Difference between revisions of "ISO 19157:2013 Geographic information - Data quality"

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|Completeness (commission)
|Completeness (commission)
|Measure 1: Excess item
|Measure 1: Excess item
Measure 2: Number of excess items
Measure 2: Number of excess items  
Measure 3: Number of duplicate feature instances
Measure 3: Number of duplicate feature instances
|Direct external
|Direct external
Direct external
Direct external  
Direct internal
Direct internal

Revision as of 11:56, 4 May 2016


Full name ISO 19157:2013, Geographic information – Data quality.
Version Edition 1
Amendments None
Corrigenda None
Published by ISO/TC 211
Languages English, French
Online overview https://www.iso.org/obp/ui/#iso:std:iso:19157:ed-1:v1:en
Type of standard ISO International Standard

Meta level.

Related standard(s) ISO 19115-1:2013, Geographic information – Metadata – Part 1: Fundamentals

ISO 19115-2:2009, Geographic information – Metadata – Part 2: Extensions for imagery and gridded data ISO 19158:2012, Geographic information – Quality assurance of data supply

Application The standard specifies the description, evaluation and reporting of the quality of geographic data.


Data quality evaluation process

Data quality metadata Standalone quality report Data quality measure


ISO 19157:2013 establishes the principles for describing the quality for geographic data. It defines components for describing data quality; specifies components and content structure of a register for data quality measures; describes general procedures for evaluating the quality of geographic data; and establishes principles for reporting data quality.

The standard also defines a set of data quality measures for use in evaluating and reporting data quality. It is applicable to data producers providing quality information to describe and assess how well a dataset conforms to its product specification and to data users attempting to determine whether or not specific geographic data are of sufficient quality for their particular application.

The standard does not attempt to define minimum acceptable levels of quality for geographic data.

Implementation benefits

ISO 19157:2013 provides a standard way for describing the quality of geographic data. Such descriptions are useful when a producer has to evaluate how well a dataset meets the criteria described in its product specification. For example, if the producer outsourced the acquisition of the data, ISO 19157:2013 could be used to evaluate and describe the quality of the received data during acceptance testing.

Geographic data are increasingly shared and exchanged. As a result, geographic data are often used for purposes that differ from the purpose for which it was originally captured. Complete descriptions of the quality of a dataset encourage and facilitate the sharing, interchange and use of appropriate datasets.

Another benefit of implementing ISO 19157:2013 is that the quality information could assist a user who has to decide whether a specific dataset is appropriate for an intended use or application. If the user has to decide between two or more datasets, standardized quality descriptions simplify comparing the datasets. If ISO 19157:2013 is implemented, quality reports are expressed in a comparable way and there is a common understanding of the quality measures that have been used. A project to develop an XML of ISO 19157:2013 has begun.

Implementation guidelines

ISO 19157:2013 cancels and replaces ISO/TS 19138:2006, ISO 19114:2003 and ISO 19113:2002. According to ISO 19157:2013, data quality comprises six elements: completeness, thematic accuracy, logical consistency, temporal quality, positional accuracy and usability. Each element is comprised of a number of sub-elements, for example, completeness (commission and omission), logical consistency (conceptual, domain, format, topological), etc. These elements are used to describe data quality, i.e. how well a specific dataset meets the criteria for the different elements set forth in its product specification or user requirements. Evaluation against the criteria is done either quantitatively or subjectively (non-quantitatively). The latter case applies if a detailed data product specification does not exist or if the data product specification lacks quantitative measures and descriptors. Three metaquality elements – confidence, ‘representativity’ and homogeneity – provide quantitative and qualitative statements about the evaluation against the criteria and its result.

Quality information can be provided for different units of data, e.g. a dataset series, a dataset or a subset of a dataset with common characteristics. A data quality unit comprises of a scope and data quality elements. The scope specifies the extent, spatial and/or temporal and/or common characteristic(s) of the unit for which the quality information is provided.

In ISO 19157:2013, quality related information provided by purpose, usage and lineage of geographic data conforms to ISO 19115-1:2014 (described in chapter 11).

ISO 19157:2013 specifies four conformance classes, i.e. the standard can be implemented for four different quality aspects of geo-spatial datasets, each briefly described below.

  1. Implementing a data quality evaluation process conforming to ISO 19157:2013
A data quality evaluation process conforming to ISO 19157:2013 comprises of four steps:
  • Step 1 - Specify the data quality units to be evaluated. Study the data product specification to identify applicable data quality units and their scope. For each data quality unit, identify the applicable data quality element(s). See example in Table 10.25.
  • Step 2 - Specify the data quality measures to be used to describe quality of each data quality element of a data quality unit. The requirements in the data product specification provide guidance on applicable data quality measures. See example in Table 10.26. The data quality measures in the table are from the list of standardized data quality measures in ISO 19157:2013. It is also possible to describe user-defined quality measures, see further below, and to maintain a collection of such measures in a catalogue or register.
  • Step 3 - Specify the data quality evaluation procedures, i.e. the evaluation method(s) to be applied. The method can be direct (based on inspection of the items in the dataset) or indirect (based on external knowledge, such as lineage metadata). Direct evaluation is further classified by the source against which the evaluation is done: internal if only the data in the dataset is evaluated or external if there is reference to external data (e.g. satellite imagery or ground truth). ISO 19157:2013 includes guidance on how to sample data for evaluation.
  • Step 4 - Determine the output of the data quality evaluation, i.e. perform the data quality evaluation described in Steps 1-3 above. Additional results may be produced by aggregating or by deriving from existing results without carrying out a new evaluation. How to report the results of the data quality evaluation is described elsewhere in this chapter.
Example: Data quality units
Data quality unit Scope Data quality elements
Topographic dataset All features in the dataset Completeness (commission and omission), thematic accuracy (correct classification)
Street network Street features in the entire dataset Logical inconsistency (topological inconsistency)
Example: Data quality measures
Data quality unit Data quality elements Data quality measure Method
Topographic dataset Completeness (commission) Measure 1: Excess item

Measure 2: Number of excess items Measure 3: Number of duplicate feature instances

Direct external

Direct external Direct internal

Topographic dataset Completeness (omission) Measure 1: Missing item

Measure 2: Number of missing items

Direct external

Direct external

Topographic dataset Thematic accuracy (correct classification) Measure 1: Number of incorrectly classified features

Measure 2: Misclassification rate

Direct external

Direct external

Street network Logical inconsistency (topological inconsistency) Measure 1: Number of missing connections due to undershoots

Measure 2: Number of missing connections due to overshoots Measure 3: Number of invalid self-intersect errors Measure 4: Number of invalid self-overlap errors

Direct internal

Direct internal Direct internal Direct internal