Abstract
Urban areas are increasingly required to balance sustainability and resilience goals; however, these two dimensions often rely on contrasting planning logics. Sustainability is commonly associated with efficiency-oriented land-use optimisation, while resilience emphasises redundancy and adaptive capacity. Despite the growing recognition of this tension, spatially explicit and indicator-based tools capable of assessing sustainability and resilience trade-offs at the intra-urban scale remain limited.
This study develops an integrated indicator-based spatial decision-support framework to operationalise the sustainability-resilience nexus in urban systems. The approach combines machine learning-based analysis of multi-temporal satellite imagery with hierarchical multi-criteria decision analysis to construct a composite Urban Global Index (UGI). Land-use and demographic indicators derived from supervised classification were used to generate six spatial indicators organized around efficiency-oriented and redundancy-oriented dimensions, which were weighted using the analytic hierarchy process based on expert judgement and implemented within a geographic information system environment. The framework is demonstrated through its application to a metropolitan case study in Morocco.
Two complementary assessment logics were examined: one treating sustainability and resilience as separate dimensions, and another integrating all indicators into a single composite index. The results show strong convergence between the two approaches, confirming the robustness of the framework while revealing intra-metropolitan disparities linked to local urban dynamics such as densification strategies, uneven resilience attributes, and large-scale housing interventions.
By integrating efficiency- and redundancy-based indicators, the UGI provides a quantitative and policy-relevant representation of urban performance. The proposed framework contributes to indicator development by offering a transferable tool for assessing sustainability and resilience trade-offs across diverse urban contexts.
•Indicator-based ML-GIS framework for spatial urban assessment.•UGI integrates sustainability and resilience via efficiency-redundancy trade-offs.•Dual scenarios enable comparison of sustainability–resilience dynamics.•Results reveal stable patterns with local spatial heterogeneity.•Framework supports indicator-based spatial decision-making.