JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 5, 493–501 https://doi.org/10.1080/13467581.2020.1799800 ARCHITECTURAL PLANNING AND DESIGN Visual preference evaluation on urban landmarks in the process of urbanization: a case study of Shanghai Oriental Pearl Radio & TV Tower a b,a a Mengmeng Zhao , Jian Zhang and Jun Cai a b School of Design, Shanghai Jiao Tong University, Shanghai, China; China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China ABSTRACT ARTICLE HISTORY Received 2 May 2020 In the context of China’s fast urbanization, the background of urban landmarks is constantly Accepted 16 July 2020 changing, which provides useful examples for the study of visual preference evaluation of urban landmarks. With the Shanghai Oriental Pearl Radio & TV Tower as research object, the KEYWORDS pictures of 24 years were used as the research material, and the height ratio, building density, Landmark; visual preference distance, and volume of the surrounding buildings were set as the physical characteristics to be evaluation; demographic studied. Photo stimulation experiments were conducted with participants of different demo- characteristics; urbanization graphic characteristics, the results of which were analyzed accordingly. The analytical results indicate that the height ratio, building density, distance, volume of Oriental Pearl Radio & TV Tower’s surrounding buildings are the main factors that influence people’s visual preference evaluation; people of different demographic characteristics (gender, age, growth experience) pay different attention to the height ratio, building density, distance, volume, and render different emotional and aesthetic responses to the fast change of this urban landmark and its surroundings. This research provides a valuable reference to urban landmark design and the development strategy of urban planning. 1. Introduction Visual preference evaluation of urban landmarks is another important aspect of urban building evaluation. 1.1. Research background Samavatekbatan, Gholami, and Karimimoshaver (2016) Urban landmarks are not merely a regional visual focus studied the aesthetic issues of urban high-rise buildings which helps people recognize and judge the surround- with computer software. Their study revealed that ings, but also an important carrier of urban space height was the most influential factor for the visual history, culture, aesthetics and emotional attachment. aesthetic evaluation of high-rise buildings. Browne Meanwhile, they are also a key component of urban (2006) observed that aesthetic value was one of the spatial form. The urban landmarks of a city are to key functions of landmark buildings. a large degree the embodiment of public impression The change of the surroundings around a particular on the city. building influences visual preference evaluation as The Oriental Pearl Radio & TV Tower (OPT, here- well. Zarghamia et al. (2019) studied the tension cre- inafter referred to as “the Tower”) is one of the ated by the height, width, and height–width ratio of important urban landmarks of Shanghai. As one of high-rise buildings and exerted upon the observers at the most representative buildings of Shanghai different distances. Yabuki, Miyashita, and Fukuda urban modernization, it is indeed the symbol of (2011) studied the integration of buildings of different modern Shanghai to many Chinese people since heights and surrounding landscapes by using the AR its completion (1995). method. Collins, Sitte, and Collins (2006) maintained Urban landmarks are remarkably influential to urban that the height of surrounding buildings around the spatial form. Karimimimoshaver and Winkemann (2018) square would influence viewers’ feeling. Lin, Homma, claimed that the influence of high-rise buildings on and Iki (2018) discovered that building height and urban skyline can be assessed from the following vegetation types around the lake exerted some impact three dimensions: aesthetic dimension, visibility dimen- on people’s visual preference evaluation upon the lake. sion, and meaning dimension. Yusoff, Noor, and Ghazali The main standard to evaluate the characteristics of (2014) maintained that skyscrapers, as important com- visual environment is visual aesthetics which refers to ponents of the skyline of Kuala Lumpur, capital of the degree people’s aesthetic experiences of visual Malaysia, mainly influenced people’s first impression landscapes can reach. To be specific, visual aesthetics on the city. is closely related not only with some objective factors CONTACT Jian Zhang email@example.com Institute of Architectural Design and Landscape Environment, Department of Architecture, School of Design, Shanghai Jiao Tong University, 200240, Shanghai, China © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 494 M. ZHAO ET AL. such as landscapes and buildings but also with the there any difference between people of different demographic characteristics of the evaluators (Wang demographic characteristics in their preference on and Zhao 2017). According to the previous researches, the physical characteristics of urban landmarks as cultural background (Yu 1995), education background well as the surrounding buildings? (Molnarova et al. 2012), gender (Lindemann-Matthies et al. 2010; Strumse 1996), age (Van den Berg and 2. Research methods Koole 2006), professional knowledge (Strumse 1996; Vouligny, Domon, and Ruiz 2009), familiarity with the 2.1. Research materials environment (Howley, Donoghue, and Hynes 2012), The research materials of this research are mainly and living environment (Yu 1995; Zube, Pitt, and a series of pictures of the Tower and its surrounding Evans 1983) are all influential to people’s visual pre- building groups. The original photo was taken in Chen ference evaluation. Yi Square, where a full view of Lujiazui financial district In terms of research methods, most studies on visual complex centered around the Tower across Huangpu preference evaluation in the field of architecture and River can be clearly obtained. Meanwhile, Chen Yi related fields were based on subject feeling (Arriaza et al. Square is also one of the most important scenic spots 2004; Kaltenborn and Bjerke 2002; Kaplan and Kaplan for all the tourists to Shanghai. Therefore, the location of 1989; Uuemaa et al. 2009). By contrast, only quite few photo shooting for this research is highly representative. studies adopted objective research method (Tveit 2009). The photo was shot at the chosen location as intro- Some evaluation methods which combine subjective duced above. According to “Report on nutrition and feelings and objective data have already been used to chronic diseases of Chinese residents” released by the evaluate visual preference on buildings as well as in State Council in 2015, the average height of Shanghai other relevant fields. Stamps Iii (1990) observed that residents (both male and female) is 168 cm. there existed a highly positive correlation between the th Accordingly, on November 22 , 2018, the photo was information people obtain from static colored photos taken with iPhone 7 Plus at the height of 165 cm above and that from actual views. Iverson (1985) put forward the ground (as shown in Figure 1). the concept “visual quality” and described visual effect The above-mentioned photo was put into computer from a quantitative viewpoint. Magill (1992) and for processing. To be specific, the surrounding buildings Geneletti (2008) attempted to define visual preference around the Tower were removed one by one according with a series of indexes and landscapes. Other research- to their respective completion time with Photoshop CS5. ers explored how the change of landscapes’ physical Similar methods are also widely used in studies of visual characteristics influenced landscape aesthetics and preference evaluation (Larsen and Harlan 2006; Tsoutsos tried to establish a correlation between physical char- et al. 2009; White and Gatersleben 2011; acteristics and landscape aesthetics (Buhyoff et al. 1994; Samavatekbatan, Gholami, and Karimimoshaver 2016; Real, Arce, and Sabucedo 2000). Lin, Homma, and Iki 2018). In total, 23 pictures were produced. These pictures reconstructed the state of the 1.2. Research questions Tower and its surrounding buildings from its completion in 1995 to November 2018 year by year. Four pictures In the context of China’s urban fast development for (the pictures of 2001, 2005, 2014 and 2018) were deleted nearly 30 years, the surroundings of many urban land- later in that no change occurred compared with the marks have been changing constantly (Z. Wang 2018). previous year. Therefore, with one of the first photos This research tried to explore people’s visual prefer- taken, there are 20 pictures shown in Figure 2. ence on urban landmarks as well as their surroundings during different periods through photo incentive method. The Tower and its surroundings in different 2.2. Physical characteristics of the pictures periods were shown through a series of pictures to the In this research, the physical characteristics of the pic- participants. Through photo incentive, aesthetic eva- tures of the Tower and its surrounding building group luation, picture analysis, and statistical analysis, the were divided into four types: height ratio (a), building visual preference laws of urban landmarks and the density (d), distance between the Tower and its sur- corresponding surroundings were thence obtained. rounding high-rise buildings (l), and volume of the This research collected participants’ visual prefer- surrounding buildings (v). ence data of these pictures (of different periods) and demographic characteristics through questionnaire survey. By analyzing the data collected the following 2.3. Calculation of the physical characteristics two questions were to be investigated: Is there any difference between different groups in their visual The 20 pictures were put in AutoCAD2014 for grid preference for urban landmarks in the context of fast- analysis. Firstly, the height of the Tower was set as changing surroundings around urban landmarks? Is H. Then, the top three tall buildings around the JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 495 Figure 1. The location and the viewpoint. Figure 2. The pictures of different years. Tower were marked out and the height ratio of the number of cells that the buildings occupy (c1) and three buildings and the Tower could be calculated by that of the sky (c2) was counted, respectively. So the the following formula: a = h/H. Secondly, the number building density could be calculated by the following of cells between the top three tallest buildings and the formula: d = c1/c2. Finally, the number of cells that Tower was counted, so that the distance between and every building takes up was counted, so the volume of the mean value (l) could be calculated. Thirdly, the the top three largest buildings in the pictures and the 496 M. ZHAO ET AL. mean value (v) were calculated. In the case that a cell is Then, the data collected were analyzed with SPSS not fully stuffed, it should be counted as 0.5 cell. The 22.0. Through correlation analysis, the influence of grid analysis and physical characteristics’ extraction are different demographic characteristics on people’s shown in Figure 3. The calculation results of the 20 visual preference evaluation was studied. On this pictures are shown in Table 1. basis, the data were further analyzed with multiple linear regression models. These analytical methods are also widely used in similar studies (Wang and 2.4. Survey of participants’ preference Zhao 2017; Zhao, Zhang, and Cai 2020). After being processed with computer software, the 20 pictures were shown to the participants of this 3. Results research. For the convenience of the participants to score the pictures, the pictures were printed on five 3.1. Overall evaluation of the pictures pieces of full-color photo paper (A4 size, four pictures At first, the intergroup reliability of preference scores on each piece) and then bound in a volume in random obtained was examined. The SPSS calculation results order so that the participants would not know the year showed that the reliability was 0.782, indicating a high that each picture represented. Then, these pictures internal reliability. It also demonstrated that the ques- were shown to random Chinese participants in Chen tionnaire survey was highly reliable and the data Yi Square of Shanghai (the location of photo shooting). obtained could be analyzed. To begin with, participants need to complete their The average score of each picture (s) was calculated, demographic characteristics according to the ques- with the maximum score being 4.23 and the minimum tions on the questionnaire. The demographic charac- being 2.22. The average preference score of all the teristics involved in this research included gender, age, pictures was 3.41. The highest average score was educational background, and rural growth experience. given to the picture of 2007 while the lowest went to Then, participants were asked to score each of the 20 the picture of 1998. pictures of the Tower (ranging from 0 to 5; 0 denoting the least preference and 5 denoting the most prefer- ence). They could revise their scores at their own will 3.2. Demographic characteristics and visual before the questionnaire was finished. The first survey preference evaluation was conducted on 29 November 2018 which included 160 participants altogether. To ensure the reliability of Single-factor analysis of variance was conducted to survey data, a second survey was conducted on study the relationship between demographic charac- 20 December 2018, with 170 participants in total. All teristics and visual preference evaluation. The results the scores given by the participants were recorded and indicate that gender difference (F = 11.204, p = 0.02), compared. The result indicated that the average pre- age difference (F = 2.692, p = 0.01), experience in rural ference scores of the two surveys were close to each areas (F = 6.230, p = 0.03) all contribute to participants’ other (single-factor analysis of variance F = 0.458, scoring process, which finally leads to the difference in p = 0.230). Accordingly, the two rounds of question- the average scores of each picture. However, partici- naire survey could be further analyzed as a whole. The pants with education difference (F = 2.021, p = 0.64) scores corresponding to the variables of each demo- does not show any difference in their average scores of graphic characteristic are shown in Table 5. Of the 330 each picture. questionnaires, 278 were valid and the rate of validity In addition, whether demographic characteristics was 84.2%. The demographic characteristics of the interact with each other (collinearity) was also studied. participants, which were in line with the statistical Based on the results of multiple linear regression ana- results of Shanghai Statistical Yearbook (2018), are lysis, collinear analysis was conducted to independent shown in Table 2. variables. As is shown in Figure 4, the standardized Figure 3. The diagram of the grid analysis and physical characteristics’ extraction. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 497 Table 1. The physical characteristics of the surrounding buildings around the Tower. Height ratio (a) Distance (l) Building density (d) Volume (v) Mean value of the height ratio of the three Mean value of the distance of the three Ratio of cells of the Mean value of the top Year tallest buildings and the Tower tallest buildings and the Tower buildings and the sky three largest buildings 1995 0.24 11.7 0.06 14.33 1996 0.27 7.2 0.06 14.33 1997 0.31 8.2 0.08 18.67 1998 0.40 11.5 0.18 18.67 1999 0.41 8.8 0.19 18.67 2000 0.41 8.5 0.21 22.00 2002 0.41 8.5 0.23 22.00 2003 0.42 22.2 0.27 28.67 2004 0.42 22.2 0.28 30.67 2006 0.42 22.2 0.30 30.67 2007 0.42 7.8 0.30 30.67 2008 0.48 13.2 0.36 32.00 2009 0.52 16.0 0.44 32.67 2010 0.52 16.0 0.46 32.67 2011 0.52 16.0 0.46 36.00 2012 0.52 16.0 0.46 36.00 2013 0.52 16.0 0.48 36.67 2015 0.52 16.0 0.48 36.67 2016 0.61 18.3 0.52 36.67 2017 0.61 18.3 0.52 36.67 residual of the models follows a normal distribution variables. The stepwise multiple linear regression models pattern. Meanwhile, for meaningful independent vari- indicate that significant predictors for different age ables (age, gender, and experience), their tolerance groups are different. For participants under 17 and and VIF value are as follows: age tolerance 0.97, between 18 and 35 years old, distance (l) is the reliable VIF = 1.031; gender tolerance 0.453, VIF = 2.206; experi- predictor of visual preference evaluation; for participants ence tolerance 0.451, VIF = 2.218. Arriaza et al. (2004), between 36 and 59 years old, height ratio (a) is the John (2008), Menard (2002) claimed that when VIF was reliable predictor of visual preference evaluation; for over 10 or tolerance smaller than 0.2, collinearity those over 60 years old, height ratio (a) and building existed in the model. In this research, all the VIF of density (d) are reliable predictors of visual preference independent variables are smaller than 10 and all tol- evaluation (as shown in Table 4). erances are over 0.2; the residual is distributed in a normal pattern. Therefore, it can be concluded that 3.5. Participants’ growth experience and pictures’ there is no collinearity in the model (as shown in physical characteristics Figure 4). Participants with and without any experiences in rural areas were asked to score each picture, respectively. 3.3. Participants’ gender and pictures’ physical The average score of each picture is set as a dependent characteristics variable and the physical characteristics of the 20 pic- Participants of different genders were asked to score tures as independent variables. The stepwise multiple each picture, respectively. The average score of each linear regression models indicate that significant pre- picture is set as a dependent variable and the physical dictors for participants with different growth experi- characteristics of the 20 pictures as independent vari- ences are different. For participants with rural growth ables. The stepwise multiple linear regression models experiences, height ratio (a) and building density (d) indicate that significant predictors for males and are reliable predictors of visual preference evaluation; females are different. For male participants, height for those without rural growth experiences, distance (l) ratio (a) is the reliable predictor of visual preference is the sole reliable predictor (as shown in Table 5). evaluation; for female participants, height ratio (a) and building density (d) are reliable predictors of visual 4. Discussions preference evaluation (as shown in Table 3). Howley, Donoghue, and Hynes (2012) maintained that people of different ages had different visual preference 3.4. Participants’ age and pictures’ physical evaluations. A similar conclusion can be drawn from characteristics this research. There exists a positive correlation Participants of different age groups were asked to score between age and visual preference scores. To be spe- each picture, respectively. The average score of each cific, the older the participants are, the higher scores picture is set as a dependent variable and the physical they would give to the Tower and its surrounding characteristics of the 20 pictures as independent buildings (as shown in Table 4). This may be justified 498 M. ZHAO ET AL. Table 2. Demographic characteristics of participants. Demographic Number of Proportion of participants Proportion of Shanghai native participants characteristics Variables participants (%) (%) Gender Female 134 48.20 49.60 Male 144 51.80 50.40 Age Below 17 26 9.35 11.20 Between 18 and 34 72 25.90 20.20 Between 35 and 59 125 44.96 38.40 Over 60 55 19.78 30.20 Educational background High school and below 111 39.93 36.50 Junior college 105 37.77 41.50 Undergraduate 50 17.99 15.60 program Postgraduate program 12 4.31 6.40 Rural growth experiences No 125 44.96 46.20 Yes 153 55.04 53.80 Figure 4. Collinearity judgment. by the fact that the older groups witnessed the stand- claimed that age was negatively correlated with the still and backwardness of China’s cityscape before the attitude to urbanization. They observed that the older reform and opening-up, and they also experienced the groups preferred living in rural areas. These researches fast development of urbanization in China. Their life drew contrary conclusions from this research. experience let them more accustomed to the fast Yu (1995) put forward that people with different change of China’s cityscape and more readily con- educational backgrounds showed different visual pre- tented with China’s urbanization process. Noticeably, ferences for landscapes. R. Wang and Zhao (2017) the average score of visual preference given by the claimed that people with higher education preferred younger group is significantly lower than those given well-vegetated landscapes. However, this research dis- by the middle-aged and old groups. However, Zube, covered that educational background did not influ - Pitt, and Evans (1983) argued that age was negatively ence people’s visual preference evaluation. One correlated with visual preference evaluation of water- reasonable explanation for this is that Chinese partici- body. Riechers, Barkmann, and Tscharntke (2018) pants have already been accustomed to the fast Table 3. Analysis of participants’ gender and pictures’ physical characteristics. Unstandardized coefficients Standardized coefficients Collinearity statistics Dependent B Std. Error Beta t Sig. Tolerance VIF Scores for male Constant 1.598 0.349 3.697 0 (R = 0.58, a 0.632 1.287 0.502 2.944 0.025 0.394 5.243 N = 144) Scores for female Constant 2.724 0.689 4.023 0 (R = 0.43, a 0.782 0.697 0.428 3.698 0.001 0.512 3.369 N = 134) d 0.482 0.859 0.721 6.369 0.004 0.485 6.674 JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 499 Table 4. Analysis of participants’ age and pictures’ physical characteristics. Unstandardized coefficients Standardized coefficients Collinearity statistics Model B Std. Error Beta t Sig. Tolerance VIF 0–17 years old (Constant) 2.482 0.257 6.258 0 R = 0.521, n = 26 d 0.521 1.471 0.852 3.158 0.001 0.521 3.258 18–35 years old (Constant) 4.528 0.582 2.257 0 R = 0.632, n = 72 d 0.365 0.912 0.632 4.325 0.002 0.369 5.685 36–59 years old (Constant) 8.368 0.251 2.963 0 R = 0.584, n = 125 a 0.369 1.363 0.261 4.517 0 0.961 7.324 60 years old or older (Constant) 7.856 0.473 5.381 0 R = 0.642, n = 55 a 0.982 2.323 0.783 3.312 0 0.421 3.313 d 1.251 0.952 0.821 3.125 0.002 0.695 4.202 Table 5. Analysis of participants’ growth experience and pictures’ physical characteristics. Unstandardized Standardized Collinearity coefficients coefficients statistics Model B Std. Error Beta t Sig. Tolerance VIF Experiences in rural areas (Constant) 3.618 0.385 8.654 0 R = 0.652, n = 125 a 0.471 2.221 0.714 2.278 0 0.541 7.321 d 0.232 1.569 0.247 3.652 0.03 0.479 6.363 No experience in rural areas (Constant) 4.528 0.582 2.257 0 R = 0.617, n = 153 l 0.471 1.052 0.397 4.747 0.01 0.636 1.258 urbanization of China. Therefore, educational back- The reason for this may lie in that the male groups may ground fails to function when the participants evaluate be more interested in scientific and technological cap- the urban landmarks and the surrounding building abilities in terms of building height. On the other hand, groups. female groups are more concerned about height and Abello and Bernáldez (1986) observed that gender density when they evaluate the Tower and its sur- difference would lead to the variation of visual pre- rounding buildings. Besides, height and density are ference evaluation. As is discovered in this research, basically positively correlated with the average score females’ score averagely higher than males do (as of pictures. This can be justified by the fact that the shown in Table 3). This may be justified by the fact height and density of high-rise buildings in some sense that the subjects of these pictures are urban land- mirror the prosperity of a city, and for Chinese female marks and surrounding building groups. Generally groups, the prosperity in the city also indicates the speaking, females may prefer bustling cities. As safety and prosperity of life. China’s urbanization advances, it is natural that Groups of different ages also display different visual females should render a higher evaluation on urban preference to urban landmarks and surrounding build- landmarks and surrounding building groups than ings. For the two groups of 0–17 and 18–35 years old, males do. However, this conclusion is opposite to when they evaluate the Tower and its surrounding that of Yao et al. (2012). building groups, their main concern is the distance Keane (1990) held that life experience did not influ - between the two. For the group of 36–59 years old, ence people’s visual aesthetic evaluation of landscape. their main concern goes to height when evaluating the However, as is revealed in this research, people with Tower and surrounding buildings. People of over life experience in rural areas are generally more appre- 60 years old are more concerned about height and ciative of the Tower and its surrounding building density. Similar to the group of 36–59 years old, they groups. This is mainly because of the relatively large are generally contented with the current situation of urban-rural gap in China. In most rural areas in China, Chinese cities. From the above analysis, it can be seen high-rise buildings are quite rare. In this case, people that the young generation is more rational than the living there or who once lived there would find dense middle-aged and old groups. They are more integrated high-rise buildings a more enjoyable sight. In contrast, into information era and more open-minded to other participants who grow up in cities and have no growth big cities in the world in that they are comparatively experience in rural areas display a much weaker pre- better educated; meanwhile, they have no direct per- ference to dense high-rise buildings. sonal experience of the standstill and backward citys- Groups of different gender usually render different cape in the past, thus failing to develop a full view of visual preference to urban landmarks and surrounding China’s fast urbanization. Consequently, they do not buildings. It can be seen in this research that male show strong preference to the current cityscape, the groups would primarily give their priority to the build- fruit of China’s fast urbanization. Contrarily, the mid- ing height in the pictures when they evaluate the dle-aged and old groups attach more importance to Tower and its surrounding building groups; as the urban development and prosperity. height increases, the score they give rises accordingly. 500 M. ZHAO ET AL. Groups with or without growth experience in anonymous participants. rural areas display different visual preference eva- luations of urban landmarks and surrounding build- Disclosure statement ing groups. For people with growth experience in rural areas, their concern is mainly directed to build- No potential conflict of interest was reported by the authors. ing height and density when they evaluate the Tower and its surrounding buildings. To be specific, the denser and the higher the buildings are, the Funding higher the average scores of pictures would be. This work was supported by the Fundamental Research Compared with those who grow up in cities, espe- Funds for the Central Universities, Shanghai Jiao Tong cially big cities, they have relatively scarce opportu- University, China (16JCCS06). nity to see high-rise buildings before. When they are exposed to these high-rise buildings around the Tower, it is natural that they display more surprise Notes on contributors and enjoyment. However, people without any living experience in rural areas are more concerned about Mengmeng Zhao is a Ph.D. Candidate at School of Design, Shanghai Jiao Tong University. Her research interests are the distance between the surrounding high-rise Architectural Design and Theory. buildings and the Tower because they are already quite familiar with the high-rise buildings. In this Jian Zhang is a Tenure Track Professor/ Researcher (Double hired) at Shanghai Jiao Tong University and China Institute case, they can analyze the coordination between for Urban Governance. He is a Ph.D., graduated from the the Tower and its surrounding buildings in the Nagoya Institute of Technology. His research interests are pictures more rationally. This coordination is highly Architectural Design and Theory. relevant to the distance, which justifies their main Jun Cai is a Professor at School of Design, Shanghai Jiao Tong concern clearly. University. She is a Ph.D., graduated from the Nagoya In addition, as has been found in this research, Institute of Technology. Her research interests are groups of different demographic characteristics show Architectural History and Theory. no concern about the volume of the Tower and sur- rounding buildings (v). References Abello, R. P., and F. G. Bernáldez. 1986. “Landscape 5. 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Journal of Asian Architecture and Building Engineering
– Taylor & Francis
Published: Sep 3, 2021
Keywords: Landmark; visual preference evaluation; demographic characteristics; urbanization