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Research on the scale of pedestrian space in underground shopping streets based on VR experiment Research on the scale of pedestrian space in underground shopping streets based on VR experiment
Yao, Gang; Yuan, Tingting; Rui, Yue; Chen, Wanjing; Duan, Zhongcheng; Sun, Liang; Si, Xianzhi; Zhang, Meng; Chen, Kaiyun; Zhu, Yusong; Chen, Yiying
2021-03-04 00:00:00
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 2, 138–153 https://doi.org/10.1080/13467581.2020.1782215 ARCHITECTURAL PLANNING AND DESIGN Research on the scale of pedestrian space in underground shopping streets based on VR experiment a a a a a a b Gang Yao , Tingting Yuan , Yue Rui , Wanjing Chen , Zhongcheng Duan , Liang Sun , Xianzhi Si , a a a a Meng Zhang , Kaiyun Chen , Yusong Zhu and Yiying Chen a b School of Architecture and Design, China University of Mining and Technology, Xuzhou, Jiangsu Province, P. R. China; School of Mines, China University of Mining and Technology, Xuzhou, Jiangsu Province, P. R. China ABSTRACT ARTICLE HISTORY Received 14 January 2020 The staying activity in shopping street has an inseparable relation with the spatial scale of the Accepted 29 May 2020 street. Neither too narrow nor too wide scale of street could bring a good walking experience to pedestrian, so it is especially important to find a suitable scale. Most of the researches are KEYWORDS qualitative investigations related to human psychology, and few of them are about under- Underground shopping ground shopping street. This research made a quantitative investigation on the relationship streets; pedestrian space; between the spatial scale of underground shopping street and the staying activity there, scale research; effective defined the concept of staying activity coefficient to quantify the staying activity in shopping staying activity coefficient; virtual reality experiment street, and used virtual reality experimental method to study the influence of spatial scale changes on the staying activity in shopping street. The research results show that the height and width of underground shopping street as well as the aspect ratio could significantly affect human activity in the pedestrian spaces of underground shopping street. Meanwhile, the most suitable scale of pedestrian spaces in underground street for people to have staying activity is also derived. 1. Introduction in the urban planning, the importance of street envir- onmental design to pedestrians’ walking quantity can In shopping streets, appropriate pedestrian spacial not be ignored. Therefore, a reasonable design of scale could bring higher staying for shopper (Xu and street scale is extremely important. Ashihara (1987) Kang 2014; Belinda and Chin 1998; Painter 1996; Zheng has presented valuable opinions in his book of 2015; Kang et al. 2015; Minhas et al. 2017). Staying is Aesthetics of Streets. After the investigation of closely related to the occurrence frequency of shop- Yokohama Chinatown, Motomachi and Yokohama ping activities, shopping duration, activity richness, bridge in Yokohama, Japan, he came to a conclusion shopping spatial perception and so on. With higher that “a street with aspect ratio of 0.8–0.9 can create an staying, the shopping activity will frequently occur for Asian-styled bustling atmosphere to the most”. a longer duration, with richer activities and better However, today’s buildings are at least three meters space perception; on the contrary, with lower staying, high, and even high-rise buildings are dozens of the shopping activity will occur in a lower frequency, meters. If we use this ratio to figure out street width, for a shorter duration, with single activity content and the result will be obviously unreasonable (Cheng and worse space perception (Yun and Yang 2008; Whitney Li. 2013). Besides, the human perception to space is 2009; Yu, Kang, and Liu 2012). So, if the scale of pedes- not only influenced by aspect ratio but also by the size trian space in shopping streets can not be well of space (Fang, Song, and Ye 2014). When the enclosed handled, it would have a greater negative impact on degree is the same, the larger the space is, the wider people’s shopping activities. people will feel. So, the viewpoint of Yoshinobu The spatial scale of streets is composed of street Ashihara would be difficult to adapt to the design of length, street width, street building height and street modern urban streets. store width. Creating a humanized street scale is the For street scale, there are more research on the key of street design (Zhang 2006). Shen et al. (2017) ground street. But in future city development, the made a quantitative research of streetscape and pro- importance of underground space will become posed that a morphological analysis of street can help increasingly prominent (Zhang, Zheng, and Wang city planners to design a higher quality urban space. 2018). The opinions and research subjects of Ameli et al. (2015) made a statistic analysis of 32 block Yoshinobu Ashihara (Tan 2008) are about the above- data and proposed that a better block design could ground shopping streets. Because the underground bring more pedestrian. Kang (2018) put forward that, CONTACT Liang Sun sunliang@cumt.edu.cn School of Architecture and Design, China University of Mining and Technology, Xuzhou, Jiangsu Province 221116, P. R. 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. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 139 space environment is more complex and depressive valuable theoretical and quantitative criterion for the compared to the above-ground shopping streets, the design and transformation of underground shopping orientation of pedestrian space in underground shop- streets. ping streets is weaker and difficult to accurately per- ceive space (Tian 2015), and the closure of 2. Principle and method underground shopping streets is strong, so it is more difficult to study their pedestrian spatial scale and 2.1. Research principle more factors need to be considered (Wu and Tian This paper studied the relationship between the spatial 2015). In addition, the opinions and research methods scale and staying of streets. Since street staying is of Yoshinobu Ashihara are too qualitative, mainly from a very vague concept, in order to conduct personal experience, and lack of judgment on the a systematic research, the paper firstly analyzed the experience of others. Therefore, it is necessary to path form of pedestrians when they pass through the apply more effective and systematic methods and shopping street, providing a basis for the follow-up tools to conduct in-depth research on the scale of investigations and the selection of experimental pedestrian space in the underground shopping streets. method. It also proposed the concept of effective Virtual Reality (VR) technology utilizes computer to activity area for the quantification and assessment of build a three-dimensional virtual world, and provides street staying. Based on the research of this concept, users with visual, auditory, tactile and other sensory the coefficient of effective staying activity was put simulation, enabling them to immerse in the environ- forward as the main criterion for evaluating staying. ment and observe the objects in the created space By studying the effective activity coefficient of the timely and unlimited. VR technology can truly restore streets with different scales, the relationship between the real scene and provide an objective condition for street scale and staying was investigated. experimental research, and the experimental results are easily to be quantified (Wu and Zhu 2016). There have been many successful experiments with VR tech- 2.1.1. Analysis of pedestrian passing path in nology. For example, Yasufuku (2014) used VR to cal- underground shopping streets culate and analyze the visual space of human walking Firstly, the plane form of underground shopping path in architectural space and concluded that it is pedestrian streets was abstracted, and a group of fig - feasible to analyze and evaluate the architectural ures with big and small square spaces juxtaposed with space with VR equipment and experiment. Natephra, each other were obtained. When there is no commu- Motamedi, and Fukuda (2017) built different 3D virtual nicative behavior between people and the shop space models and searched for the optimal indoor lighting on both sides (in this case, people’s behavior does not scheme through VR experiment. Naz, Kopper, and belong to effective staying activity occurred under the Mcmahan (2017) used VR as a tool to build indoor influence of environment), it can be considered that scenes with different colors and explored the relation- people’s walking route in the passage includes two ship between colors and human emotion. Through the elements: starting point and ending point. The starting study of three cases, Salman Azhar (2017) proposed and ending points can be anywhere on either side of that VR technology can be used for safety assessment the passage while the human behavior pattern can be in construction design so that architects and engineers thought of as a connection line between two points. can intuitively assess the safety of construction site Figure 1 shows human movement trajectory in under- before construction, so as to improve occupational ground shopping pedestrian street when people are safety. Fangfang Liu and Kang (2018) used VR technol- not affected by commercial functions. ogy to objectively evaluate the visual environment When people contact with commercial interfaces on comfort of street scale. This shows VR technology has both sides of underground shopping pedestrian been mature and can be fully used in laboratory scene streets, they will pay attention to the stores on both simulation for quantitative analysis. However, at pre- sides; if people are interested in a store, they will sent, the research on the application of VR technology inevitably walk towards that store. Then, it can be to shopping streets is still limited to the analysis of considered that the store that interests people is psychological feelings and with few quantitative a secondary goal between the starting and ending research. points of people’s journey, and this goal point is infi - In this study, VR experiment was used to quantita- nitely close to the commercial interface. The behavior tively analyze the relationship between the staying of approaching the store is the deviation of people’s activity coefficient and the scale of pedestrian space path when they move, and the path will become more in shopping streets, and further inferred the suitable zigzag and complicated compared with the above scale of pedestrian space in underground shopping situation. Because only the behavior of staying activity streets. This suitable scale of pedestrian space pro- outside the store is considered, the path stops at a goal posed in the research results will provide more point infinitely close to the store interface, and then 140 Y. GANG ET AL. Figure 1. The behavior pattern of pedestrians when they are not affected. Figure 2. The behavior pattern of pedestrians when they are attracted. the next path deviation occurs. Figure 2 shows the water tension between hydrophil wool line to naturally analysis of people’s movement pattern in the under- optimize the shape of a mass of wool thread, i.e. the ground shopping pedestrian street when they are shortest path system connecting all given points in attracted by the commercial functions. space, so as to cut down the total path length of entire According to Frei Otto’s theory (Otto 2009, 2009; system. In mathematics, finding the shortest connection Schumacher 2009) related to path research, the path of between the locating points is called a topological pro- pedestrians can be simplified when they pass the under- blem. In topology, in order to reduce the total length of ground shopping streets. Frei Otto was very good at the generated result, additional control point or control studying the natural structures of materials through lines need to be added to the path. Every newly increased their properties. It is discovered that matters have the point has three dimensions in the space, and each edge power to self-organize and can help people get the best passing through this point shows an angle of 120° with answer. Later, in the study of path, he proposed three other adjacent edges (Xing 2016). Integrated with Frei basic forms of path: Otto’s optimization of streamline form, the research can 1.Direct Path Networks 2. Minimal Path Networks 3. simplify people’s path in the street, and infer the actual Minimizing Detour Networks movement trajectory of people when they have multiple According to the theory of Minimizing Detour concerns of interest in the shopping street interface, i.e. Networks, the research can map and demonstrate the an approximate wave-shaped path form. This path form paths that people take when they have multiple goals or provides a reference basis for the coefficient of the effec - concerns of interest. In the study of Minimizing Detour tive staying activity proposed hereinafter. Figure 3 shows Networks, Frei Otto built a wool line model which using the actual route people take in the underground Figure 3. The actual route of pedestrians as they pass the shopping street. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 141 shopping pedestrian street when they are attracted by commercial functions. 2.1.2. Effective staying activity area The staying activity in the shopping street contains all behavior activity of shoppers when they go window- shopping there, which are complicated and diversified. For example that people stop, stay and ramble freely during shopping are all staying activities. (Yu. 2018) It is very difficult for a simple staying or simple activity to completely conform to the behavior state of shoppers during shopping, so the research proposed and defined the concept of staying activity. It is closely related to the spatial quality and the spatial feelings. The staying activity of a space reflects the spatial qual- ity of a space, such as safety level, contact level, pros- perity and sense of place. The magnitude of staying activity not only corresponds to the staying activities in different spaces but also determines the excellence of different spatial quality and space feeling. This research assumed that there is an area in underground shopping street where a staying activity frequently occurs, and this area is called the effective staying activity area (ESAA). Taking a section of the underground shopping street as the boundary, and Figure 4. Diagram of effective staying activity. taking personal staying activities as an example, as long as he/she travels (rambles, walk quickly, runs) or stays (waits and sees, rest) in one area of this pedes- Path β is used to indicate that people have deviated trian space, it can be said that this person takes staying from the line α due to attraction on both sides of the activities in this area, and this area is called ESAA. street. Path β will interlace with the center line α to enclose a scope, and the area of this scope can be 2.1.3. Effective staying activity coefficient calculated. This scope is ESAA. From the above analysis, it is known that the general path The ratio of the area of ESAA and the total area of of pedestrian is approximate wave shape when they pass pedestrian space is the effective staying activity coeffi - the underground shopping street (see Figure 3). In order cient (ESAC). S is set as the area of ESAA, and S is set as 1 2 to quantify the effective staying activities according to the total area of pedestrian space. Thus, S /S means the 1 2 the path form of pedestrians, it is necessary to propose value of ESAC, which is set as δ. This coefficient δ can be a quantifiable parameter as the evaluation criterion. considered as a quantitative index to evaluate whether In Figure 4, Line α is a straight path through the the staying activity of pedestrian space in an under- pedestrian space and used to illustrate that people will ground shopping street is good or bad (See Figure 4 not be deviated by the business factor on both sides. for details). Figure 5. Profile diagram of pedestrian space model of underground shopping street. 142 Y. GANG ET AL. 2.2. Experimental method 3. Experimental processes 2.2.1. Experimental samples 3.1. Variable value of virtual model In the study, in order to simulate the influence of the (1) Depth underground commercial space with difference scales on human staying activities, VR technology was used to The survey results show that the depth of under- build virtual scenes since it is unrealistic to build real ground shopping streets is mainly between scenes. In order to make the experimental scenes built 20 ~ 40 m, and the 30 m long underground shop- in line with the actual scale of underground commercial ping streets appear most frequently. According to space as possible, a survey was conducted in the under- the experiment and site need and limits, the under- ground shopping streets across China in the early stage ground shopping street space with standard length (see survey data in Appendix 1). According to the survey of 36 m was particularly selected as the experimental results, a suitable change range of width, height and space. depth were selected as the data base for the scale of virtual model of underground commercial space. In the (2) Width experiment, the depth of underground commercial space was taken as a constant, and the width and height Through investigation, the width of underground as the variables, to investigate the relationship between shopping street is mainly between 4 m and 8 m. In the spatial scale of underground shopping street and order to better reflect the influence of the width on the effective activity staying coefficient. The experiment human’s staying activities, the research expanded the simulated the actual underground commercial spatial if range of width and selected 2.5 m, 4.5 m, 6.5 m, possible and increased the sample size of width and 8.5 m and 10.5 m of virtual model as the experimen- height, so as to improve experimental accuracy. tal scene (Figure 5). (3) Height 2.2.2. Establishment of virtual model of underground shopping street Through investigation, the height of underground In order to simulate the real underground commercial commercial space does not change much. So, accord- space in virtual model better, firstly Sketch up and ing to the survey data, the frequently appeared heights 3dmax software were used to conduct space modeling of 2.5 m, 3.5 m, 4.5 m, 5.5 m and 6.5 m were selected as and lighting simulation meticulously. Secondly, the height of experimental scene (Figure 5). according to the changes of width, height and other independent variables of underground commercial (4) Other variables space, more experimental models were established. Finally, the established model was imported in Vizard To simulate the underground commercial space 5 for script editing, to generate the VR space of under- scenes in a real sense, white energy-saving lamps ground shopping street. with a color temperature of about 6000 K were used for lighting illumination, and yellow energy-saving lamps with a color temperature of about 3000 K were 2.2.3. Experimental process and data processing used cooperatively in node position (Luo 2012; Huang Participants of different ages and genders were and Weng 2017). To prevent from additional influence selected for experimental simulation. All of them of color and material on the psychological perception were told to wear the VR helmet Oculus Rift DK2 to of the participants, the underground streets and ceil- simulate passing virtual underground commercial ings in the ideal scene are white, with no material space with difference scales (Zhang 2017), and the texture, and the floor is the frequently used white Precision Position Tracking Studio (PPT Studio) was ceramic tiles in underground streets. used to track their paths (Tang 2016). After experiment, several path diagrams corresponding to different underground commercial space scale were obtained. 3.2. Establishment of VR scenes Then, the Autodesk Computer-Aided Design software (1) Build mode with Sketch Up (AutoCAD) was used to calculate the area of path dia- In total, 25 pedestrian space models of underground gram, and ESAC of different underground commercial shopping street with different scales were built by using space scales was obtained and averaged. Finally, the Sketch Up2015 software. Each model contains the basic Microsoft office Excel software was used to count ESAC elements and materials of the underground shopping of the underground commercial space with different street such as floor, shop, counter, suspended ceiling, scales, and the MATLAB software was used to analyze lamps and so on, as shown in Figure 6. the data. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 143 Figure 6. Establishment of pedestrian space model of underground shopping street with different scales. (2) Use 3d Max for rendering 3.3. VR experiment (1) Experimental equipment The rendering is to import the Sketch Up model established in the previous step in the Autodesk 3D Immersive VR technology was used to experience Studio Max (3d Max), to add white lamps with a color the experimental scenes. This experiment used temperature of about 6000 K in it, and add yellow Vizard 5.4 (Lin and Lin 2016), a VR engine based lamps with a color temperature of about 3000 K in on Python language of Oculus, the VR helmet the node position, for further rendering. OSGB format Oculus Rift DK2 and the motion capture system- is then exported (as shown in Figure 7). PPT Studio, which can realize high-quality optical motion tracking over a wide range (of more than (3) Utilize Vizard5 to build VR scene 50 m × 50 m). Optical sensors are installed in the corner of the room to track active LED indicator. The researchers write scripts in Vizard5 (Figures 8 & 9), Sensors collect data as they pass through the room. and imports the previous OSGB file to build VR scene, to Through fast processing, the sensor data was trans- provide experimental scene for follow-up experiments. formed into accurate 3d mark position. Figure 7. 3d Max rendering scene. 144 Y. GANG ET AL. Figure 8. Write script. (2) Selection of participants the process of each VR scene experiment, there is no time limit for the experiment. Participants passed the A total of 47 people participated in the experiment, of experimental space according to certain rules, started which 23 were undergraduates or graduate students from the middle point of the initial interface of the majoring in architecture from China University of Mining passage. The real and reasonable situation of walking and Technology, and 24 were undergraduates or gradu- path should be restored as possible when people pass ate students majoring in engineering. Most of the partici- through a street space. The pedestrian can move left pants had experienced underground shopping street and right, but not return back. In total, 80 groups of space. experimental data were obtained from 47 participants after the experiment, and all walking paths in the (3) Experimental process experiments were recorded. After that, all the experi- ments had been done. Before the whole experiment begins, there will be a transition period during which participants will wear VR helmets and be placed in a virtual, quiet, bright and 4. Experimental results and analysis open room environment to familiarize them with the During the experiment, a total of 80 groups of sense of space scale in the virtual environment. The experiments were completed. Due to the experi- entire transition period is 3–5 minutes. mental error, some walking paths exceeded the After that, the formal experiment was started (as maximum width of the street, so 17 groups of shown in Figures 10 & 11). In the experiment, partici- invalid data were excluded and 63 groups of valid pants were randomly assigned to experience under- data were obtained. ground pedestrian space with 3–5 different scales. In JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 145 Figure 9. Build VR scene. Figure 10. Experimental scene. Figure 11. Experimental scene. 4.1. Use AutoCAD for data conversion path and the center line of the room were mea- After the walking path obtained from the experi- sured. S /S means ESAC(δ) which can be used to 1 2 ment was fitted with the spline in AutoCAD, the evaluate the influence of the pedestrian space of cumulative area S and the space plane area S of underground shopping street on human 1 2 the graph formed by the enclosure of the walking psychology. 146 Y. GANG ET AL. AutoCAD orders and operations are as follows: (1) Insert the pedestrian path graphs (2) Shortcut key: L refers to creating a straight line of 36 long (scale:1:1) (3) Shortcut key: AL refers to aligning the length of underground shopping street with the line created (4) Shortcut key: L refers to drawing the profile of underground shopping street and the center line of the laneway (5) Shortcut key: SPL refers to spline curve fitting of walking path (vectorization) (6) Shortcut key: REG refers to select the region enclosed by walking curve and the center line of underground shopping street and build a region (7) Shortcut key: ZMJ refers to area summation order (area summation plug-in) Figure 12 and Appendix 2 show the schematic dia- gram and preliminary calculation results of the effec - tive staying activity after experimental statistics. 4.2. Use MATLAB for data analysis (1) Problem analysis In this study, different ESAC(δ) was obtained by chan- ging the height and width. In a three-dimensional space, it can be interpreted as synthesizing a number of points of ESAC(δ) values into a curved surface. Generally, there are two data processing methods for these problems: curve fitting and interpolation method (Wang 2015; Tu and Huang 2012). The inter- polation method is to use the function value of several known points in a section of the function f(x) and make appropriate specific function. In other points of this Figure 12. Diagram of effective staying activity. section, the value of this specific function is taken as an approximate value of the function f(x). (3) Data processing In the interpolation method, the spline interpola- tion method is a mathematical method to use variable The research used the interpolation method to splines to make a line going through a series of points, provide the functional value of the specific function to analyze and process the known parametric data. In in the specified section (i.e. at the height of [2.5,6.5] practical engineering, the spline interpolation method and the width of [2.5,10.5]) and used MATLAB soft- is frequently used to solve curved surface problem. ware of r2017a version to write program. In which, Therefore, this research adopted spline interpola- X-axis is the height, Y-axis is the width, and Z-axis is tion to search for its highest point and applied ESAC(δ) obtained from rough data processing, thus, MATLAB software to fit the curve, so as to find out the function curve graph was obtained (as shown in ESAC(δ) of the highest point and lowest point. Figures 13–15). (2) Data source (4) Conclusion The experiment was conducted in the VR laboratory and the street depth was constant. Through changing Within the experimental scope, when Height = 5.3939 m, the height and width value, ESAC(δ) was worked out, Width = 6.4444 m, ESAC(δ) reaches to the maximum of and each group obtained 25 groups of data in average. 0.3267. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 147 Figure 13. Pseudo-color chart. Figure 14. Contour chart. 4.3. Analysis of experimental results the effective staying efficient will be. Conversely, the further people are to the closed area of the middle Through the analysis of experimental results, we can contour line, the smaller the effective staying efficient get three charts containing the changes of the length, will be. Figure 15 is a 3d curve diagram, and also the width and staying activity of the underground shop- most intuitive one in the three charts. In this diagram, ping street (Figures 13–15). z-axis represents ESAC(δ), from which the height and Among them, Figure 13 is a pseudo-color chart. In width values corresponding to effective activity coeffi - this chart, the darker and the redder the area is, the cient can be intuitively read out. In fact, the basic greater ESAC will be. Conversely, the lighter and the meaning of these three charts is the same. bluer the area is, the smaller ESAC will be. Figure 14 is As can be seen from the diagrams, ESAC(δ) of a contour chart. In this chart, the closer people are to a space with the width of 6–7 m and the height of the closed area of the middle contour line, the greater 4–5 m (i.e. the height–width ratio is about 0.8) is 148 Y. GANG ET AL. Figure 15. 3d curve diagram. higher. Therefore, through the above experiments, it spatial scale of underground shopping street and the can be concluded that: the pedestrian space of under- behavior of people in it. ground shopping street with width of 6–7 m and height of 4–5 m is most suitable for people to stay (2) The design of research method and walk. According to the definition of effective stay- ing activity, in the pedestrian space of underground By virtue of Frei Otto’s research of path, the experi- shopping street with this scale, there will be richer ment analyzed and inferred the behavior pattern of shopping activity and better shopping space feeling, people in pedestrian street space, obtained the staying and it also helps to create a commercial atmosphere. activity path diagrams conforming to actual situation Through the interpretation of the above tables, the and proposed reasonable quantitative method. This theoretical methods of transforming underground space provides valid data for the experiment to make math- into shopping street can be found, i.e. quickly obtaining ematical modeling with MatLab and get more compre- the spatial scale corresponding to the maximum ESAC(δ) hensive and reliable results. This research method can according to the proportional relation in the charts. also be used to analyze, infer and quantify the behavior pattern of people in other kinds of public activity spaces. 5. Discussion (3) Application of VR experimental method The experimental results show that the definition of ESAC is from this experimental idea and the experi- Through 3DMax virtual model and VR equipment, mental method based on VR technology is a feasible the participants could rapidly, intuitively and truly feel quantitative research method. This method can also be the spatial scale of different underground shopping used to make corresponding adjustment measures on pedestrian streets and freely have staying activity other functional spaces and conduct related experi- there. Several groups of experiments of staying activ- ments, so as to quantify the spatial attributes caused ities of people in underground shopping streets with by human behavior or perception in other functional different scales can be completed with minimum spaces. Its innovative points are as follows: space and time, and the track of people’s behavior can be accurately recorded. This provides conditions (1) The concept of staying activity and relevant for the calculation of experimental results and further variables data processing. 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Survey data of urban underground shopping streets City Underground street Length/m Width/m Height/m Aspect ratio Xu Zhou 1 Youwo Fashion Block 20–30 2.2 3.3 1.5 2 Central Fashion Avenue 10–15 4 4.5 1.125 3 Ladies’ Street 25–31 2 3 1.5 4 CUMT’s new street 26–33 3 .4.5 1.5 5 Wanda Plaza, Yunlong 100 meters or more 6 4.8 0.8 Nan Jing 6 Nanjing New Department Store Building 32–41 5 4 0.8 7 Underground Commercial Street of Hunan Road 24–36 6 4.5 0.75 8 Fashion LaiDi 28–39 2.5 3.5 1.4 9 Nanjing International Financial Center 27–49 6.4 4.8 0.75 10 Suning Life Square 24–28 8 4.2 0.525 11 Oriental Fred 28–32 4.8 4.5 0.94 Shang Hai 12 Pacific Mori Living World 28–43 7.8 0.75 13 Sun and Moon Center 26–35 6 5.1 0.85 14 Super Brand Mall 50 meters or more 6.4 5.4 0.84 15 Shanghai Guojin Center Mall 28–36 6.4 4.5 0.7 16 Hua Run Times Square 50 meters or more 5.6 4.2 0.75 17 The first Babaiban of Shanghai 27–34 5.4 3.9 0.72 Su Zhou 18 Starlight heaven and earth 25–31 3.9 3.6 0.92 19 Jiu Guang mall 27–36 4.5 3.9 0.87 20 Vanke Plaza 31–38 6.3 4.2 0.67 21 Impression city 29–33 5.8 3.9 0.67 22 Rainbow mall 26–39 4.5 3.6 Cheng Du 23 Yinshi Underground Commercial Street 26–43 2 3.5 1.75 24 Tianfu Square 30–37 4.6 3.9 0.85 25 Cosmo Wealth Center 21–29 6.1 4.5 0.74 26 Wangfujing Department Store 22–31 7.9 4.8 0.61 27 Raffles City ChengDu 35–40 5.8 3.9 0.67 152 Y. GANG ET AL. Appendix 2. Experimental data Height/m Width/m Aspect ratio Enclosure area S Average value Total area S Encircling proportion 1 2 2.5 2.5a 1 7.23 15.32 90.00 0.17 2.5 c 20.69 2.5d 18.03 4.5a 0.55556 38.88 162.00 0.24 4.5 c 38.88 4.5d 6.5a 0.38462 36.23 234.00 0.15 6.5 c 36.23 6.5d 8.5a 0.29412 51.71 306.00 0.17 8.5 c 51.71 8.5d 10.5a 0.23810 60.04 378.00 0.16 10.5 c 60.04 10.5d 3.5 2.5a 1.4 10.25 18.75 90.00 0.21 2.5 c 15.70 2.5d 23.38 2.5e 25.69 4.5a 0.77778 31.31 162.00 0.19 4.5 c 15.87 4.5d 37.12 4.5e 40.95 6.5a 0.53846 63.55 234.00 0.27 6.5 c 63.55 6.5e 8.5a 0.411765 49.92 306.00 0.16 8.5 c 49.92 8.5e 10.5a 0.333333 107.95 378.00 0.29 10.5b 10.5 c 80.92 10.5e 134.98 4.5 2.5a 1.8 21.37 13.63 90.00 0.15 2.5 c 12.24 2.5e 7.30 4.5a 1 44.47 29.11 162.00 0.18 4.5 c 21.17 4.5d 21.68 6.5a 0.69231 75.01 58.76 234.00 0.25 6.5 c 42.85 6.5e 58.43 8.5a 0.52941 45.98 306.00 0.15 8.5 c 38.79 8.5e 99.14 10.5a 0.42857 50.87 378.00 0.13 10.5 c 36.32 10.5e 116.30 5.5 2.5a 2.2 12.60 15.50 90.00 0.17 2.5e 18.41 4.5a 1.22222 36.17 41.00 162.00 0.25 4.5e 45.82 6.5a 0.84615 55.43 76.05 234.00 0.32 6.5e 96.67 8.5a 0.64706 104.78 68.51 306.00 0.22 8.5e 32.24 10.5a 0.52381 106.30 77.74 378.00 0.21 10.5b 84.46 10.5e 42.45 (Continued) JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 153 (Continued). Height/m Width/m Aspect ratio Enclosure area S Average value Total area S Encircling proportion 1 2 6.5 2.5a 2.6 16.51 16.51 90.00 0.18 4.5a 1.44444 17.52 24.63 162.00 0.15 4.5b 34.84 4.5 c 19.94 4.5d 20.82 4.5e 30.03 6.5a 1 28.87 29.00 234.00 0.12 6.5b 43.55 6.5 c 22.60 6.5d 35.73 6.5e 14.23 8.5a 0.76471 36.78 46.24 306.00 0.15 8.5b 50.61 8.5 c 43.89 8.5d 70.75 8.5e 29.16 10.5a 0.61905 64.18 75.49 378.00 0.20 10.5b 129.94 10.5 c 71.41 10.5d 87.25 10.5e 24.65
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