Enterprise spatial data supply chains (SDSCs) increasingly support high-stakes decision-making; yet, the provenance in operational geospatial systems is often fragmented across metadata records, workflow logs, and application-specific formats. This limits traceability, reproducibility, auditability, and fitness-for-purpose assessment, particularly when organisations need to explain how spatial products were created, with which parameters, spatial references, and dependencies. This study proposes the Enterprise Spatial Data Provenance Knowledge Infrastructure (ESDPKI), a standards-aligned framework that treats provenance as enterprise knowledge infrastructure rather than passive metadata. Using a design science research approach, the study synthesised the literature-derived requirements, standards-based interoperability constraints, and representative spatial data supply chain workflows to develop four artefacts: a six-layer reference architecture, the GeoPROV minimal semantic profile, a validation-gated ingestion and governance mechanism, and a reproducible evaluation blueprint with service-level objectives. Together, these artefacts support provenance capture, semantic normalisation, validation, queryable lineage, catalogue linkage, and policy-aware disclosure across enterprise environments. The resulting design makes geospatial operations, parameters, geometry, and coordinate reference system context machine-actionable, enabling lineage tracing, impact analysis, discovery-time fitness-for-purpose assessment, and stronger governance at scale. ESDPKI therefore provides a coherent architectural pathway for operationalising trustworthy, explainable, and scalable spatial provenance in enterprise settings.
Sadiq et al. (Thu,) studied this question.