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Bars and lines: A study of graphic communication



Interpretations of graphs seem to be rooted in principles of cognitive naturalness and information processing rather than arbitrary correspondences. These predict that people should more readily associate bars with discrete comparisons between data points because bars are discrete entities and facilitate point estimates. They should more readily associate lines with trends because lines connect discrete entities and directly represent slope. The predictions were supported in three experiments--two examining comprehension and one production. The correspondence does not seem to depend on explicit knowledge of rules. Instead, it may reflect the influence of the communicative situation as well as the perceptual properties of graphs.
Bars and Lines: A Study of Graphic Communication
Jeff Zacks and Barbara.Tversky
Psychology Department
Stanford University
Stanford, CA 94305-2130
Interpretations of graphic displays of information seem to be
rooted in principles of cognitive naturalness and information
processing rather than arbitrary correspondences. Both of
these considerations predict that people should more readily
associate bars with discrete information because bars are
discrete entities and facilitate point estimates. Similarly,
people should more readily associate lines with trends
because lines connect discrete entities and directly represent
slope. In two experiments, viewers tended to describe bar
graphs in terms of discrete comparisons between individual
data points, while they tended to describe line graphs in
terms of continuous trends. In a third experiment,
participants sketched graphic displays to illustrate verbal
descriptions of data; they tended to use bar graphs to convey
discrete comparisons, and line graphs to convey trends. The
strength of the bar/line convention seems to depend on the
communicative situation as well as the perceptual and
conceptual properties of the graphic displays.
Graphs are a pervasive species of cognitive artifact, used
both to reason about data and to communicate them. Given
a particular data set, there are a large number of ways it
could be portrayed, and people develop conventions that
govern what techniques are used in a given situation. One
such convention is the use of bar graphs to depict
comparisons among discrete data points and line graphs to
depict trends. This convention is reflected in instructional
books (Kosslyn, 1993), publication manuals (American
Psychological Association, 1994), and in the frustrated
complaints of editors when it is violated. It does not seem
to be arbitrary. On the one hand, it fits with the way people
use space to convey meaning. On the other hand, it fits
with the ease with which people extract information from
In the sections that follow, we will describe how the bar-
line convention could originate from biases in the
perceptual and cognitive abilities of graph viewers. We
will then present data documenting how the convention
functions both for viewers and authors of graphs. Based on
the strength of the effect, and on the relationship of
authors’ choices to viewers’ behavior, we conclude that the
convention can’t be fully understood just in terms of the
information-processing properties of graph viewers, but
requires thinking about the larger situation in which graphs
are used to communicate.
Cognitive Naturalness
Many conventions of graphic communication have been
invented and reinvented across cultures and by children,
suggesting that they derive from cognitively natural ways
of using space to convey meaning (Tversky, 1995). This
may be the case for bars and lines as well. Some support
for this comes from research on production and
comprehension of graphic displays. In producing graphic
representations of temporal, quantitative, and preference
relations, children across cultures line up dots they perceive
as representing levels of an underlying dimension but do
not line up dots they do not perceive as related
dimensionally (Tversky, Kugelmass & Winter, 1991).
selecting what graphic displays they would use for
conveying various sorts of information, adults prefer to use
bars for conveying discrete information and lines for
conveying trends (Levy, Zacks, Tversky & Schiano, 1996).
The Gestalt principles underlying figural perception
support the naturalness of bars for categorical information
and lines for ordinal or interval data. In bar graphs, each
value is represented as a separate bar, suggesting separate
entities or categories, whereas in line graphs, values are
connected by a single line, suggesting that all the values
belong to the same entity.
Information-processing Models Of Graphical
Pinker (1990) has developed the most detailed information-
processing theory of graph comprehension. In Pinker’s
model, a graph reader first builds up a visual description of
the visual display, similar to Marr’s (1982) 2 1/2 D sketch.
This representation is constrained by several factors,
including Gestalt laws of grouping and prior experience
with other graphs. From the visual description, a graph
reader constructs conceptual messages, which are
propositions about variables depicted in the graph. The
reader can also construct conceptual questions, which are
essentially conceptual messages with missing values.
High-level inferential processes are available to operate on
conceptual messages.
From: AAAI Technical Report FS-97-03. Compilation copyright © 1997, AAAI ( All rights reserved.
Pinker argues, "different types of graphs are not easier or
more difficult across the board, but are easier or more
difficult depending on the particular class of information
that is to be extracted" (1990, p. 11). Based on the theory,
Pinker predicts that it should be easier to make discrete
comparisons between individual data points from bar
graphs and easier to make trend assessments from line
The Gestalt principles again support this
prediction. Absolute values are easier to discern when the
values are presented separately, as in bars, whereas trends
are easier to discern when values are connected, as in lines.
The prediction of an interaction between graph type (bar
vs. line) and ease of making discrete comparisons or trend
assessments is also supported by empirical results. Simcox
(1984, described in Pinker, 1990) found that viewers were
faster to make judgments about the absolute value of data
points with bar graphs than with line graphs, but faster to
make judgments of slope with line graphs than with bar
graphs. This pattern held both for a sorting task and a
classification task. In related (as yet unpublished) work,
we found that viewers were faster to make discrete
comparisons from bar graphs than from line graphs. For
trend judgments, there was no difference between the two
graph types. Interestingly, this pattern held even when the
graphs contained only two data points. In this case, the
discrete comparison and the trend assessment are formally
equivalent (Zacks, Levy, Tversky & Schiano, 1996).
Finally, Simkin and Hastie (1987) compared bar graphs (of
a type slightly different from that used in our experiments)
with pie graphs for discrete comparison judgments and
proportion-of-the-whole judgments. They found viewers
were more accurate making discrete comparisons with the
bar graphs than the pie graphs, while the opposite was true
for proportion-of-the-whole judgments. Also, they
reported that in a survey, viewers tended to spontaneously
describe bar graphs in terms of discrete comparisons, and
to describe pie graphs in terms of proportions of the whole.
Origins of the Bar-Line Convention
Together, the theory and results described above suggest
that people should be more likely to interpret information
presented in bars as deriving from discrete variables and
information presented in lines as deriving from continuous
underlying variables. This should be evident in the way
they describe the depicted relations. Information presented
as bar graphs should be described categorically, in terms of
discrete comparisons between individual data points, using
terms such as "higher," "lower," "greater than," and "less
than." On the other hand, information presented as lines
should be described as continuous trends between the data
I Graphical perception, including the comprehension of
trends and discrete comparisons from line and bar graphs,
has also been modeled quantitatively by Lohse (1993).
However, specific predictions about the interaction of
graph type (bar vs. line) and task were not reported.
points, using terms like "rising," "falling," "increasing,’"
and "decreasing." The first two experiments examine
people’s spontaneous descriptions of data graphed as bars
or lines.
Mirroring the predictions for comprehension of graphics
are the predictions for production of graphics. When asked
to graphically represent information described
categorically, using terms like "greater than," people
should produce relatively more bars than lines. When
asked to graphically represent information described
continuously, using terms like "increases," people should
produce relatively more lines than bars. The last
experiment examines people’s constructions of graphs for
representing data described discretely or continuously.
Experiment 1
In the first experiment, we presented participants with
simple bar or line graphs and asked them to describe what
they saw. We predicted that viewers would be more likely
to describe the bar graphs in terms of discrete comparisons
between the individual data points. On the other hand, we
predicted they would be more likely to describe the line
graphs in terms of continuous trends.
Participants. 69 Stanford University undergraduates
participated in this experiment to partially fulfill a course
Stimuli and procedure. Simple graphs were drawn of a
two-point data set. The two points were always drawn so
as to be appreciably different; which point was higher was
counterbalanced across viewers. The horizontal axis was
labeled "X" and the vertical axis was labeled "Y." The
data point on the left was labeled "A," and the one on the
right labeled "B." One critical factor was manipulated:
viewers either saw a version of the graph drawn as a bar
graph or as a line graph. Examples of the stimuli are
shown in Figure 1.
Below each graph was the instruction: "Please describe
in a sentence what is shown in the graph above:". The
graph and instructions took up half a page; the other half
was printed with another unrelated question about graphs.
The graphs were printed on 8.5" x 11" paper and
distributed as part of a packet of questionnaires. (A note:
though equal numbers of each version of the graph were
distributed, not all the questionnaire packets were fully
completed, so the numbers of participants viewing each
graph version was not equal. The same was true for the
two studies described below.)
Figure 1: Examples of the bar and line graph stimuli used
in Experiment I.
Three judges (the first author and two judges who were
naive to the hypotheses and blind to the conditions)
classified each response as either a "discrete comparison"
between the two points or a "trend assessment". Their
instructions were:
"Classify the way the sentence characterizes the data as a
comparison or a trend description. Comparisons use terms
like more/less, more/fewer, higher/lower, larger/smaller,
stronger/weaker; they tend to refer to discrete values.
Trend descriptions use terms like function, relationship,
correlation, varies, trend; the tend to refer to continuous
changes in the variables. Not all the sentences will have
unambiguous assignments; use your judgment. "
All three judges agreed on 59 of the 69 responses. For
those 59 responses, every bar graph was described with a
discrete comparison and every line graph was describe with
a trend assessment, ~2(1) = 54.9, p < .001. Table 1 shows
this pattern.
Bar graph Line graph
Discrete comparison
24 0
Trend assessment
Table 1: Frequency of data characterization responses as a
function of graph type.
The particular content of the responses was quite
variable. Most respondents provided conceptual
descriptions of the relationship between the data point A
and B, but some gave physical characterizations of the
graph, and others invented fictional situations to explain
the data. The following are three typical "discrete
comparison" responses, and-three typical "trend
assessment" responses:
Discrete comparisons:
"Y is greater in a than B"
"A is a larger Y quantity than B"
"B is bought more often than A"
Trend assessments:
"A line, drawn on the XY plane, descending from A to B
along the X axis"
"As x increases in value y increases"
"As X increases, Y decreases"
To summarize, the deployment of the bar-line convention
had an unambiguous affect on the disposition of the readers
to respond. When they saw bar graphs, they described
discrete contrasts in the data; when they saw line graphs,
they described trends.
Experiment 2
Experiment 1 demonstrated the existence of a bar-line
convention effect on conceptual structure. Is this effect a
"hot house" laboratory phenomenon, or does it reflect
processes that have real impact on our interactions in the
world? One way to get at this question is to add a salient
source of real-world variation in conceptual structure and
check to see that the bar-line convention effect holds up.
Experiment 2 did this by manipulating the conceptual
domain of the graph along with the graph type. The
dependent variable was always continuous (height). For
the independent variable, the discrete conceptual domain of
gender was contrasted with the continuous domain of age.
This produced situations in which the bar-line convention
conflicted with the content of the data. Most interesting is
the case where line graphs were used with gender as the
conceptual domain. Here the convention implies that a
continuous trend is being depicted, but the domain is
clearly discrete. In this situation, how will people describe
the graph?
Participants. 106 Stanford University undergraduates
participated in this experiment to partially fulfill a course
~" 30"
Female Male
~ 20-
Female Male
~. 20-
0 0
10-year-olds 12-year-olds 10-year-olds 12-year-olds
Figure 2: Examples of the bar and line graph stimuli, and the continuous and discrete conceptual domains used in
Experiment 2.
Stimuli and procedure. The stimuli were quite similar to
those used in Experiment 1. Simple graphs were drawn of
a two-point data set. The two points were always drawn so
as to be appreciably different; the second point was always
higher than the first.
In this experiment, two factors were manipulated. As in
Experiment I, viewers either saw a version of the graph
drawn as a bar graph or as a line graph. Also, the
conceptual domain of the data was manipulated by varying
the labeling of the points and axes. Two domains were
selected such that the dependent variable could be held
constant while the nature of the independent variable was
manipulated. In one version (the "gender" domain), the
two points were labeled "Female" and "Male." In the other
version, (the "age" domain) the points were labeled "10-
year-olds" and "12-year-olds." The vertical axis was
always labeled "Height (inches)." In the first version, the
two labels identified (more-or-less) discrete categories;
thus the labels denoted a discrete conceptual domain. For
the second version the labels denoted a continuous
variable, age, and therefore a continuous conceptual
domain. To summarize, a given graph stimulus was either
associated with a discrete or continuous conceptual
domain, and either was drawn as a bar graph or line graph.
Examples of the stimuli are shown in Figure 2.
In the previous experiment, it had been noted that a
minority of the responses did not describe the data shown
by the graph. Several described the physical appearance of
the figure, and several created fictional explanations of the
depicted data. To reduce the number of these types of
responses, the instructions were changed slightly. Below
each graph was the instruction: "Please describe in a
sentence the relationship shown in this graph:" (rather than
simply asking for a description of what is shown). The
graph and instructions took up half a page; the other half
was printed with another unrelated question about graphs.
The graphs were printed on 8.5" x 11" paper and
distributed as part of a packet of questionnaires.
Because there was good agreement among the judges about
the graph descriptions in Experiment 1, only the first author
Gender (discrete domain) Age (continuous domain)
Bar graph
Line graph Bar graph Line graph
Discrete comparison
28 22 28 9
Trend assessment
3 2 14
Table 2: Frequency of data characterization responses as a function of graph type (bar graph or
line graph) and conceptual domain (gender or age).
rated the descriptions. During rating, he was blind to the
type of graph presented.
Effects of graph type and conceptual domain on viewers’
propensity to make a discrete comparison or a continuous
trend assessment were investigated by fitting log-linear
models. We tested the effect of a factor by comparing the
simplest model that contained its interaction with the
dependent variable (description type) with a model with
that interaction removed. In each case, we estimated both
Pearson’s X
and the likelihood ratio Z
and report the
more conservative of the two (which in this case was
always Pearson’s E2).
Both the graph type and the conceptual domain exerted
effects on viewers’ descriptions of the graphs (see Table 2).
As in Experiment 1, participants were more likely to use a
discrete comparison for a bar graph than for a line graph,
and more likely to make a trend assessment for a line graph
than a bar graph, X2(I) = 21.5, p < .001. Also,
participants were more likely to make a discrete
comparison when the conceptual domain was gender, and
more likely to use a trend judgment when the conceptual
domain was age, Z2(1) = 14.3, p < .001. (We should
somewhat skeptical of the accuracy of the Z2
approximation, given that one of the cells in Table 2
contains a zero. As a check, we performed an analysis in
which we collapsed over graph type and domain in turn and
computed Z2 tests of independence; it gave the same
Responses were in general less variable than those in the
first experiment. Descriptions were usually of the depicted
variables, with few physical characterizations or fictional
stories. Examples of the discrete comparisons and trend
assessments follow.
Discrete comparisons:
"Male’s height is higher than that of female’s"
"The average male is taller than the average female."
"Twelve yr. olds are taller than 10 yr olds."
Trend assessments:
"The graph shows a positive correlation between a
child’s increases in age and height between the ages of
10 and 12."
"Height increases with age. (from about 46 inches at 10
to 55 inches at 12)"
"The more male a person is, the taller he/she is"
The last example deserves particular comment. The fact
that some participants were willing to use a continuous
trend assessment to describe a domain that was clearly
discrete illustrates the power of the bar-line convention. (3
of the 25 participants who saw that stimulus gave such a
response.) Comparing the odds ratios for the two effects
(15.4 for graph type, 7.2 for domain) shows that the effect
of graph type was about twice as big as that of conceptual
domain. This indicates that the biasing effect of graph type
is something to be reckoned with even in more
"ecologically valid" situations.
The effect of conceptual domain on qualitative
descriptions agrees well with research showing that
manipulating the conceptual domain can lead to
quantitative distortion in the perception of graphs. In one
experiment, Tversky and Schiano (1989) showed that
labeling a figure as a graph led to distortion of a diagonal
line, while labeling the same figure as a map did not.
Experiment 3
The two previous experiments showed that the bar-line
convention systematically influenced readers’ conceptual
understanding of a graph. If readers are sensitive to this
convention, are authors as well? Experiment 3 was
designed to answer this question.
Participants. 99 Stanford University undergraduates
participated in this experiment to partially fulfill a course
Stimuli and procedure. The stimuli for this experiment
were essentially the inverse of those used in the previous
experiment. Participants were given a description of a data
pattern together with a frame for a graph, and asked to
draw a graph. The conceptual domain and the labeling of
the frames was just as it had been in Experiment 2. In one
version (the "gender" domain), the two points were labeled
"Female" and "Male." In the other version, (the "age"
domain) the points were labeled "10-year-olds" and "12-
year-olds." The vertical axis was always labeled "Height
(inches)". The descriptions were either discrete
continuous. The discrete descriptions were:
Gender (discrete domain) Age (continuous domain)
Discrete Trend Discrete Trend
description description description description
Bar graph
14 7 11
Line graph
6 13
14 24
Table 3: Frequency of graph type drawn as a function of description type and conceptual
"In the frame above, draw a graph that depicts the
following relationship:
Height for males is greater than for females."
"Height for 12-year-olds is greater than for 10-year-
The continuous descriptions were:
"In the frame above, draw a graph that depicts the
following relationship:
Height increases from females to males."
"Height increases from 10-year-olds to 12-year-olds."
The graph and instructions took up half a page; the other
half was printed with another unrelated question about
graphs. The graphs were printed on 8.5" x I I" paper and
distributed as part of a packet of questionnaires.
Of the 99 forms that were returned, most of the drawings
were line graphs (57) or bar graphs (32). Of the remaining
10, 6 could be described as scatter plots. Only the bar
graph and line graph responses were analyzed further.
We analyzed the data in the same fashion as for
Experiment 2, by fitting log-linear models to test
differences in goodness-of-fit. Again, the presence of an
empty cell in the frequency table is problematic for the Z
approximations. As a check, we again computed Z
of independence, which gave the same results as the log-
linear analysis reported below.
The results of Experiment 3 mirror those of the previous
experiments. Given a discrete description, participants
tended to draw bar graphs; given a continuous description,
the), tended to draw line graphs, Z2(1) = 15.3, p < .001.
Also, they were more likely to use a bar graph for the
discrete conceptual domain and more likely to use a line
graph for the continuous domain, Z2(1) = 9.83, p = .002.
The data are given in Table 3.
These results show that creators of graphs are sensitive to
the bar-line convention in a fashion that parallels that of
readers. Also mirroring the results of Experiment 2, the
effect of description type was more powerful (odds ratio
6.61 ) than that of domain (odds ratio = 3.82), suggesting
that the convention exerts a significant influence in real-
world situations.
In two experiments, participants wrote descriptions of
relations portrayed in bar or line graphs. There was a
strong tendency to describe data portrayed as bars
discretely, for example, "A is higher than B," and to
describe data portrayed as lines in terms of trends, for
example, "X increases from A to B." The second
experiment also examined effects of discrete or continuous
variables in the data. The influence of graphic display was
far greater than that of the underlying variable. In a third
experiment, participants were given the reverse task.
Given relations described discretely or continuously, they
were asked to construct graphic displays. There was a
strong tendency to portray discrete descriptions as bars and
continuous descriptions as lines.
Thus, people’s comprehension and production of graphs
conform to the principles of cognitive naturalness and
information processing ease discussed in the introduction.
They also correspond to graphic convention. Where do
graphic conventions like the bar-line convention come
from? It seems likely that they originate in these same
perceptual and cognitive propensities. However, it seems
unlikely that the perceptual-cognitive biases alone could
give rise to the striking effects observed here. The
differences in ease of information extraction between bar
and line graphs are small (Zacks et al., 1996) as are the
effects of cognitive naturalness (Tversky et al., 1991).
We believe that small perceptual-cognitive biases are
parlayed into large effects due to positive feedback exerted
by communicative convention. Conforming to the biases
would initially make graphic communications more readily
understood and the information in them more easily
extracted. Once a disposition to privilege a given
relationship between graphic displays and conceptual
messages is exploited by authors, viewers can rely on that
regularity. This further enhances the disposition and
thereby its use. This process can be likened to the way
speech conventions develop in a community of users
(Clark, 1996).
While the origins of the bar-line convention can be
traced to cognitive naturalness and information processing
ease, a fuller understanding emerges when we consider the
larger situation in which authors and viewers use graphs to
communicate. Cognitive scientists interested in graphic
perception have traditionally looked at perceptual-cognitive
processes form the point of view of the solitary observer.
A complete account requires expanding the picture to
include processes of communication.
Zacks, J., Levy, E., Tversky, B., & Schiano, D. (1996).
Ease of Processing with Spatial Representations:
Interaction of Rendering Technique and Conceptual Task.
Unpublished manuscript.
The authors would like to thank Interval Research
Corporation for its support of this research and the National
Science Foundation Graduate Fellowship Program for its
support of the first author. Thanks also to Duncan Hill,
Rehan Khan, and Shelly Wynecoop for their assistance
rating the responses and Terry Winograd for his valuable
comments on an earlier draft.
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... Note that the number of line charts did not change as much as that of bar charts. Bar charts differ from line graphs in the following ways: (1) the horizontal axis of a bar chart consists of entities or processes rather than quantities (Gross & Harmon, 2014); (2) bar graphs tend to be used to convey discrete comparisons; and (3) line graphs tend to be used to convey trends (Zacks & Tversky, 1999). However, a previous study pointed out that univariate scatterplots, box plots, and histograms 1974 1979 1984 1989 1994 1999 2004 2009 2014 numbers of graphics in 20 article percentage% Fig. 22 Change in the numbers and ratios of the tables. ...
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The purpose of this study is to examine how trends in the use of images in modern life science journals have changed since the spread of computer-based visual and imaging technology. To this end, a new classification system was constructed to analyze how the graphics of a scientific journal have changed over the years. The focus was on one international peer-reviewed journal in life sciences, Cell , which was founded in 1974, whereby 1725 figures and 160 tables from the research articles in Cell were sampled. The unit of classification was defined as a graphic and the figures and tables were divided into 5952 graphics. These graphics were further classified into hierarchical categories, and the data in each category were aggregated every five years. The following categories were observed: (1) data graphics, (2) explanation graphics, and (3) hybrid graphics. Data graphics increased by more than sixfold between 1974 and 2014, and some types of data graphics including mechanical reproduction images and bar charts displayed notable changes. The representation of explanatory graphics changed from hand-painted illustrations to diagrams of Bezier-curves. It is suggested that in addition to the development of experimental technologies such as fluorescent microscopy and big data analysis, continuously evolving application software for image creation and researchers’ motivation to convince reviewers and editors have influenced these changes.
... To specify, people tend to overestimate the associated number for high-intensive space while underestimating the number for low-intensive space (23). Thus, people would like to have a stronger sense and overestimate for a large area (24)(25)(26). Considering portion size has been demonstrated to have a significant positive impact on anxiety (27,28), a reasonable prediction is that a graph with a large portion-size area, compared with a small area, could cause a higher level of anxiety in the context of COVID-19 updates. ...
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To increase public awareness and disseminate health information, the WHO and health departments worldwide have been visualizing the latest statistics on the spread of COVID-19 to increase awareness and thus reduce its spread. Within various sources, graphs are frequently used to illustrate COVID-19 datasets. Limited research has provided insights into the effect of different graphs on emotional stress and ineffective behavioral strategies from a cross-cultural perspective. The result of current research suggests a graph with a high proportion size of the colored area (e.g., stacked area graph) might increase people's anxiety and social distancing intentions; people in collectivist culture might have a high level of anxiety and social distancing intentions; the effect of different graphs on social distancing intentions is mediated by anxiety experienced. Theoretical contribution and practical implications on health communication were also discussed in this study.
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Traditional visualisations are designed to be shown on a flat surface (screen or page) but most data is not "flat". For example, the surface of the earth exists on a sphere, however, when that surface is presented on a flat map, key information is hidden, such as geographic paths on the spherical surface being wrapped across the boundaries of the flat map. Similarly, cyclical time-series data has no beginning or end. When such cyclical data is presented on a traditional linear chart, the viewer needs to perceive continuity of the visualisation across the chart's boundaries. Mentally reconnecting the chart across such a boundary may induce additional cognitive load. More complex data such as a network diagram with hundreds or thousands of links between data points leads to a densely connected structure that is even less "flat" and needs to wrap around in multiple dimensions. To improve the usability of these visualisations, this thesis explores a novel class of interactive wrapped data visualisations, i.e., visualisations that wrap around continuously when interactively panned on a two-dimensional projection of surfaces of 3D shapes, specifically, cylinder, torus, or sphere. We start with a systematic exploration of the design space of interactive wrapped visualisations, characterising the visualisations that help people understand the relationship within the data. Subsequently, we investigate a series of wrappable visualisations for cyclical time series, network, and geographic data. We show that these interactive visualisations better preserve the spatial relations in the case of geospatial data, and better reveal the data's underlying structure in the case of abstract data such as networks and cyclical time series. Furthermore, to assist future research and development, we contribute layout algorithms and toolkits to help create pannable wrapped visualisations.
The process of forming, expressing, and updating beliefs from data plays a critical role in data‐driven decision making. Effectively eliciting those beliefs has potential for high impact across a broad set of applications, including increased engagement with data and visualizations, personalizing visualizations, and understanding users' visual reasoning processes, which can inform improved data analysis and decision making strategies (e.g., via bias mitigation). Recently, belief‐driven visualizations have been used to elicit and visualize readers' beliefs in a visualization alongside data in narrative media and data journalism platforms such as the New York Times and FiveThirtyEight. However, there is little research on different aspects that constitute designing an effective belief‐driven visualization. In this paper, we synthesize a design space for belief‐driven visualizations based on formative and summative interviews with designers and visualization experts. The design space includes 7 main design considerations, beginning with an assumed data set, then structured according to: from who, why, when, what, and how the belief is elicited, and the possible feedback about the belief that may be provided to the visualization viewer. The design space covers considerations such as the type of data parameter with optional uncertainty being elicited, interaction techniques, and visual feedback, among others. Finally, we describe how more than 24 existing belief‐driven visualizations from popular news media outlets span the design space and discuss trends and opportunities within this space.
Existing research on word-of-mouth considers various descriptive statistics of rating distributions, such as the mean, variance, skewness, kurtosis, and even entropy and the Herfindahl-Hirschman index. But real-world consumer decisions are often derived from visual assessment of displayed rating distributions in the form of histograms. In this study, we argue that such distribution charts may inadvertently lead to a consumer-choice bias that we call the histogram distortion bias (HDB). We propose that salient features of distributions in visual decision making may mislead consumers and result in inferior decision making. In an illustrative model, we derive a measure of the HDB. We show that with the HDB, consumers may make choices that violate well-accepted decision rules. In a series of experiments, subjects are observed to prefer products with a higher HDB despite a lower average rating. They could also violate widely accepted modeling assumptions, such as branch independence and first-order stochastic dominance. This paper was accepted by Chris Forman, information systems.
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Resumen La comprensión de gráficos de datos, tanto a nivel científico como a nivel de divulgación cientí-fica, es un tema de creciente interés en el mundo, en particular en el caso de productores rurales, ya que las agencias y otras instituciones deben explicar las mejoras y aportes técnicos en forma sencilla y comprensible. En este trabajo, por medio de una encuesta, se investigó la preferencia por diversos tipos de gráficos a 80 productores rurales de la zona centro-sur de Entre Ríos, Argentina. Se consultó por la preferencia de gráficos de barras (horizontales o verticales) o de líneas (en el mismo gráfico o en gráficos separados). Se obtuvo que prefieren en forma signifi-cativa los gráficos de barras por sobre los de líneas (76,25%, p=0,001) y los de barras verticales por sobre los de barras horizontales (78,75%, p=0,000). No hubo diferencia en la preferencia de líneas juntas en el mismo gráfico o separadas en cuatro gráficos. La división por estratos-edad, nivel de estudios o años en la actividad rural-mostraron pequeñas variaciones respecto a los datos globales. En conclusión, los resultados señalan la importancia de diseñar gráficos que sean más sencillos en textos dirigidos a productores rurales. Abstract Understanding data graphs, both when performing scientific work and sharing scientific data, is a topic that is receiving increased attention around the world. is is particularly the case when it comes to rural producers, since agencies and other institutions must explain improvements and technical contributions in a simple, digestible manner. For this paper, we surveyed 80 rural producers in the south-central region of the Province of Entre Ríos in Argentina, investigating their preferences for certain types of data graphs. We asked for their position on bar graphs (either horizontal or vertical) as opposed to line graphs (combined in the same graph or as separate graphs). We found that respondents significantly preferred bar graphs (76.25%, p=0.001) and, among those, also preferred vertical bar graphs over horizontal (78.75%, p=0.000). ere was no preference for line graphs in a single graph or separated across four graphs. Dividing respondents by age group, academic level, and years of rural activity yielded only slight variations next to the global percentages. In conclusion, these results reveal the importance of designing simpler data graphs when sharing information with rural producers.
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Researchers have proposed that graphical efficacy may be determined, in part, by the nature of the perceptual interactions that exist between attributes used to create graphical displays. One extreme type of interaction isintegrality, in which two or more physical dimensions are represented as a single psychological dimension in the observer. An alternative type of interaction isconfigurality, in which a global emergent dimension is availableto the observer in addition to the component attributes. Thirteen stimulus sets, each composed of attributes commonly used in the design of graphs, were submitted to the performance-based diagnostics of integrality and configurality. Analyses suggest a continuum of configurality among the present stimulus sets, with little evidence for integral graphical attributes. The configural pattern of results was more common when two identical dimensions were paired (homogeneous stimuli) than when two different dimensions were paired (heterogeneous stimuli). However, there was no evidence that pairs of dimensions belonging to a single object (object integration) were any more configural than dimensions belonging to different objects. Object integration was, however, consistently related to inefficient performance in tasks requiring the filtering of one of two component dimensions.
Graphs and other visual displays of information have become a pervasive part of our environment. In Elements of Graph Design , noted psychologist Stephen Kosslyn explains step-by-step how to create effective displays of quantitative data, with guidelines based on our current understanding of how the brain processes information. Unlike any other guide to designing graphs, it demonstrates clearly why certain graph formats and elements work better than others in specific situations. For those who prepare, use and interpret graphic data, Elements of Graph Design explores the crucial connections between the design, the data, and the reader. When read cover to cover or used as a hands-on working reference, it offers a wealth of advice on effectively conveying information visually.
How does space come to be used to represent nonspatial relations, as in graphs? Approximately 1200 children and adults from three language cultures, English, Hebrew, and Arabic, produced graphic representations of spatial, temporal, quantitative, and preference relations. Children placed stickers on square pieces of paper to represent, for example, a disliked food, a liked food, and a favorite food. Two major analyses of these data were performed. The analysis of directionality of the represented relation showed effects of direction of written language only for representations of temporal concepts, where left-to-right was dominant for speakers of English and right-to-left for speakers of Arabic, with Hebrew speakers in between. For quantity and preference, all canonical directions except top-to-bottom were used approximately equally by all cultures and ages. The analysis of information represented in the graphic representations showed an age trend; more of the older children represented ordinal and some interval information in their mappings. There was a small effect of abstractness of concept on information represented, with more interval information represented by children for the more concrete concepts, space, time, quantity, and preference in that order. Directionality findings were related to language-specific left-to-right or right-to-left directionality and to universal association of more or better with upward. The difficulties in externally representing interval information were related to prevalent difficulties in expressing comparative information. Children's graphic productions were compared to other invented notation systems, by children and by cultures, particularly for numbers and language.
Recent work on graph perception has focused on the nature of the processes that operate when people decode the information represented in graphs. We began our investigations by gathering evidence that people have generic expectations about what types of information will be the major messages in various types of graphs. These graph schemata suggested how graph type and judgment type would interact to determine the speed and accuracy of quantitative information extraction. These predictions were confirmed by the finding that a comparison judgment was most accurate when the judgment required assessing position along a common scale (simple bar chart), had intermediate accuracy on length judgments (divided bar chart), and was least accurate when assessing angles (pie chart). In contrast, when the judgment was an estimate of the proportion of the whole, angle assessments (pie chart) were as accurate as position (simple bar chart) and more accurate than length (divided bar chart). Proposals for elementary information processes involving anchoring, scanning, projection, superimposition, and detection operators were made to explain this interaction.
This article describes a computer program, UCIE (Understanding Cognitive Information Engineering) that simulates graphical perception. UCIE predicts response time to answer a question posed to a graphic display from assumptions about the sequence of eye fixations, short-term memory capacity and duration limits, and the degree of difficulty to acquire information in each glance. An empirical study compared actual performance to UCIE predictions over a range of display types and question types. The results yielded some support for the cognitive model. A zero-parameter model explains 37% of the variance in average reaction times (N = 1,128). However, the zero-parameter model only explains about 10% of the individual variation in reaction times across 28 subjects (N = 15,200). Although this is an important start to understand how we interpret visual displays for meaning, additional research is needed to explain individual differences in performance.