An Explainable Multi-Task Deep Learning Framework for Service-Gap Identification and Emerging Urban Prediction

Rapid urbanization has intensified pressure on public services, infrastructure systems, and spatial equity, highlighting the need for integrated approaches that jointly assess service deficits and emerging urban growth. This study proposes an explainable urban analytics framework for identifying service-gap risk and emerging urban patterns using harmonized spatial, service, population, and digital-readiness indicators. The framework integrates an MCP-enabled data harmonization pipeline, composite service-availability and population-adjusted service-stress features, and a Dual-Head MLP architecture that supports shared representation learning across two related prediction tasks. SHAP-based explainability is employed to interpret the relative contribution of service, stress, digital-readiness, and spatial-context features. The framework was evaluated using Saudi district-level data and proxy-based external city datasets to assess cross-city transferability. Compared with classical machine-learning and single-task neural baselines, the Dual-Head MLP demonstrated stronger task-wise predictive performance, while the external evaluation indicated stable but context-dependent transfer across heterogeneous urban settings. The findings suggest that the proposed framework can support urban planners and municipal decision-makers in prioritizing infrastructure investment, identifying underresourced areas, and interpreting early urban transformation patterns. However, the outputs should be regarded as proxy-based decision-support indicators rather than official administrative measures of service adequacy.