A Novel Graph-Based Framework for Classifying Urban Functional Zones with Multisource Data and Human Mobility Patterns

Recent research has shown the advantages of incorporating multisource geospatial data into the classification of urban functional zones (UFZs), particularly remote sensing and social sensing data. However, the effects of combining datasets of varying quality have not been thoroughly analyzed. In addition, human mobility patterns from social sensing data, which capture signals of human activities, are often represented by origin-destination pairs, thus ignoring spatial relationships between UFZs embedded in mobility trajectories. To address the aforementioned issues, this study proposed a graph-based UFZ classification framework that fuses semantic features from high spatial resolution (HSR) remote sensing images, points of interest, and GPS trajectory data. The framework involves three main steps: (1) High-level scene information in HSR remote sensing imageries was extracted through deep neural networks, and multisource semantic embeddings were constructed based on physical features and social sensing features from multiple geospatial data sources; (2) UFZ mobility graph was constructed by spatially joining trajectory information with UFZs to construct topological connections between functional parcel segments; and (3) UFZ segments and multisource semantic features were transformed into nodes and embeddings in the mobility graphs, and subsequently graph-based models were adopted to identify UFZs. The proposed framework was tested on Zhuhai and Singapore datasets. Results indicated that it outperformed traditional classification methods with an overall accuracy of 76.7% and 84.5% for Zhuhai and Singapore datasets, respectively. The proposed framework contributes to literature in heterogeneous data fusion and is generalizable to other UFZ classification scenarios where human mobility patterns play a role.