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Visual Informatics 5 (2021) 28–38
Contents lists available at ScienceDirect
Visual Informatics
journal homepage: www.elsevier.com/locate/visinf
Toward flexible visual analytics augmented through
smooth display transitions
Christian Tominski a,∗, Gennady Andrienko b,c, Natalia Andrienko b,c, Susanne Bleisch d,
Sara Irina Fabrikant e, Eva Mayr f, Silvia Miksch g, Margit Pohl g, André Skupin h
aUniversity of Rostock, Rostock, Germany
bFraunhofer IAIS, Sankt Augustin, Germany
cCity, University of London, UK
dFHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
eUniversity of Zurich, Zurich, Switzerland
fDanube University Krems, Krems, Austria
gTU Wien, Vienna, Austria
hSan Diego State University, San Diego, USA
article info
Article history:
Received 11 May 2021
Received in revised form 23 June 2021
Accepted 23 June 2021
Available online 30 June 2021
Keywords:
Visual analytics
Animated transitions
Multi-faceted data
abstract
Visualizing big and complex multivariate data is challenging. To address this challenge, we propose
flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity
challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied
data. At the heart of our proposed approach are transitions that fluidly transform data between user-
relevant views to offer various perspectives and insights into the data. While smooth display transitions
have been already proposed, there has not yet been an interdisciplinary discussion to systematically
conceptualize and formalize these ideas. As a call to further action, we argue that future research
is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary
ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them,
and consider the display user for whom such depictions are produced and made available for visual
analytics. With this contribution we aim to further facilitate visual analytics on complex data sets
for varying data exploration tasks and purposes based on different user characteristics and data use
contexts.
©2021 The Author(s). Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University
Press Co. Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
1. Introduction
Analyzing multi-faceted big data is challenging (Kehrer and
Hauser,2013;Hadlak et al.,2015). To support a comprehensive
understanding of this kind of data, different views and perspec-
tives must be made available to the user during the visual data
exploration and analysis.
A common example for multivariate data offering multiple
perspectives is spatio-temporal data. Such data consist of a set
of entities and measured attributes that have been observed
at different points in time and at different locations in space.
∗Corresponding author.
E-mail addresses: christian.tominski@uni-rostock.de (C. Tominski),
gennady.adrienko@iais.fraunhofer.de (G. Andrienko),
natalia.andrienko@iais.fraunhofer.de (N. Andrienko), susanne.bleisch@fhnw.ch
(S. Bleisch), sara.fabrikant@geo.uzh.ch (S.I. Fabrikant), eva.mayr@donau-uni.ac.at
(E. Mayr), miksch@ifs.tuwien.ac.at (S. Miksch), margit@igw.tuwien.ac.at
(M. Pohl), skupin@sdsu.edu (A. Skupin).
From a visualization perspective, widely-used visualization ap-
proaches exist to display a single aspect of such data. Three
examples are shown in Fig. 1. A spiral may visualize cyclic tem-
poral patterns (Aigner et al.,2011), a choropleth map can show
spatial areal relationships (Dykes et al.,2005), and a node-link
diagram may expose the structural connections between data
entities (Tamassia,2013). When multiple perspectives on the
same data set are depicted in different views, understanding
of the interplay of these different data characteristics may be
hindered. However, once multiple data aspects are channeled
into separate and distinct views, understanding the interplay of
these aspects becomes a non-trivial task. Mechanisms like view
coordination (Tominski et al.,2009), brushing & linking (Chen,
2004), or dynamically embedded visual links (Collins and Carpen-
dale,2007) are frequently deployed to enable users to develop an
overall understanding of patterns and relationships existing in the
data shown in separate views.
One alternative to linked views (Roberts,2007) is to integrate
multiple data characteristics into one single visualization. An
https://doi.org/10.1016/j.visinf.2021.06.004
2468-502X/©2021 The Author(s). Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under
the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Fig. 1. Visualizing time, space, and structural connections in separate views.
Fig. 2. Time, space, and structural connections integrated in a single visual representation.
example is shown in Fig. 2, where data entities (white dots) and
their structural connections (gray lines) are embedded within
selected geographic regions of a perspective 3D map display
(Hadlak et al.,2010). For each time step in the data, there is
a map layer stacked along the vertical axis. Additionally, blue
and red spikes between the layers indicate where data entities
start or cease to exist across time. While this visual representa-
tion integrates time, space, and structural connections, it is also
rather complex and requires some training to decipher and some
interaction to explore.
Typically, integrating a large number of data characteristics
into a single visual representation is not feasible, because the
resulting image would be visually too dense and thus too difficult
to interpret. On the other hand, with many separate single-aspect
views, the user needs to visually integrate findings made in one
view with patterns of different data characteristics shown in
other views. In summary, both integration and separation of data
characteristics may require considerable cognitive and perceptual
load or view interaction effort by the user. In short, separate
linked data views and integrated multivariate views have their
strength and weaknesses. For this, we propose flexible visual
analytics to combine the strengths of both data visualization
approaches, as we discuss next.
2. Flexible visual analytics
We introduce an alternative approach situated at the inter-
face of integration and separation, which we call flexible visual
analytics (FVA). Our working definition of the term is as follows:
‘‘Flexible visual analytics is an approach to support the com-
prehensive visual exploration and analysis of multi-faceted
data via several smoothly integrated elastic multivariate
views’’.
The goal of FVA is to mitigate the challenges associated with
visual complexity and interaction complexity in visual analytics,
while maintaining the strengths of multiple perspectives on the
studied data. Essentially, FVA is based on the effective blending
of different data views. The main ingredient of FVA are thus
transitions that are designed to smoothly transform one view into
other data views. Conceptually, transitions are a visual and com-
putational means to transform between different data views, such
as visual encodings, visualization techniques, view types, parame-
terizations, data query results, or the results of different analytical
computations. Transitions may reduce interaction complexity and
allow users to fluidly and seamlessly study different perspectives
of the data. The start and end points of transitions are user-
selected views that highlight a particularly relevant or interesting
perspective on the data based on a user’s task or interest. These
prioritized views are designed with maximal expressiveness for
that chosen data perspective, while other data characteristics are
compressed or omitted. The prioritized views are assumed to be
balanced in terms of their visual complexity.
FVA, according to our definition, has been used in the lit-
erature before, however, as we contend, without fundamental
conceptualization in its own right. For example, Yuan et al. blend
parallel coordinates and scatter plots (Yuan et al.,2009). Their
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C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
approach smoothly pushes two parallel axes apart to make space
for embedding scattered data points. Flexibly blending time se-
ries plots with parallel coordinates is possible as well (Gruendl
et al.,2016). Tominski et al. blend 2D and 3D representations
of movement trajectories (Tominski et al.,2012). Starting from a
2D overview of the entire movement data the user can smoothly
transition to a 3D view that reveals details about individual
movement trajectories. Schulz and Hadlak study transitions in
the design space of implicit tree representations (Schulz and
Hadlak,2015). This allows visualization designers to explore new
potentially useful designs for particular data analysis tasks. Brosz
et al. developed an approach for transforming visual representa-
tions via skeleton-based image deformations (Brosz et al.,2013).
Being pixel-based, the approach can be applied to any visualiza-
tion, but is oblivious to its geometric model and the underlying
data facets. Previous work also studied morphing between vi-
sualization techniques for educational purposes (Ruchikachorn
and Mueller,2015). In the context of digital humanities, the
PolyCube approach utilizes space–time cube transformations to
switch between different perspectives on complex cultural data
collections (Windhager et al.,2020).
All these examples have in common that they involve smooth
transitions between views that focus on different aspects of the
studied data within a given application context or part of a
visualization system innovation. For several years, smooth ani-
mated transitions have been a topic of research in visualization,
for example, for data graphics (Heer and Robertson,2007), data
navigation (Pulo,2007), or data aggregation (Kim et al.,2019).
Several approaches have been developed to enhance animated
transitions, for example, by bundling trajectories (Du et al.,2015),
by grouping (Zheng et al.,2018), or via a grammar for author-
ing (Kim and Heer,2021). A design space for animated transitions
has recently been published (Thompson et al.,2020).
Our goal for this paper and future similar research is to re-
view, build upon, and extend transition research in a way that
transitions are not only possible for elementary visualizations or
charts, but for complex, multivariate visual depictions of big and
complex data. Eventually, FVA’s aim is to be able to systematically
transition between several different views, and not only between
two simple visual representations. Such a research endeavor can
also be informed by research on animated transitions for user
interfaces, which arguably, are already more complex than basic
charts (Dessart et al.,2012;Vanderdonckt,2012;Chevalier et al.,
2016).
Smooth transitions are well known for animated data graphics
such as the popular Gapminder project (Gapminder Foundation,
2021). Yet, for this current definition of FVA, we do not consider
views that change along a time line, but what cartographers have
called re-expression or non-temporal animation that is using
any numeric data dimension other than time (Harrower and
Fabrikant,2008). Nonetheless, research on animation is certainly
related to what we discuss here.
While past and current animation research and authoring
systems contain smooth transitions between static scenes out
of the box, there are many open research issues: From a con-
ceptual perspective, we do not have a clear understanding of
the requirements and principles of FVA, specifically for complex
multivariate views. What does the design space look like? It is
further unclear which data dimensions or facets can be combined
with which visual mappings. Are there general principles that
can help us find such suitable combinations? We are also lacking
a thorough understanding of how much integration, separation,
and transitioning are appropriate in the context of a specific data
domain, application type, and visualization user. Where is the
sweet spot satisfying those contextual requirements; does such
an optimal solution even exist?
In light of these open issues, we do see the need for developing
a systematic view of FVA in order to gain a better understanding
of the potentials and limitations of augmenting visual data analy-
sis by means of transitions between discrete visual states. Such a
systematic view would allow us to comparatively evaluate differ-
ent approaches, match them to tasks and contexts, and identify
the potential for not yet existing techniques to be developed in
the future.
We thus aim to position this contribution as a call to action
for more research on FVA. We propose some conceptual consid-
erations that identify key aspects of FVA in terms of views and
transitions between them. Moreover, we discuss implications of
FVA from the perspective of human perception and cognition.
Finally, we identify open research questions to spark further
research in the context of FVA contributing to the overall goal
of making big and complex multivariate data analysis not only
a fluid and seamless, but also a fruitful experience with less
cognitive load and fewer required interactions.
3. A technical perspective on FVA
As indicated earlier, FVA builds upon the idea of (i) relevant
views and (ii) smooth transitions between these views. Next, we
focus our discussion on the technical aspects involved in FVA.
3.1. Relevant multivariate views
We first need to clarify what we mean by relevant multivariate
views and what they are supposed to show. In the first place,
the data attributes Aare of interest. The data attributes may
be embedded in a temporal Tand spatial Sframe of reference.
Moreover, structural relations Rmay exist between data entities.
The different data aspects A,T,S, and Rlead to several common
data classes (Tominski and Schumann,2020): multivariate data
(A), time-oriented data (T→A), spatio-temporal data (T×S→
A), or dynamic graphs (T→R). One can imagine further data
aspects of interest such as uncertainty (Bonneau et al.,2014) or
set affiliation (Alsallakh et al.,2016).
Multivariate data offer several analysis opportunities. For ex-
ample, they may be analyzed with respect to outliers, correla-
tions, or clusters. For spatio-temporal data, the analyst may want
to study how data values develop over time or where certain
values are located in space. For a dynamic graph, one may ask
which of its parts form stable communities over time. More
generally, many data facets imply that there are many questions
one may ask about the data, which in turn lead to a more complex
data exploration and analysis process (Kehrer and Hauser,2013).
In light of this multitude of issues, it is a truism that there is
no one optimal view that will suffice. As a starting point for FVA,
we propose relevant, that is, prioritized views that emphasize one
or two selected aspects of the data while potentially hinting at or
omitting other aspects of the data.
The visualization literature is quite clear about the fact that
particular types of data require dedicated visual representations
(Hanrahan,2009;Tominski and Schumann,2020). Yet, designing
visual representations for multiple aspects of high-dimensional
and multivariate data remains challenging.
One example of a prioritized multi-aspect visualization is de-
scribed by Dübel et al. (2017), who balance the visualization of
terrain, collected geo-spatial data, and their uncertainty. When
the terrain is prioritized, it is rendered using sophisticated light-
ing algorithms, whereas the geo-spatial data are represented only
in an aggregated fashion. On the other hand, when the geo-
spatial data are prioritized, they are shown in full detail, while the
terrain is visualized only by means of contours. As this example
illustrates, the prioritization can be implemented by varying the
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C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Fig. 3. Prioritized views (red, green, blue) communicate data aspects (A,T,S,
R) at different levels of detail (++,+,−).
data’s degree of abstraction (e.g., aggregated vs. exact values) and
the degree of visual abstraction (e.g., detailed relief shading vs.
contours only).
In the context of visual analytics, one may also consider ap-
proximate, heuristic data analysis methods in contrast to ex-
act and precise computational steps. Prioritization may also be
achieved by changing the amount of data items through selective
sampling or changing data components through dimensional-
ity reduction. Although selected questions of multi-aspect views
have already been studied (Kehrer and Hauser,2013;Hadlak
et al.,2015), no comprehensive design-space for prioritizing data
aspects in visual analytics has been described in the literature.
Prioritized views as described before form the basis for FVA.
An individual view can be characterized conceptually as illus-
trated in Fig. 3. The different aspects (A,T,S,R) a view might
contain are depicted as vertical axes. For each of the aspects, we
define a continuum of the level of detail from full detail (++),
to reduced detail (+), to omitted (−). Full detail is provided
for aspects that are prioritized, reduced detail is sufficient to
provide context, omitted data aspects are not included in the
visualization. In order to be able to develop a comprehensive
understanding of the data, an analyst would need a whole set
of views, each with a different prioritization of the relevant data
aspects and dimensions. The figure shows the characteristics of
three hypothetical views as polylines in red, green, and blue. The
red line corresponds to a view that emphasizes the temporal
dependencies of the data, but does neither include space nor
structural relations (e.g., a spiral display). The green line stands
for a view that focuses on the relations, but shows space and data
attributes only to a lower degree, leaving out time completely
(e.g., a node-link diagram overlaid on a 3D globe). Finally, the
blue view emphasizes the spatial aspect and includes aggregated
data attributes, but does not convey aspects of time and relations
(e.g., a choropleth map). A challenge for FVA is to systematically
research and find concrete views that are suitable for different
applications and use contexts. The set of views should compre-
hensively accommodate all data aspects, but also strive to be
minimal to reduce cognitive load.
3.2. Smooth multivariate transitions
Conceptually, FVA is about flexibly transitioning between rel-
evant multivariate views. In a sense, FVA is a kind of navigation
between views, where transitions exist to make the navigation
smooth rather than abrupt. Robert Spence argues (Spence,1999)
(p. 938):
‘‘If change has to occur it is immensely helpful, as far as
minimizing the cognitive load associated with the mainte-
nance of a good internal model is concerned, if the external
representation can change smoothly’’.
Transitions may form bridges on different conceptual levels.
They can link views with different analytical abstractions, for
example, between the results of different time-series forecast
methods (Wang and Hornbæk,2020). However, transitions will
more commonly involve different visual representations, for ex-
ample, between a 2D and a corresponding 3D representation,
or between a geographic projection and a multi-dimensional
projection. Note that transitions are not only for communication-
oriented purposes (e.g., storytelling, onboarding), but are also
supposed to be a vehicle for data exploration.
No matter the specifics of what is being connected by a tran-
sition, it leads from one prioritized view to another one. From
there, another view and yet another view may be reached, form-
ing a chain of connected views. Alternatively, there may be a
central view from which several other views can be reached, but
no lateral transitions are available between these other views.
This would form a star-shaped topology.
In general, transitions between views may thus form different
topologies, some are illustrated in Fig. 4. However, we do not yet
know the potential impact that a particular topology may have on
the analysis and on the generation of insights involving complex
data. More research is necessary to investigate which specific
types of topologies may be suitable under which circumstances.
For the transition itself, we define two key requirements: A
transition should be (i) smooth and (ii) controllable. Smoothness
is required to support users in understanding how one visual
representation transforms into another. Achieving smoothness
typically involves some form of interpolation. A transition should
also be controllable to allow users to reverse or replay it, or to
watch it at a different speed. An appropriate user interface can
offer these operations.
An elementary transition is concerned with an atomic visual
change. An example would be to change the position of a sin-
gle dot. A transition from one visual representation to another
typically involves a whole series of elementary transitions. For
example, collapsing a set of dots might involve the temporary
display of their convex hull, which is then folded into a single
meta dot replacing the original set.
From a conceptual perspective, a transition can be based on
the underlying data model or on the view’s graphical model. On
the side of the data model, a transition can involve data attributes,
derived statistics, or parameters of any step along the visual
analytics pipeline. Transitions on the graphical side work on a ge-
ometrical scene definition or the plain pixel array. Consequently,
three different strategies for implementing transitions exist:
1. Interpolate data model,
2. Interpolate geometry model, or
3. Interpolate pixel model.
The decision on which strategy to use must be made de-
pending on the intended visual outcome for the transition. The
reason is that different strategies can lead to different outputs.
Consider, for example, the illustration in Fig. 5. Let us assume
an analytical computation is parameterized with two different
values p=vTand p=vSto convey either temporal or
spatial aspects of the data. The two resulting views show the
data as a black dot at different positions. When interpolating the
dot position (geometry model), the visual outcome is a linear
trajectory. On the other hand, when interpolating the parameter
values between vTand vSdirectly (data model), the trajectory of
the dot might be totally different, as indicated by the curve in
our example. When interpolating between images (pixel model),
for example, by means of alpha-blending, no trajectory appears
at all. Therefore, the interpolation strategy to be employed must
be chosen carefully.
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C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Fig. 4. Transitions between views may form different topologies.
Fig. 5. Different visual outcome of interpolation in data space and visual space.
Fig. 6. Four differently prioritized views of the food stall example data from Table 1. Left: Attributes and time are shown in full detail, space is omitted. Middle
top: Attributes in full detail, time and space are omitted. Right: A map showing the location and type of the food stalls but not their labels (attribute with reduced
detail, time omitted). Middle bottom: Time is fixed (reduced detail) and attributes shown, space omitted.
In our example, pis a numeric parameter that is suitable for
interpolation between vTand vS. However, what if a transition
needs to convey the change of a categorical parameter, for which
no interpolation of the parameter value is possible by definition.
In such cases, graphical interpolation is the only choice we have
for a smooth transition. Yet, the intermediate views being created
during the transition do not have a corresponding state in the
data/parameter space. It is important to make viewers of such
transitions aware of this fact. How this can be done is an open
research question.
The previous example was concerned only with an elementary
transition of a single dot. The situation gets more complex when
considering transitions between elaborate visual representations
such as those mentioned earlier—balancing the visualization of
terrain, geo-spatial data, and their uncertainty. While there are
previous works on animated transition for data graphics, we do
not yet know how these translate to more complex multivariate
views. Which aspects need to be transitioned via interpolation
in the data space, which aspects are safe to be transitioned in
the visual space? How to best group and stage individual atomic
transitions to generate an overall comprehensible and helpful
view transition? The literature does not yet provide guidelines
in this regard, which calls for more research on FVA.
3.3. Examples
In this section, we discuss examples illustrating how smooth
display transitions might connect different visual representations
better. In doing so, we also demonstrate that FVA is indeed a
concept for multi-faceted data, including attributes, time, space,
and structural relationships.
An example with a simple fictional food stall data set shall
illustrate options for different prioritized views of the same data
and how those views might be chained using transitions. Table 1
shows the example data set with four different food stalls. Each
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C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Table 1
Simple food stall data set to be visualized with different prioritized views in
Fig. 6.
Name (A) Type of food (A) Open (T) Location (S) ...
Andy’s Snacks 09 am–03 pm XA,YA...
Beans Co. Coffee 07 am–10 am XB,YB...
Cypress Lunch 11 am–01 pm XC,YC...
Delight Coffee 07 am–05 pm XD,YD...
... ... ... ... ...
stall has a name, sells a type of food, is open at certain times,
and is located somewhere. Fig. 6 shows four different prioritized
views. The parallel coordinate plots at the bottom of each frame
indicate which aspect of the data is prioritized, shown with re-
duced detail, or omitted (as illustrated earlier in Fig. 3). The thick
gray lines between the frames indicate options for transitions.
We hypothesize that to transition smoothly between prioritized
views it may be useful to stage the transition and to increase or
decrease data details along the axes of data aspects consecutively.
For example, to go from the time view (Fig. 6, left) to the map
view (Fig. 6, right), one might first collapse the timeline to a point
(decrease details of time Tfrom full to omitted, as shown in Fig. 6,
middle top) and then move the points to their location on the
map (increase space Sfrom omitted to full detail). The colors
for food stall type information are kept during the transition,
while the name of the food stall is removed (reduced detail for
attributes A).
Our second example comes from previous work on combining
the advantages of node-link diagrams and matrix representations
in a technique called NodeTrix (Henry et al.,2007). Node-link
representations and matrices are visually quite different, and
therefore, a smooth transition between them requires several
stages. Fig. 7 shows an example with five stages. Starting with
a node-link representation (1), the edges are bent (2), nodes are
rearranged (3), and edges are blended to become the cells (4) of
the final matrix representation (5). Stages (2) and (3) operate in
the geometry space, whereas stage (4) is in pixel space. This il-
lustrates that transitions between complex visual representations
might require combining interpolation in different spaces.
Finally, Fig. 8 shows screenshots from an exemplary transition
between a 3D and a 2D categorical representation (Windhager
et al.,2020;Salisu et al.,2019). The 3D view (left) clusters the
data points in eight time layers and uses a ‘hull’ to show their
flow over time. The 2D view (right) uses color to encode time on
a more fine-grained level. The transition consists of several steps:
First, the reference cube is broken up to individual time layers and
the new color coding is introduced. Then, in a smooth animation,
the layers are superimposed until the representation arrives at
the final 2D view.
4. A human perspective on FVA
From a human perspective, making sense of a visualization—be
it in a more data exploratory or in a more information com-
municative setting—requires the interplay of different perceptual
(e.g., visual search, object tracking, pattern detection) and cogni-
tive processes (e.g., build up a mental model, integrate insights
into an existing knowledge structure). These perceptual and cog-
nitive processes are bound to be more demanding and challeng-
ing for users, when they wish to make sense of big and complex
multivariate data. User studies show that extracting multivariate
spatiotemporal patterns is more difficult in separated views than
in integrated ones (Andrienko et al.,2010;Windhager et al.,
2020). Prior empirical research suggests different constraints of
the human information processing system that may explain this
effect: (1) split attention (especially with animated views) (Opach
et al.,2014;Maggi et al.,2016), (2) inattentional blindness (Ship-
ley et al.,2013), (3) change blindness (Rensink,2002;Fabrikant,
2005), (4) cognitive load (Sweller,1988;Sweller et al.,2011),
and (5) generally the lack of support for incremental construc-
tion of mental models, missing gradual augmentation of users’
conceptual models (Fabrikant et al.,2008;Ceneda et al.,2017;
Windhager et al.,2018).
The FVA approach can mitigate some of these constraints by
using transitions, the process in which one object (the unity of
all data) is moved from one visualization reference system to an-
other visualization reference system and thereby changes its ap-
pearance. To conceive a transition, the user needs to understand
(1) how the data object in one display (in one reference system)
relates to another perspective in the second reference system and
(2) how the data object transforms across the reference systems.
We argue that transitions have an augmenting function for
data exploration, visual search, cognitive processing, memory
load, and knowledge building. Cognitive load is offloaded to the
visualization system and the visual complexity is reduced by
interaction. However, to fully exploit the transitions’ augment-
ing potential, we have to take into account some cognitive and
perceptual constraints in their design.
4.1. Perceptual constraints
One of the challenges of visual analytics is that the simul-
taneous presentation of different data aspects (e.g., spatial and
temporal dependencies) raises specific problems. Using map-like
representations to show developments in time leads to occlusion
of relevant information (Kriglstein et al.,2016). Other solutions
have to be found for the representation of spatio-temporal data.
Animations could be one of these possible solutions to show spa-
tial and temporal information in one visualization. Nevertheless,
perceptual constraints have to be taken into account.
One of these constraints is change blindness and inatten-
tional blindness (Rensink,2002). Change blindness and inatten-
tional blindness indicate severe limits of our visual attention
that have consequences for how users will interact with visual
representations of multi-faceted data. Following transitions in vi-
sual analytics requires tracking of multiple aspects on the screen
simultaneously. Nevertheless, recent research on multiple object
tracking indicates that human perception is better than previ-
ously assumed (Wu and Wolfe,2018). Rensink (2002) formulated
guidelines for screen design (e.g., transitions should only consist
of two reference systems and one object) that take change blind-
ness and inattentional blindness into account. These guidelines
are also highly relevant when developing FVA.
4.2. Cognitive constraints
Visualizations providing complex information require a high
degree of attention from the users. Cognitive load theory clarifies
the cognitive processes necessary for such activities (Sweller,
1988;Sweller et al.,2011). Originally, cognitive load theory has
been developed to model learning processes with educational
systems. It distinguishes between intrinsic and extrinsic cognitive
load. Intrinsic cognitive load is related to the complexity of the
material as such, whereas extrinsic cognitive load describes the
load resulting from the way the material is presented. Sweller
et al. (2011) argue that intrinsic cognitive load is given, whereas
extrinsic cognitive load can be reduced by appropriate ways
of design. They provide several possibilities how this can be
achieved.
Sweller et al. also described several effects related to cognitive
load, among others the split-attention effect. This effect can be
observed when two or more elements belonging together are
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C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Fig. 7. Smooth transition from a node-link representation to a matrix representation.
Source: ©2007 IEEE. Reprinted, with permission, from Henry et al. (2007).
Fig. 8. Transition from a 3D categorical representation (left) to a 2D representation (right).
positioned in different areas of the screen. To interpret such
a visualization correctly, users have to integrate the presented
information in a meaningful way. This is difficult because users
cannot observe both objects simultaneously and therefore have
to keep at least one of the elements in short-term memory. A
possibility to overcome this problem is to reduce the distance be-
tween elements and create a clear connection between elements
belonging together. Previous work on visualizing spatio-temporal
data (Tominski and Schulz,2012) and supporting visual compari-
son (Tominski,2016) have successfully applied these suggestions.
Yet, the issue of split attention remains highly relevant for visual
analytics, and also very challenging and difficult to solve.
Another effect identified in the context of cognitive load the-
ory is the transient information effect (Sweller et al.,2019).
This effect occurs when information is only presented briefly,
and people have to retain this information in working memory.
Strategies that might help to mitigate this effect are self-pacing
or segmentation. These strategies can be easily supported by
visualization systems.
Animation has been put forth as a strategy for integrating
elements of a visualization into a coherent whole. In this sense,
animation—as a core component of FVA—can overcome the split-
attention effect and help us to construct relations between ele-
ments at various places on the screen. Animation has been pri-
marily suggested as an appropriate method to represent temporal
information, but other phenomena can also be represented in that
way. Within the visualization community, there is a controversial
discussion about the use of animation. Evaluation studies have
yielded mixed results (Kriglstein et al.,2014). On the one hand,
animations have advantages for tasks related to temporal devel-
opments. It has been argued that animations may convey very
small changes in the data that are easily missed when using other
techniques, like small multiples (Goldsberry and Battersby,2009;
Fabrikant et al.,2008;Fish et al.,2011). In addition, it can be
argued that an animation conveys a more holistic picture than
other visualizations. On the other hand, animations that are not
well designed or inappropriately used can be confusing.
There are several factors that influence the success of an ani-
mation (Harrower,2007). Speed can be either too fast or too slow.
The possibility to control the speed of an animation is important
for the users and helps them to understand the visualization in
more detail. There is some empirical evidence that interactivity
can help to support sensemaking processes (Amini et al.,2015).
In animations, users often get overwhelmed by the sheer amount
of data. Therefore, the possibility to filter the data is especially
important so that users can concentrate on the crucial aspects
of the visualization. Animations are also more advantageous for
small datasets than for large datasets.
Bach et al. (2014) present a user study about animated tran-
sitions for dynamic networks. Their research indicates that an-
imations decrease the error rate of study participants, but they
may increase task completion time for some types of tasks. They
also mention that it is difficult to track several different changes
occurring in different areas of the screen.
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C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Lowe (2014) describes a model to clarify learning and in-
teracting with animations, the animation processing model. This
model distinguishes between five different stages, going from
more localized, detailed processing of information to the more
general level of mental model consolidation. One basic idea is
that decomposition of animations occurring in the first stage of
the model is time-consuming and increases cognitive load of the
users. Therefore, designers of animations should decompose them
into meaningful units. These units are presented to the users
who can, at a later stage, easily integrate them into a meaningful
whole. In this way, the cognitive effort of users can be reduced
considerably. Lowe and Boucheix (2016) present empirical evi-
dence to support this notion. Lowe also argues that animations
are often animated static images. He points out that, for example,
methods of cueing adapted from static images (e.g., arrows) often
do not work in animations, and that different methods of cueing
should be adopted.
4.3. Recommendations
Several tentative recommendations can be derived from this
brief overview of the literature. User studies indicate that an in-
tegrated view is better than separated views for the presentation
of multivariate data (Andrienko et al.,2010;Windhager et al.,
2020). So, if the number of data dimensions allows, an integrated
view should be preferred. Transitions between different views can
help to overcome the split-attention effect (Sweller et al.,2011;
Fish et al.,2011), although these animations have to be designed
carefully (Harrower,2007;Kriglstein et al.,2014). To take change
blindness and inattentional blindness into account, Rensink for-
mulated as a design guideline that a transition should only consist
of two reference systems and one object (Rensink,2002). In
general, the number of elements that are modified should be
kept as small as possible (Bach et al.,2014;Fish et al.,2011).
Therefore, interactivity, especially the possibility for filtering the
data, is necessary (Amini et al.,2015). In addition, users should
be able to control the speed of the animation (Fabrikant,2005;
Kriglstein et al.,2014;Sweller et al.,2019). Finally, segmentation
and decomposition of the animation into distinct units should
be possible to reduce cognitive load (Shipley et al.,2013;Lowe,
2014;Sweller et al.,2019).
5. Related work
FVA as discussed in this paper has the goal of supporting users
in making sense of multiple visual representations of complex
data. FVA shares this goal with existing approaches from the
literature.
We already mentioned visual linking as a related concept
(Collins and Carpendale,2007). It is based on drawing links be-
tween different visual representations. The key advantage of vi-
sual linking is that relations between visual representations are
made explicit. On the down side, visual linking requires additional
visual resources for drawing the links and non-trivial measures
must be taken to prevent links from occluding the visual rep-
resentation (Steinberger et al.,2011). Moreover, visual linking
requires the visual representations to be linked be visible at the
same time. This works for classic multi-view visualizations, but
not for visual representations that are dynamically embedded
into parts of another visualization, as for example for Responsive
Matrix Cells (Horak et al.,2021).
FVA is also related to composite visualization as described
by Javed and Elmqvist (Javed and Elmqvist,2012). Composite
visualization is not a specific technique, but can be understood
as a generalization or a design space of coordinated multiple
views (Roberts,2007). The composition can be juxtaposition, su-
perposition, overloading, and nesting. The design space is mainly
focused on the spatial arrangement of visual representations,
which are shown simultaneously, but does not consider the tem-
poral arrangement, that is, the smooth transitioning of visual
representations over time across a topology. It is interesting that
Javed and Elmqvist state in their paper: ‘‘However, it is possible
to envision other ways to combine two or more visualizations, for
example using interaction or animation’’. This is exactly what we
aim for with FVA.
The work by Chen et al. (2021) further explores the design
space of multi-view visualization. They add to the notion of
composition (frequency, diversity, correlations of view types) the
notion of configuration (position and size of views). Based on
hundreds of examples from the literature, numerous composi-
tion and configuration patterns are analyzed, which are utilized
for a recommendation system for multi-view visualization. Yet,
they also do not consider smooth transitions between visual
representations.
Finally, we mention animated storytelling via Data-GIF (Shu
et al.,2021) as a related approach to make data understandable.
Data-GIF also utilizes animated transitions, yet these are pre-
designed and do not support interactive control at all. FVA is
about the user taking control and traversing several multivariate
views to gain insight into complex multivariate big data.
It can be concluded that (1) researchers studying the space of
possible approaches to combining multiple views did not inves-
tigate flexible transitions among these approaches; (2) there are
examples of the use of animated transitions but there has been no
systematic general consideration of the essence of this approach;
(3) the current state of research on flexible transitions does not
allow valid comparisons with other approaches and creation of
design guidelines for choosing a suitable approach for given data,
tasks, and users.
6. Future work and conclusion
We have proposed flexible visual analytics (FVA) with the aim
to mitigate visual complexity and interaction complexity chal-
lenges in visual analytics. The overall goal of our FVA approach
is to make the exploration and the analysis of big and complex
multivariate data a fluid and seamless process. With our work
we neither propose a new approach competing with existing ones
nor do we propose a specific design or software implementation.
Our contribution is that we make the first attempt of systematic
consideration of flexible display transitions as a general approach.
The evolving conceptual foundations of FVA offer multiple
further research avenues to make FVA a useful asset in the visual
analytics toolbox. Below we open several research avenues that
future work might wish to address.
Prioritized multivariate views. For FVA to work, we need not only
one or two prioritized data views as has been suggested before,
but potentially series of displays of varied lengths for different
tasks and contexts, to convey all relevant data views. Therefore,
aggregating and generalizing previous literature and knowledge
on multi-faceted visual analytics would be a first step for future
work. A design methodology should be devised describing the
necessary steps to consider for integrating across, and prioritizing
different views in data exploration and visual analytics tasks.
Inspiration for such a design methodology can be drawn from
Munzner’s nested model of visualization design (Munzner,2009).
Ideally, guidelines can describe how certain data views can be
emphasized visually, what combinations of views work well, in
which sequence, and where the limits of display prioritization
might lie. Based on a systematic design methodology and de-
piction guidelines, concrete exemplars of prioritized multivariate
views should be designed to form a basis for the investigation of
multivariate transitions.
35
C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Multivariate transitions. More conceptual and methodological re-
search is necessary to investigate how complex multivariate
views in visual analytics can be transformed into one another.
From a top-down perspective, we need to understand which as-
pects can be transformed from one to the other in a semantically
meaningful way. Based on that, one may ask how individual
transitions can be combined to form a topology of transitions that
might allow for the analyst to cycle through any chosen view or
series of views of the data usefully and timely. Are transitions
between all possible combinations of data aspects feasible or
necessary? Are there particularly compatible combinations of
aspects that may serve as a generic backbone for a transition
topology? What are the properties of different topologies, and
how do these affect the type of knowledge generation with FVA?
From a bottom-up perspective, it is necessary to investigate
how multivariate transitions can be implemented. Extending ex-
isting literature on animated transitions (Vanderdonckt,2012;
Chevalier et al.,2016;Thompson et al.,2020) strategies need to be
developed for transitions between complex multivariate displays.
Conceptually, we need to ask how and where transitions need to
be executed—in the data model, in the geometry model, or in the
pixel model? How can atomic transitions be integrated to form
basic composite transitions that are information-rich and mean-
ingful but do not overwhelm the analyst? This begs the ques-
tion of how to communicate the meaningfulness of intermediate
states of transitions?
Human factors of FVA. The design of flexible visualizations poses
many user challenges. Human perception and cognition follow
empirically established evidence that has to be taken into account
in early stages of the design process. Cognitive load theory or
empirical findings related to change blindness and inattentional
blindness must inform future FVA investigations. Prior research
on animations can serve as a useful stepping stone, but there are
still open questions on how to design animations to support effec-
tive and efficient sense-making. How can we educate users to use
FVA, and which level of complexity might be still graspable? How
can we aggregate data into meaningful semantic hierarchies to
guide users’ understanding of FVA views and transitions? Which
kinds of interaction mechanisms might serve users to effectively
and efficiently use FVA?
In summary, we proposed the key idea of flexible visual an-
alytics (FVA) based on user, task, and context-relevant, multi-
variate data views and one or more smooth transitions between
them. We further considered the human dimension for develop-
ing meaningful and useful FVA approaches. With this report, we
aim to put the flexible, integrated, and seamless FVA approach
for visually exploring and analyzing multi-faceted data on the
visual analytics research agenda. It remains to be seen how the
identified research questions will lead to the development of
respective solutions, empirically evaluated with actual users, that
improve the visual data analysis experience when working with
big and complex multivariate data.
Ethical Approval
This work does not contain any studies with human or animal
subjects performed by any of the authors.
CRediT authorship contribution statement
Christian Tominski: Conceptualization, Writing - original
draft, Writing - review & editing. Gennady Andrienko: Concep-
tualization, Writing - original draft, writing - review & editing.
Natalia Andrienko: Conceptualization, Writing - original draft,
Writing - review & editing. Susanne Bleisch: Conceptualization,
Writing - original draft, Writing - review & editing. Sara
Irina Fabrikant: Conceptualization, Writing - original draft,
Writing - review & editing. Eva Mayr: Conceptualization,
Writing - original draft, Writing - review & editing. Silvia
Miksch: Conceptualization, Writing - original draft, Writing
- review & editing. Margit Pohl: Conceptualization, Writing
- original draft, Writing - review & editing. André Skupin:
Conceptualization, Writing - original draft, Writing - review &
editing.
Declaration of competing interest
The authors declare that they have no known competing
financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Acknowledgments
The authors gratefully acknowledge that this work is a re-
sult of the Dagstuhl Seminar 19192 on Visual Analytics for Sets
over Time and Space (Fabrikant et al.,2019). Dagstuhl seminars
are funded by the Leibniz Association, Germany. Sara Irina Fab-
rikant gratefully acknowledges funding from the European Re-
search Council (ERC), under the GeoViSense Project, Grant num-
ber 740426.
References
Aigner, W., Miksch, S., Schumann, H., Tominski, C., 2011. Visualization of
Time-Oriented Data. Springer, https://doi.org/10.1007/978-0- 85729-079- 3.
Alsallakh, B., Micallef, L., Aigner, W., Hauser, H., Miksch, S., Rodgers, P., 2016. The
state-of-the-art of set visualization. Comput. Graph. Forum 35 (1), 234–260,
https://doi.org/10.1111/cgf.12722.
Amini, F., Rufiange, S., Hossain, Z., Ventura, Q., Irani, P., McGuffin, M.J., 2015.
The impact of interactivity on comprehending 2D and 3D visualizations of
movement data. IEEE Trans. Vis. Comput. Graph. 21 (1), 122–135, https:
//doi.org/10.1109/TVCG.2014.2329308.
Andrienko, G., Andrienko, N., ska Demˇ sar, U., Dransch, D., Dykes, J., Fab-
rikant, S.I., Jern, M., Kraak, M.-J., Schumann, H., Tominski, C., 2010. Space,
time and visual analytics. Int. J. Geogr. Inf. Sci. 24 (10), 1577–1600, https:
//doi.org/10.1080/13658816.2010.508043.
Bach, B., Pietriga, E., Fekete, J.-D., 2014. Graphdiaries: Animated transitions and
temporal navigation for dynamic networks. IEEE Trans. Vis. Comput. Graphics
20 (5), 740–754, https://doi.org/10.1109/TVCG.2013.254.
Bonneau, G., Hege, H., Johnson, C.R., Oliveira, M.M., Potter, K., Rheingans, P.,
Schultz, T., 2014. Overview and state-of-the-art of uncertainty visualization.
In: Hansen, C.D., Chen, M., Johnson, C.R., Kaufman, A.E., Hagen, H. (Eds.),
Scientific Visualization. Springer, pp. 3–27, https://doi.org/10.1007/978-1-
4471-6497- 5_1.
Brosz, J., Nacenta, M.A., Pusch, R., Carpendale, S., Hurter, C., 2013. Transmogri-
fication: Causal manipulation of visualizations. In: Proceedings of the ACM
Symposium on User Interface Software and Technology (UIST). ACM Press,
pp. 97–106, https://doi.org/10.1145/2501988.2502046.
Ceneda, D., Gschwandtner, T., May, T., Miksch, S., Schulz, H.-J., Streit, M.,
Tominski, C., 2017. Characterizing guidance in visual analytics. IEEE Trans.
Vis. Comput. Graphics 23 (1), 111–120, https://doi.org/10.1109/TVCG.2016.
2598468.
Chen, H., 2004. Compound brushing explained. Inf. Vis. 3 (2), 96–108, https:
//doi.org/10.1057/palgrave.ivs.9500068.
Chen, X., Zeng, W., Lin, Y., Al-Maneea, H.M., Roberts, J., Chang, R., 2021.
Composition and configuration patterns in multiple-view visualizations. IEEE
Trans. Vis. Comput. Graphics 27 (2), 1514–1524, https://doi.org/10.1109/
TVCG.2020.3030338.
Chevalier, F., Riche, N.H., Plaisant, C., Chalbi, A., Hurter, C., 2016. Animations 25
years later: New roles and opportunities. In: Proceedings of the International
Working Conference on Advanced Visual Interfaces (AVI). ACM, pp. 280–287,
https://doi.org/10.1145/2909132.2909255.
Collins, C., Carpendale, S., 2007. Vislink: Revealing relationships amongst vi-
sualizations. IEEE Trans. Vis. Comput. Graphics 13 (6), 1192–1199, https:
//doi.org/10.1109/TVCG.2007.70521.
Dessart, C., Motti, V.G., Vanderdonckt, J., 2012. Animated transitions between
user interface views. In: Tortora, G., Levialdi, S., Tucci, M. (Eds.), International
Working Conference on Advanced Visual Interfaces (AVI). ACM, pp. 341–348,
https://doi.org/10.1145/2254556.2254623.
Du, F., Cao, N., Zhao, J., Lin, Y., 2015. Trajectory bundling for animated transitions.
In: Proceedings of the 33rd Annual ACM Conference on Human Factors
in Computing Systems (CHI). ACM, pp. 289–298, https://doi.org/10.1145/
2702123.2702476.
36
C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Dübel, S., Röhlig, M., Tominski, C., Schumann, H., 2017. Visualizing 3D terrain,
geo-spatial data, and uncertainty. Informatics 4 (1), 1–18, https://doi.org/10.
3390/informatics4010006.
Dykes, J., MacEachren, A.M., Kraak, M.-J., 2005. Exploring Geovisualization. In:
International Cartographic Association, Elsevier Science.
Fabrikant, S.I., 2005. Towards an understanding of geovisualization with dynamic
displays: Issues and prospects. In: Reasoning with Mental and External
Diagrams: Computational Modeling and Spatial Assistance, Papers from the
2005 AAAI Spring Symposium. Technical Report SS-05-06, AAAI, Stanford,
California, USA, pp. 6–11, URL http://www.aaai.org/Library/Symposia/Spring/
2005/ss05-06- 003.php March 21-23, 2005.
Fabrikant, S.I., Miksch, S., Wolff, A., 2019. Visual analytics for sets over time
and space (dagstuhl seminar 19192). Dagstuhl Rep. 9 (5), 31–57, https:
//doi.org/10.4230/DagRep.9.5.31.
Fabrikant, S.I., Rebich-Hespanha, S., Andrienko, N., Andrienko, G., Montello, D.R.,
2008. Novel method to measure inference affordance in static small-multiple
map displays representing dynamic processes. Cartogr. J. 45 (3), 201–215,
https://doi.org/10.1179/000870408X311396.
Fish, C., Goldsberry, K.P., Battersby, S., 2011. Change blindness in animated
choropleth maps: An empirical study. Cartogr. Geogr. Inf. Sci. 38 (4),
350–362, https://doi.org/10.1559/15230406384350.
Gapminder Foundation, 2021. Gapminder tools. URL https://www.gapminder.org/
tools/ (Retrieved April, 2021).
Goldsberry, K., Battersby, S., 2009. Issues of change detection in animated
choropleth maps. Cartogr.: Int. J. Geogr. Inf. Geovis. 44 (3), 201–215, https:
//doi.org/10.3138/carto.44.3.201.
Gruendl, H., Riehmann, P., Pausch, Y., Fröhlich, B., 2016. Time-series plots
integrated in parallel-coordinates displays. Comput. Graph. Forum 35 (3),
321–330, https://doi.org/10.1111/cgf.12908.
Hadlak, S., Schumann, H., Schulz, H.-J., 2015. A survey of multi-faceted graph
visualization. In: Borgo, R., Ganovelli, F., Viola, I. (Eds.), Eurographics Confer-
ence on Visualization (EuroVis) - STARs. The Eurographics Association, pp.
1–20, https://doi.org/10.2312/eurovisstar.20151109.
Hadlak, S., Tominski, C., Schulz, H.-J., Schumann, H., 2010. Visualization of
attributed hierarchical structures in a spatio-temporal context. Int. J. Geogr.
Inf. Sci. 24 (10), 1497–1513, https://doi.org/10.1080/13658816.2010.510840.
Hanrahan, P., 2009. Systems of thought. In: Keynote presentation at the
Eurographics/IEEE Symposium on Visualization (EuroVis).
Harrower, M., 2007. The cognitive limits of animated maps. Cartogr.: Int. J. Geogr.
Inf. Geovis. 42 (4), 349–357, https://doi.org/10.3138/carto.42.4.349.
Harrower, M., Fabrikant, S., 2008. The role of map animation for geographic
visualization. In: Dodge, M., McDerby, M., Turner, M. (Eds.), Geographic
Visualization. John Wiley & Sons, Ltd, pp. 49–65, https://doi.org/10.1002/
9780470987643.ch4.
Heer, J., Robertson, G.G., 2007. Animated transitions in statistical data graphics.
IEEE Trans. Vis. Comput. Graph. 13 (6), 1240–1247, https://doi.org/10.1109/
TVCG.2007.70539.
Henry, N., Fekete, J.-D., McGuffin, M.J., 2007. Nodetrix: A hybrid visualization
of social networks. IEEE Trans. Vis. Comput. Graphics 13 (6), 1302–1309,
https://doi.org/10.1109/TVCG.2007.70582.
Horak, T., Berger, P., Schumann, H., Dachselt, R., Tominski, C., 2021. Responsive
matrix cells: A focus+context approach for exploring and editing multivariate
graphs. IEEE Trans. Vis. Comput. Graphics 27 (2), 1644–1654, https://doi.org/
10.1109/TVCG.2020.3030371 T. Horak and P. Berger are joint first authors..
Javed, W., Elmqvist, N., 2012. Exploring the design space of composite visualiza-
tion. In: Proceedings of the IEEE Pacific Visualization Symposium (PacificVis).
IEEE Computer Society, pp. 1–8, https://doi.org/10.1109/PacificVis.2012.
6183556.
Kehrer, J., Hauser, H., 2013. Visualization and visual analysis of multifaceted
scientific data: A survey. IEEE Trans. Vis. Comput. Graphics 19 (3), 495–513,
https://doi.org/10.1109/TVCG.2012.110.
Kim, Y., Correll, M., Heer, J., 2019. Designing animated transitions to convey
aggregate operations. Comput. Graph. Forum 38 (3), 541–551, https://doi.
org/10.1111/cgf.13709.
Kim, Y., Heer, J., 2021. Gemini: A grammar and recommender system for
animated transitions in statistical graphics. IEEE Trans. Vis. Comput. Graph.
27 (2), 485–494, https://doi.org/10.1109/TVCG.2020.3030360.
Kriglstein, S., Haider, J., Wallner, G., Pohl, M., 2016. Who, where, when and
with whom? Evaluation of group meeting visualizations. In: Diagrammatic
Representation and Inference. Springer, pp. 235–249, https://doi.org/10.1007/
978-3- 319-42333- 3_19.
Kriglstein, S., Pohl, M., Smuc, M., 2014. Pep up your time machine: Recommen-
dations for the design of information visualizations of time-dependent data.
In: Huang, W. (Ed.), Handbook of Human Centric Visualization. Springer, pp.
203–225, https://doi.org/10.1007/978-1- 4614-7485- 2_8.
Lowe, R.K., 2014. Dynamic visualizations: A two-edged sword?. In: Huang, W.
(Ed.), Handbook of Human Centric Visualization. Springer, pp. 581–604,
https://doi.org/10.1007/978-1- 4614-7485- 2_23.
Lowe, R.K., Boucheix, J.-M., 2016. Principled animation design improves com-
prehension of complex dynamics. Learn. Instr. 45, 72–84, https://doi.org/10.
1016/j.learninstruc.2016.06.005.
Maggi, S., Fabrikant, S.I., Imbert, J.-P., Hurter, C., 2016. How do display design
and user characteristics matter in animations? an empirical study with air
traffic control displays. Cartogr.: Int. J. Geogr. Inf. Geovis. 51 (1), 25–37,
https://doi.org/10.3138/cart.51.1.3176.
Munzner, T., 2009. A nested process model for visualization design and vali-
dation. IEEE Trans. Vis. Comput. Graph. 15 (6), 921–928, https://doi.org/10.
1109/TVCG.2009.111.
Opach, T., Golebiowska, I., Fabrikant, S.I., 2014. How do people view multi-
component animated maps?. Cartogr. J. 51 (4), 330–342, https://doi.org/10.
1179/1743277413Y.0000000049.
Pulo, K., 2007. Navani: Navigating large-scale visualisations with animated
transitions. In: International Conference on Information Visualisation (IV).
IEEE Computer Society, pp. 271–276, https://doi.org/10.1109/IV.2007.82.
Rensink, R.A., 2002. Internal vs. External information in visual perception. In:
Proceedings of the 2nd International Symposium on Smart Graphics. ACM,
pp. 63–70, https://doi.org/10.1145/569005.569015.
Roberts, J.C., 2007. State of the art: Coordinated & multiple views in exploratory
visualization. In: Proceedings of the International Conference on Coordinated
and Multiple Views in Exploratory Visualization (CMV). IEEE Computer
Society, pp. 98–102, https://doi.org/10.1109/CMV.2007.20.
Ruchikachorn, P., Mueller, K., 2015. Learning visualizations by analogy: Promot-
ing visual literacy through visualization morphing. IEEE Trans. Vis. Comput.
Graph. 21 (9), 1028–1044, https://doi.org/10.1109/TVCG.2015.2413786.
Salisu, S., Mayr, E., Filipov, V.A., Leite, R.A., Miksch, S., Windhager, F., 2019.
Shapes of time: Visualizing set changes over time in cultural heritage
collections. In: Madeiras Pereira, J.a., Raidou, R.G. (Eds.), EuroVis 2019 -
Posters. The Eurographics Association, ISBN: 978-3-03868-088-8, https://doi.
org/10.2312/eurp.20191142.
Schulz, H.-J., Hadlak, S., 2015. Preset-based generation and exploration of
visualization designs. J. Vis. Lang. Comput. 31, 9–29, https://doi.org/10.1016/
j.jvlc.2015.09.004.
Shipley, T.F., Fabrikant, S.I., Lautenschütz, Anna-Katharina, 2013. Creating percep-
tually salient animated displays of spatiotemporal coordination in events. In:
Raubal, M., Mark, D.M., Frank, A.U. (Eds.), Cognitive and Linguistic Aspects
of Geographic Space: New Perspectives on Geographic Information Research.
Springer Berlin Heidelberg, pp. 259–270, https://doi.org/10.1007/978-3- 642-
34359-9_14.
Shu, X., Wu, A., Tang, J., Bach, B., Wu, Y., Qu, H., 2021. What makes a data-
GIF understandable?. IEEE Trans. Vis. Comput. Graphics 27 (2), 1492–1502,
https://doi.org/10.1109/TVCG.2020.3030396.
Spence, R., 1999. A framework for navigation. Int. J. Hum. Comput. Stud. 51 (5),
919–945, https://doi.org/10.1006/ijhc.1999.0265.
Steinberger, M., Waldner, M., Streit, M., Lex, A., Schmalstieg, D., 2011.
Context-preserving visual links. IEEE Trans. Vis. Comput. Graphics 17 (12),
2249–2258, https://doi.org/10.1109/TVCG.2011.183.
Sweller, J., 1988. Cognitive load during problem solving: Effects on learning.
Cogn. Sci. 12 (2), 257–285, https://doi.org/10.1207/s15516709cog1202_4.
Sweller, J., Ayres, P., Kalyuga, S., 2011. Cognitive Load Theory. In: Explorations in
the Learning Sciences, Instructional Systems and Performance Technologies,
Springer, https://doi.org/10.1007/978-1- 4419-8126- 4.
Sweller, J., van Merrienboer, J.J.G., Paas, F., 2019. Cognitive architectures and
instructional design: 20 years later. Educ. Psychol. Rev. 31, 261–292, https:
//doi.org/10.1007/s10648-019- 09465-5.
Tamassia, R. (Ed.), 2013. Handbook of Graph Drawing and Visualization. CRC
Press.
Thompson, J., Liu, Z., Li, W., Stasko, J.T., 2020. Understanding the design space
and authoring paradigms for animated data graphics. Comput. Graph. Forum
39 (3), 207–218, https://doi.org/10.1111/cgf.13974.
Tominski, C., 2016. Comparing: Reducing costs of visual comparison. In: Short
Paper Proceedings of the Eurographics Conference on Visualization (EuroVis).
Eurographics Association, pp. 137–141, https://doi.org/10.2312/eurovisshort.
20161175.
Tominski, C., Abello, J., Schumann, H., 2009. CGV – An interactive graph
visualization system. Comput. Graph. 33 (6), 660–678, https://doi.org/10.
1016/j.cag.2009.06.002.
Tominski, C., Schulz, H.-J., 2012. The great wall of space-time. In: Proceedings
of the Workshop on Vision, Modeling & Visualization (VMV). Eurographics
Association, pp. 199–206, https://doi.org/10.2312/PE/VMV/VMV12/199-206.
Tominski, C., Schumann, H., 2020. Interactive Visual Data Analysis. In: AK Peters
Visualization Series, CRC Press, https://doi.org/10.1201/9781315152707.
Tominski, C., Schumann, H., Andrienko, G.L., Andrienko, N.V., 2012. Stacking-
based visualization of trajectory attribute data. IEEE Trans. Vis. Comput.
Graphics 18 (12), 2565–2574, https://doi.org/10.1109/TVCG.2012.265.
Vanderdonckt, J., 2012. Animated transitions for empowering interactive in-
formation systems. In: International Conference on Research Challenges in
Information Science (RCIS). IEEE, pp. 1–12, https://doi.org/10.1109/RCIS.2012.
6240413.
Wang, X., Hornbæk, K., 2020. Visual exploration of time-series forecasts through
structured navigation. In: Tortora, G., Vitiello, G., Winckler, M. (Eds.), Con-
ference on Advanced Visual Interfaces (AVI). ACM, pp. 38:1–38:9, https:
//doi.org/10.1145/3399715.3399906.
37
C. Tominski, G. Andrienko, N. Andrienko et al. Visual Informatics 5 (2021) 28–38
Windhager, F., Salisu, S., Leite, R.A., Filipov, V., Miksch, S., Schreder, G., Mayr, E.,
2020. Many views are not enough: Designing for synoptic insights in cultural
collections. IEEE Comput. Graph. Appl. 40 (3), 58–71, https://doi.org/10.1109/
MCG.2020.2985368.
Windhager, F., Salisu, S., Schreder, G., Mayr, E., 2018. Orchestrating overviews:
A synoptic approach to the visualization of cultural collections. Open Libr.
Humanit. 4 (2), https://doi.org/10.16995/olh.276.
Wu, C.-C., Wolfe, J.M., 2018. A new multiple object awareness paradigm shows
that imperfect knowledge of object location is still knowledge. Curr. Biol. 28
(21), 3430–3434.e3, https://doi.org/10.1016/j.cub.2018.08.042.
Yuan, X., Guo, P., Xiao, H., Zhou, H., Qu, H., 2009. Scattering points in parallel
coordinates. IEEE Trans. Vis. Comput. Graphics 15 (6), 1001–1008, https:
//doi.org/10.1109/TVCG.2009.179.
Zheng, Y., Wu, W., Cao, N., Qu, H., Ni, L.M., 2018. Focus+context grouping for
animated transitions. J. Vis. Lang. Comput. 48, 61–69, https://doi.org/10.1016/
j.jvlc.2018.06.006.
38
