Toward a Reproducible GIS Quality Assurance Framework: Quantifying Error Mechanisms Across the Spatial Workflow Through Ten Illustrative Cases

Geographic Information Systems (GIS) increasingly inform high-stakes decisions in public health, environmental management, infrastructure planning, and regulatory governance; yet GIS practice lacks enforceable professional standards equivalent to those in medicine, engineering, or law. This study presents ten illustrative cases that document specific GIS error mechanisms across four workflow stages: data acquisition and integration, coordinate reference system (CRS) and projection handling, analytical processing, and output communication. Cases are categorized into four types: quantitative-controlled (Cases 1, 3, and 6), quantitative-observational (Cases 2, 7, and 8), quantitative-computational (Cases 9 and 10), and illustrative-qualitative (Cases 4 and 5) cases. Errors examined include raster resampling artifacts, temporal misalignment, CRS misspecification, topology violations, interpolation sensitivity, classification scheme sensitivity, positional misalignment, global sensitivity analysis of model parameter weights, and pairwise error interdependency. Key quantitative findings include a +20.6% area overestimation under Web Mercator, a Kriging root mean square error (RMSE) of 3.2 mm compared with a physically implausible Spline minimum of −171 mm, and a maximum pairwise error co-occurrence score of 0.86 (Scale/Resolution × Privacy/Ethics, based on co-occurrence proportions across 50 analytical cases derived from 24 references). Based on these cases, a five-pillar standard-of-care framework is proposed, encompassing data quality and provenance, CRS integrity, metadata and documentation, analytical validation, and professional education. The framework integrates and extends ISO 19157:2013, FAIR data principles, and reproducible workflow practices, providing actionable, reproducible guidance for GIS practitioners, spatial data scientists, and policymakers seeking to improve the reliability and transparency of spatial analyses.