ISO 19113:2002

INTERNATIONAL ISO
STANDARD 19113
First edition
2002-12-01
Geographic information — Quality
principles
Information géographique — Principes qualité

Reference number
©
ISO 2002
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ii © ISO 2002 — All rights reserved

Contents Page
Foreword. iv
Introduction . v
1 Scope. 1
2 Conformance . 1
3 Normative references . 1
4 Terms and definitions. 2
5 Principles for describing the quality of geographic data . 4
5.1 Components of data quality description . 4
5.2 Data quality elements and data quality subelements . 5
5.3 Data quality overview elements. 7
6 Identifying the quality of geographic information . 8
6.1 Identifying quantitative quality information . 8
6.2 Identifying non-quantitative quality information . 10
7 Reporting quality information. 10
7.1 Reporting quantitative quality information .10
7.2 Reporting non-quantitative quality information . 10
Annex A (normative) Abstract test suite. 11
Annex B (informative) Data quality concepts and their use. 14
Annex C (informative) Data quality elements, data quality subelements and data quality overview
elements. 19

Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards bodies
(ISO member bodies). The work of preparing International Standards is normally carried out through ISO
technical committees. Each member body interested in a subject for which a technical committee has been
established has the right to be represented on that committee. International organizations, governmental and
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International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
International Standards are drafted in accordance with the rules given in the ISO/IEC Directives, Part 2.
The main task of technical committees is to prepare International Standards. Draft International Standards
adopted by the technical committees are circulated to the member bodies for voting. Publication as an
International Standard requires approval by at least 75 % of the member bodies casting a vote.
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent
rights. ISO shall not be held responsible for identifying any or all such patent rights.
ISO 19113 was prepared by Technical Committee ISO/TC 211, Geographic information/Geomatics.
iv © ISO 2002 — All rights reserved

Introduction
Geographic datasets are increasingly being shared, interchanged and used for purposes other than their
producers’ intended ones. Information about the quality of available geographic datasets is vital to the process
of selecting a dataset in that the value of data is directly related to its quality. Data users confront situations
requiring different levels of data quality. Extremely accurate data is required by some data users for certain
needs and less accurate data are sufficient for other needs. Information about the quality of geographic data is
becoming a decisive factor for its utilization as technological advances allow the collection and use of
geographic datasets whose quality can exceed that which is needed and requested by data users.
The purpose of describing the quality of geographic data is to facilitate the selection of the geographic dataset
best suited to application needs or requirements. Complete descriptions of the quality of a dataset will
encourage the sharing, interchange and use of appropriate geographic datasets. A geographic dataset can be
viewed as a commodity or product. Information on the quality of geographic data allows a data producer or
vendor to validate how well a dataset meets the criteria set forth in its product specification and assists a data
user in determining a product’s ability to satisfy the requirements for their particular application.
The objective of this International Standard is to provide principles for describing the quality for geographic
data and concepts for handling quality information for geographic data.

INTERNATIONAL STANDARD ISO 19113:2002(E)

Geographic information — Quality principles
1 Scope
This International Standard establishes the principles for describing the quality of geographic data and
specifies components for reporting quality information. It also provides an approach to organizing information
about data quality.
This International Standard is applicable to data producers providing quality information to describe and
assess how well a dataset meets its mapping of the universe of discourse as specified in the product
specification, formal or implied, and to data users attempting to determine whether or not specific geographic
data is of sufficient quality for their particular application. This International Standard should be considered by
organizations involved in data acquisition and purchase, in such a way that it makes it possible to fulfil the
intentions of the product specification. It can additionally be used for defining application schemas and
describing quality requirements.
As well as being applicable to digital geographic data, the principles of this International Standard can be
extended to identify, collect and report the quality information for a geographic dataset, its principles can be
extended and used to identify, collect and report quality information for a dataset series or smaller groupings
of data that are a subset of a dataset.
Although this International Standard is applicable to digital geographic data, its principles can be extended to
many other forms of geographic data such as maps, charts and textual documents.
This International Standard does not attempt to define a minimum acceptable level of quality for geographic
data.
2 Conformance
Any product claiming conformance with this International Standard shall pass all the requirements described
in the abstract test suite presented in Annex A.
3 Normative references
The following referenced documents are indispensable for the application of this document. For dated
references, only the edition cited applies. For undated references, the latest edition of the referenced
document (including any amendments) applies.
ISO 19108:2002, Geographic information — Temporal schema
1)
ISO 19109:— , Geographic information — Rules for application schema
1)
ISO 19114:— , Geographic information — Quality evaluation procedures
1)
ISO 19115:— , Geographic information — Metadata

1) To be published.
4 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
4.1
accuracy
closeness of agreement between a test result and the accepted reference value [ISO 3534-1]
NOTE A test result can be observations or measurements.
4.2
conformance
fulfilment of specified requirements [ISO 19105]
4.3
conformance quality level
threshold value or set of threshold values for data quality results used to determine how well a dataset meets
the criteria set forth in its product specification or user requirements [ISO 19114]
4.4
data quality date
date or range of dates on which a data quality measure is applied
4.5
data quality element
quantitative component documenting the quality of a dataset [ISO 19101]
NOTE The applicability of a data quality element to a dataset depends on both the dataset’s content and its product
specification, the result being that all data quality elements may not be applicable to all datasets.
4.6
data quality evaluation procedure
operation(s) used in applying and reporting quality evaluation methods and their results
4.7
data quality measure
evaluation of a data quality subelement
EXAMPLE The percentage of the values of an attribute that are correct.
4.8
data quality overview element
non-quantitative component documenting the quality of a dataset [ISO 19101]
NOTE Information about the purpose, usage and lineage of a dataset is non-quantitative quality information.
4.9
data quality result
value or set of values resulting from applying a data quality measure or the outcome of evaluating the
obtained value or set of values against a specified conformance quality level
EXAMPLE A data quality result of “90” with a data quality value type of “percentage” reported for the data quality
element and its data quality subelement “completeness, commission” is an example of a value resulting from applying a
data quality measure to the data specified by a data quality scope. A data quality result of “true” with a data quality value
type of “boolean variable” is an example of comparing the value (90) against a specified acceptable conformance quality
level (85) and reporting an evaluation of a kind, pass or fail.
4.10
data quality scope
extent or characteristic(s) of the data for which quality information is reported
2 © ISO 2002 — All rights reserved

NOTE A data quality scope for a dataset can comprise a dataset series to which the dataset belongs, the dataset
itself, or a smaller grouping of data located physically within the dataset sharing common characteristics. Common
characteristics can be an identified feature type, feature attribute, or feature relationship; data collection criteria; original
source; or a specified geographic or temporal extent.
4.11
data quality subelement
component of a data quality element describing a certain aspect of that data quality element
4.12
data quality value type
value type for reporting a data quality result
EXAMPLE “boolean variable”, “percentage”, “ratio”
NOTE A data quality value type is always provided for a data quality result.
4.13
data quality value unit
value unit for reporting a data quality result
EXAMPLE “metre”
NOTE A data quality value unit is provided only when applicable for a data quality result.
4.14
dataset
identifiable collection of data [ISO 19115]
NOTE A dataset may be a smaller grouping of data which, though limited by some constraint such as spatial extent
or feature type, is located physically within a larger dataset. Theoretically, a dataset may be as small as a single feature or
feature attribute contained within a larger dataset.
4.15
dataset series
collection of datasets sharing the same product specification [ISO 19115]
4.16
feature
abstraction of real world phenomena [ISO 19101]
NOTE A feature may occur as a type or an instance. Feature type or feature instance should be used when only one
is meant.
4.17
feature attribute
characteristic of a feature [ISO 19101]
NOTE A feature attribute has a name, a data type and a value domain associated with it. A feature attribute for a
feature instance also has an attribute value taken from the value domain.
4.18
feature operation
operation that every instance of a feature type may perform [ISO 19110]
EXAMPLE 1 An operation upon the feature type “dam” is to raise the dam. The result of this operation is to raise the
level of water in a reservoir.
EXAMPLE 2 An operation by the feature type “dam” might be to block vessels from navigating along a watercourse.
NOTE Feature operations provide a basis for feature type definitions.
4.19
metadata
data about data [ISO 19115]
4.20
product specification
description of the universe of discourse and a specification for mapping the universe of discourse to a dataset
4.21
quality
totality of characteristics of a product that bear on its ability to satisfy stated and implied needs [ISO 19101]
4.22
universe of discourse
view of the real or hypothetical world that includes everything of interest [ISO 19101]
5 Principles for describing the quality of geographic data
5.1 Components of data quality description
This International Standard can be used when
 identifying and reporting quality information;
 evaluating the quality of a dataset;
 developing product specifications and user requirements;
 specifying application schemas.
ISO 19114 and ISO 19115 describe schemas for reporting quality information.
ISO 19114 provides the framework for evaluating the quality of a dataset.
ISO 19109 describes the development of application schemas.
A quality description can be applied to a dataset series, a dataset or a smaller grouping of data located
physically within the dataset sharing common characteristics so that its quality can be evaluated.
The quality of a dataset shall be described using two components:
 data quality elements;
 data quality overview elements.
Data quality elements, together with data quality subelements and the descriptors of a data quality
subelement, describe how well a dataset meets the criteria set forth in its product specification and provide
quantitative quality information.
Data quality overview elements provide general, non-quantitative information.
NOTE Data quality overview elements are critical for assessing the quality of a dataset for a particular application
that differs from the intended application.
This International Standard recognizes that quantitative and non-quantitative quality information may have
associated quality.
4 © ISO 2002 — All rights reserved

The quality about quality information may include a measure of the confidence or the reliability of the quality
information. This type of information is recorded in ISO 19114’s quality evaluation report.
Figure 1 provides an overview of data quality information.
Annex B provides a discussion of data quality concepts used to establish the components for describing the
quality of geographic data.
Figure 1 — An overview of data quality information
5.2 Data quality elements and data quality subelements
5.2.1 Data quality elements
The following data quality elements, where applicable, shall be used to describe how well a dataset meets the
criteria set forth in its product specification:
 completeness: presence and absence of features, their attributes and relationships;
 logical consistency: degree of adherence to logical rules of data structure, attribution and relationships
(data structure can be conceptual, logical or physical);
 positional accuracy: accuracy of the position of features;
 temporal accuracy: accuracy of the temporal attributes and temporal relationships of features;
 thematic accuracy: accuracy of quantitative attributes and the correctness of non-quantitative attributes
and of the classifications of features and their relationships.
Additional data quality elements may be created to describe a component of the quantitative quality of a
dataset not addressed in this International Standard.
5.2.2 Data quality subelements
For the data quality elements identified in 5.2.1, the following data quality subelements where applicable shall
be used to describe aspects of the quantitative quality of a dataset:
 completeness;
 commission: excess data present in a dataset,
 omission: data absent from a dataset.
 logical consistency;
 conceptual consistency: adherence to rules of the conceptual schema,
 domain consistency: adherence of values to the value domains,
 format consistency: degree to which data is stored in accordance with the physical structure of the
dataset,
 topological consistency: correctness of the explicitly encoded topological characteristics of a dataset.
 positional accuracy;
 absolute or external accuracy: closeness of reported coordinate values to values accepted as or
being true,
 relative or internal accuracy: closeness of the relative positions of features in a dataset to their
respective relative positions accepted as or being true,
 gridded data position accuracy: closeness of gridded data position values to values accepted as or
being true.
 temporal accuracy;
 accuracy of a time measurement: correctness of the temporal references of an item (reporting of
error in time measurement),
 temporal consistency: correctness of ordered events or sequences, if reported,
 temporal validity: validity of data with respect to time.
 thematic accuracy;
 classification correctness: comparison of the classes assigned to features or their attributes to a
universe of discourse (e.g. ground truth or reference dataset),
 non-quantitative attribute correctness: correctness of non-quantitative attributes,
 quantitative attribute accuracy: accuracy of quantitative attributes.
Additional data quality subelements may be created for any of the data quality elements.
6 © ISO 2002 — All rights reserved

5.2.3 Descriptors of a data quality subelement
Quality information shall be recorded for each applicable data quality subelement. The mechanism for
completely recording information for a data quality subelement shall be the use of the seven descriptors of a
data quality subelement:
 data quality scope;
 data quality measure;
 data quality evaluation procedure;
 data quality result;
 data quality value type;
 data quality value unit;
 data quality date.
NOTE The descriptors of a data quality subelement are defined in Clause 4.
5.3 Data quality overview elements
The following data quality overview elements where applicable shall be used to describe the non-quantitative
quality of a dataset:
 purpose;
 usage;
 lineage.
Purpose shall describe the rationale for creating a dataset and contain information about its intended use.
NOTE A dataset’s intended use is not necessarily the same as its actual use. Actual use is described using the data
quality overview element usage.
Usage shall describe the application(s) for which a dataset has been used. Usage describes uses of the
dataset by the data producer or by other, distinct, data users.
Lineage shall describe the history of a dataset and, in as much as is known, recount the life cycle of a dataset
from collection and acquisition through compilation and derivation to its current form.
Lineage may contain two unique components:
 source information shall describe the parentage of a dataset;
 process step or history information shall describe a record of events or transformations in the life of a
dataset, including the process used to maintain the dataset whether continuous or periodic, and the lead
time.
Additional data quality overview element(s) shall describe an area of non-quantitative quality of a dataset not
addressed in this International Standard.
6 Identifying the quality of geographic information
6.1 Identifying quantitative quality information
6.1.1 General
Clause 6.1 describe the general process for identifying quantitative quality information. Some of the
subclauses may not be relevant in all cases.
6.1.2 Identifying applicable data quality elements
All data quality elements applicable to a dataset shall be identified. Some data quality elements may not be
applicable for a particular type of dataset.
NOTE 1 Applicability of a data quality element should be determined by reference to a dataset’s product specification.
EXAMPLE A dataset whose spatial references are postal references only will not have a data quality element of
positional accuracy.
NOTE 2 Annex C contains examples of identifying applicable data quality elements.
6.1.3 Creating additional data quality elements
New data quality element(s) may be named and defined if the data quality elements listed in this International
Standard do not sufficiently address a component of quality. The name and definition of an additional data
quality element shall be included as a part of a dataset’s quality information.
6.1.4 Identifying applicable data quality subelements
All applicable data quality subelements for each applicable data quality element shall be identified (at least
one data quality subelement shall be identified as applicable for each applicable data quality element). Some
of an applicable data quality element’s data quality subelements may not be applicable to a particular type of
dataset.
NOTE 1 Applicability of a data quality subelement should be determined by reference to a dataset’s product
specification.
NOTE 2 Annex C contains examples of identifying applicable data quality subelements.
6.1.5 Creating additional data quality subelements
New data quality subelement(s) may be named and defined if the data quality subelements listed in this
International Standard do not sufficiently address an aspect of quality. The name and definition of an
additional data quality subelement shall be included as a part of a dataset’s quality information.
6.1.6 Using the descriptors of a data quality subelement
6.1.6.1 Data quality scope
At least one data quality scope shall be identified for each applicable data quality subelement. A data quality
scope may be a dataset series to which a dataset belongs, the dataset or a smaller grouping of data located
physically within the dataset sharing common characteristics. If a data quality scope cannot be identified, the
data quality scope shall be the dataset.
NOTE Data quality scope(s) should be determined by reference to a dataset’s product specification and the
non-quantitative quality information provided for data quality overview elements.
8 © ISO 2002 — All rights reserved

Quality can vary within a dataset. Multiple data quality scopes may be identified for each applicable data
quality subelement to more completely describe quantitative quality information. A data quality scope shall be
adequately described. The following can be used to describe a data quality scope:
 the level (a dataset series to which a dataset belongs, the dataset or a smaller grouping of data located
physically within the dataset sharing common characteristics);
 the types of items (lists of feature types, feature attributes and feature relationships) or specific items (lists
of feature instances, attribute values and instances of feature relationships);
 the geographic extent;
 the temporal extent (the time frame of reference and accuracy of the time frame).
6.1.6.2 Data quality measure
One data quality measure shall be provided for each data quality scope. A data quality measure shall briefly
describe and name, where a name exists, the type of test being applied to the data specified by a data quality
scope and shall include bounding or limiting parameters.
NOTE 1 Examples of bounding or limiting parameters are confidence intervals and error rates.
This International Standard recognizes that the quality of a dataset is measured using a variety of tests. A
single data quality measure might be insufficient for fully evaluating the quality of the data specified by a data
quality scope and providing a measure of quality for all possible utilizations of a dataset. A combination of data
quality measures can give useful information. Multiple data quality measures may be provided for the data
specified by a data quality scope.
NOTE 2 ISO 19114 includes examples of names and descriptions of types of data quality measures.
6.1.6.3 Data quality evaluation procedure
One data quality evaluation procedure shall be provided for each data quality measure. A data quality
evaluation procedure shall describe, or reference documentation describing, the methodology used to apply a
data quality measure to the data specified by a data quality scope and shall include the reporting of the
methodology.
NOTE 1 Examples of documentation are published articles or accepted industry standards.
NOTE 2 ISO 19114 includes a data quality evaluation procedure framework applicable to datasets and further clarifies
the type of information to be reported in a data quality evaluation procedure.
6.1.6.4 Data quality result
One data quality result shall be provided for each data quality measure. The data quality result shall be either
 the value or set of values obtained from applying a data quality measure to the data specified by a data
quality scope, or
 the outcome of evaluating the value or set of values obtained from applying a data quality measure to the
data specified by a data quality scope against a specified acceptable conformance quality level. This type
of data quality result is referred to in this International Standard as pass-fail.
Both types of data quality results identified in this International Standard may be provided.
NOTE ISO 19114 addresses the determination of conformance quality levels.
6.1.6.5 Data quality value type
One data quality value type shall be provided for each data quality result.
NOTE The data quality value type for pass-fail is “boolean variable”.
6.1.6.6 Data quality value unit
One data quality value unit, if applicable, shall be provided for each data quality result.
6.1.6.7 Data quality date
One data quality date shall be provided for each data quality measure in conformance with the requirements
of ISO 19108’s temporal schema.
6.2 Identifying non-quantitative quality information
6.2.1 Identifying applicable data quality overview elements
Purpose of a dataset shall always be applicable.
All usage of a dataset that the producer is aware of shall be applicable.
Lineage of a dataset shall always be applicable. In extreme cases, information about lineage may not be
known. Either lineage or an explanation of the lack of lineage information shall be reported.
Lineage for smaller groupings of data within a dataset specified by a data quality scope can be collected for
and differ from the rest of the dataset’s lineage. Differing lineage may be provided for smaller groupings of
data within a dataset specified by a data quality scope as a part of a dataset’s non-quantitative quality
information for more complete non-quantitative quality information.
6.2.2 Creating additional data quality overview elements
New data quality overview element(s) may be named and defined if the data quality overview elements
identified in this International Standard do not address an area of general non-quantitative quality. The name
and definition of an additional data quality overview element shall be included as a part of its quality
information.
7 Reporting quality information
7.1 Reporting quantitative quality information
Quantitative quality information shall be reported as metadata in conformance with the requirements of
ISO 19115.
Quantitative quality information shall additionally be reported using a quality evaluation report in conformance
with the requirements of ISO 19114.
7.2 Reporting non-quantitative quality information
Non-quantitative quality information shall be reported as metadata in conformance with the requirements of
ISO 19115.
NOTE Non-quantitative quality information is not reported in ISO 19114’s quality evaluation report.
10 © ISO 2002 — All rights reserved

Annex A
(normative)
Abstract test suite
A.1 Abstract test suite
A.1.1 General
All of the test cases in this annex are of the Test Type: Basic.
A.1.2 Test case identifier: Component test
a) Test Purpose: to determine conformance by ensuring the components of quality are used in the quality
description.
b) Test Method: examine the quality description and verify data quality elements (together with data quality
subelements and the descriptors of a data quality subelement) have been used to provide quantitative
quality information.
Examine the quality description and verify data quality overview elements have been used to provide
non-quantitative quality information.
c) Reference: ISO 19113:2002, 5.1.
A.1.3 Test case identifier: Validity test
a) Test Purpose: to determine conformance by ensuring the validity of the quality description.
b) Test Method: examine the quality description and verify its data quality elements and data quality
subelements are listed in this International Standard or are additional and describe a component or
aspect of quantitative quality that is not specifically identified in this International Standard.
Examine the quality description and verify the descriptors of a data quality subelement identified in this
International Standard have been used to describe quantitative quality.
Examine the quality description and verify its data quality overview elements are listed in this International
Standard or are additional and describe an area of non-quantitative quality that is not specifically
identified in this International Standard.
c) Reference: ISO 19113:2002, 5.2 and 5.3.
A.1.4 Test case identifier: Quantitative quality applicability test
a) Test Purpose: to determine conformance by ensuring the applicability of the quantitative quality
description.
b) Test Method: identify the product specification statements relevant to quantitative quality and use them to
identify the applicable data quality elements and their applicable data quality subelements. Compare the
applicable data quality subelements with the data quality subelements used in the quality description to
ensure all data quality subelements applicable to the dataset have been identified and used in the quality
description.
NOTE  Conformance is valid if nonapplicable data quality subelements are additionally used to describe quantitative
quality. However, the non-applicable data quality subelements cannot be subjected to further conformance testing.
c) Reference: ISO 19113:2002, 6.1.
A.1.5 Test case identifier: Non-quantitative quality applicability test
a) Test Purpose: to determine conformance by ensuring the applicability of the non-quantitative quality
description.
b) Test Method: verify the applicable data quality overview elements are used to describe non-quantitative
quality.
c) Reference: ISO 19113:2002, 6.2.
A.1.6 Test case identifier: Exclusiveness test
a) Test Purpose: to determine conformance by ensuring additional items in the quality description are
exclusive and that sufficient information about an additional item is provided.
b) Test Method: examine all additional data quality elements and ensure each addresses a component of
quantitative quality that is not specifically listed and described in this International Standard.
Examine all additional data quality subelements and ensure each addresses an aspect of quantitative
quality that is not specifically listed and described in this International Standard.
Examine all additional data quality overview elements and ensure each addresses an area of
non-quantitative quality that is not specifically listed and described in this International Standard.
Ascertain the name and a description of the additional item are a part of the quality description.
c) Reference: ISO 19113:2002, 6.1.3, 6.1.5 and 6.2.2.
A.1.7 Test case identifier: Correct use of the descriptors of a data quality subelement
a) Test Purpose: to determine conformance by verifying that the descriptors of a data quality subelement
have been correctly used in the quality description.
b) Test Method: compare this International Standard and the quality information supplied for each applicable
data quality subelement (including additional data quality subelements) to determine the occurrence rules
for using descriptors of a data quality subelement have been followed.
c) Reference: ISO 19113:2002, 6.1.6.
A.1.8 Test case identifier: Reporting quality information as metadata
a) Test Purpose: to determine conformance by verifying the quality description is reported as metadata.
b) Test Method: verify that quantitative quality information has been reported as metadata in conformance
with ISO 19115.
Verify that non-quantitative quality information has been reported as metadata in conformance with
ISO 19115.
c) Reference: ISO 19113:2002, Clause 7.
12 © ISO 2002 — All rights reserved

A.1.9 Test case identifier: Reporting quantitative quality information using a quality
evaluation report
a) Test Purpose: to determine conformance by verifying the quantitative quality of the quality description is
reported as a quality evaluation report.
b) Test Method: verify that quantitative quality information is reported in a quality evaluation report in
conformance with the requirements of ISO 19114.
c) Reference: ISO 19113:2002, 7.1.
Annex B
(informative)
Data quality concepts and their use
B.1 Background
A dataset is defined as an identifiable collection of data. Those data represent entities of the real-world which
are characterized by having spatial, thematic and temporal aspects. The process of abstracting from the real-
world to the universe of discourse involves modelling the potentially infinite characteristics of real-world
entities into an ideal form defined by a position, a theme and a time for the reason of making intelligible and
representable these entities. The universe of discourse is described by a product specification, against which
the content of [parts of] a dataset is tested for its quality.
B.2 Purpose of data quality concepts
Since a dataset is not generally produced for a specific application but rather for a set of supposed
applications, the quality of the dataset can only be assessed by knowing the data quality elements and the
data quality overview elements. The data quality elements evaluate the difference between the dataset
produced and the universe of discourse (that is the perfect dataset that corresponds to the product
specification). The data quality overview elements provide general, non-quantitative information. The purpose
gives information on the reasons for creating the dataset and on the intended use of the dataset. The usage
provides information on the kind of application for which the dataset has been used. Lineage describes the
history of the dataset.
Data quality concepts provide an important framework for data producers and data users. A data producer is
given the means for specifying how well the mapping used to create a dataset reflects its universe of
discourse. Data producers can validate how well a dataset meets the criteria set forth in its product
specification. Data users are given the means for assessing a dataset derived from a universe of discourse
identified as being coincident with requirements of a data user’s application. Data users can assess quality to
ascertain if a dataset can satisfy the requirements of an application (see Figure B.1).
B.3 The structure of datasets and components for quality description
A dataset may belong to a dataset series. The quality of all member datasets belonging to a dataset series is
often the same. Data quality concepts recognize dataset series and allow for substituting and reporting the
quality of a dataset series for a dataset.
A dataset can be viewed as containing a large but finite number of smaller groupings of data. Smaller
groupings of data which share a commonality such as belonging to the same feature type, feature attribute or
feature relationship or sharing a collection criteria or geographic extent can be expected to have similar
quality. A smaller grouping of data can be as small as a feature instance, attribute value or occurrence of a
feature relationship and, theoretically, data quality concepts allow each feature instance, attribute value and
occurrence of a feature relationship of a dataset to have its own quality. The quality of smaller groupings of
data cannot be assumed to be the same as the quality of the rest of the dataset to which they belong. Data
quality concepts allow for reporting the quality of a dataset and additionally the differing quality of smaller
groupings of data by identifying these groupings as the data specified by data quality scopes. The quality
information reported for multiple data quality scopes provide a more complete picture of quality.
NOTE For a data producer, a product specification describes the universe of discourse and contains the parameters
for constructing a dataset. For a data user, user requirements describe a universe of discourse, which may or may not
match the dataset’s universe of discourse. The true quality of a dataset is how well it represents a universe of discourse.
14 © ISO 2002 — All rights reserved

Figure B.1 — The framework provided by data quality concepts
To describe the quality of a dataset, two unique components of data quality information are recognized:
quantitative quality components and non-quantitative quality components. Data quality elements are
quantitative components of quality information; data quality overview elements are non-quantitative
components of quality information.
Data quality elements allow for the measurement of how well a dataset meets the criteria set forth in its
product specification. Data quality elements have distinct aspects known as data quality subelements. Data
quality subelements can be measured or tested in various ways. Data quality concepts recognize that not all
data quality elements nor all data quality subelements and their subsequent means of measurement and
testing are applicable to a particular type of dataset. Additionally, some data quality subelements are
applicable to and measured or tested for a dataset while others are applicable to and measured or tested for
smaller groupings of data in a dataset specified by a data quality scope.
This International Standard identifies data quality elements primarily as a means of detecting and reporting
separate categories of quality information. However, this International Standard additionally recognizes that
frequently data quality subelements are interrelated. For example, a coordinate error may generate at least
two kinds of errors, a positional error and a topological error. The meaning of the data quality subelements in
terms of the product and manner in which the data quality subelements are handled are the purview of the
quality evaluator.
Whereas data quality elements allow for the measurement of how well a dataset meets the criteria set forth in
its product specification, data quality overview elements allow for additionally evaluating a dataset for a
particular application by providing purpose, usage and lineage information.
B.4 Reporting quality information
B.4.1 When to report quality information
Datasets are continually being created, updated and merged with the result that the quality or a component of
the quality of a dataset may change. The quality information of a dataset can be affected by three conditions:
a) when any quantity of data is deleted from, modified or added to a dataset;
b) when a dataset’s product specification is modified;
c) when the real world has changed.
The first condition, a modification to a dataset, may occur quite frequently. Many datasets are not static. There
is an increase in the interchange of information, the use of datasets for multiple purposes and an
accompanying update and refinement of datasets to meet multiple purposes. If the reported quality of a
dataset is likely to change with modifications to the dataset, the quality of a dataset should be reassessed and
updated as required when changes occur.
Complete knowledge of all applicable data quality elements and all data quality overview elements with the
exception of the data quality overview element usage should be available when a dataset is created. Only the
data producer’s usage (assuming the data producer actually uses the dataset) of a dataset can initially be
reported. There is a reliance on data users to report uses of a dataset that differ from its intended purpose so
that continual updates to this particular data quality overview element can be made to reflect occurring,
unforeseen uses.
The second c
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