ISSN 2186-7437

NII Shonan Meeting Report

No. 2018-12

Reimagining the Mental Map andDrawing Stability
Daniel Archambault
Karsten Klein
Kazuo Misue
September 10–13, 2018

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Reimagining the Mental Map
and Drawing Stability
Organizers:
Daniel Archambault (Swansea University, United Kingdom)
Karsten Klein (University of Konstanz, Germany)
Kazuo Misue (University of Tsukuba, Japan)
September 10–13, 2018

1

Introduction

Dynamic networks, and dynamic information in general, are an important topic
across many domains. Often, the data can be expressed as a network thatevolves over time. In a social network setting, understanding data from Twitterand Facebook can clarify the interaction between people and the evolution of
events in real time. In a biological setting, genes and proteins interact and theseinteractions can change depending on an experimental treatment and expression levels of the genes change with time. An analysis of these changes can thenfor example be used to detect disease conditions, understand their mechanism,and treat them. In a computer network scenario, links can go down and newconnections are made. In finance, trades can be expressed as a network and canbe interpreted along with information about the evolving market around them.
Regulators and market participants can then monitor and analyze market behavior, e.g. to detect fraud and suspicious behavior. In all of these applications,
we must have eﬀective visualizations that draw the dynamic perspective of these
networks in a meaningful and comprehensible way. In order for the visualization
to be successful, the user of the system must be able to follow the evolving data.
In psychology and geography, these concepts have been explored in the context of humans navigating physical environments with maps with the internal
representation of the space inside the mind of the human known as the mental
map or cognitive maps. In this work, a cognitive map is the internal representation of the physical space inside the mind of the human. In a dynamic
information context, the cognitive map is the internal representation of the information space that is evolving over time. Thus, we can begin to separate out
the mental map and drawing stability with the mental map being the internal
representation and drawing stability the external representation present in thevisualization.
Early work in the mental map of information spaces concentrated on users
following changes in a network: either through dynamic data or interaction
with this data. One dimension of comprehensibility of dynamic information is
information stability. In the early 1990’s, Misue et al. proposed methods forenhancing the stability of dynamically evolving graphs. In particular, the work1

examined what should happen to unaﬀected areas of the network once a local
change had been made to the network. The motivation for these approaches was
to increase the comprehensibility of dynamic data: if network structure changes
in a local area of the plane, then areas that do not change should remain stable.
Many interpretations of this concept of preserving the mental map have been
considered and expanded on through the years.
One of the most common interpretations of preserving the mental map is thenotion that nodes and edges of the graph should move as little as possible between successive time periods in the plane. Archambault and Purchase revealed
quantitative benefits of this definition as it helps users revisit specific nodes
in a dynamic graph and follow specific paths in a graph as the data evolves
over time. Preserving the mental map helps users oﬄoad information to therepresentation as they understand that it will remain in the same place, unlessthe network changes substantially. In dynamic graph drawing, the majority ofmethods for drawing dynamic graphs in a stable way have taken the simple
definition of keeping nodes in relatively the same area of the plane. However,in the original definitions of mental map preservation, more complex measures
were considered, including topological properties of the drawing.
Although the mental map in information visualization has frequently been
associated with dynamic data, supporting the mental map is also important forinteraction. The mental map can be aﬀected not only by changes in the representation, but by the combination of representation, interaction operations
performed by the user, and the associated cognitive processes. Moving a clusterof nodes from one corner of the screen to the other will aﬀect drawing stability
in the classical definition, but might preserve the quality of the mental map perfectly. When interacting with data, changes to the representation should only
influence the area interacted with and not the entire data set as a whole. When
information goes oﬀ screen because of an interaction, one would expect it tocome back on screen if the interaction is reverted. As such, it is not only important to engage information visualization researchers with this concept, but HCI
experts and researchers in immersive analytics need to consider visualizations
that support the cognitive map from an interaction perspective.
Thus, while there is already a significant body of research and a range ofmodels for mental map and data stability, several challenges arose in recent
years prompt us to revisit the concepts and to develop new approaches. These
challenges concern scalability, the applicability of the models in application
areas, as well as the technology of the environment in which the network analysis
is performed:• The size and complexity of the data that is represented has increased
dramatically over the last years. While layout algorithms scale well andcan draw hundreds of thousands of nodes and edges, how we support thecognitive models of networks needs to be adjusted to the change in scale.
On the other hand, when methods like aggregation or clustering are used
to reduce visual complexity, the resulting visualization will need diﬀerent,
more complex concepts for mental map preservation with respect to therelation between representation and raw data.
• Applications might require specific adaptions or requirements to mental
map preservation. Additional data annotations and semantics play a crucial role for expert users and might need to be taken into account. Many of2the original models were written from an algorithmic perspective, closely
related to the corresponding network drawing approaches. While thisallows for easy integration into dynamic network visualization implementations, it may not fit with specific application requirements.
• New technologies such as wall displays, table top displays, and 3D environments are available that facilitate novel visualization and interaction
methods, but might be a game changer in terms of mental map preservation as existing concepts may not be directly transferable to these newdevices. Topic and Aims

2

Topic and Aims

In this seminar, our goal is to revisit some of the mental map preservation
definitions and to develop new definitions for drawing stability that support thecomprehensibility of dynamic data. We plan to go beyond the basic definitionof preserving node location in space to other definitions that can support thecognitive map of the user as they navigate the dynamically evolving information
space. More specifically, we intend to pursue the following research questions:
What are new metrics and models that cover stability and mental map quality in the light of above mentioned challenges? What are new algorithms thatcan be developed to support the cognitive maps of users visualising dynamic
data? What new methods need to be developed that better support the cognitive maps of users exploring information in specific application domains? Given
new display and interaction technologies, are there new approaches for preserving the mental map that better supports the exploration of networks when using
these devices? The development of techniques that support a user’s mental map
require the combination of expert knowledge on network / information visualisation principles and algorithms, expertise in perception and cognitive processes,
as well as a good definition of requirements from practical applications. The
workshop aims at examining information visualisation techniques that are better able to support the mental map of the user. Our workshop intends to study
supporting the cognitive map both in dynamic network settings and with respect to interaction with devices. More specifically, the aims of the seminar are
as follows:
1. To bring experts in the fields information visualisation, graph drawing,interaction and devices, psychol- ogy, and relevant application We intend
to have an international audience from Europe, Asia, the Americas, andAustralia that have information visualisation problems where cognitive
maps of the information space should be supported.
2. To rethink algorithms that compute stable representations of dynamic
data and to go beyond “keeping unchanging components in relatively thesame area of the plane.” What are alternatives for supporting the mental
map of the user when investigating dynamic data?
3. To rethink implications of interaction on the mental map of information
spaces. How does interacting with information spaces on large screens,
table tops, immersive analytics environments impact cognitive maps of3

information? Are there any special requirements that need to be supported?
4. Psychology and geography have considered the cognitive map. How cantheir definitions of cognitive maps help us understand the mental map in
information visualisation?
5. What are good ways to apply our results to relevant application areas(biology, social networks, and others)?
6. To formulate future research challenges in better supporting the mental
map for information visualisation research.

3

Meeting Schedule

Check-in Day: September 9 (Sunday)• 19:00 Welcome Banquet
Day 1: September 10 (Monday)
Seminar start, welcome, and introductory presentations• 09:00 Welcome and seminar overview by the organizers, self introductionof participants• 10:30 Break• 11:00 Invited presentations• 12:00 Group photo• 12:15 Lunch• 14:00 Topic discussion (form working groups)• 15:30 Break• 16:00 Working groups• 18:00 Dinner
Day 2: September 11 (Tuesday)
Working groups and discussions• 09:00 Working groups• 10:30 Break• 11:00 Working groups• 12:00 Lunch• 14:00 Working groups• 15:30 Break• 16:00 Working groups4• 17:00 Working group report• 18:00 Dinner
Day 3: September 12 (Wednesday)
Working groups, excursion, and banquet• 09:00 Working groups• 10:30 Break• 11:00 Working groups• 12:00 Lunch• 13:30 Excursion• 19:00 Banquet
Day 4: September 13 (Thursday)
Working groups, discussions and presentations• 09:00 Working groups• 10:30 Break• 11:00 Working group reports and closing• 12:00 Lunch — the seminar closes with the final lunch

4

Working Group Reports

4.1

Lost in Translation: Alignment of Mental Representations for visual analytics

Participants: Daniel Archambault, Jessie Kennedy, Tatiana von Landesberger, Mark McCann, Fintan McGee, Benjamin Renoust, andHsiang-Yun Wu
Visual analytics, which is defined as the science of analytical reasoning facilitated by interactive visual interfaces, allows us to obtain deep insights on
information assessment and decision making [TC06]. Visual representations
and interactions often play important roles in visual analytics since they extend
humans’ capability to read, explore, and understand large amounts of information [TC05]. Scientists often create visual representations basing on their imagination of the real-world phenomenon, but the representations evolve diﬀerently
and lead to additional eﬀorts for learning their variety. Figure 1 shows an simple example. The London Underground map consists of several arbitrary zone
areas (gray and white decomposition in Figure 1(a)), which implicitly tells theprices to the travelers who consider traveling across diﬀerent zones. However,on the another hand, the Tokyo Metro company constructs the map diﬀerently,
because the prices of the transportation system in Japan are proportional torailway length between each pair of the source and the destination, which areoften written as text labels on top of each station as shown in Figure 1(b).
Due to the experiences with the London Underground map, travelers may have5

established a mental model based on the zone concept, and this mental modelneeds to be updated to align with the Tokyo Metro map when they come to
Japan, otherwise they often get confused at first sight.
Our working group was inspired by Norman’s work [Nor13] and try to summarize the visualization challenge on dynamic network together with the roleof mental representation in understanding the proposed visual representation.
In principle, a successful network visualization must show information to theusers in a meaningful and comprehensible way so that the users are capable tounderstand and follow the evolving data. We discussed four concepts that forma model of the role of mental representation in the visualization process. These
are the real world (the phenomenon being visualized), the data representation,
the visual representation, and the set of mental representations at the intersection of the other concepts (see figure 2). We place mental representations at
the center of this model as there is not one single mental representation, butseveral at play, often interacting with the other concepts. One mental representation might be that of the observer studying the real world phenomenon,
one might be a researcher modeling the data describing the phenomenon, another might be that of the person visualizing the data, and another might bethan of the consumer of the data. We discussed the role of the misalignmentof mental representations in the failure of a visualization. For example, there
may be a misalignment between the mental representation of a real world observer studying a phenomenon and the mental representation used to create avisualization of the phenomenon, resulting in an erroneous choice of visualization. For a visualization to be successful there needs to be a convergence of allmental representations involved. In the context of avoiding misalignment we
discussed how mental representations can be changed, leading to misalignment( or possibly alignment). We discussed a notion of stability with respect tomental representations. We identified four states of a mental representation. It
can be stable, (small fluctuations but on average the same), evolving (changing
over time, so that eventually it may be misaligned with other mental representation), revolutionary (a sudden large change that means a sudden misalignmentof mental representations, and converging (when mental representations change
in such a way that they become aligned).
Four use cases, including (1) Area Maps and Subway Maps, (2) Public Health
Data, (3) Biological Taxonomies, (4) biological pathways, were investigated toprove our initial assumptions on the proposed model. For Area Maps, referring
geospatial position allocentrically or egocentrically has a substantial impact onusers’ spatial cognition, which may lead to losing their way by simply misunderstanding the initial setting of the map. Subway Maps is another example has
been further discussed. Public Health Data allows us to analyze the correlation
between people, places, events, and so on along the time, while diﬀerent visual
representation such as pie charts, scatter plots, node-link diagrams, or parallel
coordinates could change the significance of number to the readers. Similarly,
the evolutionary relationships between organisms in the world are often imagined as a tree structure in Biological Taxonomies. Although in reality, taxonomy
could be represented by graph more eﬀectively, it unexpectedly changes the research workflow of domain experts and were not favored in the end. Finally,
the biological pathways provide us an abstract information about how chemicalcomponents function in nature. This abstraction has done experimentally andthe dynamic visualization relies on the experimental assumption. This leads6to pathway diagram in variety and becomes diﬃcult to be used for daily communication. In summary, the mental misalignment has a strong impact on theinterpretation of the real-world phenomenon so the next generation of visualization is expected to take the proposed factors into consideration.

(a)

(b)

Figure 1: Various visualizations for the transportation systems, including (a) atypical London Underground map, (b) a Tokyo Metro map.

(a)

(b)

Figure 2: Two models of Mental Representation, (a) emphasizing the interaction
between users (bees) and the real world A, the data C, and the visualization D.
Bees can be a single or multiple people. In (b), we highlight the importance ofuser mental representation mapping with the ‘external elements’ (A, C and D:
bA is a mental representation of the real world; bC is a mental representationof the data and its structure; bD is a mental representation of the visualization.
Each of these bees poses a risk of misalignment that a good visualization design
may address.

4.2

Metrics and Algorithms for Dynamic Clustered Graphs

Participants: Emilio Di Giacomo, Walter Didimo, Michael Kaufmann, and Giuseppe Liotta

7Let G = (V, E) be a graph. A clustering of G is a partition V = {V1 , V2 , . . . , Vh }of the vertex set V . Each partite set Vi of V is called a cluster. The pairG = (G, V) is called a clustered graph. An edge (u, v) of G is called an intracluster edge if u and v belong to the same cluster; otherwise it is called aninter-cluster edge. We studied the problem of maintaining drawing stability
for dynamic clustered graphs. More precisely, let G1 , G2 , . . . , Gk be a sequenceof clustered graphs, where Gi is the graph at time ti obtained from Gi−1 as aconsequence of one of the following operations:
Edge addition: an edge is added;
Edge removal: an edge is removed;
Cluster merging: an inter-cluster edge connecting u ∈ Vi and v ∈ Vj is added
and the clusters Vi and Vj are merged into a single cluster.
Cluster splitting: an intra-cluster edge (u, v) is removed and the cluster containing u and v is split into two distinct clusters.
Our goal is to compute a sequence of drawings D1 , D2 , . . . , Dk , such that each
Di is the drawing of Gi (for i = 1, 2, . . . , k) and has the following properties:• each vertex is drawn as a point in the plane;• each edge is drawn as a simple Jordan arc connecting its end-vertices;• each cluster is represented as a simple closed region that contains all andonly its vertices;• there is no edge-region crossings and no region-region crossings. We havean edge-region crossing if an edge intersects a region boundary more thanonce; we have a region-region crossing, if two distinct regions intersect.
To preserve the drawing stability, throughout the drawing sequence we aim atpreserving the following properties:
P1 the orthogonal relations between vertices;P2 a very simple shape for clusters and edges.
We study the problem in three scenarios that diﬀer in terms of what knowledgeof the future the algorithm has when it transforms drawing Di−1 in Di .
Full knowledge: the whole sequence of clustered graphs is known since thebeginning. This scenario is suitable for oﬀ-line analysis of an evolving
graph (e.g., analyze how a social network evolves over time within a desired
time window). In this case the visualization algorithm can take advantageof this knowledge to compute a sequence of drawings that maximizes thedrawing stability.
Zero knowledge: Di is computed with no knowledge of the future changes. In
this case, the algorithm computes Di making the best choice to preserve
the drawing stability with respect to Di−1 . This choice, however, might
be a bad choice for the future changes. This scenario is suitable for on-line
analysis of an evolving network (e.g., real-time monitoring of a network
throughout a continuous stream of changes).
8Partial knowledge: Di is computed knowing the next h changes, for some
integer h > 0. This scenario is intermediate between the previous twoand can be used for an “almost” on-line analysis of an evolving network(where changes are buﬀered to take advantage of a partial knowledge of the
future) or for oﬀ-line analysis in order to increase the eﬃciency (runtime)with respect to the full-knowledge algorithm.
We adopt a drawing model where all vertices lie on the same horizontal
line ℓ, the edges are drawn as semicircles (arcs) and the clusters are drawn
as rectangles. Notice that this model enforces property P2 at each time stepat the expenses of property P1. Indeed, to draw clusters as rectangles it isnecessary that the vertices of each cluster are consecutive along ℓ. Supposethat because of a merging operation we need to merge two clusters that are notconsecutive. In this case we have to change the ordering of the vertices thus
violating property P1. Since there are several ways to move vertices in order tomake two clusters adjacent, we adopt a cost model where the cost of each moveis computed in terms of vertex swaps. Based on this cost model we devise threealgorithms (one for each of the scenarios above) that compute the sequence ofdrawings D1 , D2 , . . . , Dk minimizing the cost function. Clearly, the algorithm
for the full-knowledge scenario minimizes the cost of the whole sequence, while
the one for the zero-knowledge scenario minimizes the cost for each single step.
The complexity of these two algorithms is O(nk), O(nk+1 ), respectively, where
n is the number of vertices and k the number of operations (i.e. the number oftime steps). The algorithm for partial-knowledge scenario minimizes the costfor the subsequence of length h that is known at each step; its time complexity
is O( hk nh+1 ).

4.3

Stable Dynamic Cartograms

Participants: Markus Chimani, Stephen Kobourov, Wouter Meulemans, Martin Nöllenburg, and Jaakko Peltonen
Our group considered the problem of mental map preservation in the context of dynamic cartograms. Cartograms combine statistical and geographicalinformation in thematic maps, where areas of geographical regions (e.g., countries) are scaled in proportion to some statistic (e.g., population). This kind ofvalue-by-area visualization has been used for many years, with the first reference
to the term “cartogram” dating back to at least 1870. Since then, cartograms
have been studied by geographers, cartographers, economists, social scientists,geometers, and information visualization researchers. Many diﬀerent types ofcartograms have been proposed and implemented, but nearly all the work is
focused on static cartograms.
When visualizing data that changes over time (e.g., year-by-year population
statistics), or when wanting to compare multiple cartograms1 (e.g., population,
GDP, crime) a natural problem that occurs is that of computing “stable” cartograms. Similar to the notion of mental map preservation, a cartogram is stable
if the layout does not change a great deal from one moment in time to the next,
1 See,

for example, an interactive cartogram of diﬀerent categories of household spendings by the New York Times https://www.nytimes.com/interactive/2008/09/04/business/
20080907-metrics-graphic.html

9except for the required re-sizing of the regions. For cartograms, the notion ofstability can be formalized by measuring the change in contacts between regions,the change in the east/west and north/south relations between pairs of regions,the change in the angles between the centroids of pairs of regions, etc.
We focused on Demers cartograms [NK16], where regions are represented
by interior-disjoint squares of size determined by the variable that is encoded.
As a first step towards optimizing stable dynamic cartograms for an arbitrary
sequence of data over time, we considered two diﬀerent sets of area variables
for the same underlying set of regions. More formally, we studied the following
problem, where we say that two squares are adjacent if they touch along theirboundaries.
Problem 1. Given a Demers cartogram A consisting of n interior-disjoint
squares {s1 , s2 , . . . , sn } in the plane with side lengths {a1 , a2 , . . . , an } and asecond set of side lengths {b1 , b2 , . . . , bn }, find a Demers cartogram B, such that
1. each square si has side length bi ,2. any two squares si and sj that are separated by a horizontal (vertical) line
in A remain separated by a horizontal (vertical) line in B,
3. as many adjacencies of pairs of squares (si , sj ) in A as possible are preserved in B, and
4. for adjacencies of pairs of squares (si , sj ) in A that cannot be preserved inB, their displacement in the vertical or horizontal direction is minimized.
We modeled Problem 1 as a linear program using real-valued variables to
encode the position of each square in terms of x- and y-coordinates with a hardconstraint on the prescribed size of each square. The separation constraints
derived from A translate into linear constraints on the relative x-/y-positions of
all pairs of squares. For all pairs of adjacent squares in A we use the objectivefunction to minimize their distance in B, such that ideally they would havedistance 0 and thus still touch. If this is not possible, their relative displacement
is minimized.
We implemented a prototype of the linear program, which confirms the practicability of our approach and also lends itself to a smooth interpolation between
A and B to animate the change in the data. Figure 3 shows an example of two
stable cartograms A and B computed by our linear program. Future work includes the extension of our linear programming model to optimize the stabilityof cartograms across multiple sets of area constraints, to represent lost adjacencies by drawing explicit short and crossing-free edges between the squares, andto experimentally evaluate the quality of the dynamic cartograms according tosuitable visual quality measures.

4.4

Challenges and Opportunities: The Mental Map for
Graph Visualisation in Immersive Environments

Participants: Peter Eades, Andreas Kerren, Karsten Klein, KwanLiu Ma, and Falk Schreiber

10

A

C

E

C
E

A

D

D

B

B

Figure 3: Input cartogram (left) and the output cartogram with minimal separation between adjacent squares in the input – the red lines indicate the two
adjacencies that were separated.
Our group was investigating the diﬀerences of mental map creation andpreservation in immersive environments compared to the standard workspace
setup (non-immersive environments). While there is already some work on
the mental map for graph visualisation in a classical desktop monitor setup,
there is not only much less research on immersive graph visualisation [CDK+ 17,
KMLM15], but also less practical experience in using immersive environments
for graph exploration and analysis for both practitioners and researchers ingraph visualisation. We thus started with a general brainstorming on the scopeof our research during the Shonan seminar, as the topic lends itself to a variety
of interesting research questions.
As a result, we decided to first focus on structuring our thoughts by defining
a design space for immersive graph visualisation. We discussed the possible
dimensions and aspects, with the further goal to later allow a more precise
design of experiments to test our hypotheses.
We then discussed related literature; in particular we had to do a literature search on publications outside of the visualisation community, e.g. regarding influence factors of spatial navigation and mental model development, e.g.
[NSSNSM+ 16, ZKH10, MJ12]. Due to the time constraints we are sure that we
did not fully cover the relevant literature, and thus will need to look deeper into
the related work.
As we are lacking a precise definition or model of a mental map, obvious
questions that we discussed in the context of potential experiments are: How
could the mental map be characterised for evaluation in experiments, and how
can aspects of a mental map model then be captured in experimental studies
not only to allow qualitative and quantitative statements, but also to falsify or
confirm the model itself.
A more fundamental question that we were not able to answer during theworkshop is, if the mental model is actually 3D, or if it is dependent on the
input. Furthermore, in the context of collaborative graph analysis, we think it
is worthwile to investigate the characterisation of a shared or common mental
map, and how it could be supported by suitable visualisations.
We designed several experiments that are intended to investigate a few of thefundamental research questions. Within the next months, we plan to conduct
the first of those experiments, and we are aiming to have a publication based11

on it.

4.5

Concepts and Processes for Mental Model

Participants: Lyn Bartram, Bongshin Lee, Luana Micallef, Kazuo
Misue, and Helen C. PurchaseWe identify key concepts and terminology for mental model (Figure 4) that
could be applicable to the visualization of dynamic data from the review ofdefinitions and characterizations of mental models in various domains such as
cognitive science, visual analytics, human-computer interaction, and visual languages.
Designer

CuratorViewerReasoning

Mental Map
Data

Abstraction

Conceptual
Model

Reification

External
Representation

Interpretation

Internal
Internal
Internal
Representation
Representation
Representation

Understanding

Mental Model

Cognitive Map

Cognitive Collage

Figure 4: Key concepts and terminology along with roles of mental model“Data” is reality and exists in the world. A curator selects a relevant subsetof the data to create a “conceptual model” [Nor14], also called the ‘represented world’ [Pal78]). This process is called “abstraction.”
The process of reification is one that is performed by a designer who creates an “external representation” (ER [LS10], also called a ‘system image’[Nor14], or the ‘representing world’ [Pal78]) of the conceptual model. The ER,
which we interpret as ‘what is perceived.’ It may be one object or a collectionof objects, which may be visual, aural, text, or multimedia.
The process of interpretation is a perceptual process where a viewer perceives
the ER and creates an “internal representation” (IR) [LS10], which is a static
representation in working memory [Mun14, War12, Mac86, CM85]. One ER may
result in several diﬀerent IRs. A “cognitive map” [Tve91, ARW98, Spi98] is ameta-IR that links a set of IRs together. These IRs can be of diﬀerent forms[JL80]; e.g., sentential, spatial, temporal. A “mental map” is a particular typeof IR that represents spatial relationships.
The process of understanding is a cognitive process where the viewer interprets the IR so as to create a “mental model” of reality [Nor14]. Several IRs(which may form a ‘cognitive collage’ [Tve91]) may contribute to building up
this mental model. Several mental models might be created from the same IR[JL80], thus creating a set of possibilities for a viewer to choose from.
Each ER is implicitly associated with a set of processing operations thatare invoked automatically - both at the perceptual [War12] and the cognitive
level [Pal78]. Gibson calls these processing operations ‘aﬀordances’ [Gib14].
Activating these processing steps strengthens the mental model; that is, makes
it closer to the conceptual model.
Reasoning is the process of developing this mental model further. The natureof the model will be influenced by prior knowledge and expertise, as well as
personal characteristics.
12

Further interaction with the ER may also develop the mental model. In
particular, if the mental model is incorrect, or contradicts prior knowledge (orjust seems a bit odd), then further perception of the ER may result in a change
in the IR and hence a change in the mental model.
Mental Model for Visualizing Dynamic Data
With regards dynamic graph drawing, the ER is the evolving graph. It does
not matter whether the change comes about naturally (as a result of change
in the conceptual model) or as a result of interaction. What is important isthat the ER has changed from one perceived visual form to another. Perceiving
this ER results in a change in the IR (which, in this case, has the special term
‘mental map’). The viewer can then update their mental model as a result of
this change in their mental map.
There are two implications of this. First, there is a perceptual load in translating the ER to the IR/mental map. In a dynamic environment, this load canbe reduced by ensuring minimal perceptual change in the ER (i.e., ‘preserving
the mental map’). Second, there is a cognitive load in translating the IR to
the mental model. In a dynamic environment, this load can be reduced firstly
by giving the viewer time to process the IR, and secondly by taking advantageof the viewers’ existing mental models where possible (these may be based onprior knowledge/expertise or personal characteristics).

5

Overview of Talks

There were three invited talks, giving rather broad overviews on diﬀerent aspectsof the visual analytics challenges. Moreover, one additional talk was given.

Towards Perceptual Optimization of the Visual Design ofScatterplots
Luana Micallef, University of Copenhagen
Designing a good scatterplot can be diﬃcult for non-experts in visualization,
because they need to decide on many parameters, such as marker size andopacity, aspect ratio, color, and rendering order. This paper contributes toresearch exploring the use of perceptual models and quality metrics to set suchparameters automatically for enhanced visual quality of a scatterplot. A key
consideration in this paper is the construction of a cost function to capture
several relevant aspects of the human visual system, examining a scatterplot
design for some data analysis task. We show how the cost function can be usedin an optimizer to search for the optimal visual design for a user’s dataset andtask objectives (e.g., “reliable linear correlation estimation is more important
than class separation”). The approach is extensible to diﬀerent analysis tasks.
To test its performance in a realistic setting, we pre-calibrated it for correlation
estimation, class separation, and outlier detection. The optimizer was able toproduce designs that achieved a level of speed and success comparable to thatof those using human-designed presets (e.g., in R or MATLAB). Case studies
demonstrate that the approach can adapt a design to the data, to reveal patterns
without user intervention.
13

https://userinterfaces.aalto.fi/scatterplot_optimization/

Layout Adjustment and the Mental Map, 1995
Peter Eades, University of Sydney
This talk discussed classical and old-fashioned models of the preservation of
the ’mental map’ of the diagram: orthogonal ordering, proximity, and topology.

Algorithmic Stability Analysis
Wouter Meulemans, Eindhoven University of Technology
Visualization of time-varying data often asks for stability – small changes
in the data should lead to small changes in the visualization. This implies a
trade-oﬀ between the quality of each frame in isolation and the amount or speedof change between frames. This trade-oﬀ has, for example, been investigated fortime-varying treemaps [SSV18]. However, algorithmic analysis of such problems
is diﬃcult has it creates a convoluted set of questions, such as: (1) when should
the visualization structurally change; (2) what is the eﬀect of requiring transitions between frames to be smoothly interpolated; and (3) what is the eﬀectof limiting the speed at which the visualization may actually change? In thistalk, I described our framework [MSVW18] for dealing with such questions bysplitting the analysis in three steps: event stability (1), topological stability (2)and Lipschitz stability (3). The focus in the talk was with topological stability
as being an intermediate analysis step which is often easier to analyze than thecomplete question with bounded speed. Even with infinite speed but continuouschange, we gain insight into the diﬃculties introduced by continuity that will
also aﬀect any further results with bounded speed. I illustrated this via recent
and ongoing work on the stability of typical kinetic computational-geometry
problems that often provide underlying structures for visualizations.

What we look at in the MRC/CSO Social and Public Health
Sciences Unit
Mark McCann, University of Glasgow
Dr Mark McCann gave a presentation at Shonan meeting 127 outlining thework of the Medical Research Council / Chief Scientist Oﬃce Social and Public
Health Sciences Unit at the University of Glasgow ‒ and how information
visualisation could make an important contribution to the Unit’s work. The
Unit’s mission is Improving Health and reducing inequalities through the studyof social influences on health and wellbeing. Dr McCann gave examples of
the Unit’s work: studying the co-evolution of friendship networks and alcohol
use frequency over adolescence, the formation of coalitions around health policy
debates such as minimum pricing for alcohol units, the use of node edge diagrams
to represent complex causal processes underpinning health inequalities, and todevelop interventions to improve health. There was a fruitful discussion about
a range of methods within information visualisation that could help support
this work, and suggestions for future ways to develop the use of information
visualisation in health improvement.
14

6

List of Participants• Daniel Archambault, Swansea University• Karsten Klein, University of Konstanz• Kazuo Misue, University of Tsukuba• Lyn Bartram, Simon Frasier University• Markus Chimani, Osnabrueck University• Walter Didimo, University of Perugia• Emilio Di Giacomo, University of Perugia• Peter Eades, University of Sydney• Michael Kaufmann, University of Tubingen• Jessie Kennedy, Edinburgh Napier University• Stephen Kobourov, University of Arizona• Andreas Kerren, Linnaeus University• Bongshin Lee, Microsoft Research• Giuseppe Liotta, University of Perugia• Kwan-Liu Ma, University of California-Davis• Mark McCann, University of Glasgow• Fintan McGee, Luxembourg Institute of Science and Technology• Wouter Meulemans, Eindhoven University of Technology• Luana Micallef, University of Copenhagen• Martin Nöellenburg, TU Wien
• Jaakko Peltonen, University of Tampere• Helen C. Purchase, University of Glasgow• Benjamin Renoust, Osaka University• Falk Schreiber, Universität Konstanz• Tatiana von Landesberger, Technische Universität Darmstadt• Hsiang-Yun Wu, TU Wien

15

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