With the intensification of urbanization across China, the underutilization of rural housing resources has emerged as a pressing socio-economic and spatial challenge. To enhance the efficiency of shared rural housing transformation and support rural revitalization strategies, a data-driven framework was developed to identify and prioritize hierarchical user demand attributes. Demand items were initially derived through an extensive literature review and subsequently refined using a modified Kano model, informed by structured questionnaire surveys. To strengthen attribute prioritization and functional alignment, the Importance-Satisfaction (I-S) model and the Configuration Index Model were employed for triangulated classification. Findings revealed that structural safety (A11) constitutes a highly attractive attribute, exerting a strong influence on user satisfaction when addressed, yet inducing minimal dissatisfaction when absent. Smart home (A4) and green materials (A7) were identified as key quality attributes, essential for functional enhancement and user experience optimization. In contrast, cost-effectiveness (A1) and investment return (A2) were classified as high-value-added attributes, playing a pivotal role in decision-making among economically motivated users. New energy utilization (A8) and green design (A9) were categorized as fundamental, non-negotiable attributes, reflecting evolving sustainability expectations. Meanwhile, cultural inheritance (A15) and cultural display (A17) exhibited characteristics of low-attractiveness attributes, indicating limited influence on user satisfaction. Salvage (A10) emerged as a potential quality attribute with latent user recognition. The resulting demand classification elucidates a structured pathway for functional optimization, offering a robust analytical lens for the adaptive transformation of idle rural properties into shared accommodation assets. The applicability of the refined Kano model in rural spatial redevelopment was thereby validated. By integrating multidimensional user preferences and sustainability considerations, this study contributes an empirically grounded decision-support tool for policymakers, designers, and stakeholders engaged in rural land use regeneration and housing innovation. The proposed framework holds significant implications for the sustainable utilization of dormant rural infrastructure within broader urban-rural integration agendas.
Urban resilience has become a central framework for advancing sustainable development in the context of escalating urban risks. To investigate the role of population density in shaping resilience, panel data from 114 large Chinese cities covering the period 2006–2021 (excluding the COVID-19 years to avoid potential distortions) were analyzed. A multidimensional urban resilience evaluation system was constructed, encompassing five key domains: economy, society, institutions, environment, and infrastructure. Resilience levels were assessed through the entropy-weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), while a panel threshold regression model was applied to capture potential nonlinearities in the density–resilience relationship. Results demonstrate that urban resilience in China has exhibited a sustained upward trajectory, largely driven by advances in infrastructure provision and economic capacity. However, population density exerts a nonlinear “double-threshold effect”. At low levels of density, the effect on resilience is statistically insignificant; within a medium-density range, a pronounced negative impact emerges, constituting a “medium-density trap”; and at high densities, the adverse effects are attenuated, suggesting that urban systems may gradually adapt to intensified population pressures. This trap is most evident in regional center cities and rapidly developing urban areas, where governance capacity, infrastructure investment, and resource allocation have lagged behind demographic expansion. These findings highlight the stage-dependent vulnerabilities embedded in urbanization processes and indicate that resilience is not solely a function of density itself but also of institutional capacity and infrastructural adequacy. Differentiated governance strategies are therefore required, including targeted improvements in public infrastructure, strengthened institutional and administrative capacities, and the optimization of spatial configurations to accommodate density-specific challenges. By identifying the thresholds at which population density alters resilience trajectories, this study contributes to a deeper theoretical understanding of urban vulnerability and offers actionable insights for policymakers seeking to enhance resilience under conditions of rapid urban growth and high-density development.
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