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The correctness of the temporal attributes and relationships of data. Important for time-sensitive applications like change detection.

The extent to which all relevant data are included in a dataset. Important for comprehensive analyses and avoiding misleading results due to missing data.

The degree of adherence to logical rules of data structure, attribution, and relationships. Vital for data quality and ensuring that GIS analyses are based on sound logic.

Refers to the ease with which geospatial data can be understood and interpreted. Important for making GIS data accessible to a wide range of users.

The degree to which information on a map or in a digital database matches true or accepted values. It's important for ensuring that decisions based on geospatial data are sound and reliable.

The smallest detectable feature in geospatial data. Higher resolution means finer detail. Important for discerning small or closely spaced features.

Indicates how closely the geospatial coordinates of a dataset correspond to their true location on the earth's surface. Essential for accurate mapping and navigation.

The ratio of a distance on the map to the corresponding distance on the ground. Determines the level of detail that can be represented on a map and is key for ensuring appropriate usage.

Ensures that geospatial data does not contain contradictions and is logical across datasets. Essential for multi-layer analysis and when combining various data sources.