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3 Non-geometry standards
Completeness
presence and absence of features, their attributes and relationships
Negative example: missing road data in a remote part of the
province.
Logical consistency degree of adherence to logical rules of data structure, attribution and relationships
Example: The application schema distinguishes between public and private buildings. The dataset distinguishes between
low buildings and highrises.
Positional accuracy accuracy of the position of features
Example: The absolute point accuracy is 10 cm (diagonal).
Temporal accuracy
accuracy of the temporal attributes and temporal relationships
offeatures
Example: The date ofthe data compilation was August 1990.
Thematic accuracy
accuracy of quantitative attributes and the correctness of nonquantitative attributes, as weIl as the classification of features
and their relationships
Example: Areas have been classified according to remotely
sensed imagery as green land although, in reality, they were
swamps.
The data quality overview elements contain the non-quantitative quality information
that is grouped in three:
Purpose
Usage
Lineage
It describes the rationale for creating a dataset and contains
information about its intended use.
It describes the application for which a dataset has been used.
It describes the history of a dataset and, in as much as it is
known, recounts the life cycle of a dataset from collection and
acquisition through compilation and derivation to its current
form.
Quality evaluation procedures
The ISO 19115 (Metadata) provides the dictionary for the data quality elements
and the data quality overview elements. Quantitative and non-quantitative information can be reported as metadata according to ISO 19115.
According to ISO 19114, the quantitative information can also be reported in a
"Quality evaluation report". The standard defines a detailed template for this report.
There are two conditions under which a quality evaluation report shall be produced:
l. Data quality results are reported as metadata and can only be reported as pass-fail
while more detailed information is needed.
2. When aggregated data quality results are generated the report explains how the
aggregation was done and how to interpret the meaning of the aggregate result.
3 Non-geometry standards
Completeness
presence and absence of features, their attributes and relationships
Negative example: missing road data in a remote part of the
province.
Logical consistency degree of adherence to logical rules of data structure, attribution and relationships
Example: The application schema distinguishes between public and private buildings. The dataset distinguishes between
low buildings and highrises.
Positional accuracy accuracy of the position of features
Example: The absolute point accuracy is 10 cm (diagonal).
Temporal accuracy
accuracy of the temporal attributes and temporal relationships
offeatures
Example: The date ofthe data compilation was August 1990.
Thematic accuracy
accuracy of quantitative attributes and the correctness of nonquantitative attributes, as weIl as the classification of features
and their relationships
Example: Areas have been classified according to remotely
sensed imagery as green land although, in reality, they were
swamps.
The data quality overview elements contain the non-quantitative quality information
that is grouped in three:
Purpose
Usage
Lineage
It describes the rationale for creating a dataset and contains
information about its intended use.
It describes the application for which a dataset has been used.
It describes the history of a dataset and, in as much as it is
known, recounts the life cycle of a dataset from collection and
acquisition through compilation and derivation to its current
form.
Quality evaluation procedures
The ISO 19115 (Metadata) provides the dictionary for the data quality elements
and the data quality overview elements. Quantitative and non-quantitative information can be reported as metadata according to ISO 19115.
According to ISO 19114, the quantitative information can also be reported in a
"Quality evaluation report". The standard defines a detailed template for this report.
There are two conditions under which a quality evaluation report shall be produced:
l. Data quality results are reported as metadata and can only be reported as pass-fail
while more detailed information is needed.
2. When aggregated data quality results are generated the report explains how the
aggregation was done and how to interpret the meaning of the aggregate result.