| Preface |
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xi | |
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Part 1 Bases and Concepts |
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1 | (72) |
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Chapter 1 Imperfection and Geographic Information |
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3 | (8) |
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3 | (2) |
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1.2 Concepts, representation, reasoning system, and data processing |
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5 | (3) |
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1.2.1 Foundations and concepts |
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5 | (1) |
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1.2.2 Representations of imperfection |
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6 | (1) |
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1.2.3 Reasoning systems and data processing |
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7 | (1) |
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1.3 Some conclusive remarks |
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8 | (1) |
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9 | (2) |
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Chapter 2 Imperfection of Geographic Information: Concepts and Terminologies |
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11 | (14) |
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11 | (2) |
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2.2 Semantics according to Humpty Dumpty |
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13 | (4) |
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2.3 Taxonomies of GI and its related uncertainty |
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17 | (2) |
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2.4 A theoretical framework of the nature of uncertainty and quality |
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19 | (2) |
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21 | (1) |
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22 | (3) |
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Chapter 3 The Origins of Imperfection in Geographic Data |
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25 | (20) |
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Ana-Maria Olteanu-Raimond |
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25 | (2) |
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3.2 Imperfection during the life cycle of geographic data |
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27 | (1) |
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3.3 The sources of the imperfections in a process |
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28 | (8) |
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3.3.1 The target model: toward what? |
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28 | (2) |
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3.3.2 The initial source: from what? |
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30 | (1) |
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3.3.3 The instrument: with what? |
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31 | (2) |
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3.3.4 The procedure: how? |
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33 | (2) |
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3.3.5 The operator: by whom? |
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35 | (1) |
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3.4 Examples of sources of imperfection in different processes |
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36 | (5) |
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41 | (1) |
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42 | (3) |
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Chapter 4 Integrity and Trust of Geographic Information |
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45 | (28) |
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45 | (1) |
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4.2 The notions of quality |
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46 | (2) |
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4.2.1 Data quality and its dimensions |
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46 | (1) |
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4.2.2 Assessing data quality |
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47 | (1) |
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4.2.3 Problems linked to data of poor quality |
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48 | (1) |
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4.3 Internal quality and integrity |
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48 | (4) |
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4.3.1 The concept of integrity |
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49 | (1) |
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4.3.2 Analyzing data integrity |
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50 | (2) |
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4.4 External quality and trust |
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52 | (6) |
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4.4.1 Definitions of trust |
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53 | (1) |
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54 | (4) |
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4.5 Applying these notions to maritime geolocation data |
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58 | (11) |
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4.5.1 The Automatic Identification System |
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59 | (1) |
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4.5.2 Integrity and trust issues linked to the AIS |
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59 | (2) |
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4.5.3 A suitable system for a range of analyses |
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61 | (1) |
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4.5.4 A suitable system for assessing integrity |
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62 | (4) |
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4.5.5 A suitable system for measuring trust |
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66 | (3) |
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69 | (1) |
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70 | (3) |
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73 | (40) |
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Chapter 5 Formalisms and Representations of Imperfect Geographic Objects |
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75 | (30) |
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5.1 Theories about the representation of an imperfect geographic object |
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75 | (1) |
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5.2 Where and when do we refer to imperfection in geographic information? |
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76 | (3) |
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79 | (17) |
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5.3.1 The notion of event |
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79 | (3) |
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5.3.2 Confidence and certainty (pre-measure and confidence measure) |
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82 | (3) |
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5.3.3 Non-additive measures and associated distributions |
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85 | (5) |
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5.3.4 Tools used to manipulate fuzzy measures and sets |
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90 | (6) |
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96 | (3) |
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5.4.1 Broad boundary objects |
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96 | (2) |
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98 | (1) |
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5.5 Reconsidering the introductory examples |
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99 | (3) |
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102 | (3) |
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Chapter 6 Representing Diagrams of Imperfect Geographic Objects |
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105 | (8) |
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105 | (1) |
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6.2 Describing the theoretical models of geographic objects |
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105 | (3) |
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6.3 Describing the theoretical models of imperfect geographic objects |
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108 | (3) |
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6.4 Toward massive databases |
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111 | (1) |
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111 | (2) |
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Part 3 Reasoning and Treatment |
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113 | (78) |
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Chapter 7 Algebraic Reasoning for Uncertain Data |
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115 | (18) |
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115 | (1) |
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7.2 Algebras used for spatial reasoning |
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116 | (7) |
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7.2.1 The definition and properties of algebras, relational algebras |
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116 | (2) |
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7.2.2 Relational algebras used for time and space |
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118 | (5) |
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123 | (3) |
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7.4 Extending these models to fuzzy regions |
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126 | (2) |
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128 | (5) |
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Chapter 8 Reasoning in Modal Logic for Uncertain Data |
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133 | (18) |
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133 | (1) |
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8.2 Reasoning in first-order predicate calculus |
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134 | (6) |
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8.3 Reasoning in modal logic |
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140 | (9) |
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149 | (2) |
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Chapter 9 Reviewing the Qualifiers of Imperfection in Geographic Information |
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151 | (24) |
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151 | (1) |
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9.2 Belief revision and update in knowledge engineering |
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152 | (1) |
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9.3 The limitations faced by GIS when representing a set of beliefs |
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153 | (1) |
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9.4 Revision in a set of binary beliefs |
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154 | (2) |
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9.5 The case of uncertain beliefs |
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156 | (4) |
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9.6 Bayesian probabilistic conditioning |
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160 | (3) |
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9.7 Revision in evidence theory |
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163 | (2) |
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9.8 Possibilistic conditioning |
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165 | (6) |
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171 | (2) |
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173 | (2) |
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Chapter 10 The Features of Decision Aid and Analysis Processes in Geography: How to Grasp Complexity, Uncertainty, and Risks? |
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175 | (16) |
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10.1 The decision-making context |
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175 | (1) |
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10.2 Geographers, decision-makers, actors, and the territory |
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176 | (2) |
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10.3 The objects, stakes, and issues involved in a decision |
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178 | (2) |
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10.4 Information, data, knowledge, uncertainties, and bias |
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180 | (2) |
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10.5 Supporting the structuring and resolution of ranking, choice, or sorting problems (issues) |
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182 | (3) |
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10.6 A decision-analysis method for risk analysis and management |
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185 | (3) |
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188 | (1) |
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189 | (2) |
| List of Authors |
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191 | (2) |
| Index |
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193 | |
Lisainfo e-raamatute kohta