HOT OR NOT – IDENTIFYING EMOTIONAL “HOT SPOTS” IN THE CITY

Abstract

Cities become increasingly populated, which calls for new approaches to ensure that cities continue being viable places for citizens to live in. The focus of these approaches should be on understanding citizens regarding their feelings, needs and behaviours. This includes an understanding of the perception of and the emotional reactions to urban structures from citizens’ points of view. Following the approach of urban emotions (Zeile et al., 2005), different objective physiological and subjective self-report measures were used in an experimental study in order to capture these emotional responses and to visualize the data in an emotional map. A small sample (N=13) of students was asked to collect positive as well as negative hot spots in a park area in the city centre of Stuttgart, i.e. spots that elicit positive or negative reactions. The results show the general potential of the park to function as a recreational area, but also identify room for improvement (e.g. concrete structures in the park). While physiological measures are useful to capture subtle emotional responses in larger areas, subjective measures seem to be more useful for understanding the reasons of the emotional responses by identifying positive as well as negative hot spots. A visualization tool introduced in this paper allows urban planners and other stakeholders (e.g. citizens, tourists) to view the results and analyse the data in an accessible way.

Full text

Hot or not – identifying emotional “hot spots” in the city
L. Kohn 2, H. Dastageeri 1, T. Bäumer 2, S. Moulin 2, P. Müller 2, V. Coors1
1 Dept. of Geo-Informatics, HFT - University of Applied Sciences Stuttgart, Schellingstrasse 24, Stuttgart, Germany –
habiburrahman.dastageeri@hft-stuttgart.de, volker.coors@hft-stuttgart.de
2 Dept. of Business Psychology, HFT - University of Applied Sciences Stuttgart, Schellingstrasse 24, Stuttgart, Germany
KEY WORDS: emotional mapping, experience sampling, mobile sensors, urban emotions, visualization
ABSTRACT:
Cities become increasingly populated, which calls for new approaches to ensure that cities continue being viable places for citizens to
live in. The focus of these approaches should be on understanding citizens regarding their feelings, needs and behaviours. This includes
an understanding of the perception of and the emotional reactions to urban structures from citizens’ points of view. Following the
approach of urban emotions (Zeile et al., 2005), different objective physiological and subjective self-report measures were used in an
experimental study in order to capture these emotional responses and to visualize the data in an emotional map. A small sample (N=13)
of students was asked to collect positive as well as negative hot spots in a park area in the city centre of Stuttgart, i.e. spots that elicit
positive or negative reactions. The results show the general potential of the park to function as a recreational area, but also identify
room for improvement (e.g. concrete structures in the park). While physiological measures are useful to capture subtle emotional
responses in larger areas, subjective measures seem to be more useful for understanding the reasons of the emotional responses by
identifying positive as well as negative hot spots. A visualization tool introduced in this paper allows urban planners and other
stakeholders (e.g. citizens, tourists) to view the results and analyse the data in an accessible way.
1. INTRODUCTION
With a growing world population there is a growing attraction of
urban areas (United Nations, 2017). As a consequence, the urban
settlements are getting closer to reaching their limits and it stands
to question, how citizens react to this development. Since
different groups come together in urban settlements, it is
important to understand their different needs. Otherwise, urban
areas could cease to function as viable, attractive living spaces.
An important first step is to understand how the people in cities
perceive and emotionally react to their surroundings. This “may
enable professionals to design more sustainable, pleasant, happy
and therefore smart places.” (Poplin, Yamu and Rico-Gutierrez,
2017, p. 74).
Thus, in the present study we are focussing on the perception and
emotional reactions of citizens in order to identify hot spots in
selected areas. With hot spots, we mean selected spots that elicit
positive or negative affective reactions among citizens. With our
current study, we want to describe and compare different
approaches to capture affective reactions by using objective
physiological as well subjective self-report measures.
In addition, the current paper presents an approach to visualize
the results in a way accessible to different stakeholders (e.g.
urban planners, citizens, tourists). The solution presented is built
around the concept of emotional city mapping (c.f. Zeile,
Höffken and Papastefanou, 2009) and tries to combine expertise
from psychology and geo-informatics.
The presented study adds insights on different methods useful for
capturing and analysing affective reactions of citizens. This is
necessary because “the use of emotions in urban design is not
well-established and far grown (Zeile, Resch, Exner and Sagl,
2015, p. 212). Urban design needs reliable and valid methods to
assess emotions in order to answer the following question: What
factors make future cities a good place to live in? The novelty of
our study is that we focus on the strengths and weaknesses of
different methods to assess affective reactions. This may help to
combine these different methods for a more comprehensive
measurement and visualization approach in the future.
1.1 Evaluating places
If one is interested in understanding the affective reactions of
citizens in urban areas, it is necessary to build a methodology that
focuses directly on citizens themselves. In new, so-called smart
cities, real-time data need to be collected in order to understand
how a city is functioning (c.f. Zeile, 2017). This also includes a
focus on citizens of a city and their needs (Resch, Summa, Sagl,
Zeile and Exner, 2015). The idea of a smart city from a human-
centred viewpoint is getting more and more important: the hope
for a more liveable, more efficient and greener city (Zeile et al.,
2009).
The approach of Poplin and colleagues (2017) follows this logic.
In their study they wanted to identify power places in selected
areas (i.e. “favourite outdoor locations that evoke positive
emotions, and are conducive to relaxation and reduction of
stress” Poplin et al., 2017, p. 73). Instead of consulting experts
(e.g. urban planners), they used an approach they called
“participatory place-making” (Poplin et al., 2017). They asked
participants to mark and describe their personal power places
directly on a map. Based on these data, a space syntax analysis
was conducted. Since they were focusing on (positively
connoted) power places, they did not assess any places eliciting
negative emotions. Secondly, they used a memory-based
approach, since participants were not in the power places while
describing them. This may be a good approach to identify
outstanding power places, but other places eliciting emotional
reactions might be missed this way.
Another approach described by Zeile (2017) also focuses on
reactions of citizens, but tries to measure emotions more directly
during or close to the actual experience. This bottom-up approach
as basis for participatory spatial planning is called urban
emotions, a term introduced by Zeile and colleagues (2015). It is
based on the idea that “feelings or perceptions are important key
factors and not negligible aspects in urban planning.” (Zeile et
al., 2009, p. 211) and includes different aspects:
Citizens are used directly as part of the measurement, which is
called “people as sensors” by Zeile et al. (2009). Their
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018
3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W7-67-2018 | © Authors 2018. CC BY 4.0 License.
67

perceptions of and (emotional) reactions to urban (spatial and
social) structures are assessed while passing through the city
(Zeile, 2017).
Emotional responses should be gathered using different
approaches, subjective ratings as well as objective physiological
measures (such as skin conductance, temperature and
cardiovascular activity).
The collected data should be mapped in the end to give urban
planners and other stakeholders in a city a viable tool to deduct
conclusions from it. Therefore, geo-referenced data needs to be
collected as well.
This urban emotions approach calls for interdisciplinary or even
transdisciplinary expertise, e.g. from the fields of urban planning,
geo-informatics, and psychology.
Following the call of Zeile et al. (2009) for intensified research
in this field, we conducted a field study using the urban emotions
approach. Adopting the idea of urban emotions, we wanted to test
different parameters based on cardiovascular activity for their
potential to function as objective physiological measures. These
measures were combined with subjective self-reported measures
to identify hot spots in a selected area, i.e. places that lead to
positive or negative emotional reactions while passing by. The
subjective ratings were assessed using an app for experience
sampling that allows capturing the ratings directly in the field,
with no time lag between experienced affect and response to the
questionnaire. This minimizes the problems usually found, when
people are asked to give ratings in retrospect (Shiffman, Stone,
Hufford, 2008).
We were especially interested to see the respective additional
benefit of both classes of measures in our study. The collected
emotional data were visualized in an emotional city map (Zeile
et al., 2009). As a case study, we wanted to investigate a park in
the centre of Stuttgart for its potential to function as a recreational
area and to identify positive and negative hot spots that may
support or hinder this function. We selected a park area for our
study, because natural settings may have positive effects on
citizens (Korpela, Hartig, Kaiser and Fuhrer, 2001; Poplin et al.,
2017).
2. METHOD / STUDY
In June 2016 and December 2017, a study at HFT Stuttgart
(university of applied sciences) was conducted to identify spots
that elicit positive or negative affective reactions (so called hot
spots) in the Stadtgarten, a park located at the inner-city
university campus. This location is in the heart of the (busy) city
centre of Stuttgart and could function as a low-stress retreat for
students, employees of nearby organizations as well as other
citizens. However, it stands to question whether it already lives
up to this potential. Assessing the park in summer and winter
gave a more comprehensive picture of the parks hot spots. While
the participants explored and rated the area, cardiovascular data
and mood were measured. Following the urban emotions idea
(Zeile et al., 2015), we assessed emotional reactions by using
objective physiological measures as well as subjective self-report
measures. Varying the approach of Zeile and colleagues (2009)
who focused on skin-conductance, we used psychophysiological
measured based on cardiovascular activities (heart rate and heart
rate variability) as an alternative. Therefore, this study not only
evaluated the emotional content of the park area and developed a
tool for visualizing the results, but also investigated a different
approach to assess affective reactions.
2.1 Sample / Participants
16 business psychology students participated in the field study
for course credit. They were recruited via a distribution list. Due
to missing values in either the questionnaires or physiological
measures (for tracking the heart rate), data from three participants
was excluded after the study. The errors from the questionnaire
stem from technical problems of the experience sampling
application used (these data were not saved). Regarding the
sensor used to track the heart rate: it shifted for some participants
leading to a large amount of missing data. Thus, data of 13
participants remained, one of which was male.
2.2 Measures / Materials
For collecting objective physiological as well as subjective
psychological data, participants were equipped as follows: a map
of the area the study was conducted in (including instructions
which path to follow), a movisens ecgMove 3 chest belt and a
tablet computer (Samsung Galaxy Tab3 10.1) with movisensXS
application as experience sampling tool.
2.2.1 Physiology: Cardiovascular measures were continually
collected using the movisens ecgMove 3 sensor – a
psychophysiological ambulatory measurement system. The raw
data was processed with movisens DataAnalyzer into secondary
parameters like heart rate (HR), heart rate variability (HRV) and
a stress index (SI). HRV was measured in Rmssd as it is seen as
the most suitable measure when it comes to connecting it with
subjective feelings (Levenson, Lwi, Brown, Ford, Otero and
Verstaen, 2017). The stress index (or index of regulation strain)
was introduced by Baevsky (e.g. Baevsky and Chernikova,
2017). Physiological parameters are best at identifying the extent
of negative emotional responses that can be described as stress
(Zeile et al., 2005).
2.2.2 Subjective data: General mood of participants was
assessed using items of the German version of the
Multidimensional Mood Questionnaire (MDBF; Steyer,
Schwenkmezger, Notz and Eid, 1997). Four items for each
bipolar scale were chosen in the following order: good-bad
(content, happy, unhappy, discontent), awake-tired (wide-awake,
alert, tired, sleepy), calm-nervous (calm, relaxed, tense, nervous).
Following the instruction “We would like to know how you
momentarily feel”, all items were rated on a 5-point Likert scale
ranging from not at all (1) to extremely (5).
The selected hot spots were rated on a visual analog scale from
do not like it at all (0) to like it very much (8) following the
question “How much do you like the selected hot spot?”.
Participants were also asked to describe briefly the identified hot
spot in an open text field. All ratings were assessed via the
movisensXS application.
2.2.3 Location: The GPS position of the participants was
continually tracked via the movisensXS application.
2.3 Procedure
The study was run individually for each participant in order to
avoid mutual influence of results. The laboratory of the business
psychology department served as starting point. After giving
informed consent, participants put on the chest belt sensor. They
were then informed about the different measures and thoroughly
instructed how to use them. Being additionally equipped with a
map (see figure 1) and a tablet computer (with the movisensXS
app) participants started with a trial task to make sure the
application worked and was understandable (note: none of the
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018
3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W7-67-2018 | © Authors 2018. CC BY 4.0 License.
68

participants did have any problems with the application; this may
be due to psychology students being used to the role of
participants in different experimental settings). The trial task also
included the first assessment of mood as a benchmark measure.
Afterwards, the participants followed the marked way on the map
and the instructions from the tablet computer, which came up via
notifications from the app. The way was not physically
challenging and participants were instructed to walk at a normal
pace.
Figure 1. Map of path during the study
The investigator followed participants with some distance in
order to help in case of upcoming technical problems. As soon as
participants entered the Stadtgarten (park) they were asked by
notification of the app to explore the area and identify positive
and negative hot spots, i.e. places they especially liked or did not
like. While in the area of the park, the app offered a questionnaire
in which a hot spot could be documented by taking a picture,
saving the corresponding GPS position, rating the hot spot and
describing why it was chosen. The questionnaire was only
available to participants while inside the park. In case the
participants left the park without recording any hot spots, they
were asked to go back and fulfill the task by the investigator.
After recording a number of hot spots (freely chosen by
participants), participants continued to follow the path on the
given map. When exiting the park, their mood was assessed for a
second time via the app. After a few minutes’ walk, participants
entered a different part of the park and were again asked to
identify positive and negative hot spots. When exiting the park
for the second time, the mood was assessed again. The walk
ended after approx. 45 minutes at the starting point where the
study was also finished.
3. VISUALIZATION: TECHNICAL
IMPLEMENTATION
As the collected data is dependent on a position we decided to
use a server- and map-based visualization.
3.1 Minimum Viable Product/prototype
Our first approach was to set up a Minimum Viable Product
(MVP), a prototype with the minimum requirement to visualize
the collected data on a map. For data collection of the participants
we used the movisens ecgMove 3 sensor which also tracked the
position via GPS. Besides the collected data, the participants
were asked to take pictures of their personal hot spots and
allocate an affective evaluation on a scale between 0 and 8. The
evaluations are visualized color-coded within an ascending
colour gradient between red (do not like it at all) and green (like
it very much).
For the implementation we used the open-source JavaScript
library Leaflet to visualize the data and provide a graphic user
interface to select the participant. Overall there were five
participants available in the MVP. Each participant can be chosen
individually or altogether. The graphic layer was placed on
OpenStreetMaps. By choosing any participant a polyline will be
drawn on the map representing the walked path of the particular
participant. Furthermore, the corresponding hot spots will be
shown as color-coded markers. If clicked, further information of
the hot spot will be shown such as a picture and respective notes
(see figure 2).
Figure 2. Screenshot of the MVP
3.2 Implementation
3.2.1 Data pre-processing: The collected data of movisens
was stored in various files and formats. The GPS data was
provided in Keyhole Markup Language (KML or KMZ) which is
an Extensible Markup Language (XML) notation and an
international standard of the Open Geospatial Consortium
(OGC). To extract the GPS coordinates and timestamps a PHP:
Hypertext Pre-processor (PHP) parser was implemented. After
assigning unique identifiers (ID) the data was stored in a
Structured Query Language (SQL) database. Further sources
provided HR, its corresponding timestamp and the affective
evaluation in the comma-separated values (CSV) format. The
evaluation was stored as a numeric value between zero to eight.
The pictures of the hot spots were added to the database in a JPEG
format.
3.2.2 Visualization: Besides OpenStreetMaps, Google Maps
was added as a further option. To represent the physiological
emotional reactions (based on HR) a heat map was used, which
simplified the visualisation of the combined emotions of all 13
participants. The emotions were color-coded between the colour
gradient of red (high values) to green (low values). Further
options of displaying the heat map are inverting the colours,
changing the radius and opacity. The hot spots were also added
as coloured markers. By clicking on a marker, a thumbnail of the
location as well as the participant’s ID, the corresponding notes
are shown. If HR is above 90 bpm the marker will start an
animation of going up and down (see figure 3). The mood ratings
as well as the HR can also be shown optionally as a bar or line
chart. By selecting a specific part of the walked path on the map,
the HR values will be displayed in a separate line chart next to
the map.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018
3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W7-67-2018 | © Authors 2018. CC BY 4.0 License.
69

Figure 3. Screenshot of visualization tool
Additionally, the ratings of the mood questionnaire of all
participants can be shown as a bar or pie chart at specific
moments (beginning and the end of the survey).
3.3 Website
The application requires an Apache server and the open-source
relational database management system MySQL. The PHP script
is using Ajax calls and the JSON format.
After setting up the website all features are accessible through
the GUI. The data of the participants can be replaced, further
information uploaded or new participants added. No coding nor
technical adjustments are needed for usage.
4. RESULTS
4.1 Hot spots
In total 42 hot spots were documented (i.e. 42 spots selected by
participants that lead to a positive or negative emotional
reaction), on average each participant identified M = 3.23 (SD =
1.46) hot spots. Data were analyzed quantitatively as well as
qualitatively, focusing on aggregated as well as individual
results. 15 hot spots are negatively ranked (0 to 3 on a 9-point
scale) and 27 hot spots are positively ranked (5 to 8). No hot spot
is categorized in the middle of the scale (4), i.e. participants
correctly followed the given instructions. The proportion of
positive to negative hot spots (about 2:1) is in line with the park
functioning as a possible retreat for relaxation-seeking citizens.
The comments given and pictures taken of the hot spots show that
negatively rated areas are those, which include grey and bleak
surfaces (e.g. an empty fountain, walls) or buildings (next to the
park), concrete and graffiti. The park includes a fitness-trail,
which is rated positively by some and negatively by some other
participants. Clearly positive locations in the park are the green
lawns, colorful flowers (during summertime), the marble statues
and the historic buildings of the university campus on the edge of
the park.
4.2 Mood
The three subscales of the MDBF (good-bad, aware-tired, calm-
nervous) show satisfactory to high reliabilities (Cronbach’s α
between .73 and .89).
There are no significant differences in the twelve MDBF items
between the three times of measurement: beginning of the study,
first exit of the park, second exit of the park. This indicates that
the short visit of the park did not strongly influence participants’
mood during the study.
Mood items (after exit of park) were also analyzed in
combination with the number of positive and negative hot spots
identified by each participant. There is a substantial negative
correlation between the number of negative hot spots and the
mood item “content” (r = -.73, p < .05). The correlation between
the number of negative hot spots and the mood item “discontent”
was positively correlated, but not statistically significant (r = .27,
p > .05). Thus, participants were feeling less content when
identifying negative hot spots. Therefore, the negative hot spots
should be considered in order to further improve the park area.
4.3 Physiology
In addition to self-reported subjective data, objective
physiological data of the participants were analyzed and
connected to the rated hot spots and subjective mood ratings.
Values of eight to nine participants were considered for the
general analysis of HR (nine participants) and HRV (eight
participants), since recordings for these participants showed a
continuous track of physiological parameters. The other
participants showed too many gaps in recordings of physiological
parameters and were, thus, excluded from this part of
physiological analysis. This may be due to not proper adjustment
of sensors to participants’ body or to the problem of continuously
tracking physiological data while moving outside the laboratory.
In consequence, the stress index showed many gaps and was
therefore not used for this global analysis.
First, physiological parameters (HR and HRV) and mood values
were correlated using data from eight (HRV) and nine (HR)
participants. HR shows no significant correlations with mood
ratings. However, HRV is correlated negatively with the items
“tense” (r = -.66, p < .05) and “nervous” (r = -.68, p < .05), i.e.
lower HRV-scores are an indicator for negative affect or stress.
Secondly, the average of physiological values (HR and HRV) of
each participant was calculated. The average HR (but not HRV)
values while being inside the park differed from the values while
participants were walking outside of the park area (see table 1).
Table 1. Means (M) and Standard Deviations (SD) of HR and
HRV inside and outside the park area
Seven out of nine participants have lower HR values while being
inside the park area. Only one participant shows increased HR in
the park (see figure 4).
Participant
MSD MSD MSD MSD
582 944 10 87 11 34 16
6104 714 3109 11 15 5
7105 915 4109 915 5
976 823 286 923 8
11 133 32 - - 117 37 - -
12 102 920 4102 13 20 8
13 117 9 8 3 118 10 8 4
14 101 32 10 4104 10 10 6
16 106 11 16 7111 619 6
Outside the park
Inside the park
HR
HRV
HR
HRV
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018
3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W7-67-2018 | © Authors 2018. CC BY 4.0 License.
70

Figure 4. Means and SD of HR inside and outside the park area
The visualization heat map of HR values (see figure 5) illustrates
that participants have higher physiological arousal outside of the
park area. This can be interpreted as a generally lower stress level
among participants while inside the park.
Figure 5. heat map of heart rate (all participants)
4.3.1 Physiology and hot spots: Not all of the 42 hot spots
documented by participants were used for this part of the
analysis, as there were too many missing values in the secondary
physiological parameters for some of them. We included 33 hot
spots for analysis with HR, 26 for analysis with HRV and 19 for
analysis with the stress index. For the latter 19 hot spots, all three
physiological measures (HR, HRV and stress index) are highly
correlated (all r ≥ |.74|, p < .05). HRV is strongly negatively
correlated with HR (r = -.74, p < .05) and the stress index (r = -
.78, p < .05). HR is strongly positively related to the stress index
(r = .81, p < .05). However, there are no significant correlations
between the three physiological parameters and the subjective
evaluations of the hot spots (HR, r = -.02 and HRV, r = -.13, both
p > .10). The correlation between stress index and evaluation of
hot spots is marginally significant with a medium effect size of r
= -.32 (p < .10), i.e. more positively evaluated hot spots
correspond with a lower level of stress (and vice versa).
Analyzing individual participants illustrate this: One participant
for example has a lower value on stress index (SI = 319) at the
positively rated hot spot (8) and a higher value (SI = 365) at the
negatively rated hot spot (0).
5. DISCUSSION
The target of the urban emotions approach is to capture and
visualize perceptions of and emotional responses to certain
structures within urban areas (Zeile et al., 2005). In the current
study, we combined different affective measures in order to reach
this goal: objective physiological data (based on cardiovascular
activity) as well as subjective self-reported ratings (of hot spots
and general mood). Participants had to identify positive as well
as negative hot spots in an area of interest (a park) and were asked
about the mood state after visiting the park. In addition to these
data, participants took pictures of hot spots and their GPS-
coordinates were tracked. In this case study of the park
Stadtgarten in the centre of Stuttgart we were interested to test
these different measures in order to understand their respective
advantages and limitations in order to capture urban emotions.
We also wanted to understand as a case study, whether the park
serves as a recreational area for citizens – and to identify ways
how to improve it.
Overall, participants found more positive (e.g. green lawns)
compared to negative hot spots (e.g. concrete structures) in the
park. This shows the overall potential of the park located within
the busy city centre of Stuttgart to offer citizens an area of stress
relieve and recreation. However, participants’ overall mood was
not influenced by the visit of the park during the study. There are
different possible explanations for that. First, the visit may have
been too short for participants to positively affect their mood. In
addition, since participants were following a task, they were not
really using the park for recreational purposes. Second, the park
might (still) have too few positive and too many negative hot
spots as it is today. As the results show, the fewer negative hot
spots participants encounter the more content they are feeling.
One third of hot spots was rated as negative, which might be a
number too high for a recreational area to have a positive effect
on visitors. A third explanation is that people are not fully aware
of their emotional states or may be unable to report them in self-
reports (Weinberger, Kelner, McClelland, 1997), especially if
these are not strongly experienced (such as moods compared to
emotions) and if they are not used to pay attention to their
emotional states. In this case, objective physiological measures
might have an advantage and give additional insights.
Physiological parameters such as HRV and HR may be good
additional indicators of these minor changes in mood. HRV for
example seems to be a good indicator of experienced stress, even
if people are not aware of this themselves, proving its incremental
value in capturing urban emotions. The results show a correlation
of HRV with the level of self-reported nervousness and
tenseness. However, even if all parameters deducted from
cardiovascular activity were highly correlated with each other,
only HR showed a somewhat lower stress level for participants
while walking inside the park area compared to areas outside the
park. Physiological measures seem also to be correlated with
different hot spots, but they seem to be more reliable to identify
larger areas of high and low stress levels compared those single
spots.
However, the sensors used in this study let to quite some amount
of missing data. This may clearly be a downside for using these
kind of data (c.f. Zeile et al., 2009) and needs to be closely
explored in future studies. Another problem is the interpretation
of the different physiological parameters. Even though the
different physiological parameters are highly correlated with
each other, they show different correlational patterns with the
subjective self-report measures. In addition, HR showed the
expected differences for participants while being inside
compared to outside the park area, but HRV did not. Thus, apart
from the measurement problems regarding the reliability of
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018
3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W7-67-2018 | © Authors 2018. CC BY 4.0 License.
71

physiological measures used in this study, the construct validity
of these measures is an open question. Here, further research with
larger samples, long-term measurements and consideration of
other physiological parameters is necessary. It might be useful to
assess the heart rate during a neutral task upfront for all
participants allowing for individual calibration. Also, the walking
speed and walk acceleration could be used to this effect. Apart
from other physiological measures it could be worthwhile to
integrate other measures as well in order to get a better
understanding of the emotional reactions of people in urban areas
(e.g. from social media posts, such as tweets; c.f. Resch et al.,
2015) and its triggers (e.g. by using an action camera, or a mobile
eye-tracking device). In addition, other factors that influence
emotional responses, such as weather conditions, should be
tracked in future studies. For example, it would be interesting to
investigate, how the different appeal of the park area over the
course of the year would influence emotional reactions (the
sample in our study was too small to investigate this question).
Taken together, subjective self-report measures used as
experience sampling proved to be helpful to identify outstanding
negative as well as positive hot spots in the park. Objective
physiological measures add information by capturing more subtle
emotional experiences (such as mood) and seem to be useful to
evaluate areas (as opposed to certain spots) within a city.
In addition to the assessment of emotional responses, we were
also interested in developing a visualization tool in order to
display these data in a way easy to understand for different (non-
scientific) target groups, such as citizens and tourists as well as
people working in urban development. The tool introduced in this
study had the ability to analyse physiological data as well as data
from self-report measures, both for individual participants and on
an aggregated level, in a way that is easy to comprehend for all
target groups. Above that, for each hot spot rich information was
offered (e.g. description and picture of spots), that may be
especially interesting for tourists, visiting the town. With tools
like this “the extraction of georeferenced emotions could be used
to identify areas where the citizens’ well-being is not optimal and
where urban planning actions are necessary.” (Resch et al.,
2015).
6. OUTLOOK
Overall, our study sets another example of how to assess and
display urban emotions in a larger city (Resch et al., 2015). To
achieve an understanding of how citizens are feeling in over-
growing cities is a very important issue for politicians, urban
planners and citizens themselves. It is extremely important to use
reliable and valid measures for assessing these emotional
variables. Following an experiencing sampling approach helps to
minimize error, as does the use of different classes of
measurement (self-report as well as physiological measures).
Physiological measures may be useful to identify areas of interest
for urban planners that elicit positive or negative affective
reactions among citizens. To get a better understanding of the
reasons for these reactions, subjective self-reports seem to be a
good complementary approach. In future research, it is important
to find self-report measures that are most informative for urban
planners. In addition, it is also important to understand the
problem with reliability and validity related to physiological data
in the field.
For the next iteration of the software solution, we aim to use the
OGC standard SensorThings API. Furthermore, additional
sensors such as thermometer or other sensing devices can be
added and interconnect more easily. The goal is to establish an
online tool for emotional mapping where participants as well as
sensors can be added and managed using a GUI. Through storing
the collected data on a server first, the information should be
called and visualized in almost real-time. In addition, The
usability of the visualization tool for different target groups (e.g.
urban planners, citizens, tourists) needs to be considered in future
development.
REFERENCES
Baevsky, R. M., Chernikova, A. G., 2017. Heart rate variability
analysis: physiological foundations and main methods.
Cardiometry, pp. 66-76, doi:
10.12710/cardiometry.2017.10.6676.
Korpela, K. M., Harting, T., Kaiser, F.G., Fuhrer, U., 2001.
Restorative Experience and Self-Regulation in Favorite Places.
Environment and Behavior, 33, pp. 572-589.
Levenson, R. W., Lwi, S. J., Brown, C. L., Ford, B. Q., Otero, M.
C., Verstaen, A., 2017. Emotion. In J. T. Cacioppo, L. G.
Tassinary, G. G. Berntson (Eds.), Handbook of
psychophysiology, pp. 444–464, Cambridge: Cambridge
University Press.
Poplin, A., Yamu, C., Rico-Gutierrez, L., 2017: Place-making:
An approach to the rationale behind the location choice of power
places. Iowa State University campus as case study. Int. Arch.
Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W3, pp. 73-
81. https://doi.org/10.5194/isprs-archives-XLII-4-W3-73-2017.
Resch, B., Summa, A., Sagl, G., Zeile, P., Exner, J.-P., 2015.
Urban Emotions – Geo-Semantic Emotion Extraction from
Technical Sensors, Human Sensors and Crowdsourced Data. In
G. Gartner & H. Huang (Eds.), Progress in Location-Based
Services 2014 (Lecture Notes in Geoinformation and
Cartography), pp. 199-212, Berlin: Springer International
Publishing
Schiffmann, S., Stone, A. A., Hufford, M. R., 2008. Ecological
momentary assessment. Annual Review of Clinical Psychology,
4, pp 1-32. doi:10.1146/annurev.clinpsy.3.022806.091415
Steyer, R., Schwenkmezger, P., Notz, P., Eid, M., 1994.
Theoretical analysis of a multidimensional mood questionnaire
(MDBF). Diagonstica, 40(4), pp. 320-328.
United Nations, 2017. World Population Prospects: The 2017
Revision, Key Findings and Advance World Population
Prospects: The 2017 Revision, Key Findings and advance Tables
(Department of Economic and Social Affairs, Population
Division, Ed.). Working Paper No. ESA/P/WP/248.
Weinberger, J., Kelner, S., McClelland, D. C., 1997. The effects
of subliminal symbiotic stimulation on free-response and self-
report mood. Journal of Nervous and Mental Disease, 185
(10), pp. 599-605.
Zeile, P., 2017. Feel the city – urban emotions. In Bundesamt für
Raumentwicklung, Eidgenössisches Departement für Umwelt,
Verkehr, Energie und Kommunikation (Ed.): Forum
Raumentwicklung 02/2017. Bern.
Zeile, P., Höffken, S., Papastefanou, G., 2009. Mapping People?:
The measurement of physiological data in city areas and the
potential benefit for urban planning. In M. Schrenk, V. V.
Popovich, D. Engelke, P. Elisei (Eds.) Real Corp 2009: Smart,
Sustainable, Integrative. Strategies, concepts and technologies
for planning the urban future, pp. 341-352.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018
3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W7-67-2018 | © Authors 2018. CC BY 4.0 License.
72

Zeile, P., Resch, B., Exner, J. P., Sagl, G., 2015. Urban emotions:
Benefits and risks in using human sensory assessment for the
extraction of contextual emotion information in urban planning.
In S. Geertman, J. Ferreira, R. Godspeed, & J. Stillwell (Eds.),
Planning Support Systems & Smart Cities (Lecture Notes in
Geoinformation and Cartography), pp. 209-225. Berlin: Springer
International Publishing.
ACKNOWLEDGEMENTS
Based on the MVP the implementation of the visualization tool
was done together with students of the lecture Geovisualisation
held by Prof. Dr. Volker Coors in collaboration with the
Department of Business Psychology at the University of Applied
Sciences Stuttgart. We would like to thank all the students
involved as well as the participants. We would also like to thank
the reviewers for their helpful comments on our paper.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W7, 2018
3rd International Conference on Smart Data and Smart Cities, 4–5 October 2018, Delft, The Netherlands
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W7-67-2018 | © Authors 2018. CC BY 4.0 License.
73