics
`drawn from 15 of the world's news(cid:173)
`papers and magazines published from
`1974 to 1980 found that more than 75
`percent of all the graphics published
`were time-series. Chapter 3 reports more
`on this.
`
`9H. Gray Funkhouser, "A Note on a
`Tenth Century Graph," Osiris, 1
`(January 1936), 26o-262.
`
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`0027
`
`

`

`It was not until the late 1700s that time-series charts began to
`appear in scientific writings. This drawing of Johann Heinrich
`Lambert, one of a long series, shows the periodic variation in soil
`temperature in relation to the depth under the surface. The greater
`the depth, the greater the time-lag in temperature responsiveness.
`Modern graphic designs showing time-series periodicities differ
`little from those of Lambert, although the data bases are far larger.
`
`GRAPHICAL EXCELLENCE 29
`
`]. H. Lambert, Pyrometrie (Berlin, 1779 ).
`
`NORTHERN SOURCE
`VOYAGER 2
`SOUTHERN SOURCE
`'E 10 1 +-~~--~~~r-~~~--~~~--~~~--~~~--~~-L-r~~~~--~~-J--+
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`JULY 5
`
`JULY 6
`
`JULY 7
`
`19.3
`JULY 8,1979
`
`This plot of radio emissions from Jupiter is based on data collected
`by Voyager 2 in its pass close by the planet in July 1979. The radio
`intensity increases and decreases in a ten-hour cycle as Jupiter
`rotates. Maximum intensity occurs when the Jovian north mag(cid:173)
`netic pole is tipped toward the spacecraft, indicating a northern
`hemisphere source. A southern source was detected on July 7, as
`the spacecraft neared the equatorial plane. The horizontal scale
`shows the distance of the spacecraft from the planet measured in
`terms of Jupiter radii (R). Note the use of dual labels on the hori(cid:173)
`zontal to indicate both the date and distance from Jupiter. The
`entire bottom panel also serves to label the horizontal scale,
`describing the changing orientation of the spacecraft relative to
`Jupiter as the planet is approached. The multiple time-series
`enforce not only comparisons within each series over time (as do
`all time-series plots) but also comparisons between the three
`different sampled radio bands shown. This richly multivariate
`display is based on 453,600 instrument samples of eight bits each.
`The resulting 3.6 million bits were reduced by peak and average
`processing to the 18,900 points actually plotted on the graphic.
`
`D. A. Gurnett, W. S. Kurth, and F. L.
`Scarf, "Plasma Wave Observations Near
`Jupiter: Initial Results from Voyager
`2," Science 206 (November 23, 1979),
`987-991; and letter from Donald A.
`Gurnett to Edward R. Tufte, June 27,
`1980.
`
`0028
`
`

`

`30 GRAP