Decoding green space supply–demand mismatch through urban morphology: Toward equitable urban planning with explainable machine learning
Lijuan Sun, Wei Liu, Qiqi Liu
TL;DR
This study uses machine learning to understand how urban design affects the fairness of green space distribution in Nanjing, aiming to improve urban planning.
Contribution
A novel framework combining explainable machine learning with urban morphology analysis to quantify and address green space inequities.
Findings
48 TAZs in Nanjing's urban core showed severe green space deficits, while 203 peripheral TAZs had surpluses.
Urban compactness and floor area ratio negatively impact green space supply-demand balance.
Public facility coverage and residential land proportion help reduce green space mismatches.
Abstract
Urban green spaces (UGS), as essential components of urban ecological infrastructure, play a vital role in improving residents’ quality of life and fostering spatial equity. However, rapid urbanization and intensive land use have intensified UGS supply–demand mismatches and associated “green inequities”, while their spatial drivers remain insufficiently understood. Taking the central urban area of Nanjing as a case study, this study developed a comprehensive framework to quantify UGS supply, demand, and spatial mismatches in 2022, thereby revealing inequities in green resource allocation. An explainable machine learning model (XGBoost–SHAP) was further applied to identify the urban morphological drivers of these mismatches. The results showed that all traffic analysis zones (TAZs) experienced varying degrees of spatial mismatch: 48 TAZs in urban core suffered from severe green space…
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Taxonomy
TopicsLand Use and Ecosystem Services · Urban Green Space and Health · Urban Transport and Accessibility