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A novel approach towards utilizing graph analyzing objects arrangement – case studies from Airbnb homes in New York and Boston

Published online by Cambridge University Press:  16 May 2024

Yanhua Yao
Yanhua Yao*
Affiliation:
Tsinghua University, China
*
yyh19@mails.tsinghua.edu.cn

Abstract

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The spatial arrangement of objects in residential environments is a crucial indicator of occupant behavior, shedding light on the complex dynamics of their interaction with the interior. This study introduces an object-based graph method for decoding urban home interiors, examining the co-presence of objects to uncover domestic behavioral patterns through indoor imagery analysis. By integrating centrality metrics with objects in graphs, we gain deeper insights into household behaviors, which provide empirical evidence for future interior design.

Keywords

interior designresearch methodologies and methodsempirical studiesgraphdomestic space
Type
Design Theory and Research Methods
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 175 - 184
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Attfield, J. (1999), Bringing modernity home: open plan in the British domestic interior, In At home: An anthropology of domestic space (Vol. 1, pp. 73-82).Google Scholar
Auslander, L. (1996), Taste and Power: furnishing modern France (Vol. 24), University of California Press.CrossRefGoogle Scholar
Cieraad, I. (Ed.). (2006), At home: an anthropology of domestic space, Syracuse University Press.Google Scholar
Corbusier, L. (2013), Towards a new architecture. Courier Corporation.Google Scholar
Douglas, M. (1991), “The idea of a home: A kind of space”, Social Research, pp: 287-307.Google Scholar
Ert, E., & Fleischer, A. (2019), “The evolution of trust in Airbnb: A case of home rental”, Annals of Tourism Research, vol. 75, pp: 279-287, https://doi.org/10.1016/j.annals.2019.01.004CrossRefGoogle Scholar
Ert, E., Fleischer, A., & Magen, N. (2016), “Trust and reputation in the sharing economy: The role of personal photos in Airbnb”, Tourism Management, vol, 55, pp:62-73, https://doi.org/10.1016/j.tourman.2016.01.013Google Scholar
Gurran, N. (2018), “Global home-sharing, local communities and the Airbnb debate: a planning research agenda”, Planning theory & practice, vol.19, no.2, pp:298-304, https://doi.org/10.1080/14649357.2017.1383731CrossRefGoogle Scholar
Hanson, J. (2003), Decoding homes and houses, Cambridge University Press.Google Scholar
Jang, H. (2022), “Judging an Airbnb booking by its cover: How profile photos affect guest ratings”, Journal of Consumer Marketing, vol.39, no.4, pp:371-382, https://doi.org/10.1108/JCM-01-2021-4341CrossRefGoogle Scholar
Lawrence, R. J. (1982), “Domestic space and society: A cross-cultural study”, Comparative Studies in Society and History, vol.24, no.1, pp:104-130, https://doi.org/10.1017/S0010417500009804CrossRefGoogle Scholar
Lipman, C. (2019), “Living with the past at home: The afterlife of inherited domestic objects”, Journal of Material Culture, vol.24, no.1, pp:83-100, https://doi.org/10.1177/1359183518801383CrossRefGoogle Scholar
Marcus, C. C. (2006), House as a mirror of self: Exploring the deeper meaning of home, Nicolas-Hays, Inc.Google Scholar
Miller, D. (Ed.). (2021), Home possessions: material culture behind closed doors, Routledge.CrossRefGoogle Scholar
Money, A. (2007), “Material culture and the living room: The appropriation and use of goods in everyday life”, Journal of Consumer Culture, vo.7, no.3, pp: 355-377, https://doi.org/10.1177/1469540507081630CrossRefGoogle Scholar
Mustafa, F. A., Hassan, A. S., & Baper, S. Y. (2010), “Using space syntax analysis in detecting privacy: a comparative study of traditional and modern house layouts in Erbil city, Iraq”, Asian Social Science, vol.6, no.8, pp: 157.CrossRefGoogle Scholar
Nguyen, L. S., et al. (2017), “Check out this place: Inferring ambiance from Airbnb photos”, IEEE Transactions on Multimedia, vol.20, no.6, pp:1499-1511, https://doi.org/10.1109/TMM.2017.2769444CrossRefGoogle Scholar
Pratt, G. J. (1980), Home decoration and the expression of identity, [PhD Thesis], University of British Columbia.Google Scholar
Rahimi, S., Liu, X., & Andris, C. (2016), “Hidden style in the city: an analysis of geolocated Airbnb rental images in ten major cities”, Proceedings of the 2nd ACM SIGSPATIAL workshop on smart cities and urban analytics, pp:1-7, https://doi.org/10.1145/3007540.3007547CrossRefGoogle Scholar
Ren, S., He, K., Girshick, R., & Sun, J. (2015), “Faster r-cnn: Towards real-time object detection with region proposal networks”.Advances in neural information processing systems, 28.Google Scholar
Rodemann, P. (1999). Patterns in interior environments: Perception, psychology, and practice, John Wiley & Sons.Google Scholar
Roche, D. (2000). A history of everyday things: the birth of consumption in France, 1600-1800, Cambridge University Press.Google Scholar
Seo, K. W. (2006). “The law of conservation of activities in domestic space”, Journal of Asian Architecture and Building Engineering, vol.5, no.1, pp. 21-28, https://doi.org/10.3130/jaabe.5.21CrossRefGoogle Scholar
Trentmann, F. (2009). “Materiality in the future of history: things, practices, and politics”, Journal of British Studies, vol. 48, no. 2, pp. 283-307, https://doi.org/10.1086/596123CrossRefGoogle Scholar
Yao, Y. (2024), Spatial Analysis Method for Housing Interior Utilizing Image Big Data, [PhD Thesis], Tsinghua University, Beijing.Google Scholar
Zhang, S., Lee, D., Singh, P. V., & Srinivasan, K. (2022), “What makes a good image? Airbnb demand analytics leveraging interpretable image features”, Management Science, vol.68, no.8, pp. 5644-5666. https://doi.org/10.1287/mnsc.2021.4175CrossRefGoogle Scholar
Zheng, C., & Zhang, J. (2023), “Inspiring guests' imagination of "home away from home" to choose Airbnb through brand storytelling”, International Journal of Contemporary Hospitality Management, vol.35, vol.6, pp. 2136-2156. https://doi.org/10.1108/IJCHM-04-2022-0444CrossRefGoogle Scholar
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017), “Places: A 10 million image database for scene recognition”, IEEE transactions on pattern analysis and machine intelligence, vol.40, no.6, pp: 1452-1464, https://doi.org/10.1109/TPAMI.2017.2723009CrossRefGoogle ScholarPubMed