Geographic data imperfection. 1, From theory to applications

221 Pages·2019·6.364 MB·English

by  Batton-Hubert, Mireille, Desjardin, Eric, Pinet, Francois

Geographic Data Imperfection 1

Geographic Data
Imperfection 1

From Theory to Applications

Edited by

Mireille Batton-Hubert
Eric Desjardin
François Pinet
First published 2019 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
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The rights of Mireille Batton-Hubert, Eric Desjardin and François Pinet to be identified as the authors of
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Library of Congress Control Number: 2019938868

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A CIP record for this book is available from the British Library
ISBN 978-1-78630-297-7
Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Part 1. Bases and Concepts . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 1. Imperfection and Geographic Information . . . . . 3
François PINET, Mireille BATTON-HUBERT and Eric DESJARDIN
1.1. Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Concepts, representation, reasoning system,
and data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1. Foundations and concepts . . . . . . . . . . . . . . . . . . . . . 5
1.2.2. Representations of imperfection . . . . . . . . . . . . . . . . . 6
1.2.3. Reasoning systems and data processing . . . . . . . . . . . . . 7
1.3. Some conclusive remarks . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Chapter 2. Imperfection of Geographic Information:
Concepts and Terminologies . . . . . . . . . . . . . . . . . . . . . . . 11
Rodolphe DEVILLERS, Eric DESJARDIN and Cyril DE RUNZ
2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2. Semantics according to Humpty Dumpty . . . . . . . . . . . . . . 13
2.3. Taxonomies of GI and its related uncertainty . . . . . . . . . . . . 17
2.4. A theoretical framework of the nature of
uncertainty and quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
vi Geographic Data Imperfection 1
Chapter 3. The Origins of Imperfection in
Geographic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Jean-Michel FOLLIN, Jean-François GIRRES, Ana-Maria
OLTEANU-RAIMOND and David SHEEREN
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2. Imperfection during the life cycle of geographic data . . . . . . 27
3.3. The sources of the imperfections in a process . . . . . . . . . . . 28
3.3.1. The target model: toward what? . . . . . . . . . . . . . . . . . 28
3.3.2. The initial source: from what? . . . . . . . . . . . . . . . . . . 30
3.3.3. The instrument: with what? . . . . . . . . . . . . . . . . . . . 31
3.3.4. The procedure: how? . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.5. The operator: by whom? . . . . . . . . . . . . . . . . . . . . . 35
3.4. Examples of sources of imperfection in different processes . . . 36
3.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Chapter 4. Integrity and Trust of Geographic Information . . 45
Clément IPHAR, Benjamin COSTÉ, Aldo NAPOLI, Cyril RAY
and Rodolphe DEVILLERS
4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2. The notions of quality . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2.1. Data quality and its dimensions . . . . . . . . . . . . . . . . . . 46
4.2.2. Assessing data quality . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2.3. Problems linked to data of poor quality . . . . . . . . . . . . . 48
4.3. Internal quality and integrity . . . . . . . . . . . . . . . . . . . . . . 48
4.3.1. The concept of integrity . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.2. Analyzing data integrity. . . . . . . . . . . . . . . . . . . . . . . 50
4.4. External quality and trust . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4.1. Definitions of trust . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.4.2. Measuring trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.5. Applying these notions to maritime geolocation data . . . . . . . 58
4.5.1. The Automatic Identification System . . . . . . . . . . . . . . 59
4.5.2. Integrity and trust issues linked to the AIS . . . . . . . . . . . 59
4.5.3. A suitable system for a range of analyses . . . . . . . . . . . . 61
4.5.4. A suitable system for assessing integrity . . . . . . . . . . . . 62
4.5.5. A suitable system for measuring trust . . . . . . . . . . . . . . 66
4.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Contents vii
Part 2. Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Chapter 5. Formalisms and Representations of
Imperfect Geographic Objects . . . . . . . . . . . . . . . . . . . . . . . 75
Mireille BATTON-HUBERT and François PINET
5.1. Theories about the representation of an
imperfect geographic object . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2. Where and when do we refer to imperfection in
geographic information? . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.3. Formalisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.3.1. The notion of event . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.3.2. Confidence and certainty (pre-measure and
confidence measure) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.3.3. Non-additive measures and associated distributions . . . . . 85
5.3.4. Tools used to manipulate fuzzy measures and sets . . . . . . 90
5.4. Spatial objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.4.1. Broad boundary objects . . . . . . . . . . . . . . . . . . . . . . . 96
5.4.2. Fuzzy objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.5. Reconsidering the introductory examples . . . . . . . . . . . . . . 99
5.6. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
Chapter 6. Representing Diagrams of
Imperfect Geographic Objects . . . . . . . . . . . . . . . . . . . . . . . 105
François PINET and Cyril DE RUNZ
6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2. Describing the theoretical models of geographic objects . . . . . 105
6.3. Describing the theoretical models of
imperfect geographic objects . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.4. Toward massive databases . . . . . . . . . . . . . . . . . . . . . . . 111
6.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Part 3. Reasoning and Treatment . . . . . . . . . . . . . . . . . . . . 113
Chapter 7. Algebraic Reasoning for Uncertain Data . . . . . . . 115
Florence LE BER
7.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
7.2. Algebras used for spatial reasoning . . . . . . . . . . . . . . . . . . 116
7.2.1. The definition and properties of algebras,
relational algebras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
viii Geographic Data Imperfection 1
7.2.2. Relational algebras used for time and space . . . . . . . . . . 118
7.3. Lattices of relation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.4. Extending these models to fuzzy regions . . . . . . . . . . . . . . 126
7.5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Chapter 8. Reasoning in Modal Logic for Uncertain Data . . . 133
Élisabeth GAVIGNET and Nadine CULLOT
8.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
8.2. Reasoning in first-order predicate calculus . . . . . . . . . . . . . 134
8.3. Reasoning in modal logic . . . . . . . . . . . . . . . . . . . . . . . . 140
8.4. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Chapter 9. Reviewing the Qualifiers of Imperfection
in Geographic Information . . . . . . . . . . . . . . . . . . . . . . . . . 151
Giovanni FUSCO and Andrea TETTAMANZI
9.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
9.2. Belief revision and update in knowledge engineering . . . . . . 152
9.3. The limitations faced by GIS when representing
a set of beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
9.4. Revision in a set of binary beliefs . . . . . . . . . . . . . . . . . . . 154
9.5. The case of uncertain beliefs . . . . . . . . . . . . . . . . . . . . . . 156
9.6. Bayesian probabilistic conditioning . . . . . . . . . . . . . . . . . . 160
9.7. Revision in evidence theory . . . . . . . . . . . . . . . . . . . . . . . 163
9.8. Possibilistic conditioning . . . . . . . . . . . . . . . . . . . . . . . . 165
9.9. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
9.10. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Chapter 10. The Features of Decision Aid and
Analysis Processes in Geography: How to Grasp
Complexity, Uncertainty, and Risks? . . . . . . . . . . . . . . . . . . 175
Myriam MERAD
10.1. The decision-making context . . . . . . . . . . . . . . . . . . . . . 175
10.2. Geographers, decision-makers, actors, and the territory . . . . 176
10.3. The objects, stakes, and issues involved in a decision. . . . . . 178
10.4. Information, data, knowledge, uncertainties, and bias . . . . . 180
10.5. Supporting the structuring and resolution
of ranking, choice, or sorting problems (issues) . . . . . . . . . . . . 182