![FIGURE E A conceptual model for mobile map use and adaptivity including a context tracker and an adaptation engine; [in parts based on Oppermann (), Oppermann and Zimmermann (), and Hou et al. ()].](https://www.researchgate.net/publication/375196270/figure/fig1/AS:11431281202560373@1698929136698/FIGURE-E-A-conceptual-model-for-mobile-map-use-and-adaptivity-including-a-context-tracker_Q320.jpg){kind=tracking}
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TYPE Perspective
PUBLISHED 01 November 2023
DOI 10.3389/fcomm.2023.1258851
OPEN ACCESS
EDITED BY
Ana Serrano Tellería,
University of Castilla La Mancha, Spain
REVIEWED BY
Dennis Edler,
Ruhr University Bochum, Germany
*CORRESPONDENCE
Tumasch Reichenbacher
tumasch.reichenbacher@uzh.ch
RECEIVED 14 July 2023
ACCEPTED 10 October 2023
PUBLISHED 01 November 2023
CITATION
Reichenbacher T and Bartling M (2023)
Adaptivity as a key feature of mobile maps in
the digital era. Front. Commun. 8:1258851.
doi: 10.3389/fcomm.2023.1258851
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does not comply with these terms.
Adaptivity as a key feature of
mobile maps in the digital era
Tumasch Reichenbacher*and Mona Bartling
Department of Geography, University of Zurich, Zurich, Switzerland
Mobile maps are an important tool for mastering modern digital life. In this paper,
we outline our perspective on the challenges and opportunities associated with
designing adaptive mobile maps that are useful, usable, and accessible to a wide
range of users in dierent contexts. If we claim for adaptive mobile maps to be
successful, we need to expand our understanding of map use context, including
the physical and digital spaces, user behavior, and individual dierences. We
identify key challenges, such as the scarcity of knowledge about mobile map
use behavior, the need for eective adaptation methods and strategies, user
acceptance of adaptive maps, and issues related to control, privacy, trust, and
transparency. We finally suggest research opportunities, such as studying mobile
map usage, employing AI-based adaptation methods, leveraging the power of
visual communication through maps, and ensuring user acceptance through user
control and privacy.
KEYWORDS
mobile maps, adaptivity, context, map design, digital well-being
Introduction
Maps are a fundamental artifact for society to solve manifold tasks relying on geographic
information, such as exploring, planning, wayfinding, orienting, and decision-making. The
latest developments in mobile technology have brought unprecedented opportunities for
communicating geographic-related information with mobile maps for nearly any facet of
modern life. While maps have become ubiquitous, designing useful, usable, and accessible
mobile maps faces various challenges. New concepts and frameworks, such as map use
context models or physiological behavior measurements, are emerging to suit these new
developments in mobile mapping technologies.
The transition of our society into a digital society manifests itself in the growing
importance of digital and digitalized lives (Lazer and Radford, 2017). Digital lives refer
to the fact that “an increasing fraction of life is intrinsically digitally mediated”, while
digitalized lives represent “the capture of non-intrinsically digital life (i.e., most of life) in
digital form” (Lazer and Radford, 2017, p. 21f.). While such digital and digitalized lives
offer many benefits and facilitations, the ever faster-growing amount of digital data and tools
also produce manifold challenges, accelerated through digital transformation processes, such
as data access, sense-making, or information overload for its mobile citizens (Bawden and
Robinson, 2020).
Despite this fast-paced technology adoption, a key challenge is ensuring
digital accessibility regarding varying usage situations, a wide range of users,
tasks, individual differences, distractions, and limitations, among many others
(Griffin et al., 2023). Eventually, we should strive for digital well-being, i.e.,
“experiencing maximal controlled pleasure and functional support, together with
minimal loss of control and functional impairment” (Vanden Abeele, 2021).
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To better support the mobility aspects of digital life with mobile
map apps, understanding the context of use more comprehensively
is essential. Digital transformation is inexorably penetrating and
impacting the daily life of citizens. In addition to the physical space,
the digital and hybrid spaces are becoming increasingly important
for mobile users (see Figure 1). Advances in digitalization now
allow for bridging the physical and digital worlds, including non-
tangible information from the digital world. Many activities that
users have performed and could only perform in physical space are
now increasingly shifted to the digital world and platforms (think of
online banking, for instance). Today, smartphones are the primary
interface between the physical and digital worlds, allowing mobile
users to access digital information and relate it to the physical
world. The interplay of activities in hybrid or “phygital” space (the
overlap of physical and digital space) becomes increasingly critical
(Del Vecchio et al., 2023).
We believe that mobile map adaptivity—if thoughtfully
designed and implemented—cannot only establish digital
accessibility, positive user experiences, and inclusive design but can
contribute to digital well-being concerning mobile map apps and
services. Digitalization profoundly affects our life and increasingly
influences our behavior. The downside of widely used apps and
services may be infantilizing/paternalism, exclusion, overload,
overprotection, overautomation, or loss of control (Thrash et al.,
2019).
Despite the wide range of maps that we have at our disposal,
these maps typically follow a one-size-fits-all approach. However,
this approach lacks in its ability to fully accommodate mobile map
usage in different environments and situations as we move fluently
between contexts while pursuing an activity. Beyond that, one-size-
fits-all approaches neglect that mobile map users can differ in many
aspects, particularly regarding spatial abilities and literacy levels,
cognitive states, individual impairments, etc. For instance, some
users have a good sense of direction, the capacity to mentally rotate
a map display, or to acquire spatial knowledge faster, while other
individuals exhibit poorer performance in those skills. Moreover,
not all mobile map users have the same experience or training in
map reading and some individuals might have color-deficiencies.
Also, the concrete map use situation can be very different in respect
to noise levels, secondary demanding tasks, or time pressure, which
could result in changes of cognitive load. All these factors might
require an adjusted map design to accommodate differences of
individual users or usage situations.
Since we have a clear understanding that not all map designs
are suitable for all map use purposes, users, and contexts (Griffin
et al., 2017;Roth R. E., 2019), there is a growing community
combining cartography and maps with questions of location-
based services (LBS), spatial cognition and neuro-adaptivity, to
devise appropriate and context-specific mobile map designs that
respond to the needs of users in their changing mobile map use
contexts. In the last decade, research fields, such as psychology
and human-computer interaction have shown increased interest
in this research. Adding to that, we argue that adapting mobile
maps to users in their contexts will benefit them in solving everyday
map-related tasks.
In this article, we present an overview of recent developments
in mobile cartography and dive into research on geographic
information adequacy, the relevance of mobile context, and context
aware, adaptive map design principles.
The main objective of our contribution is to claim the
importance of adaptive mobile maps for supporting modern digital
life and identify key challenges that need to be addressed to
proceed with adaptive designs for future mobile maps, i.e., move
research in this field forward from theory to practice. While
we are aware that adaptivity could be applied to all types and
functions of maps, we focus in this paper on mobile maps and their
communication function.
Past and current advances in mobile
map adaptivity
Although research on mobile adaptive maps has been
undertaken since the turn of the new millennium, only recently,
technology has become ready, allowing us to consume maps
on mobile devices as a commodity. A technology perspective
dominated the development of mobile map apps and LBS that
does not comprehensively acknowledge the challenges imposed
by the mobile use case. This lack of adjusted map design often
leads to problems in sense-making, usability, user experience,
and user empowerment. While user centered design (UCD) has
included usability and user experience (UX) design approaches,
user empowerment, coming with the third wave of HCI, introduces
another goal of designing technological tools. It offers users an
experience of agency or control, allows them to extend their skills
through technology, and may enable them to do things they would
not be able to do otherwise (Schneider et al., 2018).
Effectively representing relevant geographically referenced
information in maps on small screens is still a challenge. The major
persisting challenge of mobile maps rendered on small displays is
map complexity (Schnur et al., 2018;Keil et al., 2020;Barvir and
Vit, 2021) caused by the amount of information to portray and
the limited screen space. To ensure mobile map usability, design
strategies inspired mainly by mobile Human-Computer Interaction
(HCI), such as responsive design and mobile first design, have been
suggested, also in cartography and Geographic Information Science
(GIScience) (Ricker and Roth, 2018;Roth R., 2019).
Compared to paper maps or static online maps, mobile
maps offer a basic level of map automation. An example is the
displaying of the current location based on the received GPS
position and route following. Mobile maps are interactive, and
as such, they are also adaptable. An adaptive map visualization
approach that leverages the abundance of context information
available from sensors of modern smartphones used by mobile
citizens is still underdeveloped, despite the idea of adapting
mobile maps based on use context was introduced about two
decades ago (Zipf, 2002;Reichenbacher, 2004), building upon
research on adaptive systems and interfaces in the field of HCI
that emerged in the 1990s (Benyon, 1993;Schneider-Hufschmidt
et al., 1993;Oppermann, 1994;Brusilovsky, 1996). This early
and foundational work established various conceptualizations of
adaptation and proposed approaches to differentiate between
types of adaptations based on whether control and initiative
of the adaptation is with the user or the system. This body
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FIGURE 1
A conceptual model for mobile map use and adaptivity including a context tracker and an adaptation engine; [in parts based on Oppermann (1994),
Oppermann and Zimmermann (2011), and Hou et al. (2015)].
of research contributes accordingly to the basic distinction of
adaptation (user) and adaptivity (system). The body of research
on adaptive user interfaces is extensive. López-Jaquero et al.
(2021) describe seven stages of user interface adaptation in their
framework GISATIE: goals, initiative, specification, application,
transition, interpretation, and evaluation. Moreover, they propose
an adaptation design space including the seven key dimensions
autonomy level, granularity level, task resuming granularity, user
interface deployment, technological space coverage, user feedback,
and modality.
Ghiani et al. (2017) propose a methodology that allows users
without programming experience dynamically create personalized
web application versions more suitable for users’ needs in specific
contexts with trigger-action rules. This flexible application behavior
than is supported by a mechanism to detect contextual changes.
Firmenich et al. (2019) discuss methods and tools for adapting
user interfaces to make them more accessible. They describe
two types of approaches: built-in adaptation mechanisms and
adaptation techniques external to the application.
Galindo et al. (2017) propose a method to dynamically adapt a
user interface at runtime to user emotions based on face recognition
and demonstrated the feasibility of their approach in an experiment
compare three emotion detection tools.
Machado et al. (2018) demonstrate how interface adaptation
can help to lower the accessibility barrier for a specific societal
group, i.e. older adults. Their work offers a conceptual framework
for creating real-time adaptive UI that mitigate cognitive decline
and vision loss.
Alghamdi et al. (2022) present a usability study with 550 users
that measured the intensity of adaptive features of a Smartphone
in effectiveness, efficiency, and satisfaction. They studied adaptive
features, such as voice commands, face recognition, screen rotation,
night mode, gesture recognition, and fingerprint, both on iOS and
Android platforms. Their results show a higher effectiveness for the
adaptive features face recognition (87%) and voice command (85%)
and overall, the satisfaction level is higher for adaptive features than
non-adaptive features.
Abrahão et al. (2021) provide a set of conceptual adaptation
properties applicable to model-based adaptive user interfaces and
explore the prospects of machine learning for data processing and
analysis in interface adaptation.
Yigitbas et al. (2021) identify transparency and
controllability as two major challenges of “human-in-the-
loop” adaptive systems and propose the combination of
digital twins and virtual reality (VR) interfaces as alternative
human-in-the-loop strategies.
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Following the terminology of Oppermann and Zimmermann
(2011), we understand adaptation as the general term for changing
parts of a system, interface, or visualization, either by the user or
the system. Adaptability refers to manual adaptation by the user
to customize or individualize. Adaptivity means the adaptation
is initiated and automatically executed by the system. Schneider-
Hufschmidt et al. (1993) offer a taxonomy that includes the four
steps of initiative, proposal, decision and execution that can be
allocated to the system or the user. In an adaptable system, all
four steps are controlled by the user, whereas in an adaptive
system, all four steps are issued by the system. Of course, any
other combination can be thought of, which is sometimes termed
mixed-initiative adaptation, e.g., when a user confirms a proposed
adaptation that is then automatically executed by the system.
Adaptable maps allow the user to modify elements of the map, e.g.,
filter content, switch off layers, or change the map orientation mode
(north up vs. oriented toward the heading). An adaptive map “is
the adjustment of the visualization of geographic information and
associated parts in the visualization process such as the interface,
the information content, and the information encoding by a
visualization application or a geospatial web service to a specific
usage context” (Reichenbacher, 2008, p. 677).
A central part of the work on adaptive mobile maps in the
last decade has been the definition, modeling, and assessment
of the mobile use context. The fundamental factors involved in
mobile map use are the physical space, the digital space, the
users (individual factors, cognition), and their behavior (activity,
mobility, digital behavior) (see Figure 1).
This mobile context of use has been explored in mobile
cartography (Reichenbacher, 2004) and has been extended to the
concept of geographic relevance (GR) (Reichenbacher, 2005;Raper,
2007) to assess the relevance of features to be represented in a
mobile map beyond the proximity criterion commonly applied in
LBS. To mitigate the problem of cluttered and visually complex
mobile maps, Swienty et al. (2008) proposed an approach to
visualize geographically relevant information in a salient, attention-
guiding way for effective and efficient use in mobile situations.
The approach combines filtering geographic information shown
on a mobile map based on GR to reduce clutter, and a saliency-
based encoding of relevant features on the map, which has proved
effective in increasing the utility and usability of mobile maps.
Based on elicited relevance criteria (De Sabbata and
Reichenbacher, 2012;Reichenbacher et al., 2016), De Sabbata
and Reichenbacher developed a conceptual model of GR (2012).
Eventually, De Sabbata (2013) and De Sabbata et al. (2015)
implemented a computational assessment model including a
mobility factor and a geographic environment factor (see Figure 1).
Perhaps because mobile maps primarily represent geographic
space, research in cartography focused predominantly on context–
adaptive maps, and context was defined mainly by the geographic
environment or human mobility. Although the mobile cartography
(Reichenbacher, 2004) framework and the GR concept (De Sabbata
et al., 2015) conceptually include the user and activities, they had
no focus on these dimensions. Today, smartphones are ready for
sensing and collecting data from the physical environment and
behavioral data where geographic information behavior could be
derived and modeled (Zingaro and Reichenbacher, 2022b). With
these fundamental changes, the current approach to GR falls short.
It needs an extension beyond the physical space toward the digital
space, particularly by including dynamic context factors, real-time
data, user activities in physical and digital space, and interactive
digital behavior as contextual information that could contribute to
map adaptivity.
Despite its market power, Google only partly includes the
relevance concept in Google Maps. According to Google, “Local
search results in Maps are based primarily on relevance, distance,
prominence, as well as your personal interests. These factors are
combined to help find the best match for your search”1. However,
how these criteria are defined, applied, and combined remains
unclear and is not disclosed to the public.
Consequently, in subsequent work, the context of map use
has been broadened to put the user into focus. Griffin et al.
(2017) propose a comprehensive framework and research agenda
for identifying map designs that can be transferred from one
map use context to another one. Bartling et al. (2021a,b,2022,
2023) explore ways of modeling context and applying mobile
use context factors for adapting map apps. This shift toward the
user, and hence user–adaptive mobile maps, follows a research
trend in cartography and GIScience to better understand mobile
users and their cognitive processes involved in mobile map and
navigation tool usage to improve the experience with better-
fitted designs that accommodate cognitive abilities, cognitive styles,
individual differences and cognitive states (Reichenbacher et al.,
2022;Zingaro and Reichenbacher, 2022a,b;Kapaj et al., 2023).
Taking real-time user behavior, such as activities in geographic
space, as well as actions and interactions in the digital space, into
account should improve the adaptation of mobile maps (Zingaro
and Reichenbacher, 2022b). This shift marks an adaptation to usage
context and personalization of the mobile map.
Surprisingly, knowledge of mobile map use in real-world
situations is scarce within the cartographic community, even
though smartphones can be used for behavior recording. In one
of the few field studies, Savino et al. (2021) explored the order
of functionalities used by participants in Google Maps in situ
by analyzing interaction logs. Recently, a new line of study on
mobile map app use behavior by Reichenbacher et al. (2022) has
been employing tappigraphy to explore map app usage through
touchscreen interactions. They demonstrated how tappigraphy,
developed and, so far, primarily used in the field of neuroscience
[e.g., to quantify variables such as sleep, cognitive processing speed,
and disease activity] (Balerna and Ghosh, 2018;Duckrow et al.,
2021;Huber and Ghosh, 2021) can be transferred to GIScience
and used as a method to investigate map app use behavior
in situ (Zingaro and Reichenbacher, 2022a). Both interaction
logging and smartphone interaction records (such as tappigraphy)
are ambulatory assessment or ecological momentary assessment
(EMA) methods. They allow for continuous sampling of user
behavior in everyday activities in situ. As such, we can study map
use behavior of people with no or slight observational bias and
high ecological validity. Very recently, the use of neurological data
(e.g., from EEG) to realize neuro-adaptive LBS was proposed by
Fabrikant (2023). The approach goes a step further from user-
adaptivity through the inclusion of physiological data to adapt
1support.google.com/maps/answer/4610185
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a system to the user’s cognitive state in real-time. Such systems
are referred to as neuro-adaptive. Afergan et al. (2014) report a
system that adapts task difficulty according to observed boredom or
overload. The observation is derived from functional near-infrared
spectroscopy (fNIRS). Similarly, EEG can measure cognitive load
and then adjust, for instance, a game’s difficulty level (Fairclough,
2015,2022). According to Fabrikant (2022), this approach can
be transferred to mobile maps, where the map adapts based on
measured cognitive load.
While map adaptation has been conceptually studied,
implementations are still rare. One implementation of adaptive
behavior for a mobile map app MediaMaps is presented by van
Tonder and Wesson (2008). To our knowledge, Raubal and Panov
(2009) were the only ones to propose a formalized process for
mobile map adaptation. The same research group also researched
the role of visual attention in adaptive interfaces (Göbel et al.,
2016), explored gaze-based adaptation, adaptive legends (Göbel
et al., 2016), and controllability issues in adaptive maps (Kiefer
et al., 2017).
The notion of adaptive maps encompasses a mobile user’s
mobility and geographic environment, user activities, and
technology in use (Griffin et al., 2017;Reichenbacher, 2017). This
notion is reflected by more recent design approaches, such as
responsive web map design and mobile-first design, focusing on
usability issues and the fundamental characteristics and challenges
of the mobile use case. Mobile-first design acknowledges for
technological constraints of mobile devices, and responsive design
recognizes different characteristics, requirements, capabilities,
and form factors of display devices to flexibly respond to them
(Roth et al., 2018;Lee et al., 2022). Both design approaches offer
ways to compensate for the technical constraints of the mobile
use case. While mobile-first design typically integrates the mobile
map within a native app, responsive web maps are commonly
displayed in a web browser and should work on mobile and
stationary devices. Mobile-first is a user experience design pattern
optimized for the technological constraints of mobile devices [e.g.,
small screen size, performance, limited memory and battery life,
and multitouch interaction (Roth R., 2019)]. Adapting mobile
map displays to the limited screen sizes of mobile devices has
been proposed for various tasks, such as for route planning and
wayfinding (Zipf and Richter, 2002;Baudisch and Rosenholtz,
2003;Burigat and Chittaro, 2007;Schmid et al., 2010;Gedicke
et al., 2019). In a recent study, Savino et al. (2021) suggest that
mobile users of Google Maps also use the app to explore the map
and that adapting the map display based on map use context might
support such user behavior. Degbelo et al. (2023) present a vision
of “intelligent maps” that encompasses the notion of adaptive
behavior of maps.
Research in HCI and information visualization emphasizes
the importance of cognitive factors in visualization. that need
to be considered when adapting mobile maps. Steichen and Fu
(2019), for instance, have shown that task completion time from
is dependent on the cognitive style of users (verbal–visual; field-
dependent—field-independent), and cognitive abilities (perceptual
speed, working memory). In information visualization, adaptations
are, for instance, display notifications, hint provisions, search
results ranking, adaptive navigation, or the recommendation
of alternative visualizations (Steichen and Fu, 2019). Adaptive
visualizations can make use of dynamically added overlays,
reference structures (e.g., grids), highlighting, redundant encodings
(e.g., data labels), annotations, or visual prompts (Steichen and Fu,
2019). Other research investigated the relationship of visualization
layouts and users’ locus of control (Ziemkiewicz et al., 2013;
Delgado et al., 2022). Chiossi et al. (2022) provide an overview
of methods for adaptive visualization and interfaces based on
physiological, behavioral, or qualitative user input. Furthermore,
they explore the methodological approaches in mixed reality,
physiological computing, visual analytics, and proficiency-aware
systems. For instance, they discuss gaze-based recommender
systems, adaptation of virtual reality visual complexity based on
physiological arousal, or adapting notifications to visual appearance
and human perception.
With the rapid advancements in mobile device and sensor
technology, as well as the proliferation of AI, many future
directions for mobile maps open up that have the potential to
support and empower users of mobile maps (Reichenbacher and
Zingaro, 2022;Degbelo et al., 2023).
Key challenges for designing adaptive
mobile maps
While the adaptivity of mobile maps builds upon developments
from adaptive interfaces, adaptive hypermedia, and adaptive
visualizations, we argue that there is a fundamental difference.
Mobile maps exhibit three distinct domains that can be adapted,
either separately or jointly: the geographic information represented
in the mobile map, the cartographic visualization of this
information, i.e., the map symbology, and the map interface, i.e.,
the widgets. Moreover, the map can be understood as an interface
to geographically referenced information. And finally, mobile maps
are commonly used in highly variable contexts. These factors
make the adaptive process more complex and diverse. This section
will identify key challenges that must be addressed to move map
adaptivity forward.
Scarcity of knowledge about mobile map
use behavior
We see many advantages of maps for mobile citizens. Maps
afford a high information density, external cognition and cognitive
offloading (Scaife and Rogers, 1996), provide a synoptic view and
allow for parallel and holistic processing of information, contrary to
a linear, sequential information processing of other channels, such
as text or audio. Nevertheless, in some cases, other information
modes (e.g., text or audio) might be better suited and more efficient
than maps. Moreover, individual user differences and differences
in map use situations require alternative map designs. Despite its
importance, we still know little about map use behavior in everyday
situations. Hence, we need more ecologically valid studies, such as
those from Savino et al. (2021) and Zingaro and Reichenbacher
(2022a,b).
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Adaptation methods and strategies for
mobile maps
Adaptivity of mobile maps to the map use context (e.g., events,
tasks, users, etc.) is now possible thanks to the ability to sense
dynamic context information and capture user behavior with
smartphone sensors and real-time data. Yet, we still lack answers to
many fundamental questions regarding adaptation strategies and
methods for mobile maps. While research and approaches from
HCI can be partly transferred to mobile maps, the visualization
component of map adaptation is still under-explored. We do not
clearly understand when users could benefit from adaptive map
behavior and how intense and frequent such adaptations should be.
Also, questions about the degree of automation in adaptive map
designs and user control are critical [see one of the rare studies on
this by Kiefer et al. (2017)].
We believe mobile map adaptation should include two parallel,
antagonistic processes of generalization and individualization. The
generalization aims at offering universal adaptation to general
factors, such as reducing map information complexity by selecting
and filtering, simplifying the map for environmental factors,
general use contexts, specific map use tasks, or user groups.
Individualization aims to adapt to more specific factors (i.e.,
focusing on, emphasizing, or highlighting relevant information
to individual users and their specific tasks). Another challenge
is to select the right timing of adaptivity. For example, should
the selection and filtering of map features happen before or
continuously during usage? Is re-symbolization during usage
helpful to the user? How intense should the adaptation be to
attract users’ visual attention without distracting and confusing
them? How can mobile maps adapt to different map-use
tasks (searching, exploring, self-locating, wayfinding, estimating,
comparing, analyzing, etc.) and user behavior to ensure an optimal
cognitive workload offloaded to the map?
User acceptance
We argue that adaptive mobile maps bear great potential for
supporting digital citizens, empowering them, and making their
lives easier. At the same time, acceptance is fragile because of
possible problems with adaptivity (Lavie and Meyer, 2010): risk
of misfit (user’s needs are incorrectly captured or interpreted),
user cognitive disruption (user is disrupted by the adaptation),
lack of prediction (user does not know when and how the
map will be adapted), lack of explanation (user is not informed
about the reasons that triggered the adaptation), lack of control
(user does not have the opportunity to participate actively in
the adaptation process), risk for privacy (the map app maintains
personal information that the user wishes to keep private).
Borrowed from adaptive user interfaces, we believe the
following usability criteria are crucial for adaptive mobile maps
to be accepted by future users (Dhouib et al., 2017): predictability
(users need to understand the conditions of map adaptation
and how the map app functions), controllability (users should
be able to control the map adaptation process), breadth of
experience (the adaptation should limit the available map interface
functionalities to simplify the user experience), unobtrusiveness
(the map adaptation process should not interrupt the users’ main
activity), privacy and trust (users should be able to trust the
map app and be sure their privacy is protected), transparency
(users should be able to understand the map adaptation) (Höök,
2000;Jameson, 2003,2005,2009). In addition to these criteria,
Bouzit et al. (2017) propose observability (the map app should
make the adaptations perceivable for the user), intelligibility (the
map adaptation processes are communicated understandably to
the user), intelligibility (could be ensured by different ways,
explainability (the adaptation is explained), continuity (the
adaptation process is continuously rendered), awareness (the user
can perceive how the adaptation is occurring in the map app).
Control, privacy, trust, and transparency
Adaptive mobile maps can support an extensive range of
users with differences in various contexts to fulfill their tasks.
However, as above-mentioned, adaptive behavior could lead to a
lack of understandability, transparency and, in particular, control
(Peissner and Edlin-White, 2013;Graefe et al., 2021). The issue
of control has become fundamental in the context of autonomous
machines and AI. More recently, Shneiderman (2020) argued
for a two-dimensional concept of human control and computer
automation. According to Shneiderman, the design of systems
should avoid excessive automation, as well as excessive human
control. A more general taxonomy of automation for human-
automation interaction was proposed by Parasuraman et al. (2000).
This taxonomy has ten levels, from 1 (the computer offers no
assistance), 5 (the computer executes the suggestion if the human
approves), to 10 (the computer decides everything and acts
autonomously, ignoring the human).
In a user experiment, Kiefer et al. (2017) studied the issue
of controllability when using adaptive map interfaces. Comparing
non-adaptive to adaptive map user interfaces, they found that
adaptive map interfaces are more usable and cause a lower cognitive
load. However, users prefer adaptive map interfaces that show a
higher level of controllability. The challenge in designing adaptive
mobile maps is to find a way to keep the user in the loop. Promising
approaches are mixed-initiative adaptation (Yigitbas et al., 2021)
and co-adaptivity (Sperrle et al., 2021).
The second big challenge is privacy. To adapt to the context
of use, user abilities and behavior, a mobile map app requires a
substantial amount of personal and sensitive data (Bartling et al.,
2022). No individual adaptation and support are possible without
such knowledge about the individual. The challenge for the design
of mobile adaptive maps will be trustworthy, transparent, and fair.
The goal must be to maximize the support of individual users and
their activities in a specific context while minimizing intrusion and
data gathering.
Impacts of adaptivity
Finally, a big challenge is the desired and undesired impacts
of adaptive mobile maps. Real-time adaptivity is prone to various
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Reichenbacher and Bartling 10.3389/fcomm.2023.1258851
errors. If the data on context (e.g., the user abilities and states,
user behavior, etc.) is inaccurate, incomplete, or uncertain; this can
substantially affect inferences and models that are used to adapt
the map. In addition, successful adaptive maps also depend on the
availability and quality of the geospatial data to adequately being
able to respond to the detected map use context. For example,
persons in a wheelchair need routes that provide, for example,
wide enough pedestrian walks. The effects of the quality and
availability of geospatial data and context data may range from
ineffective adaptations to misfiring (Fairclough, 2022). In the worst
case, instead of supporting users, the adaptation could confuse
users, interrupt tasks, decrease performance and efficiency, or make
re-learning necessary.
Future map apps that will include adaptive behavior,
automation, and AI, hold the promise to better support mobile
users. Still, they also raise pressing questions of infantilizing,
paternalizing, overprotecting, and over-automating the users that
need to be explored and addressed. Furthermore, challenges in
data availability and quality for making proper map adaptation
decisions must be considered in the future. Another issue studied
in wayfinding support through automated navigation tools is the
danger of skill degradation (Ruginski et al., 2019). These effects of
adaptivity on users need to be carefully studied and evaluated to
guarantee user empowerment and digital well-being.
Discussion
Several key advancements in technology and digital
transformation have great potential to make our digital lives
easier. Smartphones come along with modern sensors that allow
sensing the usage context in breadth (Bartling et al., 2023). The
sensing can be complemented with data from IoT and smart
cities infrastructure. Another key advancement is the availability
of real-time data from these sensors, including user behavior
(Fabrikant, 2023). In conjunction with digital transformation
that links the physical world of mobile map app usage with the
digital world of information (e.g., linked information from other
apps, information from connected services) (Reichenbacher and
Zingaro, 2022).
To successfully support citizens in their everyday mobile
activities with adaptive maps we propose addressing follow
research opportunities:
Opportunity #1: Explore and amplify methods for empirical
research that elicit a comprehensive understanding of mobile map
usage that will help cartographers and app developers to better
design mobile map apps.
Opportunity #2: Design and analyze adaptation methods and
strategies for mobile maps that include the right information, at the
right place, in the right time, adequate to the cognitive state in an
easily accessible and understandable way.
Opportunity #3: Employ AI for adapting mobile maps in real-
time and leverage the power of visual communication of geographic
information with maps.
Opportunity #4: Design adaptive mobile maps by addressing
challenges of user acceptance through user control, privacy, trust,
and transparency.
We advocate for picking up these research opportunities by the
visualization community to strive for adaptive mobile maps that
are supportive and useful, and at the same time allow for digital
well-being in a digitally transformed world.
Data availability statement
The original contributions presented in the study are included
in the article/supplementary material, further inquiries can be
directed to the corresponding author.
Author contributions
TR: Conceptualization, Writing—original draft. MB:
Conceptualization, Writing—review & editing.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. MB was
funded by the Austrian Science Fund (FWF) J 4631-N.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
References
Abrahão, S., Insfran, E., Sluÿters, A., and Vanderdonckt, J. (2021).
Model-based intelligent user interface adaptation: challenges and future
directions. Softw. Syst. Model. 20, 1335–1349. doi: 10.1007/s10270-021-
00909-7
Afergan, D., Peck, E. M., Solovey, E. T., Jenkins, A., Hincks, S. W., Brown, E. T.,
et al. (2014). “Dynamic difficulty using brain metrics of workload” in Proceedings of
the SIGCHI Conference on Human Factors in Computing Systems [New York, NY:
Association for Computing Machinery (CHI’14)], 3797–3806.
Frontiers in Communication 07 frontiersin.org
Reichenbacher and Bartling 10.3389/fcomm.2023.1258851
Alghamdi, A. M., Riasat, H., Iqbal, M. W., Ashraf, M. U., Alshahrani, A.,
and Alshamrani, A. (2022). Intelligence and usability empowerment of smartphone
adaptive features. Appl. Sci. 12, 12245. doi: 10.3390/app122312245
Balerna, M., and Ghosh, A. (2018). The details of past actions on a smartphone
touchscreen are reflected by intrinsic sensorimotor dynamics. NPJ Digit. Med. 1, 4.
doi: 10.1038/s41746-017-0011-3
Bartling, M., Havas, C., Wegenkittl, S., Reichenbacher, T., and Resch, B. (2021a).
Modeling patterns in map use contexts and mobile map design usability. ISPRS Int. J.
Geo-Inform. 10, 527. doi: 10.3390/ijgi10080527
Bartling, M., Reichenbacher, T., and Fabrikant, S. (2023). “Leveraging on map
use context for advancing cartography in the 21st century” in Proceedings of the
ICA.31st International Cartographic Conference ICC 2023, (Capetown, South Africa:
International Cartographic Association).
Bartling, M., Resch, B., Reichenbacher, T., Havas, C., Robinson, A., Fabrikant, S.,
et al. (2022). Adapting mobile map application designs to map use context: a review
and call for action on potential future research themes. Cartography Geographic Inform.
Sci. 49, 237–251. doi: 10.1080/15230406.2021.2015720
Bartling, M., Robinson, A., Resch, B., Eitzinger, A., and Atzmanstorfer, K. (2021b).
The role of user context in the design of mobile map applications. Cartography
Geographic Inform. Sci. 48, 432–448. doi: 10.1080/15230406.2021.1933595
Barvir, R., and Vit, V. (2021). Graphic Map Load Measuring Tool – development
and verification. Int. J. Cartography. 7, 285–303. doi: 10.1080/23729333.2021.1972907
Baudisch, P., and Rosenholtz, R. (2003). “Halo: a technique for visualizing off-
screen objects” in Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems [New York, NY, USA: Association for Computing Machinery
(CHI’03)], 481–488.
Bawden, D., and Robinson, L. (2020). “Information overload: an overview” in
Oxford Encyclopedia of Political Decision Making. (Oxford: Oxford University Press).
Benyon, D. (1993). Adaptive Systems: a solution to usability problems. User Model.
User-Adapted Int. 3, 65–87. doi: 10.1007/BF01099425
Bouzit, S., Calvary, G., Coutaz, J., Chêne, D., Petit, E., and Vanderdonckt, J.
(2017). “The PDA-LPA design space for user interface adaptation” in 2017 11th
International Conference on Research Challenges in Information Science (RCIS).
353–364.
Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User
Model. User-Adapted Int. 6, 87–129. doi: 10.1007/BF00143964
Burigat, S., and Chittaro, L. (2007). “Geographical data visualization on mobile
devices for useer’s navigation and decision support activites” in Spatial Data on the
Web: Modeling and Management. Eds. A. Belussi, B. Catania, E. Clementini, and E.
Ferrari (Berlin, Heidelberg: Springer), 261–284.
Chiossi, F., Zagermann, J., Karolus, J., Rodrigues, N., Balestrucci, P., Weiskopf, D.,
et al. (2022). Adapting visualizations and interfaces to the user. Inform. Technol. 64,
133–143. doi: 10.1515/itit-2022-0035
De Sabbata, S. (2013). Assessing Geographic Relevance for Mobile Information
Services. PhD Thesis. Zurich: University of Zurich, Faculty of Science.
De Sabbata, S., Mizzaro, S., and Reichenbacher, T. (2015). Geographic dimensions
of relevance. J. Document. 71, 650–666. doi: 10.1108/JD-12-2013-0167
De Sabbata, S., and Reichenbacher, T. (2012). Criteria of geographic
relevance: an experimental study. Int. J. Geograph. Inform. Sci. 26, 1495–1520.
doi: 10.1080/13658816.2011.639303
Degbelo, A., Schmidt, B., Henzen, C., Lechler, S., Lubahn, B., Zander,F., et al. (2023).
Desiderata for intelligent maps: a multiperspective compilation. KN J. Cartogr. Geogr.
Inform. 73, 183–198. doi: 10.1007/s42489-023-00142-w
Del Vecchio, P., Secundo, G., and Garzoni, A. (2023). Phygital technologies and
environments for breakthrough innovation in customers’ and citizens’ journey. A
critical literature review and future agenda. Technol. Forecast. Soc. Change 189, 122342.
doi: 10.1016/j.techfore.2023.122342
Delgado, T., Alves, T., and Gama, S. (2022). How neuroticism and locus of
control affect user performance in high-dimensional data visualization. Computers and
Graphics, 109, 88–99. doi: 10.1016/j.cag.2022.10.006
Dhouib, A., Trabelsi, A., Kolski, C., and Neji, M. (2017). Prioritizing the
Usability Criteria of Adaptive User Interfaces of Information Systems based on
ISO/IEC 25040 Standard. Ingénierie des systèmes d information, 22, 107–128.
doi: 10.3166/isi.22.4.107-128
Duckrow, R. B., Ceolini, E., Zaveri, H. P., Brooks, C., and Ghosh, A. (2021). Artificial
neural network trained on smartphone behavior can trace epileptiform activity in
epilepsy. iScience, 24, 102538. doi: 10.1016/j.isci.2021.102538
Fabrikant, S. (2022). “Neuro-adaptive LBS: a research agenda on human- and
context-adaptive mobile maps for pedestrian navigation and spatial learning” in
Proceedings LBS 2022.LBS 2022 – 17th International Conference on Location Based
Services, Munich, Germany.
Fabrikant, S. I. (2023). Neuroadaptive mobile geographic information
displays: an emerging cartographic research frontier. Int. J. Cartography 1–17.
doi: 10.1080/23729333.2023.2253645
Fairclough, S. (2015). “A Closed-Loop Perspective on Symbiotic Human-Computer
Interaction” in Symbiotic Interaction. Eds. B. Blankertz, G. Jacucci, L. Gamberini, A.
Spagnolli, J. Freeman (Cham: Springer International Publishing) 57–67.
Fairclough, S. (2022). “Chapter 1 - designing human-computer interaction with
neuroadaptive technology” in Current Research in Neuroadaptive Technology. Eds. S.
Fairclough, T. Zander. (Cambridge, MA: Academic Press), 1–15.
Firmenich, S., Garrido, A., Patern,ò, F., and Rossi, G. (2019). “User interface
adaptation for accessibility” in Web Accessibility: A Foundation for Research. Eds. Y.
Yesilada and S. Harper (London: Springer London), 547–568.
Galindo, J. A., Dupuy-Chessa, S., and Céret, É. (2017). “Toward a generic
architecture for UI adaptation to emotions” in Proceedings of the 29th Conference
on l’Interaction Homme-Machine. New York, NY, USA: Association for Computing
Machinery (IHM’17), 263–272.
Gedicke, S., Niedermann, B., and Haunert, J.-H. (2019). “Multi-page labeling of
small-screen maps with a graph-coloring approach” in LBS 2019: 15th International
Conference on Location Based Services, November 11–13, 2019, Vienna, AT.
Ghiani, G., Manca, M., Patern,ò, F., and Santoro, C. (2017). Personalization of
context-dependent applications through trigger-action rules. ACM Trans. Comput.-
Hum. Interact. 24, 1–33. doi: 10.1145/3057861
Göbel, F., Giannopoulos, I., and Raubal, M. (2016). “The importance of
visual attention for adaptive interfaces” in MobileHCI ’16: Proceedings of the 18th
International Conference on Human-Computer Interaction With Mobile Devices and
Services Adjunct, 930–935.
Graefe, J., Engelhardt, D., and Bengler, K. (2021). “What does well-designed
adaptivity mean for drivers? A research approach to develop recommendations
for adaptive in-vehicle user interfaces that are understandable, transparent and
controllable” in 13th International Conference on Automotive User Interfaces and
Interactive Vehicular Applications. New York, NY, USA: Association for Computing
Machinery (AutomotiveUI’21 Adjunct), 43–46.
Griffin, A., Reichenbacher, T., Liao, H., Wang, W., and Cao, Y. (2023).
Cognitive issues of mobile map design and use. J. Locat. Based Serv.
doi: 10.R194/icaabs-S-79-2023
Griffin, A., White, T., Fish, C., Tomio, B., Huang, H., Sluter, C. R., et al. (2017).
Designing across map use contexts: a research agenda. Cartographic J. 40, 40–52.
doi: 10.1080/23729333.2017.1315988
Höök, K. (2000). Steps to take before intelligent user interfaces become real. Int.
Comp. 12, 409–426. doi: 10.1016/S0953-5438(99)00006-5
Hou, M., Banbury, S., and Burns, C. (2015). Intelligent Adaptive Systems an
Interaction-Centered Design Perspective. Boca Raton, FL: CRC Press.
Huber, R., and Ghosh, A. (2021). Large cognitive fluctuations surrounding sleep in
daily living. iScience. 24, 102159. doi: 10.1016/j.isci.2021.102159
Jameson, A. (2003). “Adaptive interfaces and agents” in The Human-Computer
Interaction Handbook. New Jersey, NJ: Lawrence Erlbaum Associates, 316–318.
Jameson, A. (2005). “User modeling meets usability goals” in User Modeling
2005. Eds. L. Ardissono, P. Brna, A. Mitrovic (Berlin, Heidelberg: Springer Berlin
Heidelberg), 1–3.
Jameson, A. D. (2009). Understanding and Dealing with usability side effects of
intelligent processing. AI Magaz. 30, 23. doi: 10.1609/aimag.v30i4.2274
Kapaj, A., Lanini-Maggi, S., Hilton, C., Cheng, B., and Fabrikant, S. (2023). How
does the design of landmarks on a mobile map influence way finding experts’ spatial
learning during a real-world navigation task? Cartogr. Geogr. Inform. Sci. 50, 197–213.
doi: 10.1080/15230406.2023.2183525
Keil, J., Edler, D., Kuchinke, L., and Dickmann, F. (2020). Effects of visual map
complexity on the attentional processing of landmarks. PLoS ONE 15, e0229575.
doi: 10.1371/journal.pone.0229575
Kiefer, P., Giannopoulos, I., Athanasios Anagnostopoulos, V., Schöning, J., and
Raubal, M. (2017). Controllability matters: the user experience of adaptive maps.
GeoInformatica 21, 619–641. doi: 10.1007/s10707-016-0282-x
Lavie, T., and Meyer, J. (2010). Benefits and costs of adaptive user interfaces. Meas.
Impact Person. Recom. User Behav. 68, 508–524. doi: 10.1016/j.ijhcs.2010.01.004
Lazer, D., and Radford, J. (2017). Data ex machina: introduction to big data. Annual
Rev. Sociol. 43, 19–39. doi: 10.1146/annurev-soc-060116-053457
Lee, B., Dachselt, R., Isenberg, P., and Choe, E. K. (2022). Mobile Data Visualization.
London: Chapman and Hall/CRC.
López-Jaquero, V., Motti, V., Montero, F., López, P., and Burny, N. (2021). A profile
and design space for characterizing user interface adaptation. Int. J. User-System Int. 14,
47–67. doi: 10.37789/ijusi.2021.14.2.1
Machado, E., Singh, D., Cruciani, F., Chen, L., Hanke, S., Salvago, F., et al. (2018).
“A conceptual framework for adaptive user interfaces for older adults,” in 2018 IEEE
International Conference on Pervasive Computing and Communications Workshops
(PerCom Workshops) (Athens), 782–787.
Oppermann, R. (1994). Adaptive User Support – Ergonomic Design of Manually and
Automatically Adaptable Software. Boca Raton, FL: CRC Press.
Frontiers in Communication 08 frontiersin.org
Reichenbacher and Bartling 10.3389/fcomm.2023.1258851
Oppermann, R., and Zimmermann, A. (2011). Context adaptive systems. i-com. 10,
18–25. doi: 10.1524/icom.2011.0004
Parasuraman, R., Sheridan, T. B., and Wickens, C. D. (2000). A model for types and
levels of human interaction with automation. IEEE Trans. Syst. Man Cybernet. A Syst.
Hum. 30, 286–297. doi: 10.1109/3468.844354
Peissner, M., and Edlin-White, R. (2013). “User control in adaptive user interfaces
for accessibility” in Human-Computer Interaction – INTERACT 2013. Eds. P. Kotzé, G.
Marsden, G. Lindgaard, J. Wesson, M. Winckler (Berlin, Heidelberg: Springer Berlin
Heidelberg), 623–640.
Raper, J. (2007). Geographic relevance. J. Document. 63, 836–852.
doi: 10.1108/00220410710836385
Raubal, M., and Panov, I. (2009). “A formal model for mobile map adaptation” in
Location Based Services and TeleCartography II: From Sensor Fusion to Context Models.
Eds. G. Gartner, K. Rehrl (Berlin, Heidelberg: Springer Berlin Heidelberg), 11–34.
Reichenbacher, T. (2004). Mobile Cartography – Adaptive Visualisation of
Geographic Information on Mobile Devices. PhD Thesis. Munich: Terchnical
University Munich.
Reichenbacher, T. (2005). “The importance of being relevant,” in Proceedings 22nd
International Cartographic Conference (A Coruna).
Reichenbacher,T. (2008). “Mobile usage and adaptive visualization” in Encyclopedia
of GIS. Eds. S. Shekhar and X. Hui (Berlin: Springer), 677–682.
Reichenbacher, T. (2017). Mobile usage and adaptive visualization. in Encyclopedia
of GIS. 2nd ed. Eds. S. Shekhar, X. Hui Berlin: Springer), 1289–1294.
Reichenbacher, T., Aliakbarian, M., Ghosh, A., and Fabrikant, S. (2022).
Tappigraphy: continuous ambulatory assessment and analysis of in-situ map app use
behaviour. J. Locat. Based Serv. 16, 181–207. doi: 10.1080/17489725.2022.2105410
Reichenbacher,T., De Sabbata, S., Purves, R. S., and Fabrikant, S. I. (2016). Assessing
geographic relevance for mobile search: a computational model and its validation
via crowdsourcing. J. Assoc. Inform. Sci. Technol. 67, 2620–2634. doi: 10.1002/asi.
23625
Reichenbacher, T., and Zingaro, D. (2022). “Lifting geographic relevance to the next
generation location-based services in a digitally transformed world” in Proceedings
of the 17th International Conference on Location Based Services.LBS 2022 – 17th
International Conference on Location Based Services, Munich, Germany.
Ricker, B., and Roth, R. E. (2018). Mobile Maps and Responsive Design. The
Geographic Information Science & Technology Body of Knowledge (2nd Quarter 2018
Edition), ed J. P. Wilson.
Roth, R. (2019). “What is mobile first cartographic design?” in ICA Workshop on
Mobile Map User Experience Design, Beijing, China.
Roth, R., Young, S., Nestel, C., Sack, C., Davidson, B., Janicki, J., et al. (2018).
Global landscapes: teaching globalization through responsive mobile map design. Prof.
Geographer 70, 395–411. doi: 10.1080/00330124.2017.1416297
Roth, R. E. (2019). How do user-centered design studies contribute to cartography?
Geografie, 124, 133–161. doi: 10.37040/geografie2019124020133
Ruginski, I. T., Creem-Regehr, S. H., Stefanucci, J. K., and Cashdan, E. (2019). GPS
use negatively affects environmental learning through spatial transformation abilities.
J. Environ. Psychol. 64, 12–20. doi: 10.1016/j.jenvp.2019.05.001
Savino, G.-L., Sturdee, M., Rund,é, S., Lohmeier, C., Hecht, B., Prandi, C., et al.
(2021). MapRecorder: analysing real-world usage of mobile map applications. Behav.
Inform. Technol. 40, 646–662. doi: 10.1080/0144929X.2020.1714733
Scaife, M., and Rogers, Y. (1996). External cognition: how do
graphical representations work? Int. J. Hum. Comp. Stud. 45, 185–213.
doi: 10.1006/ijhc.1996.0048
Schmid, F., Kuntzsch, C., Winter, S., Kazerani, A., and Preisig, B. (2010).
“Situated local and global orientation in mobile you-are-here maps” in Proceedings
of the 12th International Conference on Human Computer Interaction with Mobile
Devices and Services. New York, NY, USA: Association for Computing Machinery
(MobileHCI’10), 83–92.
Schneider, H., Eiband, M., Ullrich, D., and Butz, A. (2018). “Empowerment in HCI -
a survey and framework” in Proceedings of the 2018 CHI Conference on Human Factors
in Computing Systems. New York, NY, USA: Association for Computing Machinery
(CHI’18), 1–14.
Schneider-Hufschmidt, M., Kühme, T., and Malinowski, U. (1993). Adaptive User
Interfaces: Principles and Practice. Amsterdam: North-Holland (Human Factors in
Information Technology).
Schnur, S., Bektas, K., and Çöltekin, A. (2018). Measured and perceived visual
complexity: a comparative study among three online map providers. Cartography
Geographic Inform. Sci. 45, 238–254. doi: 10.1080/15230406.2017.1323676
Shneiderman, B. (2020). Human-centered artificial intelligence: reliable, safe and
trustworthy. Int. J. Hum. Comp. Int. 36, 495–504. doi: 10.1080/10447318.2020.1741118
Sperrle, F., Jeitler, A., Bernard, J., Keim, D., and El-Assady, M. (2021). Co-
adaptive visual data analysis and guidance processes. Comp. Graph. 100, 93–105.
doi: 10.1016/j.cag.2021.06.016
Steichen, B., and Fu, B. (2019). Towards adaptive information visualization - a study
of information visualization aids and the role of user cognitive style. Front. Artificial
Intel. 2, 22. doi: 10.3389/frai.2019.00022
Swienty, O., Reichenbacher, T., Reppermund, S., and Zihl, J. (2008). The role
of relevance and cognition in attention-guiding geovisualisation. Cartographic J. 45,
227–238. doi: 10.1179/000870408X311422
Thrash, T., Fabrikant, S. I., Brügger, A., Do, C. T., Huang, H., Richter, K.-F., et al.
(2019). “The future of geographic information displays from GIScience, cartographic,
and cognitive science perspectives,” in 14th International Conference on Spatial
Information Theory. COSIT 2019, eds S. Timpf, C. Schlieder,M. Kattenbeck, B. Ludwig,
and K. Stewart (Regensburg; Saarbrücken: Schloss Dagstuhl – Leibniz-Zentrum fuer
Informatik), 19.1–19.11.
van Tonder, B., and Wesson, J. (2008). “Using adaptive interfaces to improve mobile
map-based visualisation” in Proceedings of the 2008 Annual Research Conference of
the South African Institute of Computer Scientists and Information Technologists on IT
Research in Developing Countries: Riding the Wave of Technology. New York, NY, USA:
Association for Computing Machinery (SAICSIT’08), 257–266.
VandenAbeele, M. M. P. (2021). Digital wellbeing as a dynamic construct. Commun.
Theory 31, 932–955. doi: 10.1093/ct/qtaa024
Yigitbas, E., Karakaya, K., Jovanovikj, I., and Engels, G. (2021). “Enhancing human-
in-the-loop adaptive systems through digital twins and VR interfaces,” in Proceedings
of the 2021 International Symposium on Software Engineering for Adaptive and Self-
Managing Systems (SEAMS) (Madrid), 30–40.
Ziemkiewicz, C., Ottley, A., Crouser, J., Yauilla, A., Su, S., Ribarsky, W., et al. (2013).
How visualization layout relates to locus of control and other personality factors. IEEE
Trans. Visual. Comp. Graph. 19, 1109–1121. doi: 10.1109/TVCG.2012.180
Zingaro, D., and Reichenbacher, T. (2022a). “Exploratory analysis of mobile
app usage in relation to distance from home” in Proceedings of the 17th
International Conference on Location Based Services.Location Based Services (LBS),
Munich.
Zingaro, D., and Reichenbacher, T. (2022b). “Modelling and communicating
geographic relevance in a digitally transformed world using a. digital twins,” in
European Cartographic Conference - EuroCarto (Vienna: TU Wien), 1–2.
Zipf, A. (2002). User-adaptive maps for location-based services (LBS) for tourism.
in Proceedings of the 9th International Conference for Information and Communication
Technologies in Tourism, ENTER 2002. Innsbruck, Austria: Springer.
Zipf, A., and Richter, K.-F. (2002). Using focus maps to ease map reading:
developing smart applications for mobile devices. Kuenstliche Intelligenz 16, 35–37.
Frontiers in Communication 09 frontiersin.org
