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.

Urban expansion is influenced by complex and variable social, economic, natural, and policy-related factors. Given their nonlinear interactions, accurately modeling these urban expansion processes poses a challenge. While most studies treat the city as an independent entity, prioritizing internal urban factors, urban land expansion is influenced by intercity interactions and the ecological environment. This study proposes a new approach that couples the gravitational field model, ecological constraints, and the Future Land Use Simulation (FLUS) model, comprehensively considering the impact of intercity interaction and the ecological environment. The experiment in Henan Province in China assessed the effects of factors such as basic spatial variables (Slope and distance to the city center), urban gravitational field, and ecological constraints on urban expansion through the optimal parameters-based geographical detector (OPGD) model. The feasibility of the method was confirmed by this case study, which shows that it improves the simulation accuracy of the urban agglomeration scale, particularly for central cities. We identified the urban gravitational field and ecological constraints as two important factors affecting the expansion of urban agglomerations. Areas with stronger urban spatial fields are more likely to attract neighboring resources and promote urban expansion, whereas ecological factors constrain the expansion behavior of cities under the condition of ecological and environmental resource protection needs, and both of them work together to influence the expansion behavior of urban clusters. Therefore, we posit that intercity interactions and ecological constraints are important considerations for the future spatial planning of urban agglomerations and for coordinating the harmonious development of urbanization and ecological conservation.