Noise-Aware Explanatory Enrichment of Origin–Destination Frequency Tensors for Interpretable Mobility Clustering

This paper proposes a noise-aware, iterative methodology for enriching an initial mobility clustering through the sequential addition of explanatory variables. The framework separates one-off density-based cluster discovery from scalable characterisation over a fixed analytical population. Candidate variables are retained only when they improve the predefined distributional criteria and preserve continuity with the latest accepted partition. Evaluation on a large sensing-based mobility dataset from Valencia shows that the proposed architecture supports scalable and auditable explanatory enrichment while keeping the observation set unchanged and explicitly separating distributional admissibility from inter-partition continuity.