Resumen
The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis.