Missingness-Aware Spatiotemporal Graph Learning for Urban Sensor Networks: Design Choices and Evaluation Pitfalls

Abstract

Urban traffic, environmental, and utility monitoring systems generate multivariate sensor streams whose spatial relations and missing-data mechanisms evolve over time. This critical narrative review examines graph-based forecasting and reconstruction in the presence of unreliable sensor observations. Foundational diffusion and spatiotemporal graph convolutional models are considered alongside adaptive adjacency learning and bidirectional temporal imputation. The synthesis separates three frequently conflated questions: whether the graph describes physical connectivity, whether it captures statistical dependency, and whether missing entries are informative about the operating state. A protocol is developed for evaluating sensor outages, topology drift, prediction horizons, and leakage from imputation. The review concludes that a missingness-aware architecture must be paired with a missingness-aware test design; otherwise, numerical accuracy may not represent operational performance. The paper offers analytical guidance and a reproducibility checklist, not new model results.