Computational modelling of elderly preferences and spatial adaptability for predicting urban regeneration support in high-density cities
DOI:
https://doi.org/10.22399/ijcesen.5266Keywords:
Computational modeling, 'Electro-mobility', spatial adaptability, residential satisfaction, PLS-SEMAbstract
This study develops a computational modeling framework to examine how elderly preferences and spatial adaptability collectively influence urban regeneration support in high-density cities, with residential satisfaction serving as a mediating variable. Using a quantitative research design, data were collected from 327 elderly residents (aged 60 and above) residing in high-density urban communities across three metropolitan districts. Structured questionnaires employing a five-point Likert scale were administered. SPSS 27 facilitated descriptive statistics and reliability diagnostics, while SmartPLS 4 enabled Partial Least Squares Structural Equation Modeling (PLS-SEM). Results confirmed satisfactory reliability (Cronbach's Alpha > 0.80; CR > 0.85) and validity (AVE > 0.50; HTMT < 0.85). Path analysis revealed significant direct effects of elderly preferences (β = 0.421, t = 6.34, p < 0.001) and spatial adaptability (β = 0.318, t = 4.87, p < 0.001) on urban regeneration support. Residential satisfaction fully mediated the spatial adaptability–urban regeneration support relationship. The interaction term (Elderly Preferences × Spatial Adaptability) significantly moderated regeneration support (β = 0.193, t = 2.91, p < 0.01). The predictive model achieved R² = 0.623, indicating strong explanatory power. Findings provide actionable computational insights for urban planners and policymakers designing age-responsive regeneration strategies.
References
1. Al-Sayed, K., & Hassan, M. (2024). Residential satisfaction and urban renewal support among elderly residents in Dubai: A structural equation modeling approach. Journal of Urban Planning and Development, 150(1), 04023041. https://doi.org/10.1061/JUPDDM.UPENG-4213
2. Amerigo, M., & Aragones, J. I. (1997). A theoretical and methodological approach to the study of residential satisfaction. Journal of Environmental Psychology, 17(1), 47–57. https://doi.org/10.1006/jevp.1996.0038
3. Agarwal Nidhi, Hipona B. Jocelyn, Agarwal Ekansh (2024). Role of Media and Communication on Sustainable Entrepreneurship. Sustainable Partnership and Investment strategies for startups and SMEs, IGI publisher, Ch 10, 178-191. DOI: 10.4018/979-8-3693-2197-3.ch010.
4. Agarwal, N., Singh, A., & Ambojia, M.C.T (2024). Amalgamation of Disruptive Technologies for Implementation of Intelligent Manufacturing. Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities, CRC Press, ISBN-9781032630748, 42-66.
5. Buffel, T., Handler, S., & Phillipson, C. (2021). Age-friendly cities and communities: A global perspective (2nd ed.). Policy Press.
6. Cerin, E., Lee, K. Y., Barnett, A., Sit, C. H. P., Cheung, M. C., & Chan, W. M. (2023). Walking and residential satisfaction among older adults in Hong Kong: A mediation analysis. Health & Place, 81, 103011. https://doi.org/10.1016/j.healthplace.2023.103011
7. Chen, Y., Zhang, L., & Li, X. (2022). Computational approaches to urban regeneration support: Evidence from high-density Chinese cities. Urban Studies, 59(14), 2941–2960. https://doi.org/10.1177/00420980221083212
8. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
9. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2022). When to use and how to report the results of PLS-SEM (2nd ed.). European Business Review. https://doi.org/10.1108/EBR-11-2018-0203
10. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
11. Khari, D., Sharma, V., and Agarwal, N., (2020). Effect of pandemic COVID-19 on economic crisis and health issues globally. Cosmos Journal of Engineering & Technology, 10(1): 9- 15. ISSN: 2231-4210.
12. Liu, H., & Chen, W. (2023). Elderly preferences, neighborhood accessibility, and support for urban renewal in Guangzhou: A partial least squares structural equation modeling analysis. Habitat International, 131, 102715. https://doi.org/10.1016/j.habitatint.2022.102715
13. Ng, M. K., Tang, W. S., Lee, J., & Leung, D. (2023). Elderly housing preferences in high-density Asian cities: A multidimensional factor analytic investigation. Cities, 133, 104091. https://doi.org/10.1016/j.cities.2022.104091
14. Park, S., & Lee, J. H. (2022). Aging in dense cities: Walkability, green space, and elderly preferences in Seoul. Landscape and Urban Planning, 218, 104278. https://doi.org/10.1016/j.landurbplan.2021.104278
15. Pojani, D., & Stead, D. (2021). Sustainable urban transport: An overview and discussion of concepts and trends. Sustainability, 13(4), 1904. https://doi.org/10.3390/su13041904
16. Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2023). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2021.1at
17. Roberts, P., Sykes, H., & Granger, R. (2017). Urban regeneration: A handbook (2nd ed.). SAGE Publications.
18. Sarstedt, M., Hair, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2021). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
19. Tallon, A. (2020). Urban regeneration in the UK (3rd ed.). Routledge.
20. World Health Organization. (2022). Ageing and health: Key facts. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health
21. Yung, E. H. K., Chan, E. H. W., & Xu, Y. (2020). Spatial adaptability and sense of place among elderly residents in Hong Kong public housing: A multilevel regression analysis. Urban Design International, 25(3), 241–257. https://doi.org/10.1057/s41289-020-00125-4
22. Zhang, Q., & Wang, F. (2024). Geospatial computational modeling of spatial adaptability and elderly residential satisfaction in Shanghai's inner city. Computers, Environment and Urban Systems, 107, 102073. https://doi.org/10.1016/j.compenvurbsys.2024.102073
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
