Abstract
In the face of rapid urbanization and increasing environmental risks, building resilient cities has become a global priority. This study introduces a novel methodology for quantifying urban spatial resilience by integrating multi-criteria decision analysis (MCDA) and back propagation neural network (BPNN). The urban spatial resilience assessment system encompasses five critical criteria layers (urban spatial scale, structure, form, function, and network resilience) and 29 indicator layers. An empirical study of Yinchuan City, China, demonstrates the effectiveness of the MCDA-BPNN model in quantifying urban spatial resilience. The models identified significant disparities in resilience between central and peripheral urban zones, with central areas showing higher resilience scores compared to peripheral areas. This spatial pattern was most pronounced in structure, functional and network resilience. These results contribute to the field of urban environmental science by advancing the fundamental understanding of urban spatial resilience mechanisms and their spatial distribution. The study has significant implications for urban planners, policymakers, and risk management authorities, enabling data-driven decision-making and the development of targeted, resilience-oriented urban policies and strategies. The MCDA-BPNN approach offers a promising tool for comprehensive urban resilience assessment in diverse urban contexts.
Published Version
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