Traditional spatial analysis methods fail to capture the complex interactions shaping immigrant settlement patterns in metropolitan areas. This study introduces a novel deep learning framework combining Spatial Gaussian-Bernoulli DBN with GraphSAGE to analyze multi-ethnic settlement patterns. The proposed two-stage approach addresses GNN limitations with high-dimensional data by first extracting latent features through spatially constrained unsupervised learning, then applying graph-based prediction along street networks. Using Toronto data (2001–2021, 3,741 areas, 401 census features), the framework achieves exceptional accuracy for Iranian (R 2 = 0.953), Chinese (R 2 = 0.989), and Indian (R 2 = 0.987) communities—substantially outperforming traditional methods (R 2 = 0.382) and standard deep learning (R 2 = 0.885). The framework reveals three distinct settlement mechanisms: business-centered enclaves, education-transit corridors, and distributed multi-nuclear patterns. This study demonstrates that street network topology, not Euclidean distance, governs settlement diffusion, validating accessibility-based urban theory. Feature importance evolution shows how universal needs specialize into ethnic preferences—businesses for Iranians (0.15→0.30), education for Chinese (0.16→0.30), and growth dynamics for Indians (0.17→0.27). This interpretable, transferable framework establishes a new paradigm where domain knowledge amplifies machine learning for urban demographic modeling.
Moghaddam et al. (Tue,) studied this question.