Difference between revisions of "ISO 19157:2013 Geographic information - Data quality"
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| Topographic dataset
| Thematic accuracy (correct classification)
| Thematic accuracy (correct classification)
| Measure 1: Number of incorrectly classified features<br/>
| Measure 1: Number of incorrectly classified features<br/>
Revision as of 16:11, 4 May 2016
|Full name||ISO 19157:2013, Geographic information – Data quality|
|Published by||ISO/TC 211|
|Type of standard||ISO International Standard
|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
|Application||The standard specifies the description, evaluation and reporting of the quality of geographic data.|
|Conformance classes||Data quality evaluation process
Data quality metadata
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.
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.
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 Example: Data quality units.
- 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 Example: Data quality measures. 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.
|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)|
|Data quality unit||Data quality element||Data quality measure||Method|
|Topographic dataset||Completeness (commission)||Measure 1: Excess item
Measure 2: Number of excess items
|Topographic dataset||Completeness (omission)||Measure 1: Missing item
Measure 2: Number of missing items
|Topographic dataset||Thematic accuracy (correct classification)||Measure 1: Number of incorrectly classified features
Measure 2: Misclassification rate
|Street network||Logical inconsistency (topological inconsistency)||Measure 1: Number of missing connections due to undershoots
Measure 2: Number of missing connections due to overshoots
2. Implementing data quality metadata conforming to ISO 19157:2013'
Data quality metadata describes the quality of geographic data. ISO 19157:2013 specifies a conceptual model of the different components to be used when describing the quality of geographic data. Overview of the components to be used to describe data quality provides and overview of the components and their relationships to each other. A data dictionary, including definitions for all the components, is provided in the standard. Data quality metadata conforming to ISO 19157:2013 conforms to this conceptual model and is reported in conformance with ISO 19115:2003 and ISO 19115-2:2009
Overview of the components to be used to describe data quality (Source: ISO 19157:2013)
3. Implementing data quality reports conforming to ISO 19157:2013
The first (and obvious) requirement is that the quality report comprises quality metadata conforming to ISO 19157:2013 (see 2. above), i.e. it includes sections on all appropriate aspects of quality and the description of components follow the rules defined in the standard. Additional information can be added to the report, but the structure of the report is not prescribed. Example: Section of a data quality report is an example of a section of a data quality report for the quality evaluation process described above.
Example: Section of a data quality report
|Data quality unit||Data quality element||Data quality measure||Result|
|Topographic dataset||Completeness (commission)||Measure 2: Number of excess items|
|Measure 3: Number of duplicate feature instances|
|Topographic dataset||Completeness (omission)||Measure 2: Number of missing items|
|Topographic dataset||Thematic accuracy (correct classification)||Measure 1: Number of incorrectly classified features|
|Measure 2: Misclassification rate|
|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|
4. Implementing data quality measures conforming to ISO 19157:2013
A data quality measure conforming to ISO 19157:2013 is structurally and semantically well defined and described and modelled as specified in the standard. Such a measure is described by at least an identifier, a name, an element name, definition and a value type. Optional descriptors are an alias, description, a value structure, example, a basic measure and one or more source references and/or parameters. Note that full inspection is most appropriate for small populations or for tests that can be accomplished by automated means. For larger populations, checking a representative part of the data and reporting the quality result as a percentage rate is more appropriate and practical.