=Paper=
{{Paper
|id=Vol-1465/paper7
|storemode=property
|title=Mood in the city - data-driven reflection on mood in relation to public spaces
|pdfUrl=https://ceur-ws.org/Vol-1465/paper7.pdf
|volume=Vol-1465
|dblpUrl=https://dblp.org/rec/conf/ectel/Pammer15
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==Mood in the city - data-driven reflection on mood in relation to public spaces==
https://ceur-ws.org/Vol-1465/paper7.pdf
Mood in the City - Data-Driven Reflection on
Mood in Relation to Public Spaces
Viktoria Pammer1
Knowledge Technologies Institute, Graz Univ. of Technology
Know-Center
Graz, Austria
Abstract. This paper maps out the design space of urban location-
related mood self-tracking as starting point for individual and urban re-
flection: What are the benefits for key stakeholders, and what are system
design options? Data-driven reflection here means that reflection is based
on data; in this instance taking mood related to public spaces as start-
ing point for reflection, but “attaching” to mood additional data that
contains information about context such as comments, tags or pictures.
We argue that individual citizens could become aware of own mood and
act on this knowledge, e.g., intentionally seeking relaxing or stimulating
places. Urban reflection means both discourse on the liveability of public
spaces amongst citizens and by stakeholders who manage or decide on
the design of public spaces in cities, the most obvious of whom are city
government or building project organizers.
1 Mood in the City
Reality impacts humans’ affective states; in public spaces, their design as well
as the actions and interactions of other people impact the affective states of all
who pass through. The relationship of mood and places has been of interest to
all sorts of people for a variety of reasons: In “the pursuit of urban happiness”1 ,
researchers and (city) designers investigate what sort of city design makes peo-
ple feel happy and relaxed. To this purpose, plans for building highways were
cancelled in Bogota in 1998 and cycle lanes were planned instead2 . Since then,
computer scientists have taken an interest and become involved. An initiative
called urbangems 3 analysed Google street view images of London with state-of-
the art image analysis methods, and used crowdsourcing to rate images in the
dimensions beauty, happiness, quietness, deprivation. The authors found that
the amount of greenery is the most positively associated visual cue with beauty,
happiness and quietness [15]. Similar results are found in a study based on geo-
tagged tweets [1]. In [16], the authors used these findings as basis for providing
1
www.researchswinger.net
2
http://www.bbc.com/travel/feature/20130828-reclaiming-the-streets-in-bogata
3
www.urbangems.org
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Mood in the city - data-driven reflection on mood in relation to public spaces - ARTEL15
directions within a city that are not based on length of route (shortest path rec-
ommended) but on emotional pleasantness. As self-tracking technology has be-
come available and acceptable to the masses, galvanic skin response trackers are
used in the bio mapping 4 initiative to automatically track emotional arousal of
study participants in conjunction with their geographic location. The underlying
rationale is to “become aware of our own and each others’ unique body reactions
to the environment [to] create a better world” (ibid). In addition to these soci-
etally motivated works and initiatives on a socially larger scale, mood tracking
is also used for more individualistic purposes. A plethora of mood self-tracking
apps exist on the web5 in the Quantified Self spirit. Other works have inves-
tigated mood tracking more scientifically: The affective diary [17] investigated
mood representations and reflections on it throughout the day, emphasising in re-
search questions however the automatic capture of mood and its representations.
AffectAura [11] explored user reactions to long-term representation of automatic
emotion detection. The authors found that a historic representation of affective
data does support memory, but is without “cues” (contextual data) insufficient
to reconstruct memory; thus mood data cannot be the only data but needs to
be connected to contextual information. The authors did not explore reflective
learning however. In [12], a system for location-based emotion tagging has been
developed, but not evaluated or used by a significant number of users (WiMo).
Within WiMo, users can decide to share their mood tags with others via places.
All mood entries are sent to a WiMo server. Note that in all the above-described
related work, mood tracking is sometimes manual, and sometimes “automatic”
via sensors that approximate mood via physiological reactions. Sharing in these
apps fulfils as main purpose that of communicating own mood, but not that
of reflecting together, or reflecting on mood in relationship to others’ mood.
Finally, I myself have been part of a research team that has explored shared
mood tracking in the workplace, finding indications for shared mood tracking
to improve collaboration in virtual meetings [4] and work performance in call
centers [5].
2 Reflective Learning
Reflective learning (which I will use as synonymous with “reflection”) is the pro-
cess of critically exploring the past in order to learn for the future (see e.g. [2]).
As such, reflective learning means reviewing the past in order to learn for the
future. Learning is to be taken broadly: Learning means changing one’s per-
spective, one’s perception, one’s knowledge, planning to act differently in the
future, or actually doing so (ibid). It is this direction towards the future, which
distinguishes reflective learning from rumination or “mere” awareness; although
awareness is a precondition for reflective learning.
Reflective learning can be understood as a cognitive process as well as a social
process [14]. In the first case, it is the individual actor who learns (individual
4
www.biomapping.net
5
For instance: http://www.moodjam.com, http://www.moodscope.com
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Mood in the city - data-driven reflection on mood in relation to public spaces - ARTEL15
learning), while in the second case it is a social entity that learns via its members
negotiating understanding, best practices, or pre-scribed processes (collaborative
learning). In organisational contexts, reflection is seen as key driver for learning
(see e.g., [6,7]). In such contexts, individual and collaborative learning naturally
intertwine (for examples see e.g., [8]); for instance when an individual actor re-
alises that something can only be changed at a collaborative level; or when an
individual within a discussion reflects on what the discussed change in strategy
will mean for own work practice.
By data-driven reflection, I mean the concept that reflection can be based to
a significant extent on data; going so far as deriving “triggers for reflection”
(the direct reason that makes a person or group reflect, see [8]) from data. This
concept is taken by different communities, such as learning analytics, quantified
self [3] or personal informatics [10].
3 Contribution: Data-Driven Reflection on Mood in
Relation to Public Spaces
In this paper, I investigate data-driven reflection on mood in relation to public
spaces. This is a novel concept, both for the field of computers and learning, and
for the fields of social innovation and urban development. While the concept
of data-driven reflection is not new for the first; it is new for the latter. Vice
versa, while using something like “bottom-up dialogue” in an urban context is
new in the scientific discourse in computer-supported learning, it is a big part
of “business as usual” in social innovation and urban development.
I frame the task of driving social innovation and urban development bottom-up
as a similar one to reflective learning in organisations: There is a mixture and
inter-relationship of individual and collaborative reflection processes, and it is
different stakeholders and stakeholder groups who can or should learn. I concep-
tualise district communities or cities as social entities that involve people with
a variety of roles; from the role of “mere” citizen, to that of building project
manager, to that of city official.
In this paper, I try to map out the related design space: First, I discuss the
benefits for different stakeholders, as perceived benefit of use is one of the key
predictive factors of technology acceptance. Second, I discuss system and inter-
action design directions - there is a wide variety of possibilities, and decisions
will need to be taken on the path towards concretising and implementing a use
case of such urban reflection as envisioned. 6
6
An early version of this paper has been presented and discussed at the
Smart City Learning Workshop of ECTEL 2014 and is online available
at http://www.mifav.uniroma2.it/inevent/events/sclo_ectel2014/index.php?
s=201&a=362. The workshop did not publish proceedings however; in addition, the
paper has been updated to reflect discussions at said workshop, changes in my emerg-
ing understanding of the relationship between urban location-based mood tracking
and reflective learning, and comments of ARTEL 2015 reviewers.
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4 Benefits of Use
Technology acceptance has been linked, in organisational settings, to perceived
ease of use and benefit [18]. In this section we discuss the potential benefits of
location-related mood tracking for two key stakeholder groups in urban settings:
Citizens who track their own mood in relation to places and share it in relation to
public spaces, and decision makers in the public sector such as city governments
or in the private sector such as building project managers.
4.1 Individual Reflection
For individuals, location-related mood tracking can serve - at a purely individual
level, no sharing is necessary - to become aware of own mood in relation to places.
This in turn can be useful to consciously reflect on the interaction between mood
and places, and to act on this knowledge: For instance, people could use places
as resources for wellbeing, and to avoid where emotionally draining places. They
could also consciously aim to change their mood in relation to places, e.g., try to
consciously relax in typically stressful places such as crowded public transports.
Relevant individuals are not only a city’s citizens, but also tourists, or people who
come to a city for work. These processes are cognitive learning processes, and
the goal of reflection is for individuals to improve the quality of their personal
or work lives.
4.2 Urban Reflection
In prior work [13] colleagues and I analysed the functions of sharing information
in relationship to reflective learning in an organisational context. We identified
four major roles of sharing data for reflection: Data as basis for re-evaluation,
as guideline for future behaviour, as starting point for collaborative reflection,
and to integrate multiple perspectives. In urban reflection, individuals’ mood in
relation to public places would mainly serve as starting point for collaborative
reflection. The motivation of the individual person to actually share own mood
and additional information can only lie in contributing to making a city “better” -
making it more liveable and enjoyable. Thus, sharing will need to be additionally
facilitated by smooth and enjoyable user experience in terms of interaction with
technology, together with displays of respectful and actual treatment of received
input on part of responsible stakeholders, in order to achieve a suitable balance
of “ease of use” with “perceived benefit” for people to actually do share their
mood and comments.
Sharing own mood in relation to public spaces is, from the individual’s point of
view, an expression towards an audience that needs to be defined: The audience
could be other citizens; thus, sharing own mood in relation to public spaces could
be the starting point of an asynchronous public discourse on the “liveability” in
public spaces. Shared mood could also address stakeholders that decide on and
shape public spaces such as city government or building project organizers. The
role of sharing own mood data (and optionally related data such as comments,
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Mood in the city - data-driven reflection on mood in relation to public spaces - ARTEL15
tags, pictures, etc.) corresponds to what has been called “creating awareness”
in [9] as one rationale for triggering a new reflection cycle. Additionally, decision
makers could also explicitly ask for focused input from citizens, tourists, people
working in the city, etc. This would correspond to “seeking clarification” as
rationale for starting a cycle of reflection activities [9].
The multitude of individual moods would be the starting point for re-designing
cities. Such processes constitute social processes, and the goal of reflection is
for the social entities of district communities (more informal) or cities (including
also the formal structures) to improve the “key performance indicators” of a city:
Target indicators can be defined and prioritised depending on a community’s
current status, but typical indicators would be the quality of urban experience
for people living in, working in, or visiting a district or city; or the financial
standing of a district or city.
5 System and Interaction Design
In this section we discuss system and interaction design options, and emphasize
those that we currently think preferable. Concrete design decisions will need to
be explored and verified (or rejected) in future empirical studies: We will consider
“valid” or “good” design decisions those that lead to appreciable benefits for key
stakeholders as discussed in the above section.
We assume that mood tracking is done via mobile internet-enabled devices such
as smartphones or tablets. But are users prompted to enter their mood, do
they enter their mood proactively, or is a hybrid method implemented (e.g., via
reminders)? Additionally, it is a priori unclear whether users will express only
their mood or add additional context information, e.g., in the form of text, a
photo, etc. as users are increasingly used to from other social apps and platforms.
We assume, that location does not need to be manually entered into the system
but can automatically be obtained via GPS, WiFi positioning, QR-tagged public
spaces, etc. Positioning only allows for mood tracking related to the place where
one currently is. It is unclear, whether in a system as proposed, mood tracking
“after the fact” is desirable, e.g., stating in the evening that in the afternoon in
the park one was really relaxed.
At the intersection of interaction design and software architecture we place the
question of where tracked mood data are stored. In [12], all data are stored on
a server, but only shared under specific circumstances. An alternative would be
to share every mood entry, i.e., to view the system essentially as a public mood
tracking system. At the other end of the privacy spectrum, mood tracking would
be individual, and data stored on personal mobile devices. Mood data would only
be shared on specific user input. On sharing, mood entries could be shared with
or without usernames. The latter is most usual in social apps and platforms.
So far, we have discussed the capturing of mood. But how about interacting with
location-related mood entries? We argue that users should be able to visualise
their own mood in relation to places. But should all users of the system get an
overview of mood in the city, or should this be reserved for city government?
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Mood in the city - data-driven reflection on mood in relation to public spaces - ARTEL15
Should also non-users of the system, as “users of the city”, of the public spaces,
be informed about collectively tracked mood? Should shared mood be visualised
only in the respective space, or should it be accessible also remotely? In all these
cases, visualisation of collectively tracked mood and interactive exploration of
captured mood data is an issue. In the case where every visitor of a public space
should have the possibility to explore such data, interaction could be via a public
website, or be mediated by an in situ ambient device.
6 Outlook
As next steps, we will concretise the above discussed benefits for multiple stake-
holders as well as system and interaction design options in use cases and pro-
totypes around participatory district development activities in Graz. Empirical
studies will need to verify whether the above outlined benefits can be reached
with urban location-based mood tracking.
Acknowledgements
The Know-Center is funded within the Austrian COMET Program - Competence
Centers for Excellent Technologies - under the auspices of the Austrian Federal
Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry
of Economy, Family and Youth and by the State of Styria. COMET is managed
by the Austrian Research Promotion Agency FFG.
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