Visitors’ consistent stay behavior patterns within free-roaming scenic architectural complexes: Considering impacts of temporal, spatial, and environmental factors
Luying Wang, Weixin Huang
Visitors’ consistent stay behavior patterns within free-roaming scenic architectural complexes: Considering impacts of temporal, spatial, and environmental factors
GPS positioning data are increasingly utilized in environmental behavior studies to explore the spatial-temporal behavioral patterns of individuals. However, individuals’ stay behavioral pattern and its influencing factors, which are particularly significant for the design and management of scenic architectural complexes, have not been thoroughly examined. Using GPS trajectory data collected from the Palace Museum in Beijing (China), this paper investigated the visitors’ stay behavior patterns associated with temporal, spatial, and environmental influencing factors. Types of stay behavior and characteristics of stay in main stay areas were automatically recognized using Python algorithms for further and quantitative analysis. Results showed that visitors’ stay time exhibited a consistent pattern regarding psychological time allocation, a relatively unsignificant pattern regarding lunch hour, and no clear pattern regarding fatigue feature. Grouped regression analysis showed positive linear relationships with similar slopes between the average stay length and the number of stay occurrences in each type of stay area. Partial correlation analysis revealed the underlying connection between the impact of seats and greenery on stay behavior. Individually, each of the two environmental elements showed limited effect on stay frequency and stay length, while incorporating greenery into seating areas would notably increase both stay frequency and stay length.
GPS trajectory data / Free-roaming space / Stay behavior pattern / Stay area / Environmental element / The Palace Museum
[1] |
Abdou, A.H. , Mohamed, S.A.K. , Khalil, A.A.F. , Albakhit, A.I. , Alarjani, A.J.N. , 2022. Modeling the relationship between perceived service quality, tourist satisfaction, and tourists’ behavioral intentions amid COVID-19 pandemic: evidence of yoga tourists’ perspectives. Front. Psychol. 13.
|
[2] |
Baldassare, M. , 1978. Human spatial behavior. Annu. Rev. Sociol. 4, 29- 56.
|
[3] |
Baskaya, A. , Wilson, C. , Özcan, Y.Z. , 2004. Wayfinding in an unfamiliar environment: different spatial settings of two polyclinics. Environ. Behav. 36, 839- 867.
|
[4] |
Birenboim, A. , Anton-Clavé, S., Russo, A.P. , Shoval, N. , 2013. Temporal activity patterns of theme park visitors. Tourism Geogr. 15, 601- 619.
|
[5] |
Brum-Bastos, V. , Long, J. , Demsar, U. , 2018. Weather effects on human mobility: a study using multi-channel sequence analysis. Comput. Environ. Urban Syst. 71, 131- 152.
|
[6] |
Chang, D. , 2002. Spatial choice and preference in multilevel movement networks. Environ. Behav. 34, 582- 615.
|
[7] |
Chen, J. , Chen, H. , Luo, X. , 2019. Collecting building occupancy data of high resolution based on WiFi and BLE network. Autom. ConStruct. 102, 183- 194.
|
[8] |
Cheng, D. , Yue, G. , Pei, T. , Wu, M. , 2021. Clustering indoor positioning data using E-DBSCAN. ISPRS Int. J. Geo-Inf. 10, 669.
|
[9] |
Choi, J.-Y., Park, S.-A., Jung, S.-J., Lee, J.-Y., Son, K.-C., An, Y.-J., Lee, S.-W. , 2016. Physiological and psychological responses of humans to the index of greenness of an interior space. Compl. Ther. Med. 28, 37- 43.
|
[10] |
Cornacchia, G. , Pappalardo, L. , 2021. A mechanistic data-driven approach to synthesize human mobility considering the spatial, temporal, and social dimensions together. ISPRS Int. J. Geo-Inf. 10, 599.
|
[11] |
Curtis, D.S. , Rigolon, A. , Schmalz, D.L. , Brown, B.B. , 2022. Policy and environmental predictors of park visits during the first months of the COVID-19 pandemic: getting out while staying in. Environ. Behav. 54, 487- 515.
|
[12] |
De Cantis, S. , Ferrante, M. , Kahani, A. , Shoval, N. , 2016. Cruise passengers’ behavior at the destination: investigation using GPS technology. Tourism Manag. 52, 133- 150.
|
[13] |
Do, D.T. , Cheng, Y. , Shojai, A. , Chen, Y. , 2019. Public park behaviour in Da Nang: an investigation into how open space is used. Frontiers of Architectural Research 8, 454- 470.
|
[14] |
Dogu, U. , Erkip, F. , 2000. Spatial factors affecting wayfinding and orientation: a case study in a shopping mall. Environ. Behav. 32, 731- 755.
|
[15] |
East, D. , Osborne, P. , Kemp, S. , Woodfine, T. , 2017. Combining GPS & survey data improves understanding of visitor behaviour. Tourism Manag. 61, 307- 320.
|
[16] |
Evans, G.W. , Ferguson, K.T. , 2011. Built environment and mental health. In: Nriagu, J.O. (Ed.), Encyclopedia of Environmental Health. Elsevier, Burlington, pp. 446-449.
|
[17] |
Feng, Y. , Duives, D.C. , Hoogendoorn, S.P. , 2022. Wayfinding behaviour in a multi-level building: a comparative study of HMD VR and Desktop VR. Adv. Eng. Inf. 51, 101475.
|
[18] |
Grinberger, A.Y. , Shoval, N. , 2019. Spatiotemporal contingencies in tourists’ intradiurnal mobility patterns. J. Trav. Res. 58, 512- 530.
|
[19] |
Harari, G.M. , Müller, S.R. , Aung, M.S. , Rentfrow, P.J. , 2017. Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences, Big data in the behavioural sciences 18, 83- 90.
|
[20] |
Hashim, M.S. , Said, I. , 2013. Effectiveness of wayfinding towards spatial space and human behavior in theme park. In: Procedia - Social and Behavioral Sciences, AcE-Bs 2013 Hanoi (ASEAN Conference on Environment-Behaviour Studies), vol. 85. Hanoi Architectural University, Hanoi, Vietnam, pp. 282-295, 18-21 March 2013.
|
[21] |
Hu, X. , Shen, P. , Shi, Y. , Zhang, Z. , 2020. Using Wi-Fi Probe and Location Data to Analyze the Human Distribution Characteristics of Green Spaces: A Case Study of the Yanfu Greenland Park, China. Urban Forestry & Urban Greening.
|
[22] |
Huang, W. , Lin, Y. , Lin, B. , Zhao, L. , 2019. Modeling and predicting the occupancy in a China hub airport terminal using Wi-Fi data. Energy Build. 203, 109439.
|
[23] |
Huang, X. , Wu, B. , 2012. Intra-attraction tourist spatial-temporal behaviour patterns. Tourism Geogr 14 (4), 625- 645.
|
[24] |
Huang, W. , Zhang, Y. , Wu, M. , Dang, A. , 2018. Tourist behavioral analysis of smart national park based on WiFi positioning data: case study on huangshan national park. Chinese Landscape Architecture 34, 25-31 [in Chinese].
|
[25] |
Janeczko, E. , Bielinis, E. , Wójcik, R. , Woźnicka, M. , Kędziora, W. , Łukowski, A. , Elsadek, M. , Szyc, K. , Janeczko, K. , 2020. When urban environment is restorative: the effect of walking in suburbs and forests on psychological and physiological relaxation of young polish adults. Forests 11, 591.
|
[26] |
Jia, T. , Luo, X. , Li, X. , 2021. Delineating a hierarchical organization of ranked urban clusters using a spatial interaction network. Comput. Environ. Urban Syst. 87, 101617.
|
[27] |
Kim, M. , Moon, J. , 2013. A study on the correlation between viewing behavior and exhibiting methods in museums - focusing on viewing behavior on weekdays and weekends in medium sized history museums in Korea -. J. Asian Architect. Build Eng. 12, 173- 180.
|
[28] |
Kuepper, M. , Seyfried, A. , 2020. Analysis of space usage on train station platforms based on trajectory data. Sustainability 12, 8325.
|
[29] |
Kuliga, S.F. , Nelligan, B. , Dalton, R.C. , Marchette, S. , Shelton, A.L. , Carlson, L. , Hölscher, C. , 2019. Exploring individual differences and building complexity in wayfinding: the case of the Seattle Central Library. Environ. Behav. 51, 622- 665.
|
[30] |
Lago, P. , Jiménez-Guarín, C., Roncancio, C. , 2014. A case study on the analysis of behavior patterns and pattern changes in smart environments. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (Eds.), Ambient Assisted Living and Daily Activities, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 296-303.
|
[31] |
Lau, G. , McKercher, B. , 2006. Understanding tourist movement patterns in a destination: a GIS approach. Tourism Hospit. Res. 7, 39- 49.
|
[32] |
Li, L. , Li, X. , Yang, Y. , Dong, J. , 2019. Indoor tracking trajectory data similarity analysis with a deep convolutional autoencoder. Sustain. Cities Soc. 45, 588- 595.
|
[33] |
Li, M. , Ye, X. , Zhang, S. , Tang, X. , Shen, Z. , 2018. A framework of comparative urban trajectory analysis. Environ. Plan. B Urban Anal. City Sci. 45 (3), 489- 507.
|
[34] |
Li, R. , Klippel, A. , 2016. Wayfinding behaviors in complex buildings: the impact of environmental legibility and familiarity. Environ. Behav. 48, 482- 510.
|
[35] |
Li, Y. , Xie, J. , Gao, X. , Law, A. , 2021. A Method of selecting potential development regions based on GPS and social network models-from the perspective of tourist behavior. Asia Pac. J. Tourism Res. 26 (2), 183- 199.
|
[36] |
Lin, Y. , Huang, W. , 2017. Behavior analysis and individual labeling using data from Wi-Fi IPS. Presented at the ACADIA 2017: Disciplines and Disruption 366-373. Cambridge (Massachusetts), USA.
|
[37] |
Liu, S. , Long, Y. , Zhang, L. , Liu, H. , 2021. Semantic enhancement of human urban activity chain construction using mobile phone signaling data. ISPRS Int. J. Geo-Inf. 10, 545.
|
[38] |
Liu, Y. , Wang, F. , Xiao, Y. , Gao, S. , 2012. Urban land uses and traffic ‘source-sink areas’: evidence from GPS-enabled taxi data in Shanghai. Landsc. Urban Plann. 106 (1), 73- 87.
|
[39] |
Long, Y. , Han, H. , Tu, Y. , Shu, X. , 2015. Evaluating the effectiveness of urban growth boundaries using human mobility and activity records. Cities 46, 76- 84.
|
[40] |
Ma, D. , Osaragi, T. , Oki, T. , Jiang, B. , 2020. Exploring the heterogeneity of human urban movements using geo-tagged tweets. Int. J. Geogr. Inf. Sci. 34 (12), 2475- 2496.
|
[41] |
Mckercher, B. , Lau, G. , 2008. Movement patterns of tourists within a destination. Tourism Geogr. 10, 355- 374.
|
[42] |
McKercher, B. , Shoval, N. , Ng, E. , Birenboim, A. , 2012. First and repeat visitor behaviour: GPS tracking and GIS analysis in Hong Kong. Tourism Geogr. 14, 147- 161.
|
[43] |
Meagher, B.R. , Marsh, K.L. , 2015. Testing an ecological account of spaciousness in real and virtual environments. Environ. Behav. 47, 782- 815.
|
[44] |
Moussaïd, M., Helbing, D. , Garnier, S. , Johansson, A. , Combe, M. , Theraulaz, G. , 2009. Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proc. Biol. Sci. 276, 2755- 2762.
|
[45] |
Nikolopoulou, M. (Ed.), 2004. Designing Open Spaces in the Urban Environment: A Bioclimatic Approach. Centre for Renewable Energy Sources, EESD, FP5, Athens.
|
[46] |
Nikolopoulou, M. , Lykoudis, S. , 2006. Thermal comfort in outdoor urban spaces: analysis across different European countries. Build. Environ. 41, 1455- 1470.
|
[47] |
Nikolopoulou, M. , Steemers, K. , 2003. Thermal comfort and psychological adaptation as a guide for designing urban spaces. Energy and Buildings, Special issue on urban research 35, 95- 101.
|
[48] |
Oppermann, M. , Munzner, T. , 2020. Ocupado: visualizing location-based counts over time across buildings. Comput. Graph. Forum 39, 127- 138.
|
[49] |
Peng, S. , Maing, M. , 2021. Influential factors of age-friendly neighborhood open space under high-density high-rise housing context in hot weather: a case study of public housing in Hong Kong. Cities 115, 103231.
|
[50] |
Qin, K. , Xu, Y. , Kang, C. , Kwan, M.-P. , 2020. A graph convolutional network model for evaluating potential congestion spots based on local urban built environments. Trans. GIS 24, 1382- 1401.
|
[51] |
Qin, T. , Shangguan, W. , Song, G. , Tang, J. , 2018. Spatio-temporal routine mining on mobile phone data. ACM Trans. Knowl. Discov. Data 12 (5), 1- 24.
|
[52] |
Rad, P.N. , Behzadi, F. , Yazdanfar, A. , Ghamari, H. , Zabeh, E. , Lashgari, R. , 2024. Cognitive and Perceptual Influences of Architectural and Urban Environments with an Emphasis on the Experimental Procedures and Techniques.
|
[53] |
Rashid, M. , Wineman, J. , Zimring, C. , 2009. Space, behavior, and environmental perception in open-plan offices: a prospective study. Environ. Plann. Plann. Des. 36, 432- 449.
|
[54] |
Šerić, M., Mikulić, J., Ozretić Došen, Ð. , 2023. Understanding prevention measures and tourist behavior in Croatia during the COVID-19 pandemic. A mixed-method approach. Economic Research-Ekonomska Istraživanja 36, 2135556.
|
[55] |
Shahhoseini, Z. , Sarvi, M. , 2019. Pedestrian crowd flows in shared spaces: investigating the impact of geometry based on micro and macro scale measures. Transp. Res. Part B Methodol. 122, 57- 87.
|
[56] |
Sheng, Q. , Wan, D. , Yu, B. , 2021. Effect of space configurational attributes on social interactions in urban parks. Sustainability 13, 7805.
|
[57] |
Shi, L. , Huang, C. , Liu, M. , Yan, J. , Jiang, T. , Tan, Z. , Hu, Y. , Chen, W. , Zhang, X. , 2021. UrbanMotion: visual analysis of metropolitan-scale sparse trajectories. IEEE Trans. Visual. Comput. Graph. 27 (10), 3881- 3899.
|
[58] |
Sun, J. , Zhang, J.-H., Zhang, H. , Wang, C. , Duan, X. , Chen, M. , 2020. Development and validation of a tourism fatigue scale. Tourism Manag. 81, 104121.
|
[59] |
Sun, S. , Sun, C. , Duives, D.C. , Hoogendoorn, S.P. , 2021. Deviation of pedestrian path due to the presence of building entrances. J. Adv. Transport. 2021, e5594738.
|
[60] |
Takayanagi, H. , Yamada, S. , Sugahara, S. , Koumei, S. , Shibahara, H. , 2016. A study on the evaluation method for local congestion in pedestrian spaces using the Traj-scalar model. J. Asian Architect. Build Eng. 15, 397- 402.
|
[61] |
Wang, S. , Mei, G. , Cuomo, S. , 2021. A generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis. Concurrency Comput. Pract. Ex. 33, e5335.
|
[62] |
Wineman, J.D. , Peponis, J. , 2010. Constructing spatial meaning: spatial affordances in museum design. Environ. Behav. 42, 86- 109.
|
[63] |
Wu, X. , Oldfield, P. , Heath, T. , 2020. Spatial openness and student activities in an atrium: a parametric evaluation of a social informal learning environment. Build. Environ. 182, 107141.
|
[64] |
Xiao, Z. , Fang, H. , Jiang, H. , Bai, J. , Havyarimana, V. , Chen, H. , 2022. Understanding urban area attractiveness based on private car trajectory data using a deep learning approach. IEEE Trans. Intell. Transport. Syst. 23, 12343- 12352.
|
[65] |
Xu, D. , Cong, L. , Wall, G. , 2020. Visitors’ spatio-temporal behavior at a zoo in China. Asia Pac. J. Tourism Res. 25, 931- 947.
|
[66] |
Yang, L. , Cheng, B. , Deng, N. , Zhou, Z. , Huang, W. , 2019. The influence of supermarket spatial layout on shopping behavior and product sales: an application of the ultra-wideband indoor positioning system. CAADRIA 2019, Proceedings of the 24th CAADRIA Conference 1 (2019), 301- 310.
|
[67] |
Yang, L. , Huang, W. , 2019. Multi-scale analysis of residential behaviour based on UWB indoor positioning system-a case study of retired household in Beijing, China. J. Asian Architect. Build Eng. 18, 494- 506.
|
[68] |
Yi, D. , Liu, Y. , Qin, J. , Zhang, J. , 2020. Identifying urban traveling hotspots using an interaction-based spatio-temporal data field and trajectory data: a case study within the sixth ring road of beijing. Sustainability 12, 9662.
|
[69] |
Yıldırım, Ö.C., Çelik, E. , 2023. Understanding pedestrian behavior and spatial relations: a pedestrianized area in Besiktas, Istanbul. Frontiers of Architectural Research 12, 67- 84.
|
[70] |
Yin, L. , Lin, N. , Zhao, Z. , 2021. Mining daily activity chains from large-scale mobile phone location data. Cities 109, 103013.
|
[71] |
Yuan, H. , Feng, L. , Qian, Y. , 2017. Mining user movement similarity based on massive GPS trajectory data with temporal effects. J. Electron. Commer. Res. 18 (4), 303- 316.
|
[72] |
Zacharias, J. , Stathopoulos, T. , Wu, H. , 2004. Spatial behavior in San Francisco’s plazas: the effects of microclimate, other people, and environmental design. Environ. Behav. 36, 638- 658.
|
[73] |
Zaman, U. , Raza, S.H. , Abbasi, S. , Aktan, M. , Farías, P. , 2021. Sustainable or a butterfly effect in global tourism? Nexus of pandemic fatigue, COVID-19-branded destination safety, travel stimulus incentives, and post-pandemic revenge travel. Sustainability 13, 12834.
|
[74] |
Zhang, Y. , Liu, L. , Wang, H. , 2019. A new perspective on the temporal pattern of human activities in cities: the case of Shanghai. Cities 87, 196- 204.
|
[75] |
Zheng, W. , Li, M. , Lin, Z. , Zhang, Y. , 2022. Leveraging tourist trajectory data for effective destination planning and management: a new heuristic approach. Tourism Manag. 89, 104437.
|
[76] |
Zheng, W. , Liao, Z. , Qin, J. , 2017. Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction. Tourism Manag. 62, 335- 349.
|
[77] |
Zhou, G. , 2022. Research on GPS user trajectory analysis and behavior prediction based on swarm intelligence algorithm. J. Sens. 2022, 7554560.
|
/
〈 | 〉 |