Rethinking Spatial Data Quality: A Socio-Technical and Lifecycle Framework

Spatial data quality (SDQ) is commonly assessed through technical verification. However, empirical evidence demonstrates that perceived data quality often diverges from objectively measured quality due to cognitive, institutional, and lifecycle-related factors. This paper proposes a multi-layered SDQ framework that integrates technical admissibility, process and lifecycle stewardship, visual and interpretive diagnostics, and governance indicators to enable holistic quality assessment within a socio-technical system. Rather than treating quality elements in isolation, the framework supports the diagnosis of emergent quality states and associated risk patterns. The framework is demonstrated through two empirical cases: validation of planned land use data using the OPIAvalid toolkit, and semantic conflation of multiple digital elevation models (DEMs) with heterogeneous lineage. Results show that governance failures, specification misuse, and degradation of lineage can undermine trust and decision-making even when datasets formally comply with ISO-based indicators. Visual spatial forensics and lineage-aware integration proved essential for detecting undocumented methodological shortcuts and restoring justified trust in authoritative data. Artificial intelligence is positioned as a diagnostic and explanatory support, assisting in anomaly detection, prioritization, and communication of quality risks, while deterministic validation and expert judgment remain mandatory. Overall, the framework shifts SDQ management from isolated technical validation toward lifecycle-oriented, transparent, and sustainable data governance.