Blocked: When the Information Is Hidden by
the Visualization
Kyong Eun Oh
School of Library and Information Science, Simmons College, 300 The Fenway, Boston, MA 02115. E-mail:
[email protected]
Daniel Halpern
School of Communications, Pontificia Universidad Catolica de Chile, Alameda 340, Santiago 8331150, Chile.
E-mail: [email protected]
Marilyn Tremaine, James Chiang, and Deborah Silver
Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854.
E-mail: [email protected]; [email protected]; [email protected]
Karen Bemis
Institute of Marine and Coastal Sciences, School of Environmental and Biological Sciences, Rutgers, the State
University of New Jersey, 79 Dudley Road, New Brunswick, NJ 08901. E-mail: [email protected]
This study investigated how people comprehend threedimensional (3D) visualizations and what properties of
such visualizations affect comprehension. Participants
were asked to draw the face of a 3D visualization after it
was cut in half. We videotaped the participants as they
drew, erased, verbalized their thoughts, gestured, and
moved about a two-dimensional paper presentation of
the 3D visualization. The videorecords were analyzed
using a grounded theory approach to generate hypotheses related to comprehension difficulties and visualization properties. Our analysis of the results uncovered
three properties that made problem solving more difficult for participants. These were: (a) cuts that were at an
angle in relation to at least one plane of reference, (b)
nonplanar properties of the features contained in the 3D
visualizations including curved layers and v-shaped
layers, and (c) mixed combinations of layers. In contrast,
(a) cutting planes that were perpendicular or parallel to
the 3D visualization diagram’s planes of reference, (b)
internal features that were flat/planar, and (c) homogeneous layers were easier to comprehend. This research
has direct implications for the generation and use of 3D
information visualizations in that it suggests design features to include and avoid.
Received May 21, 2014; revised November 18, 2014; accepted November
18, 2014
C
V
March Online
2015 inLibrary
Wiley Online
Publishedonline
online 21
in Wiley
© 2015
2015 ASIS&T
ASIS&T Published
•
Library
(wileyonlinelibrary.com).
DOI: 10.1002/asi.23479
(wileyonlinelibrary.com).
DOI: 10.1002/asi.23479
Introduction
Three-dimensional visualizations are widely used in
information visualization to aid in comprehending large
amounts of information. One of the widely used definitions
of information visualization is “the use of computersupported interactive visual representations of abstract data
to amplify cognition” (Card, Mackinlay, & Shneiderman,
1999, p. 6). Another definition of information visualization
by Gershon and Page (2001) is “A process that transforms
data, information and knowledge into a form that relies on
the human visual system to perceive its embedded information” (p. 32). There have been a number of studies
focussing on visualizations on the web (Heo & Hirtle, 2001;
Modjeska & Chignell, 2003). In addition, types of available
visualization tools and applications are increasing in
number. However, many of them are often designed and
used without careful examination of their characteristics
or of prior empirical research, which suggests that
such visualizations cause serious user comprehension
problems (Heo & Hirtle, 2001; Keller & Tergan, 2005).
Visualizations can help users better understand information;
however, they also can hinder users’ interactions with
information when not used properly. Thus, it is critical
to understand what aspects of visualization enhance or
reduce its effectiveness.
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Among various visualization techniques, 3D visualization, which attempts to provide the visual experience of the
real world to users, has become one of the popular and
promising visualization techniques. In particular, 3D visualizations can provide rich information about concepts
and their relationships and thereby support users’ understanding of the information presented. However, 3D visualizations have routinely been found to be difficult to
comprehend solely because of a viewer’s inability to comprehend the visual relationships in the presentation
(Fabrikant, Montello, & Mark, 2010; Kali & Orion, 1996;
Velez, 2009; Velez, Silver, & Tremaine, 2005). This
problem is exacerbated by a viewer’s spatial ability, with
low spatial individuals having the most difficulties (Cohen
& Hegarty, 2007b; Keehner, Hegarty, Cohen, Khooshabeh,
& Montello, 2008; Khooshabeh & Hegarty, 2010;
Modjeska & Chignell, 2003; Velez, 2009; Velez et al.,
2005). In addition, research shows that spatial ability is
related to gender, with females repeatedly scoring lower
than males on standard spatial ability tests (Baenninger &
Newcombe, 1989; Czerwinski, Tan, & Robertson, 2002;
Hyde, 1990; Johnson & Meade, 1987; Kimura, 1999;
McGee, 1979; Tan, Czerwinski, & Robertson, 2003; Velez,
2009; Velez et al., 2005).
Because modern science and technology rely increasingly on using 3D visualizations to convey information,
because education systems do not focus on training people
in 3D image comprehension skills, and because a skill
as basic as spatial ability might be preventing many
people from entering professions that now use or need
3D visualization comprehension skills, this research
focuses on finding out how people understand 3D visualizations and what problems they might have with their
comprehension based on specific properties of the visualization. Overall, we feel that this work can contribute
to the design of comprehensible 3D information
visualizations.
This paper is organized as follows: In the next section,
we justify the importance of this work, explain how it is
related to information visualization, and connect it to other
research that has already been carried out in this area.
This is followed by an explanation of our research
methods. Then we present our results along with supportive data for our findings and a discussion of these
results. Finally, we summarize our work and present our
conclusions.
Background and Related Work
Information Visualization
Interest in information visualizations has increased both
in terms of computer science investigation into the software needed to create information visualizations and as an
area of research focused on how humans can benefit from
such visualizations in the fields of information science,
cognitive psychology, computer science, and human–
computer interaction, especially in the past 10 years
(Bederson & Shneiderman, 2003; Card et al., 1999; Chen,
2004). A number of software tools for visualizing information have been developed so that people can construct their
own visualizations. One of the good examples is Many
Eyes, which is a data visualization tool developed by IBM
(Armonk, NY). Many Eyes lets people use either existing
data sets provided by Many Eyes, or their own data sets to
produce graphic representations such as charts, graphs, tag
clouds, and diagrams. Another example is Tableau, which
is a data visualization software that allows creating different kinds of visualizations and sharing them easily on the
web. Another information visualization software, Datawatch, even lets users visualize data from streaming data.
The growing interest in information visualization becomes
more evident when the number of hits per decade for the
search term “information visualization” is counted in
Google Scholar (Julien, Leide, & Bouthillier, 2008). When
search was limited to publications from 1980 to 1990,
there were 1,060 hits. From 1990 to 2000, there were
4,690 hits, and from 2000 to 2010, the hit rate increased to
18,400.
In information science, there exists a growing number of
experimental studies that develop or propose information
visualization systems or test the usefulness and effectiveness
of such systems that are designed to support a user’s interaction with information, such as organizing, browsing,
searching, and saving information. For instance, Eick (1994)
developed a software tool called SeeSoft, which visualizes
statistics associated with texts by using colored rows within
columns. This system allows users to browse long lines of
text including a technical paper, records in a database, or a
literary corpus at a glance.
Studies also focus on the effectiveness of using information visualization in information retrieval (Sebrechts,
Vasilakis, Miller, Cugini, & Laskowski, 1999). For
instance, Hoeber and Yang (2009) examined how HotMap,
a system developed to visualize and interactively manipulate web search results, supports the users as they evaluate
and explore search results for vague queries. HotMap represents the query term frequencies in the form of a color
code on a heat scale in which multiple occurrences of a
query term are represented as a dark red color while fewer
occurrences are represented by progressively lighter shades
of red, orange, and yellow. In their study, the researchers
found an increase in speed and effectiveness and a reduction in missed documents when comparing HotMap to the
list-based presentation used by Google. Participants also
showed positive attitudes toward the HotMap interface. A
screen shot of HotMap is presented in Figure 1a. Hearst
(1995) developed a system called TileBars, which visualizes the length, query term frequency, and query term distribution of each document in the search results. She states
that her visualization interface allows users to do a quick
scan of the searched documents and supports users making
judgments about the relevance of the documents (p. 59).
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a. Hotmap: Hoeber and Yang (2009).
b. Envision: Nowell, France, Hix, Heath, & Fox (1996).
c. VIEWER: Berenci et al. (2000).
FIG. 1.
Examples of information visualizations. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
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Heimonen and Jhaveri (2005) also incorporated visualization into a search results interface. For each document their
user retrieved, query term frequencies were displayed in
the shape of a small document-shaped icon. In the user
study of its usefulness, the participants exhibited positive
attitudes toward this visualization technique (p. 882).
Another system, Envision, also visualizes the search results
by using icons that represent the various attributes of each
document via icon shape, size, and color, as shown in
Figure 1b (Fox et al., 1993; Nowell, France, Hix, Heath, &
Fox, 1996). Similarly, Berenci, Carpineto, Giannini, and
Mizzaro (2000) developed a system called VIEWER, which
is an interactive ranking system that provides a graphical
visualization of results. To be more specific, when the user
enters a query term, VIEWER first forwards this to a
selected search engine and then shows the results returned
by the search engine. It also provides a graphical visualization of the results consisting of several horizontal bars,
which are subqueries that can be formed with the original
query term within the retrieved results (p. 251). This
allows users to select subqueries and conduct a search
within search results. In the user study, the authors found
that when using VIEWER, the search results were better
than using a search engine without VIEWER. In addition,
the authors reported that VIEWER provided increased
retrieval effectiveness as well as user satisfaction (p. 249).
The screenshot of the VIEWER is displayed in Figure 1c.
Although the aforementioned studies did not examine the
use and the effect of 3D visualizations, they illustrate that
2D displays in information visualization greatly facilitate
an individual’s interaction with information. Because 3D
visualizations allow more relationships to be presented in
the visualization, the movement in information visualization design is toward 3D visualizations. The next section
discusses this trend.
3D Visualizations
Three-dimensional visualizations have become more
common in our daily lives with the increasing graphical
capabilities of computers (Hegarty, 2010; Velez, 2009; Velez
et al., 2005). A number of software tools are available to
create 3D visualizations easily without the need for people
to write programs. In addition, both the abundance of data
and the need to understand much data has increased the use
of 3D visualizations in many fields, which include architecture, astronomy, biology, dentistry, geology, engineering,
and medicine. In information science, there have been
attempts to utilize 3D visualizations in a variety of ways.
They have been used in representing document similarity by
arranging objects spatially in a virtual environment where
each object represents a document in the database
(Westerman, Collins, & Cribbin, 2005). For example,
Leuski and Allan (2000) developed a system called Lighthouse, which presents the user a ranked list and a 3D visualization of similarities among documents (Allan, Leuski,
Swan, & Byrd, 2001; Leuski & Allan, 2000), as shown in
Figure 2a. Similarly, Rennison (1994) developed a system
called Galaxy of News, which visualizes large databases of
news articles. In this system, news articles are clustered
based on the content, and users can browse and search databases in 3D information space by zooming and panning.
Wise et al. (1995) also developed a system called ThemeScape, which visualizes large text documents. In this system,
text contents are spatialized in 3D space, and the system
allows users to see the visual thematic summary of the
whole corpus while exploring the document space.
In addition, 3D visualizations have been used in representing hierarchically structured information. For example,
Robertson, Mackinlay, and Card (1991) developed Cone
Trees, which uses 3D visualizations to show hierarchical
information. In a similar vein, Jeong and Pang (1998) proposed Disc trees, which also visualizes large hierarchical
information spaces. Rekimoto and Green (1993) developed
Information Cube, which lets users intuitively browse,
search, organize, and select a large amount of information in
a 3D cube. Figure 2b displays Information Cube. Threedimensional visualizations have also been used in visualizing domain knowledge. Using visualizations in analyzing
domain structure by conducting citation analysis and link
analysis has been quite common in infometrics, bibliometrics, and webometrics, although mostly using 2D visualizations. However, some researchers worked on visualizing
domain structure using 3D visualizations. Figure 2c is a
landscape view of the author cocitation analysis map. In this
map, the height of a bar is the number of citations for the
author, and the lighter shading in the color spectrum on each
citation indicates more recent citations (Börner, Chen, &
Boyack, 2003; Chen & Paul, 2001).
Thus, there have been studies as well as systems and tools
developed that show how 3D visualizations can be used to
convey information in various ways. However, what we
know about how people comprehend these visualizations is
still minimal.
Difficulties in Comprehending 3D Visualizations
Although 3D visualizations can provide rich information,
many people experience difficulties in comprehending 3D
visualizations. Hearst (1999) stated that “although intuitively appealing, graphical overviews of large document
spaces have yet to be shown to be useful and understandable
for users” (p. 274). Even in relatively simple visualizations,
it has been found that people experience difficulties in
judging the depth, size, and position of objects (Young,
1996). Fabrikant et al. (2010) examined how people interpret 3D visualizations by conducting an experiment with 23
undergraduate students. The researchers found that inexperienced participants had difficulty interpreting the spatial
metaphor used for information visualization. Modjeska and
Chignell (2003) compared the effect of desktop virtual
reality in a 3D and 2.5D environment by investigating how
participants perform when finding topics in the 3D and 2.5D
environment. They found that people with low spatial ability
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a. Lighthouse: Leuski and Allan (2000).
b. The information cube: Rekimoto and Green (1993)
c. Author co-citation analysis map: Börner et al. (2003)
FIG. 2. Examples of 3D visualizations in the information science field. [Color figure can be viewed in the online issue, which is available at
wileyonlinelibrary.com.]
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experienced more difficulties when performing tasks within
virtual environments. They also found that all participants
performed better in the 2.5D environment than the 3D environment. Westerman et al. (2005) also compared the effect
of using 2D and 3D visualizations for information retrieval.
Their study participants used a more focused approach
when searching in the 3D environment and a more exhaustive approach in the 2D environment. The researchers
reported that there were no significant differences in terms
of the information retrieval effectiveness and efficiency in
2D versus 3D conditions. Powers and Pfitzner (2003),
who examined various information visualization interfaces,
also were skeptical about 3D visualizations, saying that
“generally 3D graphical interfaces have proven to be ineffective” (p. 534). However, the researchers mentioned
that there seems to be a general experience factor that helps
users makes better use of complex interfaces such as the 3D
interface. In a similar vein, Sebrechts et al. (1999) conducted a comparative evaluation of the effectiveness of
text, 2D, and 3D interfaces in information retrieval and
found that, while there were high interface learning costs for
the 3D visualization, they decreased significantly with experience. These results show that although 3D visualizations
can be difficult to comprehend, the ability to comprehend
these 3D visualizations can be improved with experience
and training.
Previous Research on 3D Visualization Comprehension
As we have previously stated (Oh et al., 2011), in the
fields of geology and psychology the focus has been on
spatial abilities and their relationship to 3D visualization
comprehension skills. Velez (2009) examined what spatial
abilities were correlated with 3D projection visualization
comprehension. She reported that the accuracy of participants on the visualization comprehension test had a
medium-high correlation with spatial visualization abilities.
Alle (2006) conducted a study to examine and characterize
the 3D spatial abilities of geology students by measuring
their ability to comprehend the 3D structure of folded
sedimentary rocks from surface structure visualizations.
Alle found that students had different visual penetrative
ability (VPA), which is the “ability mentally to penetrate the
image of a structure” (Kali & Orion, 1996, p. 369) when
solving geology problems. On one hand, some participants
constructed a complete 3D mental model of the visualization
quickly and drew the required cross-sectional elements
based on the presented surface information. On the other
hand, other participants were not able to associate the
surface information of the geological structures with their
interior. The participants’ attempts to draw cross-sections
showed many common nonpenetrative errors. Other
reported findings show that people exhibit large individual
differences in comprehending spatial imageries, with
some of these differences being based on the professions
the people work in (Blajenkova, Kozhevnikov, & Motes,
2006; Kozhevnikov, Kosslyn, & Shephard, 2005).
Velez’s (2009) study is one of the few studies that
attempted to identify properties of the 3D visualizations
that influenced people’s ability to create a mental picture
of an object. She found that the problems that only participants with high spatial ability answered correctly were
visualizations that had more edges and vertices, that is,
these properties make the visualization more difficult
to comprehend. Similarly, Cohen and Hegarty (2007b)
conducted an experiment and investigated how participants
solved simple 3D cross-section visualization problems
using a Santa Barbara Solid Test they developed. Cohen
and Hegarty also measured individual spatial abilities
and found that it correlated with a participant’s performance on their multiple choice spatial ability test. They
reported that oblique cutting planes were more difficult to
comprehend than 3D visualizations with orthogonal cutting
planes. Although they designed problems to represent
some of the additional features being investigated in our
study, the use of color to represent the various objects
embedded in the solid objects used in the problems led to
a presumed color advantage that aided problem solutions.
The object used in Cohen and Hegarty’s study is displayed
in Figure 3.
Our research study differs from this work in that it does not
come with pre-prepared hypotheses but, rather, is focused on
generating possible hypotheses to test later in controlled
experiments. It is a qualitative research rather than quantitative research study; something we feel is needed first to
effectively explore what the underlying comprehension
issues might be. In other words, before considering and
testing possible hypotheses as to what may make comprehension more difficult or easier, we wanted to consider multiple
possibilities and, thus, uncover potential visualization properties that affect comprehension which we might not
have thought of. To do this, we employed a think-aloud
FIG. 3. Santa Barbara Solids Test figures (Cohen & Hegarty, 2007b).
[Color figure can be viewed in the online issue, which is available at
wileyonlinelibrary.com.]
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method that allowed us to learn the cognitive processes
participants were going through as they tried to solve each
visualization problem. The research on verbal protocols indicates that people can effectively report what they are thinking
if (a) they are not performing routine problem solving and (b)
their cognitive load is not too high so that verbalization does
not interfere with the thinking processes.
Overall, the aforementioned studies investigated and
reported a number of useful and interesting findings related
to 3D visualization comprehension. However, how and why
people experience difficulty in comprehending 3D visualizations is still not understood. Thus, we have conducted our
qualitative research study to stand back and determine if
various visual properties of 3D visualizations might make
comprehension easier or more difficult. Having such knowledge might help us figure out the mechanism behind such
comprehension. Even if we cannot find the mechanism, at
least we will start to understand what properties of visualizations affect comprehension. The reader should note that
this study is only a hypothesis generation study. We do not
claim that the properties we have uncovered in this research
are correct, only that it would be interesting research to
investigate these properties further.
textbooks. For the experiment, these visualizations were
redrawn in black and white using textures to represent
the colors and also modified slightly to make it easier for
participants to redraw portions of the diagrams. (b) Characteristics of collected visualization diagrams were
analyzed using five standard geologic properties: number
of layers, number of faults, number of folds, whether layers
were orthogonal to the planes of reference, and the angle of
the layers if not orthogonal to the x–y plane the visualization was presented in. This was done to ensure a wide
representation of properties that are typically used in these
visualizations. (c) Then cross-section/slice visualization
problems that participants were to solve in the experiment
were developed (see Figure 4). Last, (d) The slice visualization problems were ordered from easiest to hardest for
presentation to participants by asking seven experts and
novices to solve the visualization problems and rank order
the problem set by difficulty. Via this process, a set of 18
3D cross-section visualization problems ordered by
increasing difficulty were created. Examples of easy and
difficult 3D visualization problems are displayed in
Figure 4.
Method
Research Questions
a
As this study was exploratory in nature, we did not have
hypotheses regarding which visual properties are easier or
more difficult to comprehend. The two primary research
questions were the following:
RQ1: What are the visual properties of 3D visualizations that make
it more difficult for users to understand the information being
presented?
RQ2: What are the visual properties of 3D visualizations that make
it easier for users to understand the information being presented?
b
Participants
Eleven undergraduate students at Rutgers University
were recruited as participants by posting notices on the
campus and via recruitment in classes. They were monetarily rewarded for their participation and were mostly from
the communication or engineering departments. We focused
on recruiting half of our participants from nonscience disciplines and being female since such individuals are likely to
have lower spatial abilities. Among 11 participants, six were
male and five were female.
Generation of 3D Visualization Diagrams
For the study, a set of 18 3D visualization diagrams
were generated. We generated these diagrams as follows:
(a) a set of 35 representative 3D visualizations were collected from introductory geology and earth science
FIG. 4.
(a) Easy and (b) difficult 3D visualization problems.
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Procedure
Analysis
In the experiment, participants were asked to visualize
the internal structure of 18 3D visualization diagrams by
drawing what the cut face of the visualization would look
like if it were cut along the dotted lines shown in Figure 4.
Each 3D visualization problem was drawn on the top part
of an individual sheet of paper and given to participants in
an order of increasing difficulty. Then the participants were
asked to draw the cut plane of the 3D visualizations on the
bottom part of the sheet of paper. In this process, a thinkaloud protocol, which asks participants to verbalize their
thoughts was used (Ericsson & Simon, 1980; Van
Someren, Barnard, & Sandberg, 1994) to examine the cognitive processes and difficulties underlying visualization
comprehension. Thus, participants were asked to verbalize
their thoughts while solving 3D visualization problems.
Each session lasted for approximately 1 hour per participant, and each session was videorecorded. Researchers
asked participants to stop when it took more than an hour
even when the participants did not finish solving all of the
problems.
The videorecordings were analyzed by focusing on participants’ verbal protocols as well as their gestures and drawings. While analyzing data, A grounded theory technique
(Strauss & Corbin, 1990) was employed to generate hypotheses. To be more specific, we first transcribed each of the
videorecordings, and as we analyzed the transcription, we
identified emerging patterns that eventually formed a set of
hypotheses as to what properties of the visualizations made
them difficult or easy to comprehend. We also noted the
support for each hypothesis. Potential disconfirming evidence was also noted and searched for. This way, some
hypotheses were supported with verbal and gesture evidence,
while some of them were either modified or eliminated.
Videorecordings were transcribed and annotated using a
video annotation tool, Transana (http://www.transana.org/).
In addition, while identifying emerging patterns that were
observed multiple times across videorecordings, a table was
developed. A portion of this table is presented in Figure 5.
Each videorecording was viewed more than five times by
multiple researchers to generate the hypotheses.
FIG. 5.
Table developed while analyzing data.
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FIG. 7. Cut. This is illustrated as the dotted line through the diagram.
[Color figure can be viewed in the online issue, which is available at
wileyonlinelibrary.com.]
FIG. 6. Planes of references. [Color figure can be viewed in the online
issue, which is available at wileyonlinelibrary.com.]
The authors note that the analysis of a verbal protocol is
an unusual use of grounded theory which is usually
applied to sociological studies. Verbal protocols have typically been used in studies of problem solving and also to
determine user problems with interfaces. Most prior
research conducted on understanding how people comprehend visualizations has been carried out with controlled
experiments. Visualizations, however, are complex and
require multiple combined skills to understand. We therefore felt that it was more appropriate to begin by generating multiple hypotheses based on observing individuals
struggle with problem solutions before beginning any controlled experiments. We used a grounded theory approach
to focus on those hypotheses that occurred frequently,
but we also added controls to the study, which is not typically done, in order to ensure that we had a representative
population.
FIG. 8. Flat/planar layers. These are illustrated as the continuous lines in
the diagram.
FIG. 9. Nonplanar layers. These are illustrated as the continuous lines in
the diagram.
Definitions
The operational definitions of key terminologies in this
study are as follows.
1. 3D visualization problems: 18 3D visualization diagrams
that are given to the participants to visualize.
2. Planes of reference: A viewer’s automatically assumed x,
y, and z coordinate system (Heo & Hirtle, 2001). The x
and y axes form one plane. The x and z axes another with
the y and z axes the third plane (Figure 6). Because the
diagram is presented as a block, the viewer automatically
assumes that the shown x, y, and z axes are the coordinate
system in use.
3. Cut: Plane that slices 3D visualization into two pieces
(Figure 7). It is often referred to as a cross-section in
different fields (Alle, 2006; Cohen & Hegarty, 2007b;
Hegarty, Keehner, Khooshabeh, & Montello, 2009; Velez,
2009).
4. Flat/planar layers: Continuous features inside the 3D
visualization that are parallel to each other or to the planes
of reference (Figure 8).
5. Nonplanar layers: Continuous features inside the 3D
visualization that are not parallel to each other or to the
planes of reference (Figure 9).
Results and Discussion
The results from our videotape analysis show that participants often struggle with visualizing the internal structure of 3D visualizations. Participant 5 described his/her
difficulty as “I’m lacking some sort of understanding or skill
or whatever that enables me to see what these would look
like from the inside. It’s like a . . . like a . . . how a child can
understand another person’s point of view. It’s almost like
that. Like I can’t look at this in any view except what is in
front of me.” An in-depth analysis of the results showed that
the following visual properties made problem solving more
difficult for participants: (a) a cut that was not parallel or
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perpendicular to at least one plane of reference (see
Figure 10a), (b) nonplanar layers that formed the visualization including curved layers (see Figure 10b) and v-shaped
layers (see Figure 10c), and (c) mixed combinations of
layers (both parallel and nonparallel to a plane of reference;
see Figure 10d).
On the other hand, (a) cuts that were perpendicular or
parallel to the diagram’s planes of reference (see
Figure 11a), (b) internal features that were flat/planar and
parallel to one plane of reference (see Figure 11b), and (c)
internal homogeneous layers, consisting of either only
curved or flat/planar layer formations that were parallel to
each other were easier to comprehend (see Figure 11c). We
could not find sufficient evidence for the effect of the shape
of the visualization, the effect of layers being at an angle to
the planes of reference, and the location of the cutting plane
in the visualization, for example, either close to the front or
back of the visualization. The paragraphs below document
support for each of the properties that were found to cause
difficulties.
a
b
c
d
FIG. 10. Properties of 3D visualizations that made problem solving more
difficult. [Color figure can be viewed in the online issue, which is available
at wileyonlinelibrary.com.]
a
b
Angled Cuts
Participants found it difficult to form a mental image of
the patterns that would appear on the cut plane when the cut
was at an angle to the planes of reference. This finding is
consistent with prior research studies (Cohen & Hegarty,
2007a, 2007b; Pani, Jeffres, Shippey, & Schwartz, 1996;
Pani, Zhou, & Friend, 1997). In particular, Cohen and
Hegarty (2007b) reported that participants in their study
consistently had difficulties in forming a mental image of the
cut plane across three different types of 3D visualizations—
simple, joined, and embedded—when the cut was at an
angle to the planes of reference.
While visualizing 3D visualization problems with angled
cuts, participants often directly mentioned about the difficulty caused by the angled cuts. For example, Participant 8
said that “This thing is a little hard because of the angle,”
and “I think I have troubles with the angle, that’s the
problem for me.” Participant 7 also mentioned that “The
difficult part for me is to visualize and imagine when the
angle goes down,” which clearly shows the difficulty caused
by the angled cuts. While visualizing, participants often
paused, pointed at the cut with a finger or pen, and used
hands to virtually cut visualization problems at an angle to
the plane of reference by covering part of the figure. For
example, while visualizing Figure 12a, participant 5 said,
“It is cutting it this way [pointing at the cut with the pen],
sort of [pausing]. Hmm . . . not actually sure [pausing]. I
don’t know . . . uh . . . [pointing at the cut with the pen
again] I can’t really tell it’s cutting at a diagonal,” indicating that the participant was having difficulty in visualizing
the cut plane because of the angled cut.
In addition, some participants tried to ignore the angle in
the cut and regard it as a cut that was parallel or perpendicular
to a plane of reference, that is, a totally vertical or horizontal
cut. For instance, while visualizing the cut plane of
Figure 12a, Participant 9 said, “I feel like this is just going to
be the face. ‘Cause if you bring it (cutting plane) up, the way
its cut will just make it similar to the first part, to the flat face.”
Here, the participant was trying to ignore the angle, and
regard the angled cut as a cut that is parallel to the x, y plane
and perpendicular to the y, z plane. Figure 12 shows the 3D
visualization problem with an angled cut (Figure 12a), the
correct answer (Figure 12b), and the examples of wrong
answers given by participants (Figure 12c,d).
c
FIG. 11. Properties of 3D visualizations that made problem solving easier. [Color figure can be viewed in the online issue, which is available at
wileyonlinelibrary.com.]
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a
b
c
d
FIG. 12. An example of a 3D visualization problem with an angled cut and its solutions. [Color figure can be viewed in the online issue, which is available
at wileyonlinelibrary.com.]
FIG. 13.
An example of a 3D visualization problem with an angled cut.
In a similar vein, before visualizing Figure 13, which has
the cutting plane that is cut with an angle, Participant 6 tried
to imagine the cut that is parallel to the x, y plane and
perpendicular to the y, z plane, saying that “If this slice is
totally horizontal, I would have five layers.” Figure 13
shows the 3D visualization problem with an angled cut.
When trying to figure out the thickness of each layer,
participants had to consider more aspects of the solution
when the 3D visualization problem has an angled cut. With
a parallel cut, the thickness is the same as seen on one of the
faces, but when the cut is angled, there is an added problem
of calculating the thicknesses via projection. As the thickness of each layer that needed to be drawn in the cut plane is
different from what is shown on the surface of the visualization, participants often tried to figure out whether a
certain layer was going to be thicker or thinner than what
was shown on the surface of the visualization as a result of
angled cut. For example, while solving the problem in
Figure 14a, participant 6 said, “Even when I take a cross
section at an angle, it is going to have three layers, but
the thickness of layers is going to be changed.” Figure 14
shows the 3D visualization problem with an angled cut
(Figure 14a), the correct answer (Figure 14b), and the
answer given by Participant 6 (Figure 14c), which was
correct.
Figuring out which part of the layer will be included in
the cutting plane also required the participants to consider
more aspects of the solution when visualizing a 3D visualization problem with an angled cut. When solving these
problems, sometimes the thicknesses of layers were wrong,
a layer was shown when it should be gone, or a layer was
gone when it should be shown, which indicated the difficulty. For instance, while visualizing Figure 15a, Participant 3 said, “I’ll not see this layer [pointing at the last
layer in the 3D visualization problem]. So I’ll see just one
thing at the end which is these dots from C to D, and the
other side here.” Figures 15 and 16 show the 3D visualization problems with angled cuts (Figures 15a and 16a), the
correct answers (Figures 15b and 16b), and the wrong
answers given by the participants (Figures 15c,d and
16c,d).
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a
b
FIG. 14.
An example of a 3D visualization problem with an angled cut and its solutions.
a
b
c
d
FIG. 15.
An example of a 3D visualization problem with an angled cut and its solutions.
Some participants made mistakes as they thought the cut
plane would not be a square because of the angle in the cut.
For example, while visualizing Figure 14a, Participant 7
drew the cut as a parallelogram, while saying that “It’s a
diagonal slice, so probably the square I will have to draw is
also diagonal.” Participant 3 also asked “Slice is always a
rectangle, right?” showing confusion caused by the angled
cut. Figure 17 shows the examples of wrong answers that
were drawn as parallelograms.
As described, participants often drew wrong answers to
this problem compared to a similar 3D visualization
problem with an orthogonal cutting plane. Wrong answers
included failing to adjust the thickness of layers, as shown in
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c
Figure 12c,d, not drawing layers that would actually be
included in the solution, or drawing layers that would not be
included in the solution, like Figure 16c,d, and drawing
a cutting plane that is parallelogram, as displayed in
Figure 17a,b.
It seems that the primary reason why participants are
having more difficulties when the cut is angled to the plane
of reference than when not is because the direction of
the cut is different from the planes of reference, that is, a
viewer’s automatically assumed x, y, and z coordinate
system (Figure 6). In fact, previous studies showed that
transforming objects mentally with axes oblique to the environment are more difficult to perform than transforming
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a
b
c
d
FIG. 16. An example of a 3D visualization problem with an angled cut and its solutions. [Color figure can be viewed in the online issue, which is available
at wileyonlinelibrary.com.]
a
objects with orthogonal axes to the environment (Cohen &
Hegarty, 2007b; Pani et al., 1997). There are two possible
reasons for this. First, as indicated by the need to do projection mapping to determine the thickness of the layers, the
cognitive load for solving an angled cut problem is higher.
Second, because most of the world we live in uses rectangular coordinates, it is likely that we do not have automatic
mappings to prior solutions or approximate solutions to
these problems.
b
Nonplanar Layers
FIG. 17. Examples of wrong answers when visualizing 3D visualization
problems with an angled cut. [Color figure can be viewed in the online
issue, which is available at wileyonlinelibrary.com.]
Nonplanar layers in the 3D visualizations including
curved layers and v-shaped layers also were more difficult to
visualize for the participants than flat/planar layers. In the
case of curved layers, while visualizing Figure 18a, Participant 5 said “This (left side of the diagram) is curvy and this
(right side of the diagram) is straight, so what will the inside
look like? I don’t know,” illustrating the confusion caused by
the curved layers. Figure 18 shows the 3D visualization
problem (Figure 18a), the correct answer (Figure 18b), and
the answer given by Participant 3 (Figure 18c), which was
correct.
Similarly, while visualizing Figure 18a, Participant 8
mentioned that “This one was more difficult because it didn’t
have straight lines any more, the curve is coming from the
inside of the slice. I don’t know why, but I can’t visualize or
even think about this arc.”
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a
b
FIG. 18.
An example of 3D visualization problem with curved layers and its solutions.
a
d
c
b
e
c
f
FIG. 19. An example of a 3D visualization problem with curved layers and its solutions. [Color figure can be viewed in the online issue, which is available
at wileyonlinelibrary.com.]
Participants often paused and did not say or do anything
for a while when they were confused. For example, while
visualizing Figure 19a, Participant 6 said, “There [pausing]
are curved layers. So this is going to be . . . [pausing]. I see
[pausing], alright. Mmm . . . [pausing],” which illustrates
the difficulty she/he was having because of the curved
layers. Figure 19 shows the 3D visualization problem
(Figure 19a), the correct answer (Figure 19b), and the wrong
answers given by participants (Figure 19c–f).
Common wrong answers caused by curved layers
included drawing flat/planar layers when they actually had
to draw curved layers, like the solution for the visualization
problem displayed in Figure 19c–f or drawing curved layers
(Figure 20) when they actually needed to be straight
(Figure 14b).
Participants found it extremely difficult to visualize what
would appear on the cut plane when the layers were
v-shaped. Participants were often lost while solving visualization problems with v-shaped layers such that they did not
do anything for a long time, and even gave up attempting to
visualize the cutting plane. For instance, while trying to
solve the visualization problem shown in Figure 21a, Participant 5 said “[Not doing anything for a long time] I don’t
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FIG. 20. An example of a wrong solution to the 3D visualization problem
with curved layers. [Color figure can be viewed in the online issue, which
is available at wileyonlinelibrary.com.]
know. I really don’t know, I just . . . can’t see . . . Yeah, I
don’t, I don’t know if it would actually be like this. Yeah, I
don’t really know. I don’t know.” Wrong answers included
not drawing anything at all or drawing parallel layers in the
cutting plane when they were actually v-shaped, such as the
solution to the visualization problem presented in
Figure 21c,d. Figure 21 shows the 3D visualization problem
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FIG. 21. An example of a 3D visualization problem with v-shaped layers
and its solutions. [Color figure can be viewed in the online issue, which is
available at wileyonlinelibrary.com.]
with v-shaped layers (Figure 21a), the correct answer
(Figure 21b), and the wrong answers given by the participants (Figure 21c,d).
However, when the layers were flat/planar, participants
found the problems easy, saying “This is pretty simple”
(Participant 9 working on Figure 4a). For most of these
cases, participants drew the cutting plane correctly without
stopping.
We hypothesize that the primary reason for the difficulty
is similar to the reason for the angled cut. To be more
specific, nonplanar layers make them difficult to comprehend because the visualization requires a projection of a
complex object on to the cutting plane, adding to the cognitive load of the visualizer. In particular, in the case of the
curved layers, it was difficult for participants to figure out
whether the images of the curved layers in the cut plane of
the 3D visualization problems would be curved or flat/
planar.
Mixed Combinations of Layers
Mixed combinations of curved and flat/planar layers
were also more difficult to visualize than homogeneous
layers, which contained either only curved or flat/planar
layer formations. As previously mentioned, visualization
problems were given to participants in an order of increasing difficulty, and visualization problems with mixed combinations of curved and flat/planar layers were problem
numbers 15, 16, and 18 among the 18 visualization
problems, showing that they were already considered to
be relatively difficult visualization problems among all of
the given problems. Thus, only a few of our participants
solved these problems, as most of the participants
stopped even before reaching these problems in the allotted
hour. Sometimes participants who reached these problems
gave up. For instance, while solving Figure 22a, Participant 1 said “I don’t understand this one at all (laughing)
. . . I’m trying to understand how to visualize this . . . I
don’t know, I really can’t. I give up on this one,” indicating
the difficulty. Figure 22 shows the 3D visualization
problem with mixed combinations of layers (Figure 22a),
the correct answer (Figure 22b), and the wrong answers
given by the participants (Figure 22c,d).
In solving these visualization problems, participants
often exhibited difficulties in figuring out whether they
should draw straight or curved lines when visualizing
certain layers in the cutting plane. For instance, while visualizing Figure 23a, Participant 6 said “This is going to be
straight? Can I see the curve over here [pointing to the cut
with an index finger]? Yeah, there should be a curve over
there for the dense layer. There should be a curve for the
next layer, too?” Similar to the visualization problems with
v-shaped layers, participants were often lost while solving
visualizations with a mixed combination of curved and flat/
planar layers. Participant 5 said, “Just as I start to think I’m
getting somewhere, I completely lose the image in . . . in my
brain. And then I can’t, think of, what it would look like.”
Wrong answers from these visualization problems include
drawing flat/planar layers in the cross-section when they
actually were curved, as in the case of Figure 23c, and
failing to draw the layers in their correct directions or size,
as in the case of the visualization problem shown in
Figure 23d. Figure 23 shows the 3D visualization problem
with mixed combinations of layers (Figure 23a), the correct
answer (Figure 23b), and the wrong answers given by the
participants (Figure 23c,d).
On the other hand, participants found it easier to visualize
3D visualization problems with homogeneous layers than
mixed combinations of curved and flat/planar layers. While
visualizing Figure 24a, which has only curved layers,
Participant 1 said, “I could go layer by layer, it’s pretty
straightforward.” Similarly, while visualizing Figure 24b,
Participant 8 said, “I didn’t have problems here because it
has the same shape for each layer, they were all rectangles,
so it was much more consistent and it didn’t have different
patterns,” indicating that consistency of layers, that is,
homogeneous layers made the cut easier to visualize. In this
case we attribute the problems with the nonhomogeneous
layers to the additional cognitive load caused by the mix of
layer types. Three-dimensional visualization problems with
homogeneous layers are presented in Figure 24.
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c
b
d
FIG. 22. An example of a 3D visualization problem with mixed combinations of layers and its solutions. [Color figure can be viewed in the online issue,
which is available at wileyonlinelibrary.com.]
Conclusion
This qualitative research study used grounded theory to
find out which properties in 3D visualizations made them
easy or difficult to comprehend. Our initial findings suggest
that cross-sections made at an angle to the planes of reference of the visualization, curved layers, v-shaped layers, and
mixed combinations of curved and flat/planar layers made
visualizations more difficult for our participants to comprehend the inner structure of the 3D visualizations. In contrast,
cross-sections that were parallel or perpendicular to the
planes of reference of the visualization, flat/planar layers,
and homogeneous layers created easy to solve visualizations. This result shows that the direction of the cut face of
the 3D visualization influences the easiness and difficulty of
the comprehension of the internal structure of 3D visualizations. In particular, this research showed that when either the
direction of the cut or the layers is different from the
assumed planes of reference, a reorienting of a participant’s
thinking to new planes of reference was required, which
made it difficult for the participants to comprehend the 3D
visualizations.
By choosing a set of 3D visualizations from introductory
geology and earth science textbooks, we were able to separate the domain knowledge from the visualization; that is,
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we did not ask any questions about what the geology layers
meant, but rather only to revisualize the visualization from a
different perspective and to draw this revisualization. We did
not do this with information visualizations because many of
those currently in use make it very difficult to separate the
domain knowledge from the visualization. We also did not
do this because many of the 3D information visualizations
are much more complicated than the geological ones
because they require a visualizer to establish their own
planes of reference and because they usually are animated;
that is, they can be rotated, zoomed in and out of, and viewed
from different perspectives. We felt that these types of capabilities would have made our study problems too difficult for
our participants and not have enabled us to obtain the information we did.
This work makes a direct contribution to the understanding of the aspects of 3D visualizations that reduce or
enhance their effectiveness. The understanding of these
aspects contributes to the design and use of any 3D visualization in a way that helps users to better interact with
information. Most directly, the research connecting 3D visualization comprehension to spatial ability in the study of
scientific visualizations can be applied to information visualization as well, since any 3D visualization, including
scientific visualizations and information visualizations,
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a
b
c
d
FIG. 23. An example of a 3D visualization problem with a mixed combination of layers and its solutions. [Color figure can be viewed in the online issue,
which is available at wileyonlinelibrary.com.]
a
b
FIG. 24.
Three-dimensional visualization problems with homogeneous layers.
requires spatial ability for comprehension. In addition, since
there is relatively little difference in the visual properties
forming both we can expect similar comprehension problems. More subtle in nature, our research suggests that individuals approach virtual visual scenes with an implied 3D
frame of reference (what we call our planes of reference)
deciding what is the up–down direction, the left–right direction, and the back–front direction. They then solve problems
based on their chosen orientation to this frame of reference
or viewpoint and have difficulties with features in the visualization that are not parallel or perpendicular to their viewpoint. The concrete nature of science visualizations often
helps a viewer construct a frame of reference. Information
visualizations typically do not. Further, they often allow
a viewer to rotate the visualization and/or perform the
rotation for the viewer. Because a viewer does not have
any knowledge of what might make a good viewpoint, they
are unlikely to achieve a rotation that will help in comprehending the visualization, making the comprehension
difficulties worse. Although future research is necessary to
form design recommendations, our work suggests possible
mechanisms for generating more comprehensible 3D
information visualizations by establishing an easily recognized frame of reference and then by avoiding visual
properties that do not have simple relationships to this frame
of reference.
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This study used a qualitative research method to generate hypotheses as to which properties make 3D visualizations easy or difficult to comprehend. This was a an
appropriate method; however, it has limitations in that
there were a small number of participants in the study. In
addition, as the study was conducted with no preconceived
beliefs as to what might cause comprehension difficulties,
it was not conducted in a controlled manner, nor were the
problems selected for specific properties. Therefore, it is
possible that the 18 3D visualization problems used in our
study may not comprehensively covered all the problems
that might cause difficulty. Also, because many of the 3D
visualization problems had multiple properties, there is
a possibility that those properties interacted with each
other. Lastly, the cross-section was an artifact of the
experiment, that is, it created a problem to solve, so in
some sense, talking about the angle of the cut is also representative of this artifact. However, an argument can be
made that a cross-section represents a mental interpretation
of the interior of a visualization and, thus, is a useful way
of determining if a person truly understands the entire
visualization.
In the future, the plan is to run confirmation studies that
test whether the visual properties that we found really do
make 3D visualizations easy or difficult to comprehend.
Controlled experiments will be conducted so that whether a
certain property makes it easier or more difficult can be
accurately measured. The first controlled experiment will
test whether the direction of the cut makes comprehension
of the internal structure of 3D visualizations easy or difficult by asking participants to find a correct cut face of the
3D visualizations when they are cut in various directions.
In the second controlled experiment, whether the shape of
the layers affects 3D visualization comprehension will be
tested by asking participants to find a correct cut face of the
3D visualizations when 3D visualizations have various
types of layers. In addition, although we only used blockshaped 3D visualizations, we plan to investigate whether
the overall shape of the 3D visualizations will make it
easier or harder to comprehend the internal structure of the
3D visualizations. Because interest in 3D information visualizations are growing in number and 3D information visualizations are getting more sophisticated and complex, we
believe that discovering specific visual properties such as
the direction of the cut, the shape of the inner layers, and
the outer shape of the 3D visualizations that make comprehending 3D visualization easier or more difficult will significantly contribute to the design of 3D information
visualizations and, also, the development of training programs that facilitate users’ understanding and interaction
with information.
Acknowledgments
This study was part of the research project, which was
supported by a grant from the National Science Foundation
(#0753176). The initial analysis of this research study was
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presented as a poster at the 74th ASIS&T conference. The
full citation is included in the references.
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DOI:
10.1002/asi
DOI:
10.1002/asi