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International Journal of
Geo-Information
Review
GIS-Based Emotional Computing: A Review of
Quantitative Approaches to Measure the Emotion
Layer of Human–Environment Relationships
Yingjing Huang 1, Teng Fei 1, * , Mei-Po Kwan 2,3,4 , Yuhao Kang 5, Jun Li 1, Yizhuo Li 1,
Xiang Li 6and Meng Bian 7
1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430000, China;
huangyingjing@whu.edu.cn (Y.H.); leejun@whu.edu.cn (J.L.); liyizhuo@whu.edu.cn (Y.L.)
2Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin,
Hong Kong, China; mkwan@illinois.edu
3Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin,
Hong Kong, China
4Department of Human Geography and Spatial Planning, Utrecht University,
3584 CB Utrecht, The Netherlands
5Geospatial Data Science Lab, Department of Geography, University of Wisconsin-Madison,
Madison, WI 53706, USA; yuhao.kang@wisc.edu
6Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450000, China;
lixiangzzchxy@163.com
7School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430000, China;
bian@whu.edu.cn
*Correspondence: feiteng@whu.edu.cn
Received: 22 July 2020; Accepted: 13 September 2020; Published: 15 September 2020
Abstract:
In recent years, with the growing accessibility of abundant contextual emotion information,
which is benefited by the numerous georeferenced user-generated content and the maturity of artificial
intelligence (AI)-based emotional computing technics, the emotion layer of human–environment
relationship is proposed for enriching traditional methods of various related disciplines such as
urban planning. This paper proposes the geographic information system (GIS)-based emotional
computing concept, which is a novel framework for applying GIS methods to collective human
emotion. The methodology presented in this paper consists of three key steps: (1) collecting
georeferenced data containing emotion and environment information such as social media and official
sites, (2) detecting emotions using AI-based emotional computing technics such as natural language
processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal
patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge
techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and
deeply explore the connection between people and their surroundings with the help of GIS methods.
Generally, the framework provides a standard workflow to calculate and analyze the new information
layer for researchers, in which a measured human-centric perspective onto the environment is possible.
Keywords: human–environment relationship; collective emotion; GIS-based emotional computing
1. Introduction
The human–environment relationship has always been a key issue in geography in terms of the
interaction between human society and its activities and geographical environment [
1
–
3
]. There is
a significant body of literature that investigates such relationship from various aspects, including
ISPRS Int. J. Geo-Inf. 2020,9, 551; doi:10.3390/ijgi9090551 www.mdpi.com/journal/ijgi
ISPRS Int. J. Geo-Inf. 2020,9, 551 2 of 15
evaluation [
4
], modeling [
5
], and application [
6
], and these studies provide a solid foundation for the
burgeoning and interdisciplinary fields, such as quality of life (QOL) [7].
Presently, there are two main forms to measure the interaction between human and environment:
the objective indices of environment attributes, such as evaluation index systems, and the subjective
indices from human perceptions, such as sense of place. As for the former, the evaluation index
systems usually are composed of indices that cover aspects such as accessibility, density, land use,
and land cover changes, and economics [
8
,
9
]. Nevertheless, the selection of such indices is limited
to current understanding of the interaction between humans and environment. In other words,
human–environment relationship may be underrepresented with such methodology. As for the latter,
the literature delivered various questionnaires to obtain indigenous people’s sense of place in three
place constructs: place identity, place dependence, and place attachment [
10
]. Although subjective
indices like sense of place seem to draw a synthetical picture of human–environment relationship
from the humanistic perspective, they emphasize portraying people’s abstract emotional connection
with their inhabited locality. Similarly, the items of questionnaires are still constrained by the state
of knowledge.
On the one hand, the concept of “place” is more than a location or a restricted space but a reality to
be understood from the perspectives of people. “Place” reflects the way people perceive and experience
the surrounding environment [
11
]. On the other hand, emotion, which dramatically influences human
consciousness [
12
], serves as a bridge between the environment (both physical and social environment)
and the final experience that a person obtained from the environment [
13
–
17
]. Therefore, exploring
collective emotion of places plays a conspicuous role in human–environment relationship research.
With the advent of big data era and the maturity of artificial intelligence (AI)-based emotional computing
techniques, massive individual-level emotional information is available to scientists. Over the last
decade, emotional computing has gained momentum, and it provides possibilities for developing a
new layer of emotion information for human–environment relationship research.
In this paper, we present a novel research framework, which equips collective emotion with
geographic information system (GIS) methods to quantitatively measure the emotion layer of
human–environment relationship, namely GIS-based emotional computing. This framework aims
to provide a standard workflow for calculating and analyzing the new information layer in different
geographical granularities. These results allow further study about understanding human behavior
in a certain environment and planning from a human-centric perspective. Crucially, we expect that
this framework provides complementary information to existing methodologies, rather than supplant
them (see Figure 1). We define the term GIS-based emotional computing as a data-driven methodology
that extracts emotional characteristics in places and analyzes it with GIS methods. Compared to
affective computing proposed by Picard [
18
], GIS-based emotional computing focuses on collective
emotion in places rather than individual emotional states. We advocate that the GIS-based emotional
computing can be a prominent research framework, and a useful tool, for dynamic diagnosis of the
human–environment relationship in different geographical and temporal granularities, with collective
emotions obtained from on-the-fly user-generated contents (UGCs).
ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 3 of 16
Figure 1. The methodologies of quantitatively and qualitatively describing human–environment
relationship.
As illustrated in Figure 2, the framework comprises three key steps: first, collecting environment
and emotion related data in various context from data sources such as social network sites and official
sites; second, exploring and cleaning data and extracting emotional information from georeferenced
emotion related data based on its data structure; and third, conducting spatiotemporal analysis using
GIS methods such as spatial interpolation and kernel density analysis in order to provide researchers
with additional insights into the complex human–environment relationship. To elaborate the
contents of each step, the rest of this paper is structured as follows. In Section 2, step 1 and step 2 of
GIS-based emotional computing will be stated. Specifically, we classify three types of data sources of
human emotions in the existing literature and elaborate their current advantages and weakness. On
the basis of data sources and data structure, we introduce several popular methods of emotion
recognition. Additionally, Section 3 presents the step 3 of GIS-based emotional computing, and three
analysis directions show the potential of GIS methods in emotion analysis. Section 4 summarizes the
current challenges and opportunities on GIS-based emotional computing. Finally, in Section 5, we
end the paper with a number of key conclusions.
Figure 2. The conceptual framework of geographic information system (GIS)-based emotional
computing.
2. Emotion Recognition
Emotion organizes our cognitive processes and action tendencies [19] and influences
individuals’ social interactions in systematic ways [20–23]. Furthermore, studies suggest that
emotional expressions have a potential impact on personality, even can predict life outcomes (e.g.,
marriage and personal well-being) of decades later [24,25]. Since measuring a person’s emotional
state is one of the most vexing problems in emotional studies, emotion recognition plays a dominant
role in GIS-based emotional computing. Generally, the data sources of human emotions include the
following three types: self-report, body sensor, and UGC. According to data structure, the methods
of emotion recognition can be classified into four types: self-reported, body sensor-based, UGC text-
based, and UGC image-based. As such methods continue to be improved, we will introduce several
popular methods of each type in this section.
Figure 1.
The methodologies of quantitativelyand qualitatively describing human–environment relationship.
ISPRS Int. J. Geo-Inf. 2020,9, 551 3 of 15
As illustrated in Figure 2, the framework comprises three key steps: first, collecting environment
and emotion related data in various context from data sources such as social network sites and official
sites; second, exploring and cleaning data and extracting emotional information from georeferenced
emotion related data based on its data structure; and third, conducting spatiotemporal analysis using
GIS methods such as spatial interpolation and kernel density analysis in order to provide researchers
with additional insights into the complex human–environment relationship. To elaborate the contents
of each step, the rest of this paper is structured as follows. In Section 2, step 1 and step 2 of GIS-based
emotional computing will be stated. Specifically, we classify three types of data sources of human
emotions in the existing literature and elaborate their current advantages and weakness. On the basis
of data sources and data structure, we introduce several popular methods of emotion recognition.
Additionally, Section 3presents the step 3 of GIS-based emotional computing, and three analysis
directions show the potential of GIS methods in emotion analysis. Section 4summarizes the current
challenges and opportunities on GIS-based emotional computing. Finally, in Section 5, we end the
paper with a number of key conclusions.
Figure 2.
The conceptual framework of geographic information system (GIS)-based emotional computing.
2. Emotion Recognition
Emotion organizes our cognitive processes and action tendencies [
19
] and influences individuals’
social interactions in systematic ways [
20
–
23
]. Furthermore, studies suggest that emotional expressions
have a potential impact on personality, even can predict life outcomes (e.g., marriage and personal
well-being) of decades later [
24
,
25
]. Since measuring a person’s emotional state is one of the most
vexing problems in emotional studies, emotion recognition plays a dominant role in GIS-based
emotional computing. Generally, the data sources of human emotions include the following three types:
self-report, body sensor, and UGC. According to data structure, the methods of emotion recognition can
be classified into four types: self-reported, body sensor-based, UGC text-based, and UGC image-based.
As such methods continue to be improved, we will introduce several popular methods of each type in
this section.
2.1. Self-Reported
Self-report usually collects emotional information by online or offline questionnaires and
interviews. It is a traditional and classic data source. Although alternative data sources of human
emotions emerged one after another, self-report remains a popular choice.
A substantial body of research on self-reported emotional information proves its easy
interpretability, the richness of information, and sheer practicality [
26
–
28
]. For example, a recent study
obtained the daily time, location, activity, mode of transportation, and emotions of female sex workers
in their diaries [
29
]. However, the response rate of questionnaires, in most studies, remains relatively
low [
10
,
30
], and these studies rest upon the assumption that respondents can represent those who
refused to respond. Moreover, prior literature has also shown that people have blind spots in their
self-knowledge, and they may not always understand their emotional states very accurately [31,32].
There are two mainstream self-reported scales wildly utilized in emotional research. One common
test called Satisfaction With Life (SWL) was put forward by Diener, Larsen [
33
]: its score reflects the
ISPRS Int. J. Geo-Inf. 2020,9, 551 4 of 15
extent to which a person feels that his/her life is worthwhile [
34
,
35
]. Continued efforts have been
made by scholars and policymakers to measure and promote subjective well-being for individuals
and groups at the community level with the help of SWL [
36
,
37
]. Applications of SWL have been
implemented at regional, national [38], and global levels [39–41].
However, the SWL test is restricted to only rate people’s happiness. A two-factor model of
Positive and Negative Affect Schedule (PANAS), developed by Watson et al. [
42
], has been used
more extensively according to the self-report emotion literature. This model is comprised of two
10-item emotion scales. These items are words that describe different feelings and emotions in Positive
Affect (PA) and Negative Affect (NA), such as interested and irritable to describe a person’s emotional
state. Updated versions of the PANAS were developed. For instance, to assess specific emotional
states, Watson et al. [
42
] created a 60-item extended version of the PANAS (the PANAS-X) that can
measure 11 specific emotions including fear, sadness, guilt, hostility, shyness, fatigue, surprise, joviality,
self-assurance, attentiveness, and serenity. Meanwhile, a 30-item, modified version of the PANAS
designed for children (PANAS-C) was proposed by Laurent et al. [
43
], and provides a brief, useful way
to differentiate anxiety from depression in children.
2.2. Body Sensor
In recent decades, with the motivation of making computers that can assess and even understand
users’ emotional states, existing literature of human-computer interaction (HCI) has applied sensing
technology to collect users’ physiological signals in different emotional states [
44
–
46
]. Stationary and
wearable sensors are both commonly utilized to collect the changes in the physiological signals of
users [
47
]. As an example, a wearable sensor platform was developed by Choi et al. [
48
], which monitored
mental stress.
Even if people do not explicitly express their emotions through facial expressions, changes in their
physiological patterns are inevitable and collectible [
49
]. However, the inherent noise in physiological
signals and their non-standard data structures has hampered the wide utilization of such data [
49
].
Even more, they can only provide datasets with limited sample sizes and short time durations [
50
–
52
].
There is a popular workflow of body sensor-based methods. Once the physiological signals were
collected from multi-sensory devices, signal processing methods were used to extract applicable features
from the physiological signals. Then, machine learning algorithms utilize, such features as model inputs
to predict emotional state. Generally, five types of physiological signals are widely captured because
they are show the correlation of underlying emotional fluctuations [
53
], including: (1) cardiovascular
activities, (2) electrodermal activities, (3) the respiratory system, (4) the electromyogram activities,
and (5) brain activities. Likewise, there are numerous options of signal processing methods (e.g., Fourier
transform, wavelet transform, thresholding, and peak detection) and machine learning algorithms
(e.g., k-nearest neighbor, regression trees, Bayesian network, and support vector machine) in the
workflow [
49
]. For instance, Choi et al. [
48
] used the k-nearest-neighbor algorithm and the discriminant
function analysis to analyze the physiological signals such as galvanic skin response and heat flow,
when classifying the emotions.
2.3. UGC Text-Based
When entering the 21st century, the increasing development of social networking sites (SNS)
provides unprecedented opportunities to collect massive individual emotional information. Geo-tagged
UGC (e.g., microblogs, blogs, and reviews) usually collect from various SNS such as Twitter, Amazon,
Weibo, and Flickr.
These UGC offer rich information about users’ emotions in different settings such as family,
work, and travel. Moreover, those petabytes of data have high spatiotemporal resolution, and their
collection is convenient and timesaving. Nevertheless, abundant evidence shows that the bias
(including emotional bias) exists in big data, and its spatial sparsity still needs to be addressed [
54
].
Furthermore, although geo- information shows that UGC can be related to places, emotions may not
ISPRS Int. J. Geo-Inf. 2020,9, 551 5 of 15
be directly affected by the surrounding environments since they may be influenced by the activities
at specific places. As for UGC text, it is difficult to extract emotional information within complex
sentences (e.g., multiple negations and metaphors). There is no common model or algorithm to detect
emotions in different languages. Besides, the same sentence may have different meanings in diverse
contexts and cultures.
Early research in this area focused on identifying and quantifying the polarity (i.e., positive
or negative) of natural language text. For example, Pang, Lee [
55
], and Read [
56
] utilized support
vector machine and Naïve Bayes (NB) classifier to extract emotional polarity from large volumes of
movie reviews and emoticons. Since human emotions are very subjective and complex, setting just
positive, negative, and neutral categories is too coarse to capture the full details of human emotions [
57
].
Recently, there has been an increased emphasis on extracting multi-dimensional human emotions from
text by developing emotion lexicons such as WordNet-Affect (WNA) [
58
], EmoSenticNet (ESN) [
59
],
and word-emotion lexicon [60].
Moreover, there is research that aims to improve the existing emotion lexicons to make it suitable
for different settings. For example, a novel emotion lexicon was developed by Chakraverty et al. [
61
],
which was compiled by integrating information from three aspects: the domain of psychology,
the lexical ontology WordNet, and the set of emoticons and slangs commonly used in web jargon.
2.4. UGC Image-Based
UGC images contain the advantages and disadvantages of UGC we discussed above. With regard to
images, their quantity is less than UGC text. Although images are informative, they resist interpretation.
With the development of technology in computer vision, image-based emotion extraction methods are
becoming more and more mature. Detecting facial expressions is a fashionable image-based extraction
method. Human faces provide one of the most powerful, versatile, and natural means of communicating
a wide array of mental states [
62
], and the relationship between facial muscles and discrete emotion
in various cultures is consistent [
63
]. Most of the techniques on facial expression-based emotion
extraction methods are inspired by the work of Ekman et al. [
64
], who produced the facial action coding
system (FACS). Still, many early facial-expression datasets [
65
,
66
] were collected under “lab-controlled”
settings where participants were asked to artificially generate some specific expressions, which do not
provide a good representation of natural facial expressions [
67
]. In recent years, several studies have
utilized robust computational algorithms to automatically capture human emotions from individuals’
facial expressions in photos. Recent efforts like that of Yu [
68
] have proposed a method that contains
a face detection module based on the ensemble of three face detectors, followed by a classification
module with the ensemble of multiple deep convolutional neural networks (CNN). What’s more,
several commercial application programming interfaces (APIs), such as Face++ Detect API [
14
] and
Microsoft Azure Emotion API [69], are available for scientific research.
3. Analyzing Collective Emotion with GIS
Generally, there are following three analysis directions in the current emotion studies of
human–environment relationship: (1) the temporal and spatial distribution of human emotions,
(2) the impact of environment on collective emotion, and (3) collective emotion as indicator. In this
section, we will illustrate how to apply GIS methods to these studies.
3.1. The Temporal and Spatial Distribution of Human Emotions
Due to the changes of the environment, people may have different emotional experiences at
different times and places. Understanding the distribution of human emotions is a basic topic in
GIS-based emotional computing, and it is broadly observed at different granularities in the existing
literature [
70
–
73
]. For example, the diurnal and seasonal rhythms of the changes in individual-level
emotions can be identified by natural language processing from Twitter text [
74
]. Additionally,
Flickr photos with geotags are traced and analyzed to extract the trend in the changes of human
ISPRS Int. J. Geo-Inf. 2020,9, 551 6 of 15
emotions between 2004 and 2014 [
75
] at the international level. Moreover, the World Happiness
Report [
40
] surveys the state of global happiness. Visualization of the spatiotemporal distribution
of human emotions at the national scale is widely carried out in different countries [
38
,
76
,
77
].
Moreover, researchers have begun to study the distribution of human emotions at fine granularities
including communities and parks [78,79]. However, the previous emotion maps either displayed the
discontinuous sample points or a simple regionalization of emotions averages to various areal units at
a certain scale because of spatial sparsity of the sampling data. In the GIS-based emotional computing
framework, evenly distributed sampling points and GIS methods, such as spatial sparsity would be
used to improve the accuracy. Further improvements will be discussed in Section 4.
3.2. The Impact of Environment on Collective Emotion
Scholars have shown that the surrounding environment has impacts on collective emotion [
10
–
12
].
It appears that both physical and social environmental factors are related to collective emotion [
80
–
82
].
On the one hand, literature from environmental psychology has explored the interactions between
collective emotion and physical environmental factors such as naturalness [
83
], density, accessibility,
and so forth. Most of these studies suggested that happiness is lower in less natural landscapes,
denser populations, and in areas with more traffic inconveniences. On the other hand, the relationships
between collective emotion and socio-economic attributes have been reported widely in social science.
For instance, Easterlin [
13
] found that there is a significant positive association between income and
happiness within countries. Table 1shows what kinds of environmental factors and at what scales
have related works examined the impact of environment on human emotions.
Table 1. Previous works on the impact of environment on human emotions.
Data Source Sample Size Study Area Results Citation
Flickr photos 2,416,191 faces Global
Environmental factors such as
natural landscape and water body
have significant impact on
tourists’ happiness.
Kang et al. [84]
Flickr photos 60,013 images
Greater Boston
Area, the United
States
Components of exposure to
nature including green vegetation,
proximity to water bodies,
and undeveloped areas have a
robust, positive effect on
happiness.
Svoray et al. [82]
self-report app
records
1,138,481
responses from
21,947 users
The United
Kingdom
The relationships between
environmental factors (land cover
type and weather) and happiness
are highly statistically significant.
MacKerron,
Mourato [85]
self-reports 25 participants
Dundee, the United
Kingdom
More green space in the
surrounding environment can
help people to adapt to stress.
Ward
Thompson et al.
[86]
self-reports 158 participants NA
There is a positive, linear
association between the density of
urban street trees and
self-reported stress recovery.
Jiang et al. [87]
tweet text of Sina
Weibo
210 million
microblog tweets China Air quality is associated with
happiness. Zheng et al. [80]
self-reports NA Multiple countries
Air pollution plays a statistically
significant role as a predictor in
subjective well-being.
Welsch [88]
self-reports 564 households
Communities in
Ann Arbor,
Michigan,
the United States
Having natural elements in the
view from the window contributes
to residents’ sense of well-being.
Kaplan [89]
self-reports 953 participants
Nine Swedish cities
Statistically significant
relationships were found between
the use of urban open green
spaces and self-reported
experiences of stress.
Grahn, Stigsdotter
[90]
ISPRS Int. J. Geo-Inf. 2020,9, 551 7 of 15
Table 1. Cont.
Data Source Sample Size Study Area Results Citation
self-reports over 10,000
individual adults
The United
Kingdom
The individuals are happier when
living with greater amounts of
urban green space.
White et al. [36]
self-reports 17,000
individuals The Netherlands
Self-reported distress is greater in
areas with lower levels of green
space.
de Vries et al. [91]
tweet text of
Twitter
34 metropolitan
statistical areas The United States
Climate factors like relative
humidity and temperature
contribute to local depression
rates.
Yang et al. [92]
self-reports NA The United States
There is a significant positive
association between income and
happiness within countries
Easterlin [13]
NA—not available.
Nevertheless, such studies are usually limited to a fixed granularity, and it is difficult to tell whether
scale affects the interactions between collective emotion and environmental factors. Furthermore,
the interactions are mostly qualitative rather than quantitative. With integrating GIS methods to
emotion analysis, solving these problems can be possible. For example, as for the interaction between
collective emotion and the accessibility of an environmental feature such as a water body or green
vegetation, separately establishing several buffers will help us to explore how distance from an
environmental feature has an impact on collective emotion.
3.3. Collective Emotion as Indicators
Since Goodchild [
93
] proposed the concept of volunteered geographic information (VGI),
which suggests that general individuals can be compared to environmental sensors, a variety of
studies have tried to explore urban development patterns using individual-level big geospatial data,
called “social sensing” [
94
]. In the context of human–environment relationship, collective emotion
has been served as a system of indicators describing the interaction of human and environment and
supporting policymakers to make decisions [95].
Collective emotion provides a new insight to understand crisis events that range from natural
disasters to man-made conflicts and how people respond to such rapid environment changes [
96
,
97
].
For example, Chien et al. [
98
] evaluated sentiment analysis of Flickr text in disaster management at
the time of the strike of a typhoon in Taiwan, China in 2009. Likewise, Dewan et al. [
99
] analyzed
the emotion of textual and visual content obtained from Facebook during the terror attacks in Paris,
France, 2015.
In recent years, collective emotion in places is gradually applied to guide urban planning [
100
,
101
].
A recent work analyzed the spatial characteristics of residents’ emotions in the city and at different
types of places in the city of Nanjing, China, to provide evidence that could help optimize urban space
development [
102
]. Likewise, another research measured pedestrians’ emotions, and results offered
initial evidence that certain spaces or spatial sequences do cause emotional arousal [
103
]. A semantic
and sentiment analysis was conducted to understand the perceptions of people towards their living
environments by examining online neighborhood textual reviews [
79
] and nearby neighborhood street
view images [104].
Although discovering valuable insights, these studies have great possibilities to obtain more
accurate results by GIS-based emotional computing. Firstly, the framework focuses on the multisource
data collection methods, which improve the volume and tolerance to the noise of emotion data.
Moreover, the integration of multiple disciplines, such as GIScience, computer science, and social
science, brings excellent calculation and analysis abilities that enable researches to perceive dynamic and
complex responses to places in near real-time. For instance, poorly timed traffic lights at crossroads and
a situation of severe earthquake both became detectable for immediately deciding the assistance policies.
ISPRS Int. J. Geo-Inf. 2020,9, 551 8 of 15
4. Challenges and Opportunities
While GIS-based emotional computing offers rich insights into a better understanding of
human–environment relationship, it poses a number of challenges, highlighted below: firstly, different
emotional baselines may exist in different regions and even between individuals. In other words,
emotional experiences may be influenced by many factors such as individuals’ memory, life history,
culture, age, and gender. Diener, Diener [
105
] found that self-esteem is strongly related to subjective
well-being (analogous to general positive emotions such as happiness) in individualist cultures
(such as the United States), but only has limited effects in collectivist cultures (such as China).
In fact, prior literature has shown that how and when emotions are experienced may differ from
one culture to another [
106
–
109
]. This difference is also affected by population’s age and gender
characteristics [
110
,
111
]. Therefore, researchers should take the demographic composition and culture
of different places into account when conducting research with GIS-based emotional computing.
Spatial sparsity of data on human emotions is an important issue to be solved. Although emotion
maps have been created by studies at different spatial scales [
84
,
112
], the sampling data is an occurrence
collection. In other words, these are presence-only data without absence data. Therefore, the previous
emotional studies were either the interpolations of sampling points, which inevitably involved
overfitting, the discontinuous display of sample points [
112
], or simply the regionalization of emotions
averages to various areal units at a certain scale [
113
]. However, for emotional expressions that cannot
be observed, it is hard to determine the emotions that are associated with places. In a recent work,
Li et al. [
114
] utilized MaxEnt [
115
], a species distribution model, which is intensively applied in
ecology, to map the geographic distribution of human emotions at a global scale but fell short of
applying to other granularity such as city and community. Yet, there is still no model available that all
scholars have agreed upon through a consensus to describe and predict the continuous distribution of
human emotions based on presence-only data.
Another challenge is that spaces with various land use mix (LUM) [
116
] may trigger different
emotions. People usually express emotional responses to “place” rather than “space” [
8
], but multiple
places may overlap in the same space at different times. For a specific street, people may stay on the
street for work during the daytime while visiting bars at night. The locale and its spatiotemporal
dynamics may influence human emotions and are supposed to be taken into consideration for GIS-based
emotional computing.
It is important to note that SNS emotional information may bring systematic bias for GIS-based
emotional computing. SNS users as a sample may not be representative of the total population [
117
,
118
].
Besides, due to the potential social pressures imposed by SNS [
119
,
120
], users may suppress or
exaggerate their emotions. For instance, Huang et al. [
121
] suggest that the majority of Weibo users
tend to post more photos with positive emotions instead of negative emotions, and there are significant
differences in place emotion extracted from Weibo and in-situ. Since there is no model that is suitable
for all places to rectify the emotions extracted from SNS yet, it is wise to pay attention to the bias of big
data when conducting emotion research.
The impact of GIS-based emotional computing is multi-fold. With the help of the framework,
the informative emotion layer of human–environment relationship can potentially enrich a variety
of fields such as traffic planning, urban safety, human-centric tourism, and evaluating current
planning projects. One the one hand, GIS-based emotional computing aims to collect massive
multisource georeferenced data and provide state-of-the-art, multidisciplinary techniques for effectively
and accurately detecting normalized emotion information from such data. On the other hand,
the map from individual emotion to place emotion is promised by using GIS-based spatial analysis.
Furthermore, geostatistics is a useful tool for deducing the causality between collective emotion and
environmental factors.
There are several opportunities in the current development of GIS-based emotional computing.
There has also been research into the connection between human perception and urban space through
urban street view imagery, which is another promising dataset that can be employed in GIS based
ISPRS Int. J. Geo-Inf. 2020,9, 551 9 of 15
emotion computing [
104
,
122
]. Building a multi-source emotional data fusion model can greatly
advance the development of GIS-based emotional computing. A good way to obtain a wide range
of human emotions in real-world settings is by combining big data (human emotions extracted from
UGC) with small data [
123
] (human emotions captured in reality) based on different cultures and
demographic characteristics to calibrate online emotion. Moreover, why people are satisfied with
some places instead of others has not yet been extensively investigated. It remains unclear which
environmental factors will influence people’s emotions at all scales and how to properly quantify the
extent of their influence.
5. Example of Implementing GIS-Based Emotional Computing
The emotion information analyzed by GIS-based emotional computing plays an increasingly
vital role in human–environment relationship research, and it serves as a critical component of
various applications including resource management, conservation, human geography, crime analysis,
real estate, psychology, environmental justice, etc. Hereby we give an example that exhibits the potential
to quantify human emotion and serves as a layer in GIS for human–environment relationships study.
The recommendation of tourist sites is a key topic in tourism studies. With GIS-based emotional
computing techniques, georeferenced contents uploaded by tourists to photo services in the public
domain enrich traditional recommendation systems with an emotion layer. One of our previous studies
collected Flickr photos of 80 tourist sites all over the world, and applied spatial clustering to emotion
information extracted from photos, for constructing an emotion layer for these tourist sites. Afterward,
a map of tourist sites with emotion tendency and a ranking list of global tourist sites based on emotion
were drawn, which serve as references for potential tourists. By calculating and analyzing the emotion
layer and other layers in GIS, we have also attempted to identify, which natural and non-natural
environmental factors may have an impact on visitor’s emotions [
84
]. The workflow of the example
can be seen in Figure 3. This example illustrated that, with GIS-based emotional computing, it is
possible to cater to tourist preferences for accurate advertising and management of the tourist industry.
ISPRS Int. J. Geo-Inf. 2019, 8, x FOR PEER REVIEW 10 of 16
emotion information extracted from photos, for constructing an emotion layer for these tourist sites.
Afterward, a map of tourist sites with emotion tendency and a ranking list of global tourist sites based
on emotion were drawn, which serve as references for potential tourists. By calculating and analyzing
the emotion layer and other layers in GIS, we have also attempted to identify, which natural and non-
natural environmental factors may have an impact on visitor's emotions [84]. The workflow of the
example can be seen in Figure 3. This example illustrated that, with GIS-based emotional computing,
it is possible to cater to tourist preferences for accurate advertising and management of the tourist
industry.
Figure 3. The workflow of an example implementing GIS-based emotional computing.
6. Conclusions
In this paper, we propose a new conceptual framework: GIS-based emotional computing, for
providing a new approach to measure the emotion layer of human–environment relationship. The
methodology comprises three steps: (1) collecting environment and emotion related data from
different data sources, (2) detecting emotional information from georeferenced emotion related data
by AI-based emotional computing techniques, and (3) conducting spatiotemporal analysis using GIS.
The current literature related to each step was reviewed, and the improvements of GIS-based
emotional computing can be done were discussed. The emotion layer reveals deep interactions
between human and their surrounding environment, and it reveals “what people real feel” instead
of “what people would feel”. GIS-based emotional computing consolidates the cutting-edge
technologies of multidisciplinary, such as GIScience, sociology, and computer science, for providing
a more effective and accurate avenue to calculate and analyze the emotion layer. It is important to
note that GIS-based emotional computing of this scope has only been possible recently, due to the
increasing capability of both massive UGC with emotional information and the technologies that take
Figure 3. The workflow of an example implementing GIS-based emotional computing.
ISPRS Int. J. Geo-Inf. 2020,9, 551 10 of 15
6. Conclusions
In this paper, we propose a new conceptual framework: GIS-based emotional computing,
for providing a new approach to measure the emotion layer of human–environment relationship.
The methodology comprises three steps: (1) collecting environment and emotion related data from
different data sources, (2) detecting emotional information from georeferenced emotion related data
by AI-based emotional computing techniques, and (3) conducting spatiotemporal analysis using
GIS. The current literature related to each step was reviewed, and the improvements of GIS-based
emotional computing can be done were discussed. The emotion layer reveals deep interactions between
human and their surrounding environment, and it reveals “what people real feel” instead of “what
people would feel”. GIS-based emotional computing consolidates the cutting-edge technologies of
multidisciplinary, such as GIScience, sociology, and computer science, for providing a more effective
and accurate avenue to calculate and analyze the emotion layer. It is important to note that GIS-based
emotional computing of this scope has only been possible recently, due to the increasing capability
of both massive UGC with emotional information and the technologies that take advantage of these
resources. This implied that GIS-based emotional computing may have unlimited potential because
of developing and advancing technologies. However, while the promise of collective emotion in
describing the human–environment relationship is alluring, the challenges above have to be addressed
for increased uptake of GIS-based emotional computing.
Author Contributions:
Conceptualization and Writing-Original Draft Preparation, Teng Fei and Yingjing Huang;
Methodology, Mei-Po Kwan, Xiang Li, and Meng Bian; Investigation, Jun Li and Yizhuo Li; Resources, Yuhao
Kang; Writing-Review & Editing, Mei-Po Kwan. All authors have read and agreed to the published version of
the manuscript.
Funding:
This work is supported by Open Fund of State Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing, Wuhan University (Grant No. 19E02).
Conflicts of Interest: The authors declare no conflict of interest.
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