A Machine Learning-Based Framework for Predicting Place Attachment in Senior Housing: Toward Human-Centered and Age-Friendly Environmental Design
محورهای موضوعی : Built Environment
1 - Department of Art Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran.
کلید واژه: Place Attachment, Environmental Design, Machine Learning, Elderly Housing, Ridge Regression, Human-Centered Design,
چکیده مقاله :
The psychological bond between elderly residents and their living environment—termed place attachment—plays a critical role in aging-in-place strategies. This study investigates the impact of environmental design characteristics on place attachment and evaluates the predictive capabilities of machine learning in this context.
Methods: A cross-sectional survey was conducted among 490 elderly residents in Tehran using a 38-item Likert-scale questionnaire. The study applied three regression-based algorithms—Linear, Polynomial, and Ridge Regression—to model the relationship between 20 environmental design variables and place attachment scores.
Results: "Positive Home Experiences" (r = 0.68), "Freedom from Confinement" (r = 0.64), and "Safety Features" (r = 0.53) emerged as the most influential predictors. Ridge Regression achieved the highest prediction accuracy, with an R² value of 0.6792.
Conclusion: The findings demonstrate the potential of machine learning to support human-centered design by enabling the early-stage evaluation of housing for the elderly. The proposed predictive framework can inform architecture curricula, computer-aided design (CAD) tools, and age-friendly housing policies.
چکیده انگلیسی:
The psychological bond between elderly residents and their living environment—termed place attachment—plays a critical role in aging-in-place strategies. This study investigates the impact of environmental design characteristics on place attachment and evaluates the predictive capabilities of machine learning in this context.
Methods: A cross-sectional survey was conducted among 490 elderly residents in Tehran using a 38-item Likert-scale questionnaire. The study applied three regression-based algorithms—Linear, Polynomial, and Ridge Regression—to model the relationship between 20 environmental design variables and place attachment scores.
Results: "Positive Home Experiences" (r = 0.68), "Freedom from Confinement" (r = 0.64), and "Safety Features" (r = 0.53) emerged as the most influential predictors. Ridge Regression achieved the highest prediction accuracy, with an R² value of 0.6792.
Conclusion: The findings demonstrate the potential of machine learning to support human-centered design by enabling the early-stage evaluation of housing for the elderly. The proposed predictive framework can inform architecture curricula, computer-aided design (CAD) tools, and age-friendly housing policies.
منابع و مأخذ:
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1. Aboagye, E. A., Lehr, A. L., Shumaker, E., Longo, J., Pazik, J., Hesketh, R. P., & Yenkie, K. M. (2024). Machine learning methods for the forecasting of environmental impacts in early-stage process design. Systems & Control Transactions, 3, 621–628. https://doi.org/10.69997/sct.141240
2. Almevall, A., Nordmark, S., Niklasson, J., & Zingmark, K. (2021). Experiences of home as an aspect of well-being in people over 80 years: A mixed-method study. Journal of Advanced Nursing. https://doi.org/10.1111/jan.15093
3. Annink, A., & van Hees, S. (2022). Meaning over things: How objects and places matter to the residential happiness of older adults. Journal of Aging and Environment, 37(3), 277–294. https://doi.org/10.1080/26892618.2022.2092926
4. Arghiani, M., & Mirhashemi, S. M. (2022). Evaluation of the components affecting the promotion of attachment to the place in the open spaces of residential complexes. MMI, 12(29), 6. http://mmi.aui.ac.ir/article-1-1120-fa.html
5. Ariyawansa, R. G., Perera, M. A. N. R., & Priyanka, H. E. S. (2023). Suitable home environment for elderly people in Colombo, Sri Lanka. Journal of Aging and Environment, 37(1), 85–99. https://doi.org/10.1080/26892618.2021.2001708
6. Atta-ur-Rahman, Dash, S., Luhach, A. K., Chilamkurti, N., Baek, S., & Nam, Y. (2019). A Neuro-Fuzzy Approach for User Behavior Classification and Prediction. Journal of Cloud Computing, 8(1). https://doi.org/10.1186/s13677-019-0144-9
7. Bengtsson, A., Hägerhäll, C., Englund, J. E., & Grahn, P. (2015). Outdoor environments at three nursing homes: Semantic environmental descriptions. Journal of Housing for the Elderly, 29(1–2), 53–76. https://doi.org/10.1080/02763893.2014.987863
8. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. https://link.springer.com/book/9780387310732
9. Board, M., & McCormack, B. (2018). Exploring the meaning of home and its implications for the care of older people. Journal of Clinical Nursing, 27, 3070–3080. https://doi.org/10.1111/jocn.14495
10. Boccagni, P., & Duyvendak, J. W. (2021). Homemaking in the public. On the scales and stakes of framing, feeling, and claiming extra‐domestic space as "home." Sociology Compass.
11. Brown, G., & Raymond, C. (2007). The Relationship between Place Attachment and Landscape Values: Toward Mapping Place Attachment. Applied Geography, 27(2), 89–111. https://doi.org/10.1016/j.apgeog.2006.11.002
12. Carneiro, T., Medeiros Da Nobrega, R. V., Nepomuceno, T., Bian, G.-B., De Albuquerque, V. H. C., & Filho, P. P. R. (2018). Performance analysis of Google Colaboratory as a tool for accelerating deep learning applications. IEEE Access, 6, 61677–61685. https://doi.org/10.1109/access.2018.2874767
13. Castellanos-Nieves, D., & García-Forte, L. (2023). Improving automated machine-learning systems through Green AI. Applied Sciences, 13(20), 11583–11583. https://doi.org/10.3390/app132011583
14. Dahlan, A., Ibrahim, S. A. S., & Masuri, M. G. (2016). Role of the physical environment and quality of life amongst older people in institutions: A mixed methodology approach. Procedia - Social and Behavioral Sciences, 234, 106–113. https://doi.org/10.1016/j.sbspro.2016.10.225
15. Derix, C. (2012). Implicit space. ECAADe Proceedings, 2, 583–591. https://doi.org/10.52842/conf.ecaade.2012.2.583
16. E, J., Xia, B., Chen, Q., Buys, L., Susilawati, C., & Drogemuller, R. (2024). Impact of the built environment on ageing in place: A systematic overview of reviews. Buildings. https://doi.org/10.3390/buildings14082355
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