Rethinking Spatial Data Quality: A Socio-Technical and Lifecycle Framework for Trustworthy Geospatial Information

Spatial data quality (SDQ) assessment often emphasizes technical conformance, but conformance alone does not establish whether a data product remains maintainable, interpretable, reusable, or trustworthy throughout its lifecycle. This paper proposes a socio-technical framework that uses the data product specification as its normative reference and evaluates quality across four dimensions: technical, lifecycle, visual–interpretive, and governance quality. A weighted geometric composite quality index (CQI) summarizes the dimension scores, while a diagnostic profile preserves dimension-specific deficits. A recursive architecture links heterogeneous sources and traceable spatial data refinement to analytics-ready data products, SDQ assessment, functional quality, decision-support capability, and independently assessed decision quality. The competent authority evaluates this evidence alongside applicable criteria and other case-specific information when making decisions and determining associated actions. Standard operating procedures are proposed to operationalize defined responsibilities, evidence requirements, validation, documentation, corrective action, and reassessment. An operationally deployed toolkit illustrates selected components of the framework, particularly automated technical validation and preliminary visual diagnostics; the complete framework and CQI require further empirical calibration and validation. The framework provides a testable basis for investigating whether, and under what conditions, improved SDQ supports analytical performance and decision quality.