Sensing Multi-level Urban Functional Structures by Using Time Series Taxi Trajectory Data

Geospatial big data has been widely applied to distinguish urban functions. Especially, the research of functional hierarchy is of profound significance in understanding the structural characteristics and distributional forms of urban functions thoroughly, but related studies are still vacant. Therefore, this study depicts human mobility patterns based on time-series taxi trajectory data and point of interests (POIs) data, and identifies urban functional properties and spatial structures through combining dynamic time warping and K-MEDOIDS clustering algorithm. The results show that the urban functions in the central area of Guangzhou possess obvious hierarchical characteristics. With the refinement of hierarchies, the functional properties are developed from the dual-structure of working-living into the triple-structure of working-living-entertainment gradually. Moreover, the spatial structure is consistent with the ring-shaped structure, and its urban functions are gradually transformed from the living-entertainment function at outer rings into the commercial-entertainment function at central rings, and present different tendencies at different rings. This study provides efficient references for urban planners to understand the property changes and structural characteristics of urban functions.