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The effects of land use and socioeconomic changes on urban landscape patterns and functional zones have been increasingly investigated around the world; however, our knowledge on these effects is still inadequate for sustainably managing urban ecosystems. The urban functional zone (UFZ) refers to a kind of regional space that provides specific functions for human activities and reflects the land use type in a city. They are important for urban planning and exploring urban texture dynamics. UFZs improve understanding of sustainable development for urban ecosystems with extreme environments and unique social backgrounds. However, the identification methods for UFZs are incomplete because of a lack of socioeconomic attributes, as well as their hierarchical relations. Here, we present a hierarchical weighted clustering model to identify UFZs based on the entropy weight method. The data included points of interest (POIs), land use type data, road network data, socioeconomic data, and population density. We found that the adjusted cosine metric and the average criterion were the optimal distance metric and linkage strategy, respectively, to cluster urban zone data. The performance with weighted data was better than that with raw data, and the level of the POI classification scheme and landscape pattern affected the accuracy of identification UFZs. The research indicated that the hierarchical weighted clustering model was a useful method to classify UFZs in order to improve urban planning and environmental management schemes.

A Method for Identifying Urban Functional Zones Based on Landscape Types and Human Activities