Identification of Urban Functional Areas and Urban Spatial Structure Analysis by Fusing Multi-Source Data Features: A Case Study of Zhengzhou, China

Abstract

The identification and delineation of urban functional zones (UFZs), which are the basic units of urban organisms, are crucial for understanding complex urban systems and the rational allocation and management of resources. Points of interest (POI) data are weak in identifying UFZs in areas with low building density and sparse data, whereas remote sensing data lack the necessary semantic information for functional zoning, and single-source data cannot perform a highly comprehensive characterization of complex UFZs. To address these issues, this study proposes a method for identifying UFZs by fusing multi-attribute features from multi-source data and introduces nighttime light and land surface temperature (LST) indicators as functional zoning references, taking the main urban area of Zhengzhou as an example. The experimental results show that the POI data with integrated three-level semantic information can characterize the semantic information of functional areas well, and the incorporation of multi-spectral, nighttime light, and LST data can further improve the recognition accuracy by approximately 10.1% compared with the POI single-source data. The final recognition accuracy and kappa coefficient reached 84.00% and 0.8162, respectively, indicating that the method is largely consistent with the actual situation and is feasible. The analysis showed that the main urban area of Zhengzhou as a whole is characterized by the coordinated development of single and mixed functional areas, in which a distinct residential-commercial-public complex is formed, and the urban functional areas on the block scale have diverse attributes. This study can provide a decision-making reference for the future development planning and management of Zhengzhou, China.