Data measures

Ordnance Survey measures the data in its products in one or more of the ways set out in the definitions of data measures table below.

Data measure	Definition	Sub-measure	Definition
Completeness	Presence and absence of features against the specified data content*	Omission	Features representing objects that conform to the specified data content but are not present in the data.
		Commission	Features representing objects that do not conform to the specified data content but are present in the data.
Logical consistency	Degree of adherence to logical rules of data structure, attribution and relationships	Conceptual consistency	How closely the data follows the conceptual rules (or model).
		Domain consistency	How closely the data values in the dataset match the range of values in the dataset specification.
		Format consistency	The physical structure (syntax): how closely the data stored and delivered fits the database schema and agreed supply formats.
		Topological consistency	The explicit topological references between features (connectivity) – according to specification.
Positional accuracy	Accuracy of the position of features	Absolute accuracy	How closely the coordinates of a point in the dataset agree with the coordinates of the same point on the ground (in British National Grid EPSG: 27700 for shapefile, TAB, MID/MIF and GeoPackage formats, and Web Mercator projection EPSG: 3857 for vector tile format).
		Relative accuracy	Positional consistency of a data point or feature in relation to other local data points or features within the same or another reference dataset.
		Geometric fidelity	The ‘trueness’ of features to the shapes and alignments of the objects they represent*.
Temporal accuracy	Accuracy of temporal attributes and temporal relationships of features	Temporal consistency	How well-ordered events are recorded in the dataset (life cycles).
		Temporal validity (currency)	Validity of data with respect to time: the amount of real-world change that has been incorporated in the dataset that is scheduled for capture under current specifications.
Thematic accuracy (attribute accuracy)	Classification of features and their attributes	Classification correctness	How accurately the attributes within the dataset record the information about objects*.

* When testing the data according to the dataset specification against the ‘real world’ or reference dataset.