AbstractOver decades, many cities have been expanded and functionally diversified by population activities, socio-demographics and attributes of the built environment. Urban expansion and development have led to the emergence of spatial structures of cities. Uncovering cities’ spatial structures is critical to understanding various urban phenomena such as segregation, equity of access, and sustainability. In this study, we propose using a neural embedding model—graph neural network (GNN)—that leverages the heterogeneous features of urban areas and their interactions captured by human mobility networks to obtain vector representations of these areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components (such as distribution of facilities) and population attributes and activities. The clustered representations of urban areas show the shared characteristics among urban areas in the same cluster. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the spatial structure in another county, allowing us to make cross-county comparisons and inferences. The findings reveal complex relationships among urban components in cities. Since the identified multifaceted spatial structures capture the combined effects of various mechanisms, such as segregation, disparate facility distribution, and human mobility, the findings could help identify the limitations of the current city structure to inform planning decisions and policies. Also, the model and findings set the stage for a variety of research in urban planning, engineering and social science through an integrated understanding of how the complex interactions between urban components and population activities and attributes shape the spatial structures in cities.