geassessd

Geassessd is a term used in geospatial analysis to describe a framework and set of practices for assessing the quality and suitability of geospatial data for analysis and decision making. It focuses on creating transparent, reproducible assessments that enable researchers and practitioners to compare datasets across projects and time.

The framework typically comprises data ingestion and metadata standardization, quality assessment metrics, uncertainty quantification, and reproducible workflows. It promotes interoperability through common schemas, controlled vocabularies, and standardized scoring schemes that rate datasets on attributes such as completeness, positional accuracy, thematic accuracy, temporal resolution, lineage, and provenance. Geassessd emphasizes documentation of data lineage and methods to support traceability.

Methodologies combine automated quality checks with expert evaluation, using statistical analyses, validation against reference datasets, and domain-specific criteria. Outputs usually include quantitative quality scores, uncertainty maps or indicators, and a transparent report detailing assumptions, limitations, and intended uses. The approach supports evidence-based decision making, risk assessment, and model validation in fields relying on geospatial data.

Geassessd has seen uptake in environmental monitoring, urban planning, disaster risk management, and applied geography. It remains an evolving concept with no single canonical implementation, existing as a family of practices, guidelines, and open-source tools that aim to harmonize geospatial data quality assessment across sectors.

See also: geographic information system, spatial data quality, metadata, uncertainty in geospatial data.