Data measures

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

Definitions of data measures table

Data measureDefinitionSub-measureDefinition

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 the British National Grid reference system)

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

Temporal consistency

How well-ordered events are recorded in the dataset (life cycles)

attributes and temporal relationships of features

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.

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