Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Humanities and Social Sciences Communications

DOI: 10.1057/s41599-026-08426-y

Authors: Rui Yang, Chuanli Liu, Zihao Chen

Institution: Jiangxi University of Science and Technology

Language: English

Topic: Urban Design and Spatial Analysis

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,890