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The Uncertainty We Do Not Measure: The Problem of Data Quality in Geoinformation
Abstract
This article examines the problem of uncertainty in GIS data that extends beyond the standard technical parameters of the “big five”. It is argued that traditional reports overlook semantic and conceptual uncertainty, which is crucial for poorly-defined geographical objects. Using examples of marsh and forest definition changes, it demonstrates how a lack of conceptual context leads to erroneous analytical conclusions. The paper proposes extending metadata to include ontologies, institutional context, and the opacity of GeoAI models. The goal is to shift towards a holistic fit-for-purpose approach, integrating measurement precision with a reflection on the meaning of geographic information.
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DOI: http://dx.doi.org/10.17951/b.2025.80.0.299-315
Date of publication: 2026-01-11 16:34:21
Date of submission: 2025-12-17 15:29:46
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