Quantitative identification of urban functional zones driven by POI and AOI coupling

Urban functional zones are fundamental spatial units for understanding urban structure and guiding planning interventions, yet their identification in rapidly urbanizing Chinese cities faces two persistent challenges: the opacity of multi-source data fusion mechanisms and the inadequate treatment of functional hybridization. This study proposes a quantitative urban functional zone identification framework that addresses both challenges through an “externalized fusion” paradigm—making data fusion weights explicit and traceable—coupled with a classification system that explicitly incorporates mixed functional zones. Using Zhengzhou, a rapidly urbanizing national transportation hub, as the study area and OSM road networks as spatial units, we systematically evaluated random forest, SVM, and KNN classifiers, and conducted nine controlled fusion-ratio experiments (POI:AOI from 1:9 to 9:1). The results reveal three substantive findings: (1) a stable fusion ratio of POI:AOI = 3:7 achieves optimal spatial pattern stability, quantitatively demonstrating the synergistic balance between “functional signals” (POI) and “spatial substrates” (AOI); (2) mixed functional zones predominate in Zhengzhou’s urban fabric, with residential-involved mixtures accounting for 55.5% of all zones, challenging conventional single-use zoning assumptions; and (3) a clear “core-residential versus periphery-circulation” spatial differentiation pattern emerges, directly reflecting the city’s strategic positioning as a national logistics hub. Validation against EULUC_China data shows 67.6% overall consistency (90.3% for residential zones), with field verification confirming geographic credibility. This study contributes not only a replicable methodological pathway, but more importantly, empirical and theoretical insights into how macro-level urban strategies shape micro-level land-use hybridization—offering implications for evidence-based planning in data-limited contexts and advancing the application of urban spatial theories to developing-world megacities.