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It is common to think of statistical graphics and data visualization as relatively modern developments in statistics. In fact, the graphic representation of quantitative information has deep roots. These roots reach into the histories of the earliestmap making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields. Along the way, developments in technologies (printing, reproduction), mathematical theory and practice, and empirical observation and recording enabled the wider use of graphics and new advances in form and content.
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A Brief History of Data Visualization
Michael Friendly
Psychology Department and Statistical Consulting Service
York University
4700 Keele Street, Toronto, ON, Canada M3J 1P3
in: Handbook of Computational Statistics: Data Visualization. See also BIBTEXentry below.
author = {M. Friendly},
title = {A Brief History of Data Visualization},
year = {2006},
publisher = {Springer-Verlag},
address = {Heidelberg},
booktitle = {Handbook of Computational Statistics: Data Visualization},
volume = {III},
editor = {C. Chen and W. H\"ardle and A Unwin},
pages = {???--???},
note = {(In press)},
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document created on: March 21, 2006
created from file: hbook.tex
cover page automatically created with CoverPage.sty
(available at your favourite CTAN mirror)
A brief history of data visualization
Michael Friendly
March 21, 2006
It is common to think of statistical graphics and data visualization as relatively modern
developments in statistics. In fact, the graphic representation of quantitative information
has deep roots. These roots reach into the histories of the earliest map-making and visual
depiction, and later into thematic cartography, statistics and statistical graphics, medicine,
and other fields. Along the way, developments in technologies (printing, reproduction)
mathematical theory and practice, and empirical observation and recording, enabled the
wider use of graphics and new advances in form and content.
This chapter provides an overview of the intellectual history of data visualization from
medieval to modern times, describing and illustrating some significant advances along the
way. It is based on a project, called the Milestones Project, to collect, catalog and document
in one place the important developments in a wide range of areas and fields that led to mod-
ern data visualization. This effort has suggested some questions of the use of present-day
methods to analyze and understand this history, that I discuss under therubric of “statistical
1 Introduction
The only new thing in the world is the history you don’t know. —Harry S Truman
It is common to think of statistical graphics and data visualization as relatively modern de-
velopments in statistics. In fact, the graphic portrayal of quantitative information has deep roots.
These roots reach into the histories of the earliest map-making and visual depiction, and later
into thematic cartography, statistics and statistical graphics, with applications and innovations
in many fields of medicine and science that are often intertwined with each other. They also
connect with the rise of statistical thinking and widespread data collection for planning and
commerce up through the 19th century. Along the way, a variety of advancements contributed
to the widespread use of data visualization today. These include technologies for drawing and
reproducing images, advances in mathematics and statistics, and new developments in data col-
lection, empirical observation and recording.
From above ground, we can see the current fruit and anticipate future growth; we must look
below to understand their germination. Yet the great variety of roots and nutrients across these
This paper was prepared for the Handbook of Computational Statistics on Data Visualization. Michael Friendly
is Professor, Psychology Department, York University, Toronto, ON, M3J 1P3 Canada, E-mail:
This work is supported by Grant 8150 from the National Sciences and Engineering Research Council of Canada. I
am grateful to the archivists of many libraries and to les Chevaliers des Album de Statistique Graphique: Antoine
de Falguerolles, Ruddy Ostermann, Gilles Palsky, Ian Spence, Antony Unwin, and Howard Wainer for historical
information, images, and helpful suggestions.
domains, that gave rise to the many branches we see today, are often not well known, and have
never been assembled in a single garden, to be studied or admired.
This chapter provides an overview of the intellectual history of data visualization from me-
dieval to modern times, describing and illustrating some significant advances along the way. It
is based on what I call the Milestones Project, an attempt to provide a broadly comprehensive
and representative catalog of important developments in all fields related to the history of data
There are many historical accounts of developments within the fields of probability (Hald,
1990), statistics (Pearson,1978,Porter,1986,Stigler,1986), astronomy (Riddell,1980), car-
tography (Wallis and Robinson,1987), which relate to, inter alia, some of the important de-
velopments contributing to modern data visualization. There are other, more specialized ac-
counts, which focus on the early history of graphic recording (Hoff and Geddes,1959,1962),
statistical graphs (Funkhouser,1936,1937,Royston,1970,Tilling,1975), fitting equations to
empirical data (Farebrother,1999), economics and time-series graphs (Klein,1997), cartogra-
phy (Friis,1974,Kruskal,1977) and thematic mapping (Robinson,1982,Palsky,1996), and
so forth; Robinson (Robinson,1982, Ch. 2) presents an excellent overview of some of the im-
portant scientific, intellectual, and technical developments of the 15th–18th centuries leading
to thematic cartography and statistical thinking. Wainer and Velleman (2001) provide a recent
account of some of the history of statistical graphics.
But there are no accounts that span the entire development of visual thinking and the visual
representation of data, and which collate the contributions of disparate disciplines. In as much
as their histories are intertwined, so too should be any telling of the development of data vi-
sualization. Another reason for interweaving these accounts is that practitioners in these fields
today tend to be highly specialized, and unaware of related developments in areas outside their
domain, much less their history.
2 Milestones Tour
Every picture tells a story. —Rod Stewart, 1971
In organizing this history, it proved useful to divide history into epochs, each of which
turned out to be describable by coherent themes and labels. This division is, of course somewhat
artificial, but it provides the opportunity to characterize the accomplishments in each period in a
general way, before describing some of them in more detail. Figure 1, discussed in Section 3.2,
provides a graphic overview of the epochs I describe in the subsections below, showing the
frequency of events considered milestones in the periods of this history. For now, it suffices to
note the labels attached to these epochs, a steady rise from the early 18th century to the late 19th
century, with a curious wiggle thereafter.
In the larger picture— recounting the history of data visualization— it turns out that many
of the milestones items have a story to be told: What motivated this development? What was the
communication goal? How does it relate to other developments— What were the pre-cursors?
How has this idea been used or re-invented today? Each section below tries to illustrate the gen-
eral themes with a few exemplars. In particular, this account attempts to tell a few representative
stories of these periods, rather than to try to be comprehensive.
For reasons of economy, only a limited number of images could be printed here, and these
only in black and white. Others are referred to by web links, mostly from the Milestones
Early maps
& diagrams Measurement
& Theory
New graphic
forms Begin modern
Golden age
Modern dark
High-D Vis
Milestones: Time course of developments
1500 1600 1700 1800 1900 2000
Figure 1: The time distribution of events considered milestones in the history of data visualiza-
tion, shown by a rug plot and density estimate.
Project,, where a color
version of this chapter will also be found.
2.1 Pre-17th Century: Early maps and diagrams
The earliest seeds of visualization arose in geometric diagrams, in tables of the positions of stars
and other celestial bodies, and in the making of maps to aid in navigation and exploration. The
idea of coordinates was used by ancient Egyptian surveyors in laying out towns, earthly and
heavenly positions were located by something akin to latitude and longitude at least by 200 BC,
and the map projection of a spherical earth into latitude and longitude by Claudius Ptolemy [c.
85–c. 165] in Alexandria would serve as reference standards until the 14th century.
Among the earliest graphical depictions of quantitative information is an anonymous 10th
century multiple time-series graph of the changing position of the seven most prominent heav-
enly bodies over space and time (Figure 2), described by Funkhouser (1936) and reproduced
in Tufte (1983, p. 28). The vertical axis represents the inclination of the planetary orbits, the
horizontal axis shows time, divided into thirty intervals. The sinusoidal variation, with different
periods is notable, as is the use of a grid, suggesting both an implicit notion of a coordinate
system, and something akin to graph paper, ideas that would not be fully developed until the
In the 14th century, the idea of a plotting a theoretical function (as a proto bar graph), and
Figure 2: Planetary movements shown as cyclic inclinations over time, by an unknown as-
tronomer, appearing in a 10th century appendix to commentaries by A. T. Macrobius on Cicero’s
In Somnium Scripionus. Source: Funkhouser (1936, p. 261).
the logical relation between tabulating values and plotting them appeared in a work by Nicole
Oresme [1323–1382] Bishop of Liseus1(Oresme,1482,1968), followed somewhat later by the
idea of a theoretical graph of distance vs. speed by Nicolas of Cusa.
By the 16th century, techniques and instruments for precise observation and measurement
of physical quantities, and geographic and celestial position were well-developed (for example,
a “wall quadrant” constructed by Tycho Brahe [1546–1601], covering an entire wall in his ob-
servatory). Particularly important were the development of triangulation and other methods to
determine mapping locations accurately (Frisius,1533,Tartaglia,1556). As well, we see ini-
tial ideas for capturing images directly (the camera obscura, used by Reginer Gemma-Frisius in
1545 to record an eclipse of the sun), the recording of mathematical functions in tables (trigono-
metric tables by Georg Rheticus, 1550), and the first modern cartographic atlas (Teatrum Orbis
Terrarum by Abraham Ortelius, 1570). These early steps comprise the beginnings of data visu-
2.2 1600-1699: Measurement and theory
Among the most important problems of the 17th century were those concerned with physical
measurement— of time, distance, and space— for astronomy, surveying, map making, naviga-
tion and territorial expansion. This century also saw great new growth in theory and the dawn
of practical application— the rise of analytic geometry and coordinate systems (Descartes and
1Funkhouser (1936, p. 277) was sufficiently impressed with Oresme’s grasp of the relation between functions
and graphs that he remarked, “if a pioneering contemporary had collected some data and presented Oresme with
actual figures to work upon, we might have had statistical graphs four hundred years before Playfair.
Fermat), theories of errors of measurement and estimation (initial steps by Galileo in the anal-
ysis of observations on Tycho Brahe’s star of 1572 (Hald,1990, §10.3)), the birth of probability
theory (Pascal and Fermat), and the beginnings of demographic statistics (John Graunt) and
“political arithmetic” (William Petty)— the study of population, land, taxes, value of goods,
etc. for the purpose of understanding the wealth of the state.
Early in this century, Christopher Scheiner (1630, recordings from 1611) introduced an idea
Tufte (1983) would later call the principle of “small multiples” to show the changing configu-
rations of sunspots over time, shown in Figure 3. The multiple images depict the recordings of
sunpots from 23 October 1611 until 19 December of that year. The large key in the upper left
identifies seven groups of sunspots by the letters A–F. These groups are similarly identified in
the 37 smaller images, arrayed left-to-right and top-to-bottom below.
Figure 3: Scheiner’s 1626 representation of the changes in sunspots over time. Source: Scheiner
Another noteworthy example (Figure 4) shows a 1644 graphic by Michael Florent van Lan-
gren [1600–1675], a Flemish astronomer to the court of Spain, believed to be the first visual
representation of statistical data (Tufte,1997, p. 15). At that time, lack of a reliable means to
determine longitude at sea hindered navigation and exploration.2This 1D line graph shows all
2For navigation, latitude could be fixed from star inclinations, but longitude required accurate measurement of
time at sea, an unsolved problem until 1765 with the invention of a marine chronometer by John Harrison. See Sobel
(1996) for a popular account.
12 known estimates of the difference in longitude between Toledo and Rome, and the name of
the astronomer (Mercator, Tycho Brahe, Ptolemy, etc.) who provided each observation.
Figure 4: Langren’s 1644 graph of determinations of the distance, in longitude, from Toledo to
Rome. The correct distance is 16300. Source: Tufte (1997, p. 15).
What is notable is that van Langren could have presented this information in various tables—
ordered by author to show provenance, by date to show priority, or by distance. However, only a
graph shows the wide variation in the estimates; note that the range of values covers nearly half
the length of the scale. Van Langren took as his overall summary the center of the range, where
there happened to be a large enough gap for him to inscribe “ROMA.” Unfortunately, all of the
estimates were biased upwards; the true distance (16300) is shown by the arrow. Van Langren’s
graph is also a milestone as the earliest-known exemplar of the principle of “effect ordering for
data display” (Friendly and Kwan,2003).
In the 1660s, the systematic collection and study of social data began in various European
countries, under the rubric of “political arithmetic” (John Graunt 1662 and William Petty 1665),
with the goals of informing the state about matters related to wealth, population, agricultural
land, taxes and so forth,3as well as for commercial purposes such as insurance and annuities
based on life tables (Jan de Witt, 1671). At approximately the same time, the initial statements
of probability theory around 1654 (see Ball (1908)) together with the idea of coordinate systems
were applied by Christiaan Huygens in 1669 to give the first graph of a continuous distribution
function4(from Gaunt’s life table based on the bills of mortality). The mid 1680s saw the first
bivariate plot derived from empirical data, a theoretical curve relating barometric pressure to
altitude, and the first known weather map,5showing prevailing winds on a map of the earth
By the end of this century, the necessary elements for the development of graphical methods
were at hand— some real data of significant interest, some theory to make sense of them, and a
few ideas for their visual representation. Perhaps more importantly, one can see this century as
giving rise to the beginnings of visual thinking, as illustrated by the examples of Scheiner and
van Langren.
3For example, Graunt (1662) used his tabulations of London births and deaths from parish records and the bills
of mortality to estimate the number of men the king would find available in the event of war (Klein,1997, 43–47).
2.3 1700-1799: New graphic forms
With some rudiments of statistical theory, data of interest and importance, and the idea of
graphic representation at least somewhat established, the 18th century witnessed the expansion
of these aspects to new domains and new graphic forms. In cartography, map-makers began to
try to show more than just geographical position on a map. As a result, new data representations
(isolines and contours) were invented, and thematic mapping of physical quantities took root.
Towards the end of this century, we see the first attempts at the thematic mapping of geologic,
economic, and medical data.
Abstract graphs, and graphs of functions became more widespread, along with the early
beginnings of statistical theory (measurement error) and systematic collection of empirical data.
As other (economic and political) data began to be collected, some novel visual forms were
invented to portray them, so the data could ‘speak to the eyes.
For example, the use of isolines to show contours of equal value on a coordinate grid (maps
and charts) was developed by Edmund Halley (1701). Figure 5, showing isogons— lines of
equal magnetic declination— is among the first examples of thematic cartography, overlay-
ing data on a map. Contour maps and topographic maps were introduced somewhat later by
Phillippe Buache (1752) and Marcellin du Carla-Boniface (1782).
Figure 5: A portion of Edmund Halley’s New and Correct Sea Chart Shewing the Variations
in the Compass in the Western and Southern Ocean, 1701. Source:Halley (1701), image from
Palsky (1996, p. 41).
Timelines, or “cartes chronologiques” were first introduced by Jacques Barbeu-Dubourg in
the form of an annotated chart of all of history (from Creation) on a 54-foot scroll (Ferguson,
1991). Joseph Priestley, presumably independently, used a more convenient form to show first a
timeline chart of biography (lifespans of 2,000 famous people, 1200 B.C. to 1750 A.D., Priestley
(1765)), and then a detailed chart of history (Priestley,1769).
The use of geometric figures (squares or rectangles) and cartograms to compare areas or de-
mographic quantities by Charles de Fourcroy6(1782) and August F.W. Crome (1785) provided
another novel visual encoding for quantitative data using superimposed squares to compare the
areas of European states.
As well, several technological innovations provided necessary ingredients for the produc-
tion and dissemination of graphic works. Some of these facilitated the reproduction of data
images, such as three-color printing, invented by Jacob le Blon in 1710 and lithography by
Aloys Senefelder in 1798. Of the latter, Robinson (1982, p. 57) says “the effect was as great as
the introduction [of the Xerox machine].” Yet, likely due to expense, most of these new graphic
forms appeared in publications with limited circulation, unlikely to attract wide attention.
A prodigious contributor to the use of the new graphical methods, Johann Lambert [1728–
1777] introduced the ideas of curve fitting and interpolation from empirical data points. He used
various sorts of line graphs and graphical tables to show periodic variation, for example, in air
and soil temperature.7
William Playfair [1759–1823] is widely considered the inventor of most of the graphical
forms widely used today— first the line graph and bar chart (Playfair,1786), later the pie chart
and circle graph (Playfair,1801). Figure 6shows a creative combination of different visual
forms: circles, pies and lines, re-drawn from Playfair (1801, Plate 2).
The use of two separate vertical scales for different quantities (population and taxes) is
today considered a sin in statistical graphics (you can easily jiggle either scale to show different
things). But Playfair used this device to good effect here to try to show taxes per capita in various
nations and argue that the British were overtaxed, compared with others. But alas, showing
simple numbers by a graph was hard enough for Playfair— he devoted several pages of text in
Playfair (1786) describing how to read and understand a line graph. The idea of calculating and
graphing rates and other indirect measurements was still to come.
In this figure the left axis and line on each circle/pie graph shows population, while the
right axis and line shows taxes. Playfair intended that the slope of the line connecting the two
would depict the rate of taxation directly to the eye; but, of course, the slope also depends on
the diameters of the circles. Playfair’s graphic sins can perhaps be forgiven here, because the
graph clearly shows the slope of the line for Britain to be in the opposite direction of those for
the other nations.
A somewhat later graph (Playfair,1821), shown in Figure 7, exemplifies the best that Play-
fair had to offer with these graphic forms. Playfair used three parallel time series to show the
price of wheat, weekly wages, and reigning monarch over a 250 year span from 1565 to 1820,
and used this graph to argue that workers had become better off in the most recent years.
By the end of this century (1794), the utility of graphing in scientific applications prompted a
Dr. Buxton in London to patent and market printed coordinate paper; curiously, a patent for lined
notepaper was not issued until 1815. The first known published graph using coordinate paper
is one of periodic variation in barometric pressure (Howard,1800). Nevertheless, graphing of
Figure 6: Re-drawn version of a portion of Playfair’s 1801 pie-circle-line chart, comparing
population and taxes in several nations.
data would remain rare for another 30 or so years,8perhaps largely because there wasn’t much
data (apart from widespread astronomical, geodetic, and physical measurement) of sufficient
complexity to require new methods and applications. Official statistics, regarding population
and mortality, and economic data were generally fragmentary and often not publicly available.
This would soon change.
2.4 1800-1850: Beginnings of modern graphics
With the fertilization provided by the previous innovations of design and technique, the first half
of the 19th century witnessed explosive growth in statistical graphics and thematic mapping, at
a rate which would not be equalled until modern times.
In statistical graphics, all of the modern forms of data display were invented: bar and pie
charts, histograms, line graphs and time-series plots, contour plots, scatterplots, and so forth.
In thematic cartography, mapping progressed from single maps to comprehensive atlases, de-
picting data on a wide variety of topics (economic, social, moral, medical, physical, etc.), and
introduced a wide range of novel forms of symbolism. During this period graphical analysis
of natural and physical phenomena (lines of magnetism, weather, tides, etc.) began to appear
regularly in scientific publications as well.
In 1801, the first geological maps were introduced in England by William Smith [1769–
1839], setting the pattern for geological cartography or “stratigraphic geology” (Smith,1815).
8William Herschel (1833), in a paper that describes the first instance of a modern scatterplot, devoted three pages
to a description of plotting points on a grid.
Figure 7: William Playfair’s 1821 time series graph of prices, wages, and ruling monarch over
a 250 year period. Source:Playfair (1821), image from Tufte (1983, p. 34)
These and other thematic maps soon led to new ways to show quantitative information on maps,
and, equally importantly, to new domains for graphically-based inquiry.
In the 1820s, Baron Charles Dupin [1784–1873] invented the use of continuous shadings
(from white to black) to show the distribution and degree of illiteracy in France (Dupin,1826)—
the first unclassed choropleth map,9and perhaps the first modern-style thematic statistical map
(Palsky,1996, p. 59). Later given the lovely title, “Carte de la France obscure et la France
ee,” it attracted wide attention, and was also perhaps the first application of graphics in the
social realm.
More significantly, in 1825, the Ministry of Justice in France instituted the first centralized
national system of crime reporting, collected quarterly from all departments and recording the
details of every charge laid before the French courts. In 1833, Andr´
e-Michel Guerry, a lawyer
with a penchant for numbers used this data (along with other data on literacy, suicides, donations
to the poor and other “moral” variables) to produce a seminal work on the moral statistics of
France (Guerry,1833)— a work that (along with Quetelet (1831,1835)) can be regarded as the
foundation of modern social science.10
Guerry used maps in a style similar to Dupin to compare the ranking of departments on
pairs of variables, notably crime vs. literacy, but other pairwise variable comparisons were
made.11 He used these to argue that the lack of an apparent (negative) relation between crime
10Guerry showed that rates of crime, when broken down by department, type of crime, age and gender of the
accused and other variables, remained remarkably consistent from year to year, yet variedwidely across departments.
He used this to argue that such regularity implied the possibility of establishing social laws, much as the regularity
of natural phenomena implied physical ones. Guerry also pioneered the study of suicide, with tabulations of suicides
in Paris, 1827–1830, by sex, age, education, profession, etc. and a content analysis of suicide notes as to presumed
11Today, one would use a scatterplot, but that graphic form was only just invented (Herschel,1833) and would not
and literacy contradicted the arm-chair theories of some social reformers who had argued that
the way to reduce crime was to increase education.12 Guerry’s maps and charts made somewhat
of an academic sensation both in France and the rest of Europe; he later exhibited several of
these at the 1851 London Exhibition, and carried out a comparative study of crime in England
and France (Guerry,1864), for which he was awarded the Moynton Prize in statistics by the
French Academy of Sciences.13 But Guerry’s systematic and careful work was unable to shine
in the shadows cast by Adolphe Quetelet, who regarded moral and social statistics as his own
Figure 8: A portion of Dr. Robert Baker’s cholera map of Leeds, 1833, showing the districts
affected by cholera. Source: Gilbert (1958, Fig. 2).
In October 1831, the first case of asiatic cholera occurred in Great Britain, and over 52,000
enter common usage for another 50 years; see Friendly and Denis (2005).
12Guerry seemed reluctant to take sides. He also contradicted the social conservatives who argued for the need to
build more prisons or impose more severe criminal sentences. See Whitt (2002).
13Among the 17 plates in this last work, seven pairs of maps for England and France each included sets of small
line graphs to show trends over time, decompositions by subtype of crime and sex, distributions over months of the
year, and so forth. The final plate, on general causes of crime is an incredibly detailed and complex multivariate
semi-graphic display attempting to relate various types of crimes to each other, to various social and moral aspects
(instruction, religion, population) as well as to their geographic distribution.
people died in the epidemic that ensued over the next 18 months or so (Gilbert,1958). Sub-
sequent cholera epidemics in 1848–1849 and 1853–1854 produced similarly large death tolls,
but the water-born cause of the disease was unknown until 1855 when Dr. John Snow produced
his famous dot map14 (Snow,1855) showing deaths due to cholera clustered around the Broad
Street pump in London. This was indeed a landmark graphic discovery, but it occurred at the
end of the period, roughly 1835–1855, that marks a high-point in the application of thematic
cartography to human (social, medical, ethnic) topics. The first known disease map of cholera
(Figure 8), due to Dr. Robert Baker (1833), shows the districts of Leeds “affected by cholera”
in the particularly severe 1832 outbreak.
I show this figure to make another point— why Baker’s map did not lead to a “eureka”
experience, while John Snow’s did. Baker used a town plan of Leeds that had been divided
into districts. Of a population of 76,000 in all of Leeds, Baker mapped the 1800 cholera cases
by hatching in red “the districts in which the cholera had prevailed. In his report, he noted
an association between the disease and living conditions: “how exceedingly the disease has
prevailed in those parts of the town where there is a deficiency, often an entire want of sewage,
drainage, and paving” (Baker,1833, p. 10). Baker did not indicate the incidence of disease on
his map, nor was he equipped to display rates of disease (in relation to population density)15
and his knowledge of possible causes, while definitely on the right track, was both weak and
implicit (not analyzed graphically or by other means). It is likely that some, perhaps tenuous,
causal indicants or evidence were available to Baker, but he was unable to connect the dots, or
see a geographically distributed outcome in relation to geographic factors in even the simple
ways that Guerry had tried.
At about the same time, 1830–1850, the use of graphs began to become recognized in
some official circles for economic and state planning— where to build railroads and canals?
what is the distribution of imports and exports? This use of graphical methods is no better illus-
trated than in the works of Charles Joseph Minard [1781–1870], whose prodigious graphical
inventions led Funkhouser (1937) to call him the Playfair of France. To illustrate, we choose
(with some difficulty) an 1844 “tableau-graphique” (Figure 9) by Minard, an early progenitor of
the modern mosaic plot (Friendly,1994). On the surface, mosaic plots descend from bar charts,
but Minard introduced two simultaneous innovations: the use of divided and proportional-width
bars so that area had a concrete visual interpretation. The graph shows the transportation of com-
mercial goods along one canal route in France by variable-width, divided bars (Minard,1844).
In this display the width of each vertical bar shows distance along this route; the divided bar
segments have height amount of goods of various types (shown by shading), so the area of
each rectangular segment is proportional to cost of transport. Minard, a true visual engineer
(Friendly,2000), developed such diagrams to argue visually for setting differential price rates
for partial vs. complete runs. Playfair had tried to make data ‘speak to the eyes, but Minard
wished to make them ‘calculer par l’œil’ as well.
It is no accident that, in England, outside the numerous applications of graphical methods
in the sciences, there was little interest in or use of graphs among statisticians (or “statists” as
14 Image:
15The German geographer Augustus Petermann produced a “Cholera map of the British Isles” in 1852 us-
ing national data from the 1831–1832 epidemic, (image:
10000/1000/800/S0011888.jpg) shaded in proportion to the relative rate of mortality using class intervals
(<1/35,1/35 : 1/100,1/100 : 1/200,... ). No previous disease map allowed determination of the range of
mortality in any given area.
Figure 9: Minard’s Tableau Graphique, showing the transportation of commercial goods along
the Canal du Centre (Chalon–Dijon). Intermediate stops are spaced by distance, and each bar
is divided by type of goods, so the area of each tile represents the cost of transport. Arrows
show the direction of transport. Source: ENPC:5860/C351 (Col. et clich´
e ENPC; used by
they called themselves). If there is a continuum ranging from “graph people” to “table people,”
British statisticians and economists were philosophically more table-inclined, and looked upon
graphs with suspicion up to the time of William Stanley Jevons around 1870 (Maas and Morgan,
2005). Statistics should be concerned with the recording of “facts relating to communities of
men which are capable of being expressed by numbers” (Mouat,1885, p.15), leaving the gen-
eralization to laws and theories to others. Indeed, this view was made abundantly clear in the
logo of the Statistical Society of London (now the Royal Statistical Society): a banded sheaf of
wheat, with the motto Aliis Exterendum— to others to flail the wheat. Making graphs, it seemed,
was too much like bread-making.
2.5 1850–1900: The Golden Age of statistical graphics
By the mid-1800s, all the conditions for the rapid growth of visualization had been established—
a “perfect storm” for data graphics. Official state statistical offices were established throughout
Europe, in recognition of the growing importance of numerical information for social plan-
ning, industrialization, commerce, and transportation. Statistical theory, initiated by Gauss and
Laplace, and extended to the social realm by Guerry and Quetelet, provided the means to make
sense of large bodies of data.
What started as the Age of Enthusiasm (Funkhouser,1937,Palsky,1996) for graphics ended
with what can be called the Golden Age, with unparalleled beauty and many innovations in
graphics and thematic cartography. So varied were these developments, that it is difficult to be
comprehensive, but a few themes stand out.
2.5.1 Escaping flatland
Although some attempts to display more than two variables simultaneously had occurred earlier
in multiple time-series (Playfair,1801,Minard,1826), contour graphs (Vauthier,1874) and a
variety of thematic maps, (e.g., Berghaus (1838)) a number of significant developments ex-
tended graphics beyond the confines of a flat piece of paper. Gustav Zeuner [1828–1907] in
Germany (Zeuner,1869), and later Luigi Perozzo [?–1875] in Italy (Perozzo,1880) constructed
3D surface plots of population data.16 The former was an axonometric projection showing var-
ious slices, while the latter (a 3D graph of population in Sweden from 1750–1875 by year and
age group) was printed in red and black and designed as a stereogram.17
Contour diagrams, showing iso-level curves of 3D surfaces, had also been used earlier in
mapping contexts (Nautonier,1604,Halley,1701,von Humboldt,1817), but the range of prob-
lems and data to which they were applied expanded considerably over this time in attempts
to understand relations among more than two data-based variables, or where the relationships
are statistical, rather than functional or measured with little error. It is more convenient to de-
scribe these under Galton, below. By 1884, the idea of visual and imaginary worlds of varying
number of dimensions found popular expression in Edwin Abbott’s (1884)Flatland, implicitly
suggesting possible views in four and more dimensions.
2.5.2 Graphical innovations
With the usefulness of graphical displays for understanding complex data and phenomena es-
tablished, many new graphical forms were invented and extended to new areas of inquiry, par-
ticularly in the social realm.
Minard (1861) developed the use of divided circle diagrams on maps (showing both a total,
by area, and sub-totals, by sectors, with circles for each geographic region on the map). Later
he developed to an art form the use of flow lines on maps of width proportional to quantities
(people, goods, imports, exports) to show movement and transport geographically. Near the
end of his life, the flow map would be taken to its highest level in his famous depiction of the
fate of the armies of Napoleon and Hannibal, in what Tufte (1983) would call the “best graphic
ever produced.” See Friendly (2002) for a wider appreciation of Minard’s work.
The social and political uses of graphics is also evidenced in the polar area charts (called
“rose diagrams” or “coxcombs”) invented by Florence Nightingale [1820–1910] to wage a
16 Image:
17Zeuner used one axis to show year of birth and another to show present age, with number of surviving persons
on the third, vertical axis, giving a 3D surface. One set of curves thus showed the distribution of population for a
given generation; the orthogonal set of curves showed the distributions across generations at a given point in time,
e.g., at a census.
campaign for improved sanitary conditions in battlefield treatment of soldiers (Nightingale,
1857). They left no doubt that many more soldiers died from disease and the consequences
of wounds than at the hands of the enemy. From around the same time, Dr. John Snow [1813–
1858] is remembered for his use of a dot map of deaths from cholera in an 1854 outbreak in
London the cholera deaths in London. Plotting the residence of each deceased provided the in-
sight for his conclusion that the source of the outbreak could be localized to contaminated water
from a pump on Broad Street, the founding innovation for modern epidemiological mapping.
Figure 10: Lallemand’s L’abaque du bateau Le Triomphe, allowing determination of magnetic
deviation at sea without calculation. Source: courtesy Mme. Marie-No¨
elle Maisonneuve, Les
fonds anciens de la biblioth`
eque de l’Ecole des Mines de Paris.
Scales and shapes for graphs and maps were also transformed for a variety of purposes,
leading to semi-logarithmic graphs (Jevons,1863,1958) to show percentage change in com-
modities over time, log-log plots to show multiplicative relations, anamorphic maps by ´
Cheysson (Palsky,1996, Fig. 63-64) using deformations of spatial size to show a quantita-
tive variable (e.g., the decrease in time to travel from Paris to various places in France over 200
years), and alignment diagrams or nomograms using sets of parallel axes. We illustrate this slice
of the golden age with Figure 10, a tour-de-force graphic for determination of magnetic devia-
tion at sea in relation to latitude and longitude without calculation (“L’Abaque Triomphe”) by
Charles Lallemand (1885), director general of the geodetic measurement of altitudes throughout
France, that combines many variables into a multi-function nomogram, using 3D, juxtaposition
of anamorphic maps, parallel coordinates and hexagonal grids.
2.5.3 Galton’s contributions
Special note should be made of the varied contributions of Francis Galton [1822-1911] to data
visualization and statistical graphics. Galton’s role in the development of the ideas of correlation
and regression are well-known. Less well-known is the role that visualization and graphing
played in his contributions and discoveries.
Galton’s statistical insight (Galton,1886)— that, in a bivariate (normal) distribution (say,
height of child against height of parent), (a) the isolines of equal frequency would appear as
concentric ellipses, and (b) that the locus of the (regression) lines of means of y|xand of x|y
were the conjugate diameters of these ellipses — was based largely on visual analysis from
the application of smoothing to his data. Karl Pearson would later say, “that Galton should
have evolved all this from his observations is to my mind one of the most noteworthy scientific
discoveries arising from pure analysis of observations. (Pearson,1920, p. 37). This was only
one of Galton’s discoveries based on graphical methods.
In earlier work, Galton had made wide use of isolines, contour diagrams and smoothing
in a variety of areas. An 1872 paper showed the use of “isodic curves” to portray the
joint effects of wind and current on the distance ships at sea could travel in any direction. An
1881 “isochronic chart” (Galton,1881) showed the time it took to reach any destination in the
world from London by means of colored regions on a world map. Still later, he analyzed rates
of fertility in marriages in relation to the ages of father and mother using “isogens,” curves of
equal percentage of families having a child (Galton,1894).
But perhaps the most notable non-statistical graphical discovery was that of the “anti-cyclonic”
(counter-clockwise) pattern of winds around low-pressure regions, combined with clockwise ro-
tations around high-pressure zones. Galton’s work on weather patterns began in 1861 and was
summarized in Meteorographica (1863). It contained a variety of ingenious graphs and maps
(over 600 illustrations in total) one of which is shown in Figure 11. This remarkable chart, one
of a two-page trellis-style display, shows observations on barometric pressure, wind direction,
rain and temperature from 15 days in December 1861.18 For each day, the 3×3grid shows
schematic maps of Europe, mapping pressure (row 1), wind and rain (row 2) and temperature
(row 3), in the morning, afternoon and evening (columns). One can clearly see the series of
black areas (low pressure) on the barometric charts for about the first half of the month, corre-
sponding to the counter-clockwise arrows in the wind charts, followed by a shift to red areas
(high pressure) and more clockwise arrows. Wainer (2005, p. 56) remarks, “Galton did for the
collectors of weather data what Kepler did for Tycho Brahe. This is no small accomplishment.”
2.5.4 Statistical Atlases
The collection, organization and dissemination of official government statistics on population,
trade and commerce, social, moral and political issues became widespread in most of the coun-
18 In July 1861, Galton distributed a circular to meterologists throughout Europe, asking them to record these data
synchonously, three times a day for the entire month of December, 1861. About 50 weather stations supplied the
data; see Pearson (1930, p. 37–39).
Figure 11: One page of Galton’s 1863 multivariate weather chart of Europe showing barometric
pressure, wind direction, rain, and temperature for the month of December, 1861. Source:
Pearson (1930, pl. 7).
tries of Europe from about 1825 to 1870 (Westergaard,1932). Reports containing data graphics
were published with some regularity in France, Germany, Hungary, and Finland, and with tabu-
lar displays in Sweden, Holland, Italy and elsewhere. At the same time, there was an impetus to
develop standards for graphical presentation at the International Statistical Congresses that had
begun in 1853 in Belgium (organized by Quetelet), and these congresses were closely linked
with state statistical bureaus. The main participants in the graphics section included Georg
von Mayr, Hermann Schwabe, Pierre ´
Emile Levasseur and ´
Emile Cheysson. Among other rec-
ommendations was one from the 7th Statistical Congress in 1869 that official publications be
accompanied by maps and diagrams. The state-sponsored statistical atlases that ensued provide
additional justification to call this period the Golden Age of Graphics, and some of its most
impressive exemplars.
The pinnacle of this period of state-sponsored statistical albums is undoubtedly the Albums
de Statistique Graphique published annually by the French ministry of public works from 1879-
1897 under the direction of ´
Emile Cheysson.19 They were published as large-format books
19Cheysson had been one of the major participants in committees on the standardization of graphical methods at
(about 11 x 17 in.), and many of the plates folded out to four- or six-times that size, all printed
in color and with great attention to layout and composition. We concur with Funkhouser (1937,
p.336) that “the Albums present the finest specimens of French graphic work in the century and
considerable pride was taken in them by the French people, statisticians and laymen alike.
The subject matter of the albums largely concerned economic and financial data related to
the planning, development and administration of public works— transport of passengers and
freight, by rail, on inland waterways and through seaports, but also included such topics as
revenues in the major theaters of Paris, attendance at the universal expositions of 1867, 1878
and 1889, changes in populations of French departments over time, and so forth.
More significantly for this account the Albums can also be viewed as an exquisite sampler of
all the graphical methods known at the time, with significant adaptations to the problem at hand.
The majority of these graphs used and extended the flow map pioneered by Minard. Others used
polar forms— variants of pie and circle diagrams, star plots and rose diagrams, often overlaid
on a map and extended to show additional variables of interest. Still others used sub-divided
squares in the manner of modern mosaic displays (Friendly,1994) to show the breakdown of
a total (passengers, freight) by several variables. It should be noted that in almost all cases
the graphical representation of the data was accompanied by numerical annotations or tables,
providing precise numerical values.
The Albums are discussed extensively by Palsky (1996), who includes seven representative
illustrations. It is hard to choose a single image here, but my favorites are surely the recursive,
multi-mosaic of rail transportation for the 1884–1886 volumes, the first of which is shown in
Figure 12. This cartogram uses one large mosaic (in the lower left) to show the numbers of
passengers and tons of freight shipped from Paris from the four principal train stations. Of the
total leaving Paris, the amounts going to each main city are shown by smaller mosaics, colored
according to railway lines; of those amounts, the distribution to smaller cities is similarly shown,
connected by lines along the rail routes.
Among the many other national statistical albums and atlases, those from the U.S. Cen-
sus bureau also deserve special mention. The Statistical Atlas of the Ninth Census, produced in
1872–1874 under the direction of Francis A. Walker [1840–1897] contained 60 plates, including
several novel graphic forms. The ambitious goal was to present a graphic portrait of the nation,
and covered a wide range of physical and human topics: geology, minerals, weather; population
by ethnic origin, wealth, illiteracy, school attendance and religious affiliation; death rates by
age, sex, race and cause, prevalence of blindness, deaf mutism and insanity, and so forth. “Age
pyramids” (back-to-back, bilateral frequency histograms and polygons) were used effectively to
compare age distributions of the population for two classes (gender, married/single, etc.). Sub-
divided squares and area-proportional pies of various forms were also used to provide com-
parisons among the states on multiple dimensions simultaneously (employed/unemployed, sex,
schooling, occupational categories). The desire to provide for easy comparisons among states
and other categorizations was expressed by arranging multiple sub-figures as “small multiples”
in many plates.
Following each subsequent decennial census for 1880 to 1900, reports and statistical atlases
were produced with more numerous and varied graphic illustrations. The 1898 volume from
the Eleventh Census (1890), under the direction of Henry Gannett [1846–1914] contained over
400 graphs, cartograms and statistical diagrams. There were several ranked parallel coordinate
the International Statistical Congresses from 1872 on. He was trained as an engineer at the ENPC, and later became
a professor of political economy at the ´
Ecole des Mines.
Figure 12: Mouvement des voyageurs et des marchandises dans les principales stations de
chemins de fer en 1882. Scale: 2mm2= 10,000 passengers or tons of freight. Source: Album,
1884, Plate 11 (author’s collection).
plots comparing states and cities over all censuses from 1790–1890. Trellis-like collections
of shaded maps showed interstate migration, distributions of religious membership, deaths by
known causes, and so forth.
The 1880 and 1890 volumes produced under Gannett’s direction are also notable for (a) the
multi-modal combination of different graphic forms (maps, tables, bar charts, bilateral poly-
gons) in numerous plates, and (b) the consistent use of effect-order sorting (Friendly and Kwan,
2003) to arrange states or other categories in relation to what was to be shown, rather than for
lookup (e.g., Alabama–Wyoming).
Figure 13: Interstate migration shown by back-to-back bar charts, sorted by emigration. Source:
Statistical Atlas of the Eleventh Census, 1890, diagram 66, p. 23 (author’s collection).
For example, Figure 13 shows interstate immigration in relation to emigration for the 49
states and territories in 1890. The right side shows population loss sorted by emigration, ranging
from NY, Ohio, Penn. and Illinois at the top to Idaho, Wyoming and Arizona at the bottom. The
left side shows where the emigrants went: Illinois, Missouri, Kansas and Texas had the biggest
gains, Virginia the biggest net loss. It is clear that people were leaving the eastern states and
were attracted to those of the midwest Mississippi valley. Other plates showed this data in
map-based formats.
However, the Age of Enthusiasm and the Golden Age were drawing to a close. The French
Albums de Statistique Graphique were discontinued in 1897 due to the high cost of production;
statistical atlases appeared in Switzerland in 1897 and 1914, but never again. The final two U.S.
Census atlases, issued after the 1910 and 1920 censuses, “were both routinized productions,
largely devoid of color and graphic imagination” (Dahmann,2001).
2.6 1900-1950: The modern dark ages
If the late 1800s were the “golden age” of statistical graphics and thematic cartography, the early
1900s can be called the “modern dark ages” of visualization (Friendly and Denis,2000).
There were few graphical innovations, and, by the mid-1930s, the enthusiasm for visualiza-
tion which characterized the late 1800s had been supplanted by the rise of quantification and
formal, often statistical, models in the social sciences. Numbers, parameter estimates, and, es-
pecially, those with standard errors were precise. Pictures were— well, just pictures: pretty or
evocative, perhaps, but incapable of stating a “fact” to three or more decimals. Or so it seemed
to many statisticians.
But it is equally fair to view this as a time of necessary dormancy, application, and popu-
larization, rather than one of innovation. In this period statistical graphics became main stream.
Graphical methods entered English20 textbooks (Bowley,1901,Peddle,1910,Haskell,1919,
Karsten,1925), the curriculum (Costelloe,1915,Warne,1916), and standard use in government
(Ayres,1919), commerce (Gantt charts and Shewart’s control charts) and science.
These textbooks contained rather detailed descriptions of the graphic method, with an appre-
ciative and often modern flavor. For example, Sir Arthur Bowley’s (1901)Elements of Statistics
devoted two chapters to graphs and diagrams, and discussed frequency and cumulative fre-
quency curves (with graphical methods for finding the median and quartiles), effects of choice
of scales and baselines on visual estimation of differences and ratios, smoothing of time-series
graphs, rectangle diagrams in which three variables could be shown by height, width and area
of bars, and “historical diagrams” in which two or more time series could be shown on a single
chart for comparative views of their histories.
Bowley’s (1901, p. 151-154) example of smoothing (see Figure 14) illustrates the character
of his approach. Here he plotted the total value of exports from Britain and Ireland over 1855–
1899. At issue was whether exports had become stationary in the most recent years and the
conclusion by Sir Robert Giffen (1899), based solely on tables of averages for successive five
year periods,21 that “the only sign of stationariness is an increase at a less rate in the last periods
than in the earlier periods” (p. 152). To answer this, he graphed the raw data, together with
curves of the moving average over three, five and ten year periods. The three- and five-year
moving averages show strong evidence of an approximately 10 year cycle, and he noted, “no
argument can stand which does not take account of the cycle of trade, which is not eliminated
until we take decennial averages” (p. 153). To this end, he took averages of successive 10-year
periods starting 1859 and drew a freehand curve “keeping as close [to the points] as possible,
without making sudden changes in curvature,” giving the thick curve in Figure 14.22 Support
for Sir Robert’s conclusion and the evidence for a 10-year cycle owe much to this graphical
Moreover, perhaps for the first time, graphical methods proved crucial in a number of new
insights, discoveries, and theories in astronomy, physics, biology, and other sciences. Among
these, one may refer to (a) E. W. Maunder’s (1904) “butterfly diagram” to study the variation of
sunspots over time, leading to the discovery that they were markedly reduced in frequency from
1645–1715; (b) the Hertzsprung-Russell diagram (Hertzsprung,1911,Spence and Garrison,
1993), a log-log plot of luminosity as a function of temperature for stars, used to explain the
changes as a star evolves and laying the groundwork for modern stellar physics; (c) the dis-
20The first systematic attempt to survey, describe, and illustrate available graphic methods for experimental data
was ´
Etienne Jules Marey’s (1878)La M´
ethode Graphique. Marey [1830–1904] also invented several devices for
visual recording, including the sphymograph and chronophotography to record motion of birds in flight, people
running, and so forth.
21Giffen, an early editor of The Statist, also wrote a statistical text published posthumously in 1913; it contained
an entire chapter on constructing tables, but not a single graph (Klein,1997, p. 17).
22A reanalysis of the data using a loess smoother shows that this is in fact over-smoothed, and corresponds closely
to a loess window width of f= 0.50. The optimal smoothing parameter, minimizing AICCis f= 0.16, giving a
smooth more like Bowley’s three- and five-year moving averages.
Figure 14: Arthur Bowley’s demonstration of methods of smoothing a time series graph. Mov-
ing averages of three, five and ten years are compared with a freehand curve drawn through four
points representing the averages of successive ten year periods. Source: Bowley (1901, opposite
p. 151).
covery of the concept of atomic number by Henry Moseley (1913) based largely on graphical
analysis. See (Friendly and Denis,2005) for more detailed discussion of these uses.
As well, experimental comparisons of the efficacy of various graphics forms were begun
(Eells,1926,von Huhn,1927,Washburne,1927), a set of standards and rules for graphic presen-
tation was finally adopted by a joint committee (Joint Committee on Standards for Graphic Presentation,
1914), and a number of practical aids to graphing were developed. In the latter part of this pe-
riod, new ideas and methods for multi-dimensional data in statistics and psychology would
provide the impetus to look beyond the 2D plane.
Graphic innovation was also awaiting new ideas and technology: the development of the
machinery of modern statistical methodology, and the advent of the computational power and
display devices which would support the next wave of developments in data visualization.
2.7 1950–1975: Re-birth of data visualization
Still under the influence of the formal and numerical zeitgeist from the mid-1930s on, data
visualization began to rise from dormancy in the mid 1960s. This was spurred largely by three
significant developments:
In the USA, John W. Tukey [1915–2000], in a landmark paper, The Future of Data Anal-
ysis (Tukey,1962), issued a call for the recognition of data analysis as a legitimate branch
of statistics distinct from mathematical statistics; shortly, he began the invention of a wide
variety of new, simple, and effective graphic displays, under the rubric of “Exploratory
Data Analysis” (EDA)— stem-leaf plots, boxplots, hanging rootograms, two-way table
displays, and so forth, many of which entered the statistical vocabulary and software im-
plementation. Tukey’s stature as a statistician and the scope of his informal, robust, and
graphical approach to data analysis were as influential as his graphical innovations. Al-
though not published until 1977, chapters from Tukey’s EDA book (Tukey,1977) were
widely circulated as they began to appear in 1970–1972, and began to make graphical
data analysis both interesting and respectable again.
In France, Jacques Bertin [1918–] published the monumental Semiologie Graphique
(Bertin,1967). To some, this appeared to do for graphics what Mendeleev had done for
the organization of the chemical elements, that is, to organize the visual and perceptual
elements of graphics according to the features and relations in data. In a parallel but sep-
arate steam, an exploratory and graphical approach to multidimensional data (“L’analyse
des donn´
ees”) begun by Jean-Paul Benz´
ecri [1932–] provided French and other European
statisticians with an alternative, visually-based view of what statistics was about.
But the skills of hand-drawn maps and graphics had withered during the dormant “mod-
ern dark ages” of graphics (though nearly every figure in Tukey’s EDA (Tukey,1977)
was, by intention, hand-drawn). Computer processing of statistical data began in 1957
with the creation of FORTRAN, the first high-level language for computing. By the late
1960s, widespread mainframe university computers offered the possibility to construct
old and new graphic forms by computer programs. Interactive statistical applications,
e.g., Fowlkes (1969), Fishkeller et al. (1974) and true high-resolution graphics were de-
veloped, but would take a while to enter common use.
By the end of this period significant intersections and collaborations would begin: (a) com-
puter science research (software tools, C language, UNIX, etc.) at Bell Laboratories (Becker,
1994) and elsewhere would combine forces with (b) developments in data analysis (EDA, psy-
chometrics, etc.) and (c) display and input technology (pen plotters, graphic terminals, digitizer
tablets, the mouse, etc.). These developments would provide new paradigms, languages and
software packages for expressing statistical ideas and implementing data graphics. In turn, they
would lead to an explosive growth in new visualization methods and techniques.
Other themes began to emerge, mostly as initial suggestions: (a) various novel visual repre-
sentations of multivariate data (Andrews’ (1972) Fourier function plots, Chernoff (1973) faces,
star plots, clustering and tree representations); (b) the development of various dimension-
reduction techniques (biplot (Gabriel,1971), multidimensional scaling, correspondence analy-
sis), providing visualization of of multidimensional data in a 2D approximation; (c) animations
of a statistical process; and (d) perceptually-based theory and experiments related to how graphic
attributes and relations might be rendered to better convey the data visually.
By the close of this period, the first exemplars of modern GIS and interactive systems for
2D and 3D statistical graphics would appear. These would set goals for future development and
2.8 1975–present: High-D, interactive and dynamic data visualization
During the last quarter of the 20th century data visualization has blossomed into a mature, vi-
brant and multi-disciplinary research area, as may be seen in this Handbook, and software tools
for a wide range of visualization methods and data types are available for every desktop com-
puter. Yet, it is hard to provide a succinct overview of the most recent developments in data
visualization, because they are so varied, have occurred at an accelerated pace, and across a
wider range of disciplines. It is also more difficult to highlight the most significant develop-
ments, that may be seen as such in a subsequent history focusing on this recent period.
With this disclaimer, a few major themes stand out:
the development of highly interactive statistical computing systems. Initially, this meant
largely command-driven, directly programmable systems (APL, S), as opposed to com-
piled, batch processing;
new paradigms of direct manipulation for visual data analysis (linking, brushing (Becker and Cleveland,
1987), selection, focusing, etc.);
new methods for visualizing high-dimensional data (the grand tour (Asimov,1985), scat-
terplot matrix (Tukey and Tukey,1981), parallel coordinates plot (Inselberg,1985,Wegman,
1990), spreadplots (Young,1994a), etc.);
the invention (or re-invention) of graphical techniques for discrete and categorical data;
the application of visualization methods to an ever-expanding array of substantive prob-
lems and data structures, and
substantially increased attention to the cognitive and perceptual aspects of data display.
These developments in visualization methods and techniques arguably depended on ad-
vances in theoretical and technological infrastructure, perhaps more so than in previous periods.
Some of these are:
large-scale statistical and graphics software engineering, both commercial (e.g., SAS)
and non-commercial (e.g., Lisp-Stat, the R project). These have often been significantly
leveraged by open-source standards for information presentation and interaction (e.g.,
Java, Tcl/Tk);
extensions of classical linear statistical modeling to ever wider domains (generalized lin-
ear models, mixed models, models for spatial/geographical data, and so forth).
vastly increased computer processing speed and capacity, allowing computationally in-
tensive methods (bootstrap methods, Bayesian MCMC analysis, etc.), access to massive
data problems (measured in terabytes) and real-time streaming data. Advances in this area
continue to press for new visualization methods.
From the early 1970s to mid 1980s, many of the advances in statistical graphics concerned
static graphs for multidimensional quantitative data, designed to allow the analyst to see rela-
tions in progressively higher dimensions. Older ideas of dimension reduction techniques (prin-
cipal component analysis, multidimensional scaling, discriminant analysis, etc.) led to gener-
alizations of projecting a high-D dataset to “interesting” low-D views, as expressed by various
numerical indices that could be optimized (projection pursuit) or explored interactively (grand
The development of general methods for multidimensional contingency tables began in the
early 1970s, with Leo Goodman (1970), Shelly Haberman (1973) and others (Bishop et al.,
1975) laying out the fundamentals of log-linear models. By the mid 1980s, some initial, spe-
cialized techniques for visualizing such data were developed (fourfold display (Fienberg,1975),
association plot (Cohen,1980), mosaic plot (Hartigan and Kleiner,1981) and sieve diagram
(Riedwyl and Sch¨
upbach,1983)), based on the idea of displaying frequencies by area (Friendly,
1995). Of these, extensions of the mosaic plot (Friendly,1994,1999) have proved most gener-
ally useful, and are now widely implemented in a variety of statistical software, most completely
in the vcd package (Meyer et al.,2005) in R.
It may be argued that the greatest potential for recent growth in data visualization came from
the development of dynamic graphic methods, allowing instantaneous and direct manipulation
of graphical objects and related statistical properties. One early instance was a system for in-
teracting with probability plots (Fowlkes,1969) in realtime, choosing a shape parameter of a
reference distribution and power transformations by adjusting a control. The first general
system for manipulating high-dimensional data was PRIM-9, developed by Fishkeller, Fried-
man and Tukey (1974), and providing dynamic tools for Projecting, Rotating (in 3D), Isolating
(identifying subsets) and Masking data in up to 9 dimensions. These were quite influential, but
remained one-of-a-kind, “proof of concept” systems. By the mid 1980s, as workstations and
display technology became cheaper and more powerful, desktop software for dynamic graphics
became more widely available (e.g., MacSpin, Xgobi). Many of these developments to that
point are detailed in the chapters of Dynamic Graphics for Statistics (Cleveland and McGill,
In the 1990s, a number of these ideas were brought together to provide more general systems
for dynamic, interactive graphics, combined with data manipulation and analysis in coherent and
extensible computing environments. The combination of all these factors was more powerful
and influential than the sum of their parts. Lisp-Stat (Tierney,1990) and its progeny (Arc,
Cook and Weisberg (1999); ViSta, Young (1994b)), for example, provided an easily extensible
object-oriented environment for statistical computing. In these systems, widgets (sliders, se-
lection boxes, pick lists, etc.), graphs, tables, statistical models and the user all communicated
through messages, acted upon by whomever was a designated “listener,” and had a method to re-
spond. Most of the ideas and methods behind present day interactive graphics are described and
illustrated in Young et al. (2006). Other chapters in this Handbook provide current perspectives
on other aspects of interactive graphics.
3 Statistical historiography
As mentioned at the outset, this review is based on the information collected for the Milestones
Project, which I regard (subject to some caveats) as a relatively comprehensive corpus of the
significant developments in the history of data visualization. As such, it is of interest to consider
what light modern methods of statistics and graphics can shed on this history, a self-referential
question we call “statistical historiography” (Friendly,2005). In return, this offers other ways
to view this history.
3.1 History as “data”
Historical events, by their nature, are typically discrete, but marked with dates or ranges of
dates, and some description— numeric, textual, or classified by descriptors (who, what, where,
amount, and so forth). Among the first to recognize that history could be treated as data and
Figure 15: A specimen version of Priestley’s Chart of Biography.Source:Priestley (1765).
portrayed visually, Joseph Priestley (1765,1769) developed the idea of depicting the lifespans
of famous people by horizontal lines along a time scale. His enormous (2’ by 3’) and detailed
Chart of Biography showed two thousand names from 1200 BC to 1750 AD by horizontal lines
from birth to death, using dots at either end to indicate ranges of uncertainty. Along the vertical
dimension, Priestly classified these individuals, e.g., as statesmen or men of learning. A small
fragment of this chart is shown in Figure 15.
Priestley’s graphical representations of time and duration apparently influenced Playfair’s
introduction of time-series charts and bar charts (Funkhouser,1937, p. 280). But these inven-
tions did not inspire the British statisticians of his day, as noted earlier; historical events and
statistical facts were seen as separate, rather than as data arrayed along a time dimension. In
1885 at the Jubilee meeting of the Royal Statistical Society, Alfred Marshall (1885) argued that
the causes of historical events could be understood by the use of statistics displayed by “histori-
cal curves” (time-series graphs): “I wish to argue that that the graphic method may be applied as
to enable history to do this work better than it has hitherto” (p. 252). Maas and Morgan (2005)
discuss these issues in more detail.
3.2 Analyzing Milestones data
The information collected in the Milestone Project is rendered in print and web forms as a
chronological list, but is maintained as a relational database (historical items, references, im-
ages) in order to be able to work with it as “data. The simplest analyses examine trends over
time. Figure 1shows a density estimate for the distribution of 248 milestones items from 1500
to the present, keyed to the labels for the periods in history. The bumps, peaks and troughs all
seem interpretable: note particularly the steady rise up to 1880, followed by a decline through
the “modern dark ages” to 1945, then the steep rise up to the present. In fact, it is slightly
surprising to see that the peak in the Golden Age is nearly as high as that at present, but this
probably just reflects under-representation of the most recent events.23
Other historical patterns can be examined by classifying the items along various dimensions
(place, form, content, and so forth). If we classify the items by place of development (Europe
vs. North America, ignoring Other), interesting trends appear (Figure 16). The greatest peak
in Europe around 1875–1880 coincided with a smaller peak in North America. The decline in
Europe following the Golden Age was accompanied by an initial rise in North America, largely
due to popularization (e.g., text books) and significant applications of graphical methods, then a
steep decline as mathematical statistics held sway.
Figure 16: The distribution of milestone items over time, comparing trends in Europe and North
Finally, Figure 17 shows two mosaic plots for the milestones items classified by Epoch,
Subject matter and Aspect. Subject was classed as having to do with human (e.g., mortality,
disease), physical or mathematical characteristics of what was represented in the innovation.
Aspect classed each item according to whether it was primarily map-based, a diagram or statis-
23Technical note: In this figure an optimal bandwidth for the kernel density estimate was selected (using the
Sheather-Jones plugin estimate) for each series separately. The smaller range and sample size of the entries for
Europe vs. North America gives a smaller bandwidth for the former, by a factor of aabout 3. Using a common band-
width, fixed to that determined for the whole series (Figure 1) undersmooths the more extensive data on European
developments and oversmooths the North American ones. The details differ, but most of the points made in the
discussion about what was happening when and where hold.
tical innovation or a technological one. The left mosaic shows the shifts in Subject over time:
Most of the early innovations concerned physical subjects, while the later periods shift heavily
to mathematical ones. Human topics are not prevalent overall, but were dominant in the 19th
century. The right mosaic, for Subject ×Aspect indicates that, unsurprisingly, map-based in-
novations were mainly about physical and human subjects, while diagrams and statistical ones
were largely about mathematical subjects. Historical classifications clearly rely on more detailed
definitions than described here, however, it seems reasonable to suggest that such analyses of
history as “data” are a promising direction for future work.
-1600 17th C 18th C 19th C 1900-50 1950-75 1975+
Physical Human Mathematical
Maps Diagrams Technology
Physical Human Mathematical
Figure 17: Mosaic plots for milestones items, classified by Subject, Aspect and Epoch.
3.3 What was he thinking?: Understanding through reproduction
Historical graphs were created using available data, methods, technology, and understanding
current at the time. We can often come to a better understanding of intellectual, scientific, and
graphical questions by attempting a re-analysis from a modern perspective.
Earlier, we showed Playfair’s time-series graph (Figure 7) of wages and prices, and noted
that Playfair wished to show that workers were better off at the end of the period shown than
at any earlier time. Presumably he wished to draw the reader’s eye to the narrowing of the gap
between the bars for prices and the line graph for wages. Is this what you see?
What this graph shows directly is quite different than Playfair’s intention. It appears that
wages remained relatively stable, while the price of wheat varied greatly. The inference that
wages increased relative to prices is indirect and not visually compelling.
We cannot resist the temptation to give Playfair a helping hand here—by graphing the ratio
of wages to prices (labor cost of wheat), as shown in Figure 18. But this would not have occurred
to Playfair, because the idea of relating one time series to another by ratios (index numbers)
would not occur for another half-century (due to Jevons). See Friendly and Denis (2005) for
further discussion of Playfair’s thinking.
As another example, we give a brief account of an attempt to explore Galton’s discovery of
regression and the elliptical contours of the bivariate normal surface, treated in more detail in
Figure 18: Redrawn version of Playfair’s time series graph showing the ratio of price of wheat
to wages, together with a loess smoothed curve.
Friendly and Denis (2005). Galton’s famous graph showing these relations (Figure 19) portrays
the joint frequency distribution of the height of children and the average height of their parents.
It was produced from a “semi-graphic table” in which Galton averaged the frequencies in each
set of four adjacent cells, drew iso-curves of equal smoothed value, and noted that these formed
“concentric and similar ellipses.”
A literal transcription of Galton’s method, using contour curves of constant average fre-
quency, and showing the curves of the means of y|xand x|yis shown in Figure 20. It is not
immediately clear that the contours are concentric ellipses, nor that the curves of means are
essentially linear and have horizontal and vertical tangents to the contours.
A modern data analyst following the spirit of Galton’s method might substitute a smoothed
bivariate kernel density estimate for Galton’s simple average of adjacent cells. The result, using
sunflower symbols to depict the cell frequencies, and a smoothed loess curve to show E(y|x)is
shown in Figure 21. The contours now do emphatically suggest concentric similar ellipses, and
the regression line is near the points of vertical tangency. A reasonable conclusion from these
figures is that Galton did not slavishly interpolate iso-frequency values as is done in the contour
plot shown in Figure 20. Rather, he drew his contours to the smoothed data by eye and brain
(as he had done earlier with maps of weather patterns), with knowledge that he could, as one
might say today, trade some increase in bias for a possible decrease in variance, and so achieve
a greater smoothing.
4 Final thoughts
This chapter is titled “A brief history ... out of recognition that it it impossible to do full
justice to the history of data visualization in such a short account. This is doubly so because I
Figure 19: Galton’s smoothed correlation diagram for the data on heights of parents and chil-
dren, showing one ellipse of equal frequency. Source: (Galton,1886, Plate X.).
have attempted to present a broad view spanning the many areas of application in which data
visualization took root and developed. That being said, it is hoped that this overview will lead
modern readers and developers of graphical methods to appreciate the rich history behind the
latest hot new methods. As we have seen, almost all current methods have a much longer
history than is commonly thought. Moreover, as I have surveyed this work and traveled to many
libraries to view original works and read historical sources, I have been struck withthe exquisite
beauty and attention to graphic detail seen in many of these images, particularly those from the
19th century. We would be hard-pressed to recreate many of these today.
From this history one may also see that most of the innovations in data visualization arose
from concrete, often practical goals: the need or desire to see phenomena and relationships
in new or different ways. It is also clear that the development of graphic methods depended
fundamentally on parallel advances in technology, data collection and statistical theory. Finally,
I believe that the application of modern methods of data visualization to its own history, in this
self-referential way I call “statistical historiography,” offers some interesting views of the past
and challenges for the future.
Mid-parent height
Child height
61 63 65 67 69 71 73 75
Figure 20: Contour plot of Galton’s smoothed data, showing the curves of ¯y|x(filled circles,
solid line), ¯x|y(open circles, solid line) and the corresponding regression lines (dashed).
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Abbott, Edwin, 14
age pyramid, 18
discriminant, 25
principal component, 25
political, 5,6
atlascartographic, 4
statistical, 17,20
atomic number, 21
Baker, Robert, 12
bar chart, 8,20,26
divided, 12
Barbeu-Dubourg, Jacques, 7
Bell Laboratories, 23
ecri, Jean-Paul, 23
Bertin, Jacques, 23
bilateral frequency polygon, 18
bills of mortality, 6
biplot, 23
bivariate normal surface, 28
bootstrap, 24
Bowley, Arthur, 21,22
boxplot, 23
Brahe, Tycho, 46,16
brushing, 24
Buache, Phillippe, 7
C, 23
camera obscura, 4
chartbar, 8,12,20,26
control, 21
Gantt, 21
isochronic, 16
pie, 8
time-series, 26
Cheysson, ´
Emile, 15,17
clustering, 23
continuous distribution function, 6
parallel, 16
coordinate paper, 8
coordinate systems, 6
correlation, 16
coxcomb, 14
Crome, August F.W., 8
contour, 29
cumulative frequency, 21
freehand, 21
frequency, 21
historical, 26
isodic, 16
isogen, 16
loess, 29
curve fitting, 8
Cusa, Nicolas of, 4
datamultivariate, 23
de Fourcroy, Charles, 8
de Witt, Jan, 6
density estimation, 29
Descartes, Ren´
e, 5
alignment, 16
butterfly, 21
cartogram, 8,18
Chernoff faces, 23
circle, 18
contour, 14,16
coxcomb, 14
divided circle, 14
geometric, 3
Hertzsprung-Russell, 21
historical, 21
nomogram, 16
pie, 18
rose, 14,18
sieve, 25
statistical, 18
stereogram, 14
tree, 23
3, 16
fourfold, 25
trellis, 16
two-way table, 23
du Carla-Boniface, Marcellin, 7
Dupin, Charles, 10
dynamic graphics, 25
Fermat, Pierre, 5
geometric, 8
flow map, 14,18
Friendly, Michael, 25
Galilei, Galileo, 5
Galton, Francis, 16,17,2829
Gannett, Henry, 18
Gauss, Carl Friedrich, 14
Gemma-Frisius, Reginer, 4
Giffen, Robert, 21
Golden Age, 13,27
Goodman, Leo, 25
grand tour, 24
age pyramid, 18
bilateral polygon, 20
circle, 8
hanging rootogram, 23
high-resolution, 23
line, 5,8
time-series, 3,8,26,28
graph paper, 8
Graunt, John, 5,6
grid coordinate, 7
hexagonal, 16
Guerry, Andr´
e-Michel, 10,14
Haberman, Shelly, 25
Halley, Edmund, 7
Harrison, John, 5
Herschel, William, 9
Huygens, Christiaan, 6
interactive graphics, 25
interpolation, 8
isoline, 16
Java, 24
Jevons, William Stanley, 13
Lallemand, Charles, 16
Lambert, Johann, 8
Laplace, Pierre Simon, 14
le Blon, Jacob, 8
Levasseur, Pierre ´
Emile, 17
life table, 6
line contour, 7
isogon, 7
isoline, 7,16
slope as rate, 8
timeline, 7
line graph
1D, 5
linking, 24
Lisp-Stat, 24,25
lithography, 8
longitude, 6
MacSpin, 25
mapanamorphic, 16
cartography, 7
chloropleth, 10
contour, 7
disease, 12
epidemiological, 15
flow, 14,18
geological, 9
shaded, 20
thematic, 7,14
topographic, 7
weather, 6,17
Marey, ´
Etienne Jules, 21
Marshall, Alfred, 26
scatterplot, 24
Maunder, E. W., 21
physical, 5
measurement error, 7
Minard, Charles Joseph, 12,14
generalized linear, 24
mixed, 24
mosaic plot, 12,25,28
Moseley, Henry, 22
multidimensional scaling, 23,25
Nightingale, Florence, 14
nomogram, 16
Oresme, Nicole, 4
Ortelius, Abraham, 4
parallel coordinates plot, 24
Pascal, Blaise, 5
Pearson, Karl, 16
Perozzo, Luigi, 14
Petermann, Augustus, 12
Petty, William, 5,6
pie chart, 8
Playfair, William, 8,26,28
plot association, 25
bivariate, 6
Fourier function, 23
log-log, 16,21
mosaic, 12,25,28
parallel coordinate, 18
probability, 25
star, 18,23
stem-leaf, 23
political arithmetic, 6
Priestley, Joseph, 8,26
PRIM-9, 25
three-colour, 8
probability theory, 6
axonometric, 14
Ptolemy, Claudius, 3,6
Quetelet, Adolphe, 11,14,17
R, 24,25
regression, 16,29
Rheticus, Georg, 4
Royal Statistical Society, 13,26
SAS, 24
multidimensional, 23,25
scatterplot matrix, 24
Scheiner, Christopher, 5
Schwabe, Hermann, 17
Senefelder, Aloys, 8
sieve diagram, 25
small multiple, 18
small multiples, 5
Smith, William, 9
smoothing, 21,29
Snow, John, 12,15
spreadplot, 24
star plot, 18,23
statistical atlas, 17
demographic, 5
moral, 10
stem-leaf plot, 23
stereogram, 14
stratigraphic geology, 9
sunflower, 29
tablegraphical, 8
life, 6
semi-graphic, 29
tableau-graphique, 12
Tcl/Tk, 24
thematic cartography, 7
time-series, 8
multiple, 14
timeline, 7,26
tour grand, 24
triangulation, 4
Tufte, Edward, 5
Tukey, John W., 23
two-way table, 23
UNIX, 23
van Langren, Michael F., 5