Integrating Geospatial Technologies and Multi-Criteria Decision Analysis for Sustainable and Resilient Urban Planning

Résumé

The increasing pace of urbanization has heightened the need for urban systems that are both sustainable and resilient.While extensive research has been conducted on these two concepts, the interplay between them remains insufficiently explored.In particular, sustainability is often associated with efficiency-maximizing resource utilization-whereas resilience emphasizes redundancy, ensuring the presence of backup systems to mitigate risks.To address this critical gap, a comprehensive framework is proposed that integrates these dual objectives within urban land-use planning.Geospatial technologies and multi-criteria decision analysis are employed to systematically assess the balance between efficiency and redundancy in urban environments.A machine learning (ML)-based classification of land use and built-up area changes, combined with demographic and infrastructural data, is utilized to quantify these factors.The proposed approach provides urban planners and policymakers with an adaptable decision-making tool, enabling context-specific prioritization of efficiency or redundancy based on local requirements.In high-density urban areas experiencing rapid expansion, efficiency is emphasized to optimize land and resource use, whereas in regions vulnerable to environmental hazards, redundancy is strategically incorporated to enhance resilience without undermining overall urban functionality.The flexibility of this method offers a significant advantage over rigid, predefined planning policies that may not be suited to specific urban contexts.By facilitating informed decision-making, the framework enhances risk management, optimizes resource allocation, and supports the development of customized urban strategies, ultimately improving long-term urban performance under diverse developmental scenarios.

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