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AS/NZS ISO 19157.1:2023
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Geographic information – Data quality, Part 1: General requirements
AS/NZS ISO 19157.1:2023 identically adopts ISO 19157 1:2023, which establishes the principles for describing the quality of geographic data.
Table of contents
Header
About this publication
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Abbreviated terms and packages
4.1 Abbreviated terms
4.2 Abbreviated packages
5 Conformance
5.1 General
5.2 Content of a data quality model
5.3 XML encoding of a data quality model
6 General requirements for geographic information quality
6.1 General
6.2 Data quality — general requirements, recommendations and permissions
7 Overview of data quality
8 Components of data quality
8.1 Overview of the components
8.2 Data quality unit
8.3 Data quality elements
8.3.1 General
8.3.2 Completeness
8.3.3 Logical consistency
8.3.4 Positional accuracy
8.3.5 Temporal quality
8.3.6 Thematic quality
8.3.7 Metaquality elements
8.4 Extending the data quality information model
8.5 Descriptors of data quality elements
8.5.1 General
8.5.2 Measure reference
8.5.3 Evaluation method
8.5.4 Quality result
8.5.4.1 General
8.5.4.2 Quantitative result
8.5.4.3 Conformance result
8.5.4.4 Descriptive result
8.5.4.5 Coverage result
8.5.5 Descriptors of a metaquality element
9 Data quality measures
9.1 General
9.2 Standardized data quality measures
9.2.1 General
9.2.2 Measure identifier
9.2.3 Name
9.2.4 Alias
9.2.5 Element name
9.2.6 Basic measure
9.2.7 Definition
9.2.8 Description
9.2.9 Parameter
9.2.10 Value type
9.2.11 Value structure
9.2.12 Source reference
9.2.13 Example
9.3 User-defined data quality measures
10 Data quality evaluation
10.1 The process for evaluating data quality
10.1.1 Introduction
10.1.2 The process flow
10.1.3 Process steps
10.2 Data quality evaluation methods
10.2.1 Classification of data quality evaluation methods
10.2.2 Direct evaluation
10.2.3 Indirect evaluation
10.3 Aggregation and derivation
11 Data quality reporting
11.1 General
11.2 Particular cases
11.2.1 Reporting aggregation (aggregated results)
11.2.2 Reporting derivation (derived results)
11.2.3 Reference to the original data quality result
11.2.4 Hierarchy principle
12 Requirements for XML encoding
Annex A
A.1 Content of a data product specification
A.2 XML encoding
Annex B
B.1 Framework of data quality concepts
B.2 The structure of datasets and components for quality description
B.3 When to use quality evaluation procedures
B.4 Reporting quality information
B.4.1 Why report data quality
B.4.2 When to report quality information
B.4.3 How to report quality information with metadata and a quality evaluation report
B.4.3.1 General
B.4.3.2 Reporting quality information as metadata
B.4.3.3 Reporting quality information within a quality evaluation report
Annex C
C.1 Data dictionary overview
C.1.1 Introduction
C.1.2 Name/role name
C.1.3 Definition
C.1.4 Obligation/condition
C.1.4.1 General
C.1.4.2 Mandatory (M)
C.1.4.3 Conditional (C)
C.1.4.4 Optional (O)
C.1.5 Maximum occurrence
C.1.6 Data type
C.1.7 Domain
C.2 Data quality package data dictionary
C.2.1 Data quality
C.2.1.1 General
C.2.1.2 Data quality element
C.2.1.3 Measure reference
C.2.1.4 Data quality evaluation
C.2.1.5 Data quality result
C.2.1.6 Quality evaluation report information
C.2.2 Data quality measure
C.2.2.1 General
C.2.2.2 Data quality measures
C.2.2.3 Data quality basic measure
C.2.2.4 Data quality parameter
C.2.2.5 Data quality measure description
C.2.2.6 Data quality measure source reference
C.2.2.7 Data quality formula type
C.3 Code lists
C.3.1 Introduction
C.3.2 Evaluation method type
C.3.3 Value structure
C.3.4 Formula language
Annex D
D.1 Introduction
D.2 Dataset description
D.2.1 Data product specification
D.2.1.1 General
D.2.1.2 Feature types
D.2.1.3 Rules
D.2.1.4 Conformance quality levels
D.2.2 Representation of the real world, the universe of discourse and the dataset
D.3 Quality evaluation process
D.3.1 Specify data quality unit(s)
D.3.2 Specify data quality measures
D.3.3 Specify data quality evaluation procedures
D.3.4 Specify conformance quality levels
D.3.5 Determine the output of the data quality evaluation (result)
D.3.5.1 Identification of errors
D.3.5.2 Logical consistency
D.3.5.3 Completeness
D.3.5.3.1 General
D.3.5.3.2 Quantitative result
D.3.5.3.3 Derived conformance result
D.3.5.3.4 Aggregated conformance result
D.3.5.4 Thematic quality — classification correctness
D.3.5.4.1 General
D.3.5.4.2 Quantitative result
D.3.5.4.3 Derived conformance result
D.3.5.4.4 Aggregated conformance result
D.3.5.5 Thematic quality – quantitative attribute accuracy
D.3.5.5.1 General
D.3.5.5.2 Quantitative result
D.3.5.5.3 Derived conformance result
D.3.5.6 Aggregated conformance to data product specification
D.4 Reporting data quality
D.4.1 Reporting as metadata
D.4.1.1 General
D.4.1.2 Reporting commission
D.4.1.3 Reporting classification correctness
D.4.1.4 Reporting conformance to the data product specification
D.4.2 Reporting in a quality evaluation report
D.5 Additional examples
D.5.1 General
D.5.2 Reporting descriptive results as metadata
D.5.3 Reporting metaquality as metadata
D.5.4 Reporting alternatives
D.5.4.1 Example of quality report using coverage result
D.5.4.2 Example of quality report for lidar data height model using free text
D.5.4.3 Example of quality report for INSPIRE hydrography positional accuracy
D.5.5 How to report sampling procedure
Annex E
E.1 Introduction
E.2 Lot and item
E.3 Sample size
E.4 Sampling strategies
E.4.1 Introduction
E.4.2 Probabilistic versus judgemental sampling
E.4.2.1 Differences
E.4.2.2 Simple random sampling
E.4.2.3 Stratified random sampling
E.4.2.4 Semi-random sampling
E.4.3 Feature-guided versus area-guided sampling
E.4.3.1 Feature-guided sampling (non-spatial sampling)
E.4.3.2 Area-guided sampling (spatial sampling)
E.5 Probability-based sampling
E.5.1 General considerations
E.5.2 Existing standard for inspection by sampling
E.5.2.1 General
E.5.2.2 Useful tables based on these International Standards — Sample size and rejection limits
E.5.2.2.1 Overview
E.5.2.2.2 Evaluating conforming/non-conforming items with samples
E.5.2.2.3 Standard deviation
E.5.3 Sampling process
E.5.3.1 Define items
E.5.3.2 Define data quality scopes of a dataset to be inspected
E.5.3.3 Divide the data quality scope into lots
E.5.3.4 Divide the lot into sampling units
E.5.3.5 Select sampling units by simple random sampling for inspection
E.5.3.6 Inspection of selected sampling units
Annex F
F.1 Overview
F.2 Data quality element
F.2.1 General
F.2.2 Other candidate quality elements
F.2.3 Ordering in data quality evaluation
F.3 The relationships between the data quality elements
F.3.1 General
F.3.2 Data quality elements related to missing attribute values
F.3.3 Relationships between the different aspects of accuracy
F.3.4 Dependency between completeness and accuracy
F.4 Data quality elements — Example of use
F.4.1 Completeness
F.4.1.1 General
F.4.1.2 Commission — excess data present in a dataset
F.4.1.3 Omission — data absent from a dataset
F.4.2 Logical consistency
F.4.2.1 General
F.4.2.2 Conceptual consistency — adherence to rules of the conceptual schema
F.4.2.3 Domain consistency — adherence of values to the value domains
F.4.2.4 Format consistency — degree to which data are stored in accordance with the physical structure of the dataset
F.4.2.5 Topological consistency — correctness of the explicitly encoded topological characteristics of a dataset
F.4.3 Positional accuracy
F.4.4 Temporal quality
F.4.4.1 General
F.4.4.2 Accuracy of a time measurement — closeness of reported time measurements to values accepted as or known to be true
F.4.4.3 Temporal consistency — correctness of the order of events
F.4.4.4 Temporal validity – validity of data with respect to time
F.4.5 Thematic quality
F.4.5.1 General
F.4.5.2 Classification correctness — comparison of the classes assigned to features or their attributes to a universe of discourse (e.g. ground truth or reference dataset)
F.5 Discussions on difficult cases
F.5.1 Relation between misclassification and completeness at feature type level
F.5.2 Quality elements related to unique identifiers
Annex G
G.1 Introduction
G.2 100 % pass/fail
G.3 Weighted pass/fail
G.4 Maximum/minimum value
Annex H
H.1 Introduction
H.2 XML namespaces
H.3 XML schema
Annex I
Bibliography
Cited references in this standard
Content history
[Current]
[Superseded]
[Superseded]
DR AS/NZS ISO 19157.1:2023
Please select a variation to view its description.
| Published | 29/09/2023 |
|---|---|
| Pages | 91 |
Please select a variation to view its pdf.
