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Article

A Demographic Characteristics-Based Study on the Visual Impact Assessment of the External Form of Entrance Pavilions to the Underground Stations of China’s Subway

by
Guanjun Li
,
Chenping Han
*,
Jiamin Shen
,
Xinyu Wang
,
Tao Gu
,
Zhongju Yang
and
Lin Zhang
School of Architecture and Design, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(6), 4030; https://doi.org/10.3390/app13064030
Submission received: 7 February 2023 / Revised: 17 March 2023 / Accepted: 20 March 2023 / Published: 22 March 2023
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Abstract

:
The subway is one of urban residents’ main means of public transportation. The design of entrance pavilions to subway stations has shown a diverse development trend over time. Since most of the subway building space is underground, it is very difficult for subway users or the public to view the structure fully. In this instance, the ground-level entrance pavilion becomes the most eye-catching element in the subway system. From the perspective of the urban spatial environment, the external form of entrance pavilions is one of the components of urban architecture. The design of the external form of entrance pavilions has an important impact on the urban spatial form and affects people’s feelings about the urban space. Therefore, it is essential to study the external form of entrance pavilions. This study investigated 42 subway entrance pavilions in 17 cities in China. The following features were studied: exterior contour, façade permeability, cultural characteristics, decorative materials, and the number of exterior colors. The photostimulation method was adopted to evaluate the influence of the physical features of the external form of the entrance pavilions on respondents’ visual impact assessment. The data analysis showed that respondents with different demographic characteristics provided different visual impact assessments of the external form of the entrance pavilions, and all five physical features influenced their assessment. This study offers a valuable reference for constructing entrance pavilions and urban spaces in Chinese cities in the future.

1. Introduction

1.1. Entrance Pavilions to Subway Stations

As a traffic node in the subway system, the entrance pavilion to an underground station is the space through which passengers enter and exit the subway [1]. It is also a transition space connecting the exterior and interior of the subway station. In addition to serving as a doorway, it also represents an architectural entity. The entrance pavilion also protects passengers from external natural conditions, acting as a protective shield against temperature changes and strong winds [2]. Entrance pavilions are mostly arranged in a scattered form. There are usually two to four entrance pavilions for a subway station to facilitate the evacuation of passengers. There is no uniform regulation on what to name the building above the ground entrance and exit of a subway station in China. It can be called an entrance pavilion [3,4] or a subway ground entrance–exit [5,6]. This study adopted the term “entrance pavilion” to precisely define the research object. There are several types, including independent entrance pavilions, entrance pavilions combined with other buildings, etc. Independent entrance pavilions can be divided into two types: fully closed and semiclosed (as shown in Figure 1). The former has a roof and a closed maintenance structure [7,8]. The research object in this study was the fully closed independent entrance pavilion, which is widely designed and constructed in China (as shown in Figure 2).
Since the first subway in China was constructed in Beijing in 1965, more Chinese cities have constructed rail transit and expanded the subway network to reduce the pressure on urban traffic. Consequently, the number of entrance pavilions has also increased [9]. Currently, subway development is the fastest in China compared to other nations. China has been ranked first worldwide for subway mileage due to its large-scale construction [10]. As of April 2018, the total length of operating subways was 4520.4 km, covering 30 cities [11]. China is expected to have 289 subway lines by 2050, with a total length of 11,740 km [12]. In the urban development of China, the scale of subway construction will increase, especially in megacities with large populations and traffic volume, and the number of entrance pavilions to subway stations will grow accordingly.
The subway is not just an important means to reduce urban traffic problems. Many underground stations are close to city centers, and many entrance pavilions are located along both sides of city streets. As a part of urban architecture, these pavilions have a certain impact on the urban space form. He and Kim [13] stated that entrance pavilions are public facilities that also beautify the city. Entrance pavilions represent not only the image of a city but also the characteristics of an era [2]. Kim, Kim [14] found that entrance pavilions influence the street landscape and the city’s charm. van der Hoeven and Juchnevic [15] studied selected cases of metro station design, which included the Canary Wharf Underground Station designed by Foster and Partners. They argued that an entrance pavilion indicates the station’s location in the transportation network and its position in the urban environment. Torigoe [16] proposed that the design and aesthetic features of entrance pavilions should harmonize with the urban environment. Zhang and Zhu [17] outlined design reforms for entrance pavilions to create more engaging street landscapes. Moreover, the design and planning of entrance pavilions should meet traffic needs and exhibit beauty to attract crowds [18]. Academic attempts have also been made in the following aspects: evaluating entrance pavilions after use [19], interior space environmental design [20,21], and ground landscape design [22,23]. With the development of urbanization in China, the scale and speed of subway construction will increase rapidly. Therefore, the influence of entrance pavilions on the urban landscape is highlighted. The external form of entrance pavilions affects people’s feelings about the urban space. This study explores people’s preferences with regard to the external space of subway station entrance pavilions and evaluates the effectiveness of the current design in order to improve the design quality.

1.2. Visual Impact Assessment

Hernández, García [24] suggested that visual impact assessment can be widely used to evaluate the influence of different elements on people. By reviewing the existing relevant research, it was found that visual impact assessment has been used to study landscape design and construction [25], agroindustrial buildings [26], wind power plants [27], photovoltaic power plants [28], and high-voltage power lines [29,30]. Visual impact assessment is also used to evaluate the impact of the different physical attributes of buildings on people’s impressions [31].
A series of studies have been carried out to evaluate the influence of the different physical attributes of buildings on people’s visual preferences by using visual impact assessment. Askari [32] studied people’s visual preference for buildings in Kuala Lumpur and determined the physical features that affected building façades with this method. Yuan, Luo [33], Zhang, Yuan [34], Bu, Chen [35], and Huang, Han [36] used this method to study people’s views of buildings and revealed that there was a relationship between visual impact assessment and the external physical features of buildings. Ng, Chau [37] employed images to assess the complexity of the built environment and reported a link between urban design and the quantification of visual streetscape features. However, little research has been carried out on evaluating the external form of subway station entrance pavilions using visual impact assessment.
There are limitations to using photo displays for visual impact assessment by the public [38,39,40]; however, the use of photographs rather than actual scenes remains the most prevalent and reliable way to evaluate aesthetic quality [38,41]. Many scholars have made remarkable progress in this field. Stamps III [42] reported a highly positive correlation between the information people obtained from static color pictures and the information obtained from actual views. Abello and Bernáldez [43], Kaplan and Kaplan [44], Hami, Moula [45], Ng, Chau [37], and Ernawati [46] used photos instead of actual landscapes to study respondents’ visual impact assessments.

1.3. Physical Features

The external form of a building comprises various physical characteristics, which directly impact the visual impact assessment. Previous studies have shown that the color, materials, and shape of a building affect people’s assessment of its exterior form [47].
Many scholars have studied the influence of colors on people’s visual impact assessment, as colors are known to have a certain influence [48,49]. Utaberta, Jalali [50] found that colors on the building façade have an influence on people’s visual impact assessment. A variety of façade colors is a crucial element in architectural design; it is also a prominent feature that the public could consider important.
Building materials represent another important physical feature [51]. Ghomeishi [52] and Ghomeshi, Nikpour [53] discovered that changing the building materials influenced people’s visual impact assessment. Fawcett, Ellingham [54] argued that building materials serve as the physical feature by which buildings can be distinguished. Jennath and Nidhish [47] explored the visual impact assessment of a library by some of the students of Mahatma Gandhi University and discovered that the building materials had an influence on the student’s assessment. Therefore, the type of building material is an important attribute that affects people’s visual impact assessment.
According to studies, people’s preference for a building is related to the richness of its contour [55,56]. Chang and Park [57] not only described a method for defining the shape of the building facade but also clarified the geometric differences between building facades. Stamps III [58] pointed out that the contour complexity of residential buildings can influence people’s visual impact assessment. A building’s scale and proportion are influential physical attributes that affect residents’ assessment of the external space of the building [59]. According to Baper and Hassan (2012), the scale and proportion of a building are the determining factors in its visual appeal. Carmelino and Hanazato [60] found that the volume of buildings on the street affects people’s visual impact assessment. Hence, the contour and form of a building significantly influence people’s visual impact assessment of its external space.
Decorations and details on the exterior of buildings are important physical characteristics when evaluating structures [61]. Hossein Askari, Dola [62] and Baper and Hassan (2012) noted that decorations and details are key physical features that affect people’s visual perception of buildings. In addition, the exterior architectural decoration can also reflect the history of a city [61,62]. Therefore, the exterior details and decoration of buildings have a strong influence on people’s visual impact assessment.
According to earlier research, permeability is recognized as a factor influencing people’s visual impact assessment of the exterior shape of buildings [63]. Alkhresheh [64] found that façades with varying permeability have a certain influence on how people assess the visual impact of residential buildings; compared with men, women were found to prefer façades with higher permeability. Pan, Yuan [65] concluded that permeability influences people’s preference for the appearance of a building façade. Since it can positively influence visual impact assessment to some degree, it can be employed not only as a criterion for aesthetic evaluation but also as a design strategy for architecture. Kearney and Winterbottom [66] conducted an experiment on building permeability and discovered that respondents usually expected high permeability in the building facade in order to see the scenery outside.
Based on the related research, in this study, we selected five physical features as research dimensions to explore the external form of subway station entrance pavilions: the number of exterior colors, the decorative material, the exterior contour of the pavilion, the cultural characteristics, and the permeability of the façade.

1.4. Demographic Characteristics

Demographic characteristics influence people’s visual impact assessment [67,68,69,70]. In their studies, Lindemann-Matthies, Briegel [71], Yamashita [72], and Howley [73] all reported that age affected respondents’ visual impact assessment. Balling and Falk [74], Lyons [75], and Van den Berg and Koole [76] stated that people’s visual impact assessment of the landscape changes based on age. Bu, Chen [35] found that men had higher visual impact assessments than women. Yu [77] reported that education level is a key factor affecting people’s landscape preferences. Sevenant and Antrop [78] verified that there was a negative correlation between education and visual impact assessment. Weinberger, Garside [79] found that expertise in architecture and design influenced people’s responses and preferences regarding the built environment. According to a study by Vouligny, Domon [80], professional knowledge influences respondents’ visual impact assessment. Zhen, Ma [81] found that occupation and design experience influenced respondents’ visual impact assessment of the same building exterior. Based on previous studies, demographic characteristics, education level [82], and gender [83,84] strongly influence people’s visual impact assessment. Consequently, the impact of demographic variables on visual impact assessment cannot be disregarded.
Therefore, in this study, we selected four demographic characteristics as the research dimensions: age, gender, education level, and design experience.
This study addressed the following questions:
  • What physical features influence people’s assessment of the visual impact of subway station entrance pavilions?;
  • Would respondents with different demographic characteristics assess the visual impact of subway station entrance pavilions differently?;
  • If the answer to the second question is yes, what physical features lead to this difference?

2. Research Method

2.1. Research Plan

The investigation sites of this study were set in 17 cities across China, including Beijing, Shanghai, and Suzhou (Table 1). The 17 cities cover 23 provinces, 5 autonomous regions, and 4 municipalities directly under the Central Government. Their radiation coverage basically includes the whole country and also includes megacities, large cities, and large and medium-sized cities. The study was highly representative of the numerous subway projects built in these areas. In total, 42 entrance pavilions of different types were investigated, and their images were analyzed. Five physical features were selected as the research objects: the exterior contour of subway station entrance pavilions, the permeability of the façade, the cultural characteristics, the decorative materials, and the number of exterior colors (Table 2).

2.1.1. Exterior Contour

The roofs of buildings have always been a central concern in research on the visual impact assessment of structures. Given that an entrance pavilion is comparatively lower than other types of buildings, the roof and the façade were considered together. In this case, the exterior contour was obtained by the parallel projection of the entrance pavilion from the side façade then divided into four types: orthogonal polylines, orthogonal and oblique polylines, orthogonal polylines and curves, and orthogonal and oblique polylines and curves.

2.1.2. Façade Permeability

The permeability of an entrance pavilion façade was expressed by the ratio of the window area to the wall area (Figure 3). The side façade of the entrance pavilion was taken to calculate the ratio. The relative area of the window in the side façade (denoted as a) was calculated by the equation a = S1/S2, where S1 and S2 are the window and wall areas of the side façade, respectively. After calculating the ratio of each of the 42 entrance pavilions, a mean value of 0.427 was obtained. Accordingly, for a given pavilion, a ratio greater than 0.427 indicated high façade permeability, and a ratio lower than 0.427 indicated low permeability.

2.1.3. Cultural Characteristics

Many of the existing entrance pavilions in China have regional cultural patterns on their facades. Accordingly, they were also studied as cultural characteristics. The entrance pavilions were divided into two categories: with or without cultural characteristics.

2.1.4. Decorative Materials

Due to the small number of materials used in the architectural design of entrance pavilions, the decorative materials of the entrance pavilions in this study were investigated statistically and then classified into three types: metal and glass, stone and glass, and metal, stone, and glass.

2.1.5. Number of Exterior Colors

The entrance pavilions were divided into 4 groups according to the number of exterior colors: single color: 1 color was used; two-colored: 2 colors were used; relatively multicolored: 3 colors were used; and multicolor: four or more colors were used. Colored glass was also considered part of the exterior colors; clear glass was not included.
After the 5 physical features were determined and quantified, photos of the 42 entrance pavilions were sent to 6 architecture experts (professors, designers, and critics). They were asked to screen out 9 photos that best represented the 5 variables. As the photos were taken with a camera, there were inevitable differences in brightness. Moreover, there were issues such as varying viewing heights and passersby in the photographs. To avoid the influence of these differences, the 9 selected photographs were modeled by Google SketchUp 2019 (created by Google Inc., located in Mountain View, CA, USA) and rendered by Vray 4.2 (created by Chaos Group located in Sofia, Bulgaria) [81]. According to the “Report on the Nutrition and Chronic Diseases Status of Chinese Residents” by the State Council of China in 2017, the average height of adults aged 18 and above is 167.1 cm. Thus, the viewing height for the model rendering was set at 167 cm from the ground, and the climatic background of the images was unified to be sunny and cloudless. Finally, 9 renderings were produced (see Figure 4).

2.2. Demographic Characteristics of Respondents

The questionnaire survey to select the Chinese citizens as respondents was conducted over the internet. Since the end of the 20th century, experimental research on visual impact assessment has increasingly utilized the internet [85,86]. Bishop [86] observed that the internet provides a convenient medium for research and experimentation on visual preferences. Internet research is more convenient for participants than on-site research, allowing researchers to employ a sample of respondents with a broad range of demographic characteristics [85,87]. Lindhjem and Navrud [88] compared internet-based surveys with face-to-face interviews and found that the differences between the two did not alter the findings of identical experiments conducted independently using each strategy. In this study, we selected 4 demographic characteristics as variables: gender, age, education level, and design experience. As shown in Table 3, these demographic characteristics were classified according to previous studies [35,65].

2.3. Questionnaire Survey

The questionnaire for the experiment was powered by www.wjx.cn (accessed on 28 September 2021). The initial questionnaire included a brief introduction to the experiment, determined the willingness of respondents to participate, and collected demographic characteristics. With the setting that each user could only answer once, the first questionnaire was completed. The production process of the second questionnaire included uploading the 9 pictures on the wjx platform for editing. After editing, the questionnaire recovery options were set, with the number of answers set to only one for each user, along with submission control to allow the user to preview the answer before submitting it. The settings were confirmed, completing the production of the second questionnaire. The questionnaire was distributed via 20 internet platforms, including WeChat, Baidu, and Zhihu. The respondents clicked a link generated by the wjx platform to access the questionnaire. The 2 questionnaire links were open from 8:00 on 3 October to 24:00 on 13 October 2021, and 8:00 on 15 October to 24:00 on 30 October 2021. The second questionnaire contained all 9 photos with high resolution and quality, generated in random order, and respondents were asked to score them. During this process, respondents could flip back and forth at will and were free to revise their score for any rendering before submitting it. A Likert scale was used, with scores ranging from 1–5, with 1 being “strongly dislike” and 5 being “strongly like” (Table 4). At the end of the questionnaire, basic demographic information about the respondents was obtained.
Before the formal questionnaire, a presurvey was conducted from 28 September to 1 October 2021. A total of 35 participants participated in the presurvey. In addition, 7 architectural experts, including 2 professors of statistics, 3 professors of architecture, and 2 architects from the China University of Mining and Technology, were asked to complete the questionnaire. The presurvey was aimed at testing the questionnaire, verifying the clarity and relevance of the questions, and determining whether the questionnaire needed to be extended. No flaws in the questionnaire were found in the presurvey.
The formal survey was conducted from 3–30 October 2021, and the 2 questionnaires were administered. The first questionnaire was carried out from 3–13 October. The link was distributed from 8:00 on 3 October to 24:00 on 13 October 2021, which mainly determined the willingness of respondents to participate and collected their demographic information. Statistical analysis found that 830 respondents were willing to participate. After the collected data from the first questionnaire were sorted, the second questionnaire was conducted from 15–30 October 2021. According to the distribution of demographic characteristics from China’s sixth population census, 352 respondents who were willing to participate in the second questionnaire were selected by quota sampling method, and the link was distributed from 8:00 on 15 October to 24:00 on 30 October 2021. A total of 352 questionnaires were distributed, and 349 responses were collected. There were 320 valid questionnaires, and the validity rate was 90.81%. The distribution of demographic characteristics of respondents is shown in Table 5. Statistical analysis found that the demographic distribution was consistent with China’s sixth population census, indicating that the survey was representative.
The collected data were analyzed by SPSS 22.0 (created by IBM located in Armonk, NY, USA) to explore the impact of different demographic characteristics on the visual impact assessment of the 5 physical features (exterior contour of subway station entrance pavilions, the permeability of the façade, cultural characteristics, decorative material, and the number of exterior colors). Based on this information, multivariable linear regression was performed.

3. Results

3.1. Overall Assessment of Photos

SPSS 22.0 was used to test the intergroup reliability of the nine photos. The result was 0.763, indicating a relatively high level of reliability. Consequently, the questionnaire survey was reliable, and the data were used to conduct a further detailed analysis.
The average score for each photo given by each respondent was expressed as S. The highest average score among the nine photos was 4.15, and the lowest was 2.07 (scoring range: 1–5). The average score for all photos was 4.13. Photo H received the highest average score, and photo G had the lowest average score.
In the experiments that used photographs instead of actual buildings, the average score for photographs can be considered valid data with regard to respondents’ visual impact assessment [56].

3.2. Correlation between Physical Features and Visual Impact Assessment

A regression analysis was conducted to examine the correlation between the five physical features and the visual impact assessment. The exterior contour of the entrance pavilion (M), façade permeability (D), cultural characteristics (C), decorative materials (E), and the number of exterior colors (P) were taken as factors, and the average score (S) of each photo was taken as the dependent variable. The analysis results are shown in Table 6.
Based on the regression analysis, all five physical features influenced the average score for each photo. The statistical results indicate significant differences among all five factors: M: F = −2.305, p = 0.030; D: F = −1.828, p = 0.049; C: F = 3.885, p = 0.025; E: F = −6.330, p = 0.002; and p: F = 4.384, p = 0.022.
Hence, it was concluded that when the average score was used as the dependent variable, there were significant differences among all five physical features. These physical features affected the average scores of the photos.

3.3. Correlation between Demographic Characteristics and Visual Impact Assessment

A one-way analysis of variance was carried out to study the correlation between the demographic characteristics and the visual impact assessment. The results show that there was a significant difference between the demographic characteristics and the average scores given by respondents: gender: F = 7.530, p = 0.001; age: F = 2.265, p = 0.031; education level: F = 3.327, p = 0.042; and design experience: F = 6.323, p = 0.005.
Kendall rank correlation analysis was used to examine the relationship between the demographic factors and the visual impact assessment, and a significant correlation between average score and demographic characteristics was found. Specifically, the correlation between average score and gender, age, education level, and design experience was highly significant. The results are shown in Table 7.
The collected data were further analyzed using a stepwise linear multivariation regression model. In this model, the independent variables were gender, age, education level, and design experience, and the dependent variable was the average score of the photos (S) (Table 8). Based on the results, all four independent variables significantly impacted the average score of the photos.
Furthermore, we explored whether there were any interactions among the demographic characteristics. A collinear analysis of independent variables was carried out based on the result of the multiple linear regression model. The values were as follows: gender: tolerance = 0.958, VIF = 1.044; age: tolerance = 0.998, VIF = 1.582; education level: tolerance = 0.463, VIF = 1.254; and design experience = 0.958, VIF = 1.044. When the VIF of a model is greater than 10 or the tolerance is less than 0.2, the model has collinearity problems [25,89]. The VIF of the independent variables calculated by SPSS was less than 10, their tolerance was greater than 0.2, and the residuals were normally distributed. Therefore, it could be concluded that the model had no collinearity problem.

3.4. Demographic Differences among Respondents and Physical Features of Photos

The average score for each picture given by the respondents with different demographic characteristics was set as a dependent variable, and the five physical features (M, D, C, E, and P) were set as the independent variables. Multivariant linear stepwise regression analysis exhibited that age, gender, education level, and design experience resulted in different significant predictors (Table 9).
For the male respondents, the exterior contour of the entrance pavilion (M), decorative materials (E), and the number of exterior colors (P) were reliable predictors. For the female respondents, the exterior contour of the entrance pavilion (M), façade permeability (D), and the number of exterior colors (P) were reliable predictors.
For the respondents aged 18–34 years, the exterior contour of the entrance pavilion (M), façade permeability (D), cultural characteristics (C), and decorative materials (E) were reliable predictors. For the respondents aged 35–59 years, the exterior contour of the entrance pavilion (M) and decorative materials (E) were reliable predictors. For the respondents aged 60 years and above, the exterior contour of the entrance pavilion (M), façade permeability (D), cultural characteristics (C), and the number of exterior colors (P) were reliable predictors.
For the respondents with higher education, cultural characteristics (C), decorative materials (E), and the number of exterior colors (P) were reliable predictors. For the respondents without a higher education, the exterior contour of the entrance pavilion (M), façade permeability (D), and decorative materials (E) were reliable predictors.
For the respondents with design experience, façade permeability (D), cultural characteristics (C), and the number of exterior colors (P) were reliable predictors. For the respondents no design experience, façade permeability (D) and decorative materials (E) were reliable predictors.
An analysis of collinearity between the models was conducted using K-S. According to the calculation results, the residual errors were normally distributed, indicating that the models had no collinearity.

4. Discussion

4.1. Physical Features and Visual Impact Assessment of Subway Station Entrance Pavilions

4.1.1. Exterior Contour

Bar and Neta (2006) and Vartanian, Navarrete [90] showed that curve contour could activate a certain brain area leading to emotional salience. The contour of buildings has some impact on people’s visual preference, which is consistent with the findings of Stamps [58]. In this study, the combination of orthogonal polylines and curves in the exterior contour scored higher than other contours. This might be because the sharpness of the other three exterior contours might have exerted a certain pressure on the respondents. This is consistent with the findings of Corradi, Belman [91] regarding the public’s distaste for certain shapes and forms.

4.1.2. Façade Permeability

van der Hoeven and Juchnevic (2016) recommended the use of transparent materials on entrance pavilions to bring in daylight, thus emphasizing the transition between the aboveground and underground space. Şen, Özdemir [92] suggested that buildings with high permeability are more popular than those with low permeability as they provide people with a sense of relaxation and ease because public buildings are not expected to be tightly closed up. In this study, entrance pavilions with high façade permeability scored higher than those with low permeability, perhaps as a result of people’s expectation that public buildings should have high visual permeability, similar to the findings of Ozdemir [63].

4.1.3. Cultural Characteristics

The visual impact assessment of the external form of the entrance pavilions was also influenced by cultural characteristics to some extent. Entrance pavilions with cultural characteristics scored higher than those without. This is probably because entrance pavilions are usually used to characterize urban image, culture, and history while fulfilling their designed functions [61,62]. The cultural characteristics of entrance pavilions can stimulate excitement in people when they recognize the regional culture.

4.1.4. Decorative Materials

Ghomeishi (2021) noted that the different building materials will influence respondents’ visual impact assessment. In this study, the combination of metal and glass had the highest score among all decorative materials. Kim, Kim [14] drew a similar conclusion in their study, reporting that metal (aluminum–plastic composite panel and steel) and glass were highly popular decorative materials among the public, as they enhance the city’s charm.

4.1.5. Number of Exterior Colors

It has been shown that architectural design with various colors can win public support more easily [93], which is consistent with the conclusion obtained in this study. The entrance pavilion with rich exterior colors scored higher. This might be attributed to the fact that multiple colors can give people a lively, happy, exciting feeling. This finding is inconsistent with the study of Gou and Wang [94], who reported that residents showed no clear inclination toward colors. However, other studies had the opposite conclusion, showing that people did have different preferences for colors [95,96]. Moreover, young people will prefer achromatic colors compared to older people [97].

4.2. Demographic Characteristics and Visual Impact Assessment

4.2.1. Age

This study demonstrates that, as the age of respondents increased, so did the average scores of the photos. This could be because, during the childhood or youth of senior respondents, transportation buildings were relatively simple and backward. When they saw the photos of the entrance pavilions, they may have concluded that these were modern or advanced and were inclined to accept them. Younger respondents might have had higher expectations for the external form of entrance pavilions. This also justifies the finding that young respondents generally had a lower visual impact assessment than senior participants. The findings obtained in this study are contrary to those in the study by [98]. Meanwhile, Howley, Donoghue [99] observed that people in different age groups gave different visual impact assessments, and this difference was influenced by various factors, such as the living environment and the experience of respondents. Wang and Zhao [70] studied the visual impact assessments of people in different age groups; their results were similar to those of Zube, Pitt [98] but are inconsistent with the conclusion of this study. Therefore, when exploring the visual impact assessments of different age groups, researchers should fully consider differences in the culture and life experience of the respondents instead of jumping to conclusions.

4.2.2. Gender

Gender also plays a role in the visual impact assessment of the external form of entrance pavilions [100]. Zhang, Wen [101] observed that gender differences led to different visual impact assessments, which is similar to the findings of this study. Moreover, this study revealed that women had higher visual impact assessments than men. The reason for this difference might be that women can be seen as more emotional and romantic; thus, they may prefer entrance pavilions with a soft external space, while men can be more rational and critical [71,83], which is contrary to the conclusion drawn by Yao, Zhu [102].

4.2.3. Education Level

Bjerke, Østdahl, Thrane, et al. (2006) suggested that education level influences people’s visual impact assessment of landscapes, and the results of this study are consistent with those findings. In this study, respondents with relatively low education levels gave higher scores. The main reason for this might be that people with a higher education usually have some understanding of engineering and architectural beauty. Accordingly, when evaluating entrance pavilions, they would be stricter and give a lower visual impact assessment. Keane [103] noted that education level had no influence on people’s visual impact assessment, which is inconsistent with the conclusion of this study.

4.2.4. Design Experience

People with some design experience gave lower visual impact assessments than those without design experience. The main reason for this is that people with design experience can convert the physical characteristics of objects into design language (shapes, lines, and color) and assess the objects with multiple indexes, such as diversity, harmony, and contrast ratio. In other words, they can quantify the aesthetic value of objects [104]. This is similar to a study by Akalin, Yildirim [105] but contrary to the conclusion drawn by PalumboPalumbo, Rampone [106], who reported that professional knowledge did not have an influence on people’s visual impact assessment.
Although no consensus has been reached on the influence of demographic characteristics on people’s visual impact assessment, this study reveals that researchers should focus on the research object, the characteristics of the times, and respondents’ life experiences when exploring visual impact assessment.

4.3. Demographic Characteristics and Physical Features

4.3.1. Gender and Physical Features

Female respondents rated the combination of orthogonal polylines and curves significantly higher than the other contours. This might be because curves can be perceived as visually similar to the female body and associated with gentleness and delicacy. Accordingly, female respondents preferred buildings featuring curves. In addition, female respondents gave higher scores for the photos of entrance pavilions with high permeability and rich exterior colors. This might be because women can be seen as more sentimental and romantic. In contrast, male respondents chiefly considered three physical features: the exterior contour of the entrance pavilion (M), decorative materials (E), and the number of exterior colors (P). In terms of exterior contour, male respondents favored the combination of orthogonal and oblique polylines, the combination of metal and glass decorative materials, and a single exterior color. This might be because men may perceive the combination of bent metal and glass and monochrome as rugged, congruent with a certain masculine personality.

4.3.2. Age and Physical Features

Respondents who were 18–34 years old gave higher visual impact assessments for entrance pavilions that had an exterior contour with a combination of orthogonal polylines, decorative materials made of metal and glass, high permeability, and no cultural characteristics. The main reason might be that people in this age group are highly receptive to newly emerging objects, while they lack contact with local history and culture. Respondents aged 35–59 attached more importance to the exterior contour (M) and decorative materials (E). Comparing the average scores for each material revealed that the combination of stone and glass scored the highest among this age group. This might be because these individuals are in the prime of their profession. Respondents aged 60 and above preferred entrance pavilions with orthogonal contours, low permeability, diverse cultural characteristics, and multiple exterior colors. The reason for this could be that they recalled features from houses they once inhabited when looking at the entrance pavilions; seniors may also have a sense of connection to buildings with historical, regional, and cultural characteristics. As is evident from the above analysis, younger groups preferred avant-garde design mainly because their aesthetic judgment has been greatly improved with the rapid development of aesthetic education in China. This gives them more radical aesthetic judgment than the seniors. In contrast, the seniors were more conservative in their aesthetic judgment due to the influence of economic conditions when they were young.

4.3.3. Education Level and Physical Features

Respondents who received a higher education considered entrance pavilions with a combination of metal and glass, cultural characteristics, and multiple colors. This is because they may have learned about certain aspects of architecture and engineering during their education, or they have experience appreciating many buildings and landscapes. Respondents who did not receive a higher education favored entrance pavilions that had an exterior contour with orthogonal and polylines and curves, stone, metal, and glass as decorative materials, and high permeability. In other words, they rated such types of pavilions highly. This is because those three physical features may have a direct influence on the overall appearance of buildings. People who have not received a higher education may select physical features that can be easily captured visually.

4.3.4. Design Experience and Physical Features

Respondents with design experience selected entrance pavilions with cultural characteristics, high permeability, and a bi-color exterior. This is because they could weigh the merits and demerits of the design elements and fully consider the design based on past experience. In other words, they had clear cognition and measurement standards for the buildings. In contrast, respondents without any design experience preferred entrance pavilions with high permeability and stone and glass decoration, as they associate these physical features with advanced technology.

5. Conclusions

It is of great significance to determine the design elements for the external form of entrance pavilions [107]. Buildings that suit the needs of the general public are more likely to garner public favor [108]. In contrast, structures that disregard people’s psychological responses may ultimately fail [109]. This study explored the relationship between the physical features of the external form of entrance pavilions and visual impact assessment. The results indicate that the exterior contour, façade permeability, cultural characteristics, decorative materials, and the number of exterior colors influenced people’s visual impact assessment. These experimental and analytical findings can serve as a reference for architects and planners in their actual design practices. When designing entrance pavilions, architects can focus on five physical features: the exterior contour, façade permeability, cultural characteristics, decorative materials, and the number of exterior colors. Entrance pavilions designed with a combination of orthogonal polylines and curves in the exterior contour, four or more exterior colors, high permeability, cultural characteristics, and a combination of metal and glass as decorative materials may be more widely accepted. For the design of entrance pavilions in different cities, the demographic characteristics of the city should also be considered, and more attention should be paid to the preferences of groups with different demographic characteristics.

Author Contributions

Conceptualization, G.L.; Methodology, X.W.; Software, J.S.; Validation, G.L. and J.S.; Formal analysis, X.W.; Investigation, T.G.; Resources, Z.Y.; Data curation, L.Z.; Writing—original draft, G.L.; Writing—review & editing, G.L.; Visualization, X.W.; Supervision, J.S.; Project administration, C.H.; Funding acquisition, C.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study did not require ethical approval.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All relevant data are within the paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Fully closed and semiclosed entrance pavilions.
Figure 1. Fully closed and semiclosed entrance pavilions.
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Figure 2. Schematic diagram of research object.
Figure 2. Schematic diagram of research object.
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Figure 3. Schematic diagram of exterior contour and relative area of façade windows.
Figure 3. Schematic diagram of exterior contour and relative area of façade windows.
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Figure 4. Nine renderings.
Figure 4. Nine renderings.
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Table
Table 1. Investigation sites of this study.
Table 1. Investigation sites of this study.
CityNumber of InvestigationsCityNumber of Investigations
Shanghai4Wuhan3
Beijing5Tianjin3
Guangzhou2Xi’an3
Shenzhen2Suzhou2
Chengdu3Changsha1
Hangzhou2Dalian1
Chongqing1Xiamen3
Nanning3Jinan1
Taiyuan3
Table
Table 2. Physical features of entrance pavilions.
Table 2. Physical features of entrance pavilions.
Physical FeaturesContents
Exterior contourOrthogonal polylines, orthogonal and oblique polylines, orthogonal polylines and curves, or orthogonal and oblique polylines and curves
Façade permeabilityHigh or low
Cultural characteristicsWith or without
Decorative materialsMetal and glass, stone and glass, or metal, stone, and glass
Number of exterior colorsSingle color, two-colored, relatively multicolored, or multicolored
Table
Table 3. Classification of demographic characteristics of surveyed respondents.
Table 3. Classification of demographic characteristics of surveyed respondents.
Demographic CharacteristicVariable
GenderMale
Female
Age (years)18–34
35–59
≥60
Education levelReceived higher education
Did not receive higher education
Design experienceWith
Without
Table
Table 4. Description of scores.
Table 4. Description of scores.
ScoreDescription
1Strongly dislike
2Mildly dislike
3Neutral
4Mildly like
5Strongly like
Table
Table 5. Statistics of demographic characteristics of survey respondents.
Table 5. Statistics of demographic characteristics of survey respondents.
Demographic CharacteristicVariablesNumber of RespondentsProportion (%)
Gendermale18056.25
female14043.75
Age (years)18–349830.62
35–599329.06
≥6012940.31
Education levelReceived higher education13742.81
Did not receive higher education18357.19
Design experienceWith12639.34
Without19460.62
Table
Table 6. Regression analysis.
Table 6. Regression analysis.
Unstandardized CoefficientsStandardized CoefficientstSig.Collinearity Statistics
BStd. ErrorBetaToleranceVIF
(Constant)1.9530.780 3.5040.057
M−0.0430.140−0.107−2.3050.0300.2224.513
D−0.1800.217−0.287−1.8280.0490.2254.447
C0.4290.1600.6053.8850.0250.5341.873
E−0.3760.161−1.113−6.3300.0020.1198.429
P0.3390.0771.3254.3840.0220.2963.375
Adjusted R2 = 0.431, n = 320.
Table
Table 7. Kendall rank correlation analysis.
Table 7. Kendall rank correlation analysis.
SAED
Kendall’s tau_bSCorrelation Coefficient
Sig. (two-tailed)
N
ACorrelation Coefficient−0.128
Sig. (two-tailed)0.370
N45
ECorrelation Coefficient−0.0060.163
Sig. (two-tailed)0.9680.254
N4545
DCorrelation Coefficient−0.109−0.145−0.012
Sig. (two-tailed)0.4680.3100.936
N454545
ScoreCorrelation Coefficient−0.289 *−0.493 **−0.179 *−0.230 *
Sig. (two-tailed)0.0230.0010.0500.037
N45454545
*, ** Correlation is significant at the 0.05 and 0.01 level (two-tailed), respectively.
Table
Table 8. Stepwise linear multivariation regression analysis.
Table 8. Stepwise linear multivariation regression analysis.
ModelUnstandardized CoefficientsStandardized CoefficientstSig.Collinearity Statistics
BStd. ErrorBetaToleranceVIF
4(Constant)2.6680.343 7.7680
S−0.6480.146−0.520−4.43900.9581.044
A0.3930.136−0.520−1.5200.0120.9981.582
E0.3780.186−0.5200.9200.0430.4631.254
D0.4060.1450.3292.8070.0080.9581.044
Adjusted R2 = 0.521, n = 320.
Table
Table 9. Linear regression analysis of physical features of the photos for respondents with different demographic characteristics.
Table 9. Linear regression analysis of physical features of the photos for respondents with different demographic characteristics.
Dependent Variable Unstandardized CoefficientsStandardized CoefficientstSig.Collinearity Statistics
BStd. ErrorBeta ToleranceVIF
Men
(adjusted
R2 = 0.509,
n = 180)		(Constant)1.1881.702 1.6980.235
M−0.0640.306−0.133−2.2110.0470.2224.513
E0.3950.3520.9682.1210.0440.1198.429
P−0.1040.169−0.337−4.6160.0110.2963.375
Women
(adjusted
R2 = 0.613,
n = 140)		(Constant)5.1673.350 2.5420.121
M−0.3400.602−0.556−4.5660.0110.2224.513
D−0.9930.934−1.037−5.0640.0060.2254.447
P0.1150.3320.2942.3470.0420.2963.375
18–34 years old
(adjusted
R2 = 0.581,
n = 98)		(Constant)1.9741.879 3.0500.071
M0.2270.3380.3562.7720.0500.2224.513
D0.1460.5240.1465.0780.0090.2254.447
C−0.3310.386−0.294−3.8600.0390.5341.873
E−0.0460.389−0.085−4.1170.0140.1198.429
35–59 years old
(adjusted
R2 = 0.711,
n = 93)		(Constant)1.9501.771 2.6010.151
M−0.1330.318−0.280−5.4190.0030.2224.513
E−0.4500.366−1.120−3.2290.0270.1198.429
60 years old or older
(adjusted
R2 = 0.539,
n = 129)		(Constant)2.3570.381 6.1920.008
M−0.1880.068−0.423−7.7500.0010.2224.513
D−0.5710.106−0.820−5.3760.0130.2254.447
C0.6180.0780.7839.9070.0010.5341.873
P0.3610.0381.2722.5680.0720.2963.375
Higher education
(adjusted
R2 = 0.714,
n = 137)		(Constant)2.7810.653 4.2620.024
C0.6680.1340.7424.9860.0160.5341.873
E−0.6800.135−1.590−3.0420.0500.1198.429
P0.3930.0651.2126.0720.0090.2963.375
No higher education
(adjusted
R2 = 0.631,
n = 183)		(Constant)1.2281.160 2.2590.067
M0.1380.2080.3253.6620.0450.2224.513
D−0.0160.323−0.024−6.4540.0060.2254.447
E−0.1090.240−0.305−5.5370.0250.1198.429
With design experience
(adjusted
R2 = 0.58,
n = 126)		(Constant)5.2311.129 4.6340.019
D−0.9880.315−1.061−4.1400.0420.2254.447
C−0.1900.232−0.180−4.1190.0430.5341.873
P0.2760.1120.7265.7660.0190.2963.375
Without design experience
(adjusted
R2 = 0.629,
n = 194)		(Constant)1.4711.242 3.1840.022
D−0.0060.346−0.007−6.0160.0080.2254.447
E−0.4680.257−1.122−3.8220.0360.1198.429
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Li, G.; Han, C.; Shen, J.; Wang, X.; Gu, T.; Yang, Z.; Zhang, L. A Demographic Characteristics-Based Study on the Visual Impact Assessment of the External Form of Entrance Pavilions to the Underground Stations of China’s Subway. Appl. Sci. 2023, 13, 4030. https://doi.org/10.3390/app13064030

AMA Style

Li G, Han C, Shen J, Wang X, Gu T, Yang Z, Zhang L. A Demographic Characteristics-Based Study on the Visual Impact Assessment of the External Form of Entrance Pavilions to the Underground Stations of China’s Subway. Applied Sciences. 2023; 13(6):4030. https://doi.org/10.3390/app13064030

Chicago/Turabian Style

Li, Guanjun, Chenping Han, Jiamin Shen, Xinyu Wang, Tao Gu, Zhongju Yang, and Lin Zhang. 2023. "A Demographic Characteristics-Based Study on the Visual Impact Assessment of the External Form of Entrance Pavilions to the Underground Stations of China’s Subway" Applied Sciences 13, no. 6: 4030. https://doi.org/10.3390/app13064030

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