AS/NZS ISO 19157:2015
Geographic information - Data quality
Establishes the principles for describing the quality of 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. It 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 data set 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. Identical to and reporduced from ISO 19157:2013.
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AS/NZS ISO 19157:2015
COPYRIGHT
PREFACE
CONTENTS
INTRODUCTION
1 Scope
2 Conformance
3 Normative references
4 Terms and definitions
5 Abbreviated terms
5.1 Abbreviations
5.2 Package abbreviations
6 Overview of data quality
7 Components of data quality
7.1 Overview of the components
7.2 Data quality unit
7.3 Data quality elements
7.4 Descriptors of data quality elements
7.5 Metaquality elements
7.6 Descriptors of a metaquality element
8 Data quality measures
8.1 General
8.2 Standardized data quality measures
8.3 User defined data quality measures
8.4 Catalogue of data quality measures
8.5 List of components
8.6 Component details
9 Data quality evaluation
9.1 The process for evaluating data quality
9.2 Data quality evaluation methods
9.3 Aggregation and derivation
10 Data quality reporting
10.1 General
10.2 Particular cases
Annex A(normative)Abstract test suites
A.1 Test case identifier: Quality evaluation process
A.2 Test case identifier: Data quality metadata
A.3 Test case identifier: Metadata conformity
A.4 Test case identifier: Standalone quality report
A.5 Test case identifier: Data quality measures
Annex B(informative)Data quality concepts and their use
B.1 Framework of data quality concepts
B.2 The structure of data sets and components for quality description
B.3 When to use quality evaluation procedures
B.4 Reporting quality information
Annex C(normative)Data dictionary for data quality
C.1 Data dictionary overview
C.2 Data quality package data dictionary
C.3 Code lists
Annex D(normative)List of standardized data quality measures
D.1 Introduction
D.2 Completeness
D.3 Logical consistency
D.4 Positional accuracy
D.5 Temporal quality
D.6 Thematic accuracy
D.7 Aggregation Measures
Annex E(informative)Evaluating and reporting data quality
E.1 Introduction
E.2 Data set description
E.3 Quality evaluation process
E.4 Reporting data quality
E.5 Additional examples
Annex F(informative)Sampling methods for evaluating
F.1 Introduction
F.2 Lot and item
F.3 Sample size
F.4 Sampling strategies
F.5 Probability-based sampling
Annex G(normative)Data quality basic measures
G.1 Purpose of data quality basic measures
G.2 Counting-related data quality basic measures
G.3 Uncertainty-related data quality basic measures
Annex H(informative)Management of data quality measures
H.1 Introduction
H.2 Storage of data quality measures
Annex I(informative)Guidelines for the use of Quality Elements
I.1 Overview
I.2 Data quality element categories
I.3 The relationships between the data quality elements
I.4 Data quality elements – example of use
I.5 Discussions on difficult cases
Annex J(informative)Aggregation of data quality results
J.1 Introduction
J.2 100 % pass/fail
J.3 Weighted pass/fail
J.4 Maximum/minimum value
Bibliography
Table
Table 1 — Process steps
Table B.1 — Hierarchical levels
Table C.1 — Data quality
Table C.2 — Data quality element
Table C.3 — Measure reference
Table C.4 — Data quality evaluation
Table C.5 — Data quality result
Table C.6 — Standalone quality report information
Table C.7 — Data quality measures
Table C.8 — Data quality basic measure
Table C.9 — Data quality parameter
Table C.10 — Data quality measure descriptor
Table C.11 — Data quality measure source reference
Table C.12 — Evaluation method type
Table C.13 — Value structure
Table D.1 — Excess item
Table D.2 — Number of excess items
Table D.3 — Rate of excess itemsLine
Table D.4 — Number of duplicate feature instances
Table D.5 — Missing item
Table D.6 — Number of missing items
Table D.7 — Rate of missing items
Table D.8 — Conceptual schema non-compliance
Table D.9 — Conceptual schema compliance
Table D.10 — Number of items not compliant with the rules of the conceptual schema
Table D.11 — Number of invalid overlaps of surfaces
Table D.12 — Non-compliance rate with respect to the rules of the conceptual schema
Table D.13 — Compliance rate with the rules of the conceptual schema
Table D.14 — Value domain non-conformance
Table D.15 — Value domain conformance
Table D.16 — Number of items not in conformance with their value domain
Table D.17 — Value domain conformance rate
Table D.18 — Value domain non-conformance rate
Table D.19 — Physical structure conflicts
Table D.20 — Physical structure conflicts number
Table D.21 — Physical structure conflict rate
Table D.22 — Number of faulty point-curve connections
Table D.23 — Rate of faulty point-curve connections
Table D.24 — Number of missing connections due to undershoots
Table D.25 — Number of missing connections due to overshoots
Table D.26 — Number of invalid slivers
Table D.27 — Number of invalid self-intersect errors
Table D.28 — Number of invalid self-overlap errors
Table D.29 — Mean value of positional uncertainties
Table D.30 — Bias of positions
Table D.31 — Mean value of positional uncertainties excluding outliers
Table D.32 — Number of positional uncertainties above a given threshold
Table D.33 — Rate of positional errors above a given threshold
Table D.34 — Covariance matrix
Table D.35 — Linear error probable
Table D.36 — Standard linear error
Table D.37 — Linear map accuracy at 90 % significance level
Table D.39 — Linear map accuracy at 99 % significance level
Table D.40 — Near certainty linear level
Table D.41 — Root mean square error
Table D.42 — Absolute linear error at 90 % significance level of biased vertical data (NATO)
Table D.43 — Absolute linear error at 90 % significance level of biased vertical data
Table D.44 — Circular standard deviation
Table D.45 — Circular error probable
Table D.46 — Circular map accuracy standard
Table D.47 — Circular error at 95 % significance level
Table D.48 — Circular near certainty error
Table D.49 — Root mean square error of planimetry
Table D.50 — Absolute circular error at 90 % significance level of biased data (NATO)
Table D.51 — Absolute circular error at 90 % significance level of biased data
Table D.52 — Uncertainty ellipse
Table D.53 — Confidence ellipse
Table D.54 — Relative vertical error
Table D.55 — Relative horizontal error
Table D.56 — Time accuracy at 68,3 % significance level
Table D.57 — Time accuracy at 50 % significance level
Table D.58 — Time accuracy at 90 % significance level
Table D.59 — Time accuracy at 95 % significance level
Table D.60 — Time accuracy at 99 % significance level
Table D.61 — Time accuracy at 99,8 % significance level
Table D.62 — Chronological order
Table D.63 — Number of incorrectly classified features
Table D.64 — Misclassification rate
Table D.65 — Misclassification matrix
Table D.66 — Relative misclassification matrix
Table D.67 — Kappa coefficient
Table D.68 — Number of incorrect attribute values
Table D.69 — Rate of correct attribute values
Table D.70 — Rate of incorrect attribute values
Table D.71 — Attribute value uncertainty at 68,3 % significance level
Table D.72 — Attribute value uncertainty at 50 % significance level
Table D.73 — Attribute value uncertainty at 90 % significance level
Table D.74 — Attribute value uncertainty at 95 % significance level
Table D.75 — Attribute value uncertainty at 99 % significance level
Table D.76 — Attribute value uncertainty at 99,8 % significance level
Table D.77 — Data product specification passed
Table D.78 — Data product specification fail count
Table D.79 — Data product specification pass count
Table D.80 — Data product specification fail rate
Table D.81 — Data product specification pass rate
Table E.1 — Feature types
Table E.2 — Conformance result for logical consistency
Table E.3 — Completeness by feature class
Table E.4 — Completeness conformance
Table E.5 — Aggregated completeness conformance
Table E.6 — Feature misclassification matrix
Table E.7 — Thematic accuracy conformance
Table E.8 — Aggregated classification correctness conformance
Table E.9 — Feature attribute height misclassification matrix – Tree height
Table E.10 — Thematic accuracy conformance
Table E.11 — Usability – conformance to the data product specification
Table E.12 — Reporting commission as metadata
Table E.13 — Reporting classification correctness as metadata
Table E.14 — Reporting usability as metadata
Table E.15 — Reporting descriptive result as metadata
Table E.16 — Reporting metaquality as metadata
Table E.17 — Procedure for sampling
Table E.18 — Reporting sampling evaluation as metadata
Table F.1 — Different basis for defining population
Table F.2 — Statistical values for testing of number of conforming/non-conforming itemsSignificance level 95 %
Table F.3 — Symbols and Formulas
Table F.4 — Statistical numbers for testing standard deviation. 95 % significance level
Table G.1 — Data quality basic measures for counting-related data quality measures
Table G.2 — Relation between the quantiles of the normal distribution and the significance level
Table G.3 — Relation between the quantiles of the Student’s t-distribution and the significancelevel for different redundancies r
Table G.4 — Data quality basic measures for different probabilities P of a one-dimensionalquantity, where the standard deviation is estimated from redundant measurements
Table G.5 — Relationship between the probability P and the corresponding radius of thecircular area
Table G.6 — Relationship between the probability P and the corresponding radius of thespherical volume
Table H.1 — Measures, basic measures and parameters attributes corresponding to registereditems attribute
Table H.2 — Example of registered item element - Measure 11
Table I.1 — Quality elements related to unique identifiers
Table J.1 — Example of computation of an aggregated quality evaluation result
Figure
Figure 1 — Conceptual model of quality for geographic data
Figure 2 — Overview of the components of data quality
Figure 3 — Data quality unit
Figure 4 — Overview of the data quality elements
Figure 5 — Data quality element descriptors
Figure 6 — Data quality measure reference
Figure 7 — Data quality evaluation method
Figure 8 — Data quality result
Figure 9 — Metaquality elements
Figure 10 — Metaquality descriptors
Figure 11 — Data quality measures
Figure 12 — Evaluating data quality
Figure 13 — Data quality evaluation methods
Figure 14 — Reporting data quality
Figure B.1 — Framework of data quality concepts
Figure B.2 — Data quality information
Figure E.1 — Graphical representation of the “real world”
Figure E.2 — Graphical representation of the universe of discourse
Figure E.3 — Graphical representation of the data set
Figure E.4 — Graphical representation of data set error locations
Figure F.1 — Effect of sample area location on representativity of items in the sample
Figure F.2 — Sampling strategy relationships
Figure F.3 — Example of area-guided random sampling
Figure F.4 — Example of area-guided regular and non-random sampling
Figure H.1 — Registered items, catalogue and data quality measures
Figure H.2 — Structural similarities between registered items and data quality measures
Figure I.1 — Ordering in data quality evaluation
Figure I.2 — Accuracy versus completeness
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AS/NZS ISO 19157:2015
COPYRIGHT
PREFACE
CONTENTS
INTRODUCTION
1 Scope
2 Conformance
3 Normative references
4 Terms and definitions
5 Abbreviated terms
5.1 Abbreviations
5.2 Package abbreviations
6 Overview of data quality
7 Components of data quality
7.1 Overview of the components
7.2 Data quality unit
7.3 Data quality elements
7.4 Descriptors of data quality elements
7.5 Metaquality elements
7.6 Descriptors of a metaquality element
8 Data quality measures
8.1 General
8.2 Standardized data quality measures
8.3 User defined data quality measures
8.4 Catalogue of data quality measures
8.5 List of components
8.6 Component details
9 Data quality evaluation
9.1 The process for evaluating data quality
9.2 Data quality evaluation methods
9.3 Aggregation and derivation
10 Data quality reporting
10.1 General
10.2 Particular cases
Annex A(normative)Abstract test suites
A.1 Test case identifier: Quality evaluation process
A.2 Test case identifier: Data quality metadata
A.3 Test case identifier: Metadata conformity
A.4 Test case identifier: Standalone quality report
A.5 Test case identifier: Data quality measures
Annex B(informative)Data quality concepts and their use
B.1 Framework of data quality concepts
B.2 The structure of data sets and components for quality description
B.3 When to use quality evaluation procedures
B.4 Reporting quality information
Annex C(normative)Data dictionary for data quality
C.1 Data dictionary overview
C.2 Data quality package data dictionary
C.3 Code lists
Annex D(normative)List of standardized data quality measures
D.1 Introduction
D.2 Completeness
D.3 Logical consistency
D.4 Positional accuracy
D.5 Temporal quality
D.6 Thematic accuracy
D.7 Aggregation Measures
Annex E(informative)Evaluating and reporting data quality
E.1 Introduction
E.2 Data set description
E.3 Quality evaluation process
E.4 Reporting data quality
E.5 Additional examples
Annex F(informative)Sampling methods for evaluating
F.1 Introduction
F.2 Lot and item
F.3 Sample size
F.4 Sampling strategies
F.5 Probability-based sampling
Annex G(normative)Data quality basic measures
G.1 Purpose of data quality basic measures
G.2 Counting-related data quality basic measures
G.3 Uncertainty-related data quality basic measures
Annex H(informative)Management of data quality measures
H.1 Introduction
H.2 Storage of data quality measures
Annex I(informative)Guidelines for the use of Quality Elements
I.1 Overview
I.2 Data quality element categories
I.3 The relationships between the data quality elements
I.4 Data quality elements – example of use
I.5 Discussions on difficult cases
Annex J(informative)Aggregation of data quality results
J.1 Introduction
J.2 100 % pass/fail
J.3 Weighted pass/fail
J.4 Maximum/minimum value
Bibliography
Table
Table 1 — Process steps
Table B.1 — Hierarchical levels
Table C.1 — Data quality
Table C.2 — Data quality element
Table C.3 — Measure reference
Table C.4 — Data quality evaluation
Table C.5 — Data quality result
Table C.6 — Standalone quality report information
Table C.7 — Data quality measures
Table C.8 — Data quality basic measure
Table C.9 — Data quality parameter
Table C.10 — Data quality measure descriptor
Table C.11 — Data quality measure source reference
Table C.12 — Evaluation method type
Table C.13 — Value structure
Table D.1 — Excess item
Table D.2 — Number of excess items
Table D.3 — Rate of excess itemsLine
Table D.4 — Number of duplicate feature instances
Table D.5 — Missing item
Table D.6 — Number of missing items
Table D.7 — Rate of missing items
Table D.8 — Conceptual schema non-compliance
Table D.9 — Conceptual schema compliance
Table D.10 — Number of items not compliant with the rules of the conceptual schema
Table D.11 — Number of invalid overlaps of surfaces
Table D.12 — Non-compliance rate with respect to the rules of the conceptual schema
Table D.13 — Compliance rate with the rules of the conceptual schema
Table D.14 — Value domain non-conformance
Table D.15 — Value domain conformance
Table D.16 — Number of items not in conformance with their value domain
Table D.17 — Value domain conformance rate
Table D.18 — Value domain non-conformance rate
Table D.19 — Physical structure conflicts
Table D.20 — Physical structure conflicts number
Table D.21 — Physical structure conflict rate
Table D.22 — Number of faulty point-curve connections
Table D.23 — Rate of faulty point-curve connections
Table D.24 — Number of missing connections due to undershoots
Table D.25 — Number of missing connections due to overshoots
Table D.26 — Number of invalid slivers
Table D.27 — Number of invalid self-intersect errors
Table D.28 — Number of invalid self-overlap errors
Table D.29 — Mean value of positional uncertainties
Table D.30 — Bias of positions
Table D.31 — Mean value of positional uncertainties excluding outliers
Table D.32 — Number of positional uncertainties above a given threshold
Table D.33 — Rate of positional errors above a given threshold
Table D.34 — Covariance matrix
Table D.35 — Linear error probable
Table D.36 — Standard linear error
Table D.37 — Linear map accuracy at 90 % significance level
Table D.39 — Linear map accuracy at 99 % significance level
Table D.40 — Near certainty linear level
Table D.41 — Root mean square error
Table D.42 — Absolute linear error at 90 % significance level of biased vertical data (NATO)
Table D.43 — Absolute linear error at 90 % significance level of biased vertical data
Table D.44 — Circular standard deviation
Table D.45 — Circular error probable
Table D.46 — Circular map accuracy standard
Table D.47 — Circular error at 95 % significance level
Table D.48 — Circular near certainty error
Table D.49 — Root mean square error of planimetry
Table D.50 — Absolute circular error at 90 % significance level of biased data (NATO)
Table D.51 — Absolute circular error at 90 % significance level of biased data
Table D.52 — Uncertainty ellipse
Table D.53 — Confidence ellipse
Table D.54 — Relative vertical error
Table D.55 — Relative horizontal error
Table D.56 — Time accuracy at 68,3 % significance level
Table D.57 — Time accuracy at 50 % significance level
Table D.58 — Time accuracy at 90 % significance level
Table D.59 — Time accuracy at 95 % significance level
Table D.60 — Time accuracy at 99 % significance level
Table D.61 — Time accuracy at 99,8 % significance level
Table D.62 — Chronological order
Table D.63 — Number of incorrectly classified features
Table D.64 — Misclassification rate
Table D.65 — Misclassification matrix
Table D.66 — Relative misclassification matrix
Table D.67 — Kappa coefficient
Table D.68 — Number of incorrect attribute values
Table D.69 — Rate of correct attribute values
Table D.70 — Rate of incorrect attribute values
Table D.71 — Attribute value uncertainty at 68,3 % significance level
Table D.72 — Attribute value uncertainty at 50 % significance level
Table D.73 — Attribute value uncertainty at 90 % significance level
Table D.74 — Attribute value uncertainty at 95 % significance level
Table D.75 — Attribute value uncertainty at 99 % significance level
Table D.76 — Attribute value uncertainty at 99,8 % significance level
Table D.77 — Data product specification passed
Table D.78 — Data product specification fail count
Table D.79 — Data product specification pass count
Table D.80 — Data product specification fail rate
Table D.81 — Data product specification pass rate
Table E.1 — Feature types
Table E.2 — Conformance result for logical consistency
Table E.3 — Completeness by feature class
Table E.4 — Completeness conformance
Table E.5 — Aggregated completeness conformance
Table E.6 — Feature misclassification matrix
Table E.7 — Thematic accuracy conformance
Table E.8 — Aggregated classification correctness conformance
Table E.9 — Feature attribute height misclassification matrix – Tree height
Table E.10 — Thematic accuracy conformance
Table E.11 — Usability – conformance to the data product specification
Table E.12 — Reporting commission as metadata
Table E.13 — Reporting classification correctness as metadata
Table E.14 — Reporting usability as metadata
Table E.15 — Reporting descriptive result as metadata
Table E.16 — Reporting metaquality as metadata
Table E.17 — Procedure for sampling
Table E.18 — Reporting sampling evaluation as metadata
Table F.1 — Different basis for defining population
Table F.2 — Statistical values for testing of number of conforming/non-conforming itemsSignificance level 95 %
Table F.3 — Symbols and Formulas
Table F.4 — Statistical numbers for testing standard deviation. 95 % significance level
Table G.1 — Data quality basic measures for counting-related data quality measures
Table G.2 — Relation between the quantiles of the normal distribution and the significance level
Table G.3 — Relation between the quantiles of the Student’s t-distribution and the significancelevel for different redundancies r
Table G.4 — Data quality basic measures for different probabilities P of a one-dimensionalquantity, where the standard deviation is estimated from redundant measurements
Table G.5 — Relationship between the probability P and the corresponding radius of thecircular area
Table G.6 — Relationship between the probability P and the corresponding radius of thespherical volume
Table H.1 — Measures, basic measures and parameters attributes corresponding to registereditems attribute
Table H.2 — Example of registered item element - Measure 11
Table I.1 — Quality elements related to unique identifiers
Table J.1 — Example of computation of an aggregated quality evaluation result
Figure
Figure 1 — Conceptual model of quality for geographic data
Figure 2 — Overview of the components of data quality
Figure 3 — Data quality unit
Figure 4 — Overview of the data quality elements
Figure 5 — Data quality element descriptors
Figure 6 — Data quality measure reference
Figure 7 — Data quality evaluation method
Figure 8 — Data quality result
Figure 9 — Metaquality elements
Figure 10 — Metaquality descriptors
Figure 11 — Data quality measures
Figure 12 — Evaluating data quality
Figure 13 — Data quality evaluation methods
Figure 14 — Reporting data quality
Figure B.1 — Framework of data quality concepts
Figure B.2 — Data quality information
Figure E.1 — Graphical representation of the “real world”
Figure E.2 — Graphical representation of the universe of discourse
Figure E.3 — Graphical representation of the data set
Figure E.4 — Graphical representation of data set error locations
Figure F.1 — Effect of sample area location on representativity of items in the sample
Figure F.2 — Sampling strategy relationships
Figure F.3 — Example of area-guided random sampling
Figure F.4 — Example of area-guided regular and non-random sampling
Figure H.1 — Registered items, catalogue and data quality measures
Figure H.2 — Structural similarities between registered items and data quality measures
Figure I.1 — Ordering in data quality evaluation
Figure I.2 — Accuracy versus completeness
Previous versions
AS/NZS ISO 19157:2015
Online Library subscription information
Online library subscriptions provide a cost-effective solution for organisations of any size to access the latest standards, offering 24/7 access to a personalised catalogue. The service ensures subscribers have have the most up-to-date version of selected standards, incluing automatic updates for any amendments or versions. Pricing is dependent on the chosen standards and the number of concurrent users, with discounts applied from the standards PDF price. For customised subscriptions or further inquiries, contact the Online Library team.