Visual Explanations
Images and Quantities, Evidence and
Narrative
Edward R. Tufte
Goals
Heart of thinking/explaining: assessment of change,
dynamics, cause and effect
Goal: Proper arrangement in space and time of
images, words and numbers – to present
information about motion, process, mechanism,
cause and effect.
Strategies are independent of content area and
technology of display.
First Statistical Graph Ever
1644 Michael Florent van Langren, Flemish astronomer
Items measured/scaled on common grid
Supercomputer Scientific
Animations
• 65% of 134 color images in one compilation
had no scales or labeled dimensions at all.
• Only 13% had complete labels and scales.
• Animation of Venus, based on radar data
collected during 1992 Magellan space probe
used scale exaggerated by 22.5 times. Good
TV, bad science. Turned rolling hills into
mountains.
Image from “Study of a Numerically Modeled
Severe Storm
How big is the
cloud?
What direction
is it moving?
What are the
dimensions of
the grid?
Redesigned image
How to assess integrity of visual
evidence?
Ethical standards:
• Show unprocessed image along with
manipulated image.
• Identify manipulators and methods.
• Make clear what the pool of images the
displayed image was selected from. Is it
representative?
Scale
Appropriate re-expressions or
transformations of scales are
among the most powerful
strategies for exploring data.
In this example, chose aspect
ratio that centers absolute values
of line segments on 45 degrees.
Revised graph shows cycle rises
rapidly, declines slowly. More
pronounced when peak is sharp.
Thoughts on clarity of graphs
Cholera Epidemic in London, 1854
• Example of using displays of evidence for
making decisions.
• When we reason about qualitative evidence,
certain methods for displaying and
analyzing data are better than others.
• Dr John Snow was able to determine the
cause of the epidemic and bring it to an end.
Cholera Epidemic
• Original data was victim’s names, ordered
by date of death.
• Snow had theory about cause of cholera,
collected data to support his theory.
Cumulative data just show overall problem
Located deaths and pumps on map
Details
• No deaths at brewery. They drink beer, also have
their own well in basement.
• Few deaths at workhouse. They have their own
well, never go to Broad Street.
• Deaths outside the area.
– children attending school in area
– family who preferred water from Broad Street
• Need to show all evidence, not just the evidence
which supports theory.
Pros and Cons of graph
Pros
• Deceptive affects of
aggregation are
avoided.
• Aid in identifying
individual cases to be
analyzed.
Cons
• Death rates not shown
• Doesn’t show number
people in area… could
be dense population
near Broad Street.
• Map may become
cluttered.
Must be careful how data are
aggregated
• Daily aggregation shows trend
• Disease was already in decline when pump handle removed.
• Aggregation by week makes effect appear more dramatic.
• Snow: “There is no doubt the mortality rate was much diminished, as I said
before, by the flight of the population, which commenced soon after the
outbreak; but the attacks had so far diminished, by the time the use of the
water was stopped, that it is impossible to decide whether the well still
contained the cholera poison in an active state or whether, from some cause,
the water had become free of it.”
Pop journalism example
More aggregation
effects
• Aggregated data does not show
quarterly dips at all
• Time-series are exquisitely
sensitive to choice of intervals
and endpoints.
• Some aggregations are sensible,
reduce tedious redundancy and
uninteresting complexity (e.g.,
daily not hourly for cholera
charts).
Challenger
• January 8, 1986, space shuttle Challenger exploded and seven
astronauts died because two o-rings leaked.
• O-rings had lost resiliency because shuttle was launched on very cold
day.
• Day before the flight, engineers who designed the rocket opposed the
launch.
• Created 13 charts to support their case.
• NASA official was “appalled” by the recommendation not launch,
asked them to reconsider (even though only no-launch
recommendation in 12 years).
• NASA officials pointed out serious weaknesses in the charts.
• The Thiokol managers changed their minds, decided the evidence was
inconclusive.
• Serves as example of groupthink, technical decision-making in the face
of political pressure, and bureaucratic failures to communicate.
• But it could have been avoided, with more convincing arguments…
• Title chart (not shown) does not provide names of preparers. Would indicate
responsibility, use authors’ reputation & credibility.
• This chart is too detailed, need summary. Shows effect, no data about cause. Same
rocket has 3 names (NASA#, Thiokol #, launch date). Some evidence not included,
such as important flight two weeks earlier that sustained damage.
• Drawings show vital effects, but again no link to cause.
• Two of these already reported on previous report.
• Nozzle blow-by not relevant in cold weather.
• SRM-15 had worst damage, not really clear.
• Shows SRM-15 which was coldest launch.
• Also includes SRM-22 on a warm day.
• “We had blow-by on hottest motor and coldest motor” – est is extreme
characterization for sample of two!
• Focus on blow-by doesn’t emphasize erosion, which was much worse on
SRM-15
Basis for decision
• Blow-by, not erosion, and temperature for 2
launches.
• Included data from development motors which are
fixed rockets on horizontal test stands. Not same
stress as real launch.
• Omitted 22 previous shuttles, temperature
variations, O-ring performance.
• Selection of data – whether partisan, hurried,
haphazard, uninformed or thoughtful and wise –
can make all the difference.
• Based on charts that don’t present evidence well, engineers
presented their recommendations.
Alternative chart by Tufte
• complete history
• ordered by possible
cause
• damage is
summarized in
severity - weighted
index.
• “numbers become
evidence by being
in relation to”
• engineers were
thinking causally,
not displaying
causally.
Scatterplot of same data
•Shows large extrapolation to predicted launch temperature
Chart prepared after accident
•cross-hatching varies
from dark to medium
to medium dark. No
numeric scale.
•Data are from
development motors,
not launches.
Related chart
• damage chart not
repeated, depicted by
little marks rather than
score.
• “chartjunk” – rockets,
indicates statistical
stupidity
• sideways temperature
obscures causal
variable.
• data is shown as timeseries, not ordered by
causality.
• “If the substantive
matter is a cause-effect
relationship, graphs
should be organized to
illuminate such a link.
Feynman’s testimony
• showed crowd that o-ring loses resiliency when clamped in cold water for brief
time.
• “O-rings were not designed to erode. Erosion was a clue something was wrong.”
• Credible experiment would require 2 glasses, to rule out whether effect is due to
wet or cold. Also rules out clamp.
• “The one-glass method is not an experiment, it is an experience” – and an effective
one!
• Variations in cause must be explicably and measurably linked to variations in
effect.
Summary of principles
Design of statistical graphs must include
• documenting the sources and characteristics of data
• insistently enforcing appropriate comparisons
• demonstrating mechanisms of cause and effect
• expressing mechanisms quantitatively
• recognizing the inherently multivariate nature of analytic
problems
• inspecting and evaluating alternative explanations.
Smallest Effective Difference
• Make all visual distinctions as subtle as possible, but still clear and effective
• Mute secondary elements (arrows, pointer lines, tic marks, grids, meshes,
legends, highlights, accents, bevels, shadows and fills).
• “When everything is emphasize, nothing is emphasized.”
Calming grid clarifies data
Dark field generates spatial clutter and temporal lurches.
General Bathymetric Chart of Oceans
Same chart, different colors
Two scales for depth
• Minimal distinctions reduce visual clutter.
• Small contrasts increase the number of distinctions that can be made within
a single image.