1. Introduction
Since 2008 more than half of the world population lived in cities and this is expected to reach 70% by 2050 [
1]. Cities are widely regarded as important areas in the pursuit of global sustainability [
2]. A sustainable society comprises five distinct elements for every human-being such as the proper education, a clean environment, a well-balanced safety, abundant resources for future generation, and contribution to a sustainable world [
3]. Easy to collect metrics that rate environmental, economic, educational, and social variables are important to evaluate the global strategies for urban transformation towards sustainability that builds upon national and local scales [
4]. A sustainable city should respond to residents’ needs through sustainable solutions for social and economic aspects [
1]. The tradeoff between resource consumption and citizens’ demand can be sustainably solved by the use of information and communication technologies (ICT) and the internet of things (IoT), which can be accessed in a smart local university campus [
5,
6]. Therefore, a future-like relationship emerges between city variables and public attitude towards local university campuses.
Universities have a central role to create knowledge and tools to transfer information for societal transformations towards sustainability [
2,
7]. Universities can be a driving force to provide urban sustainable development by embedding knowledge to the local social and economic networks [
8,
9,
10]. Besides the responsibilities of teaching and research, universities are increasingly expected to turn knowledge into innovation [
11]. Universities can also be a partner with their host-city to develop the transformation to a smart community [
1,
5,
6,
7]. To test design principles, a university campus can be taken as a socioeconomic organization like a mini-city and the management and demands for resources therein can be acquired by the using data through the IoT. In China a model is being implemented to construct cities that are famous due to universities therein, but many of these programs are not successful as expected because of the poor educational outcomes and economic productivity [
12]. The shortage of objective evaluation on local universities was at least partly responsible for the failure. People around the campus are frontiers that can give a precise evaluation on universities hence their attitude is the key to evaluate the university in its host-city.
City variables are known to affect the satisfaction of residents towards local universities [
13,
14]. An investigation using self-reported scores revealed that most undergraduates indicated satisfaction with settings of the city where their campus was located [
13]. The group of variables out of these settings includes socializing [
2,
14], resident environment [
2,
13,
14,
15,
16], socio-economic status [
8,
13,
17], and industrialization [
8] that have all been detected to have some relationship with perceived satisfaction towards campus although the magnitude was ever either positive or negative. The model of multiple city variables was proposed to measure the performance in the creativity of universities in cities [
18]. This means that the multivariable model may also have contributions to the perceived satisfaction of people in university campuses. In addition, the current development of ICT can enable new methods and metrics to assess perceived satisfaction instead of questionnaire methodology. However, results about public attitude towards university campus were limited by the testing method and information that have been published on this matter.
Traditionally, questionnaires provided a common method to evaluate people perception. Evaluation through self-reported scores has several apparent biases from subjective emotion of respondents, real-time mood, problematic questions, and social-role restricted results [
19]. Facial expression represents an emotional response to a stimulus and/or a communicative behavior in a social situation, which can be termed as Duchenne (a felt expression with an emotion cue) and non- Duchenne ways (an unfelt expression with a communicative cue), respectively [
20]. The facial expression of a visitor’s photo at a place provides a novel way to show performative emotional satisfaction in the location. A selfie taken and shared by a person through social media is one way to collect the information of emotional expressions that would like to be exposed to the public. Facial expression scores with a check-in-recorded location enable geographical analysis of posed emotion towards environments in a visited location. Highly popularized social networking service (SNS) results in millions of facial-expression images uploaded to the data-cloud [
21,
22]. Therefore, to collect and analyze facial images from SNS with check-in locations supplies a new approach to assess satisfaction of people with a wide range of geographical locations. Regarding that people expose their selfies with check-in records to show posed facial expression at the location, all variables about the city where visitors is located can be used as the explanatory independent factors in a regression model together to build the relationship with expressional scores. To utilize data from SNS enables the precise evaluation of public attitude towards universities at the large geographical scale. However, to the best of our knowledge, the use of this methodology has not been tested.
In mainland China, 116 universities are classified in the ‘211-Project’. These universities were authorized by Ministry of Education of the People’s Republic of China (MEPRC) and are being distributed in 82 cities from 31 Provinces [
23]. All universities within the 211-Project derive more financial and political support than other universities with an expectation of greater corresponding outcomes of science and technology. Therefore, cities with 211-Project universities are generally promoted by an enhanced scholar population, public services, daily livelihood, and social infrastructures. In this study, campuses of key universities in the 211-Project from mainland China were chosen as the research plots wherein selfies at check-in locations at campuses were collected and analyzed for intended facial expressions. We aimed to assess scores of happy and sad expressions of people in 211-Project universities and map them at the national scale. It was hypothesized that (i) people would pose more positive facial expressions at university campuses in cities with more development in economy and technology, and (ii) city variables about socializing and socio-economy had contrasting contributions to intended positive and negative expressions in university campuses.
4. Discussion
The most significant result in our study is that the happy score in Shandong university (Shandong Province) was highest among universities while geographical distribution also revealed that positive scores tended to be higher in Jinan City (Shandong Province). We surmise that the high happy expression score in a city was the result of the happy expression score in the campus therein. Another example is Changchun City, which obtained a medium-high happy score while the happy scores in the two universities in Jilin Province were medium or high. However, the facial expression score for the city may be null to be indicated by that for the universities therein. For example, overall universities in Beijing City showed a moderate score of positive expressions although some university campuses therein obtained higher happy expression scores (e.g., China University of Political Science and Law). In contrast, the positive expression score in some other universities at Beijing City was lower than the average level (e.g., China Mining University and Beijing Sport University). Changchun, Jinan, and Guangzhou were three cities with high scores for both happy expression and PRI and hence university campuses in these three cities result can be taken to have the highest perceived satisfaction.
Student satisfaction with a university can affect student enrollment and retention. Hajrasouliha [
36] investigated quality scores of university campuses in the United States and the scores were associated with freshman retention and graduation rates. The authors also revealed the geographical distribution of scores in selected campuses across the United States and found higher scores in the northeastern cities. Thus, both results from our study and Hajrasouliha [
36] revealed no response of distribution to a geographical gradient.
Several public facility parameters were tested for a relationship with facial expression scores. The number of regular institutions of higher education within a city was one parameter that had positive contribution to values of both a happy score and PRI. From the SNS platform of SMB we aimed to collect selfies about young people who can mainly come from students or a new teacher enrolled in the university. Undergraduates and most mater candidates look young, but some PhD candidates may look old as they may have spent several years to earn their degree. For students, it was found that faculty, advising staff, and the class itself all had significant impact on their perceived satisfaction in higher learning institutions [
37]. Some lecturers and even associate and full-time professors can also look young if they achieved high scholar scores at young age. Therefore, all young adults who would like to pose their selfies in a campus are likely to feel the emotional cue that originated from campus-life. Our results can be interpreted that young adults as either students or tutors would enjoy the city with more educational institutions where they may show Duchenne expressions as they felt being accompanied by large group of other youths. Other studies also reported that variables about the learning organization could account for the significant satisfaction for both teachers and researchers of higher learning institution [
38]. A concentration of university campuses may result in more socializing opportunities, leading to greater satisfaction with a campus through more socializing of young people in their generation group [
2,
14].
It is surprising that a number of internet accesses had a negative contribution to the happy score. This suggests more internet users in a host-city decreased the ratio of showing happy expression of youths in university campuses. Youths of intense internet users were found to have overconfidence in the web-world, but they were also reported depressive symptomatology, problem behavior, and targeting of traditional bullying in the real world [
39]. Another investigation reported that adolescents as frequent internet users reported depression by isolation from their family members [
40]. In addition, more internet access may result in a user’s habitual internet use and might result in a greater population of “internet addicts”. Internet addiction tended to evoke perceived stress, which our results were consistent with less happy expressions [
41]. People with an internet gaming disorder manifested in above average time spent with this activity were found to have different kinds of unconscious neutral facial expressions, which depressed the expression of a smiling face [
42]. Thus, these studies were all consistent with our results with the higher probability of a reduction with a happy expression in a population with higher ratio of internet users. More direct and explanatory evidence is needed to further verify these results and it is also possible the concentration on an activity itself depresses a happy face.
In our study, the sad expression score was positively correlated with the area of residential lands adjoining a university. These findings concur with those found in England [
43] and South Africa [
44], where residential housing-density was negative to the perceived satisfaction of neighbors. This negative relationship should be more apparent for a residence-surrounded university campus because of more open space in the campus than in resident communities. However, we detected a weak effect of the area of green space in urban parks on sad expression. Urban green space has been shown to alleviate perceived stress [
16]. This may be because people in our study were mainly grouped in the university campus rather than spending time in the green space of parks at the city scale. Or if they did any residual affect was not detected in this study.
We found no environmental variables were associated with our scores of happy, sad, or the PRI. This is because our data showed an obvious ceiling that cities with heavy contamination were unlikely to be included to the database. It is not recommended to install industries with heavy contamination for a city, which has been assigned to develop mainly through intellectual promotion of local universities. A sustainable and healthy community has effective ways to dispose of garbage and sewage, which can increase human disease if not abated [
45,
46]. Likewise, excessive levels of air pollution and particulate matter from industrial dust can reduce human health [
47]. In this study, it is likely that a person would not see these variables and as such no effect on facial images seems reasonable.
This study may have been limited by several aspects. The first may come from the number of users in the selfies. The initially collected selfies were either taken by a single person in a photo or a person cropped from selfies with a group of people. Since individuals can more accurately perceive emotions expressed than in-group members [
48], our facial expressions that were analyzed from selfies should have been controlled by the individual and in-group participants. However, this was not available in this study because many factors failed to be concerned, such as the number of people that were separated from a group, genders of them (this probably matters), failure of selfies for cropping (un-intact image of face, unclear face, deflected faces), group of young and mature adults, etc. Therefore, our results may have suffered some bias from the difference between faces of individual and in-group persons. As we omitted this bias for all check-in locations, it was reasonable to assume the technical error was uniform for all campuses.
Another limit to this study comes from the negative impact from pseudoreplication on our data, which resulted in a pretended independence. According to Waller et al. [
34], pseudoreplication can occur when more than one datum was observed per individual. It can also occur when data points result from the same stimulus. Both situations were also found in ecological studies [
35]. Our data may have suffered a pseudoreplication impact because different types of facial expressions may have been rated from the same subject. More than one facial expression score may have been collected from the same person in a university campus. This would impact the significance of difference of facial expression scores across universities and city variables because some of the replicated observations were not independent. A gross summary suggested that the incidence of pseudoreplication ranged between 12% and 40% in studies on primate communication research [
34]. The incidence in our study was far lower than this range by manual screening of selfies. Therefore, it is reasonable to assume the impact from pseudoreplication on our data points can be negligible due to the review of each person. However, we still suggest future work can employ a process of excluding more than one observation from the same individual or at the same location to eliminate any potential pseudoreplication bias.