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Spatial Similarity Relations in Multi-scale Map Spaces 2015 ed. [Kõva köide]

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  • Formaat: Hardback, 188 pages, kõrgus x laius: 235x155 mm, kaal: 4757 g, 40 Illustrations, color; 79 Illustrations, black and white; XVIII, 188 p. 119 illus., 40 illus. in color., 1 Hardback
  • Ilmumisaeg: 27-Oct-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319097423
  • ISBN-13: 9783319097428
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Spatial Similarity Relations in Multi-scale Map Spaces 2015 ed.Suurem pilt
  • Formaat: Hardback, 188 pages, kõrgus x laius: 235x155 mm, kaal: 4757 g, 40 Illustrations, color; 79 Illustrations, black and white; XVIII, 188 p. 119 illus., 40 illus. in color., 1 Hardback
  • Ilmumisaeg: 27-Oct-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319097423
  • ISBN-13: 9783319097428
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319097428.html
Märksõnad:
How does one determine how similar two maps are? This book aims at the theory of spatial similarity relations and its application in automated map generalization, including the definitions, classification and features of spatial similarity relations. Included also are calculation models of spatial similarity relations between arbitrary individual objects and between arbitrary object groups, and the application of the theory in the automation of the algorithms and procedures in map generalization.
1 Introduction
1(14)
1.1 Background and Motivation
1(3)
1.2 Significances of Spatial Similarity Relations
4(4)
1.2.1 Theory of Spatial Relations
4(1)
1.2.2 Spatial Description, Spatial Reasoning, and Spatial Query/Retrieval
4(1)
1.2.3 Spatial Recognition
5(1)
1.2.4 Automated Map Generalization
6(2)
1.3 Classification of Objects in Multiscale Map Spaces
8(2)
1.4 Definitions of Map Scale Change
10(1)
1.5 Research Objectives
11(1)
1.6 Scope of the Study
12(1)
1.7 Book Outline
12(3)
References
13(2)
2 Literature Review and Analysis
15(30)
2.1 Definitions of Similarity
15(7)
2.1.1 Definitions of Similarity in Various Fields
16(5)
2.1.2 Critical Analysis of the Definitions
21(1)
2.2 Features of Similarity
22(3)
2.2.1 Features of Similarity in Different Fields
22(2)
2.2.2 Critical Analysis of the Features
24(1)
2.3 Classification for Spatial Similarity Relations
25(1)
2.4 Calculation Models/Measures for Similarity Degree
25(11)
2.4.1 Models in Psychology
27(1)
2.4.2 Models/Measures in Computer Science
28(2)
2.4.3 Models/Measures in Music
30(1)
2.4.4 Models/Measures in Geography
31(5)
2.4.5 Critical Analyses of Existing Models/Measures
36(1)
2.5 Raster-based Approaches for Map Similarity Comparison
36(4)
2.5.1 Per Category Comparison Method
37(1)
2.5.2 Kappa Comparison Method
37(1)
2.5.3 Fuzzy Kappa Approach
38(1)
2.5.4 Fuzzy Inference System
38(1)
2.5.5 Fuzzy Comparison with Unequal Resolutions
39(1)
2.5.6 Aggregated Cells
39(1)
2.5.7 Moving Window-Based Structure
39(1)
2.5.8 Numerical Comparison Methods
39(1)
2.6
Chapter Summary
40(5)
References
40(5)
3 Concepts of Spatial Similarity Relations in Multiscale Map Spaces
45(36)
3.1 Definitions
45(4)
3.1.1 Definitions of Similarity Relation
46(1)
3.1.2 Definitions of Spatial Similarity Relation
47(2)
3.2 Discussion
49(3)
3.2.1 Definitions of Spatial Similarity Relation in Multiscale Map Spaces
49(3)
3.2.2 Definition of Difference
52(1)
3.3 Features
52(8)
3.3.1 Equality
52(1)
3.3.2 Finiteness
53(1)
3.3.3 Minimality
53(1)
3.3.4 Auto-Similarity
53(1)
3.3.5 Symmetry (Reflectivity)
54(1)
3.3.6 Nontransitivity
54(1)
3.3.7 Weak Symmetry
55(1)
3.3.8 Asymmetry
56(2)
3.3.9 Triangle Inequality
58(1)
3.3.10 Scale Dependence
59(1)
3.4 Factors in Similarity Judgments
60(17)
3.4.1 Factors for Individual Objects
60(4)
3.4.2 Factors for Object Groups
64(6)
3.4.3 Psychological Tests for Determining the Weights of the Factors
70(7)
3.5 Classification
77(2)
3.5.1 A Classification System of Spatial Similarity Relations in Geographic Spaces
77(1)
3.5.2 A Classification System of Spatial Similarity Relations on Line Maps
78(1)
3.6
Chapter Summary
79(2)
References
80(1)
4 Models for Calculating Spatial Similarity Degrees in Multiscale Map Spaces
81(34)
4.1 Models for Individual Objects
81(4)
4.1.1 Model for Individual Point Objects
81(1)
4.1.2 Model for Individual Linear Objects
82(3)
4.1.3 Model for Individual Areal Objects
85(1)
4.2 Models for Object Groups
85(23)
4.2.1 Model for Point Clouds
86(5)
4.2.2 Model for Parallel Line Clusters
91(3)
4.2.3 Model for Intersected Line Networks
94(3)
4.2.4 Model for Tree-Like Networks
97(4)
4.2.5 Model for Discrete Polygon Groups
101(4)
4.2.6 Model for Connected Polygon Groups
105(3)
4.3 Model for Calculating Spatial Similarity Degrees Between Maps
108(4)
4.3.1 Similarity in Topological Relations
109(1)
4.3.2 Similarity in Direction Relations
109(1)
4.3.3 Similarity in Metric Distance Relations
110(1)
4.3.4 Similarity in Attributes
111(1)
4.4
Chapter Summary
112(3)
References
112(3)
5 Model Validations
115(42)
5.1 General Approaches to Model Validation
115(2)
5.2 Strategies for Validating the New Models
117(1)
5.2.1 Strategy 1:Theoretical Justifiability
117(1)
5.2.2 Strategy 2: Third Party Involvement
118(1)
5.2.3 Strategy 3: Experts' Participation
118(1)
5.3 Psychological Experiment Design
118(3)
5.4 Samples in Psychological Experiments
121(31)
5.4.1 Rules Obeyed in Sample Selection
121(3)
5.4.2 Samples Used
124(28)
5.5 Statistical Analysis and Discussion
152(2)
5.6
Chapter Summary
154(3)
References
154(3)
6 Applications of Spatial Similarity Relations in Map Generalization
157(26)
6.1 Relations Between Map Scale Change and Spatial Similarity Degree
157(3)
6.1.1 Description of the Problem
158(1)
6.1.2 Conceptual Framework for Solving the Problem
158(2)
6.2 Formulae for Map Scale Change and Spatial Similarity Degree
160(11)
6.2.1 Individual Point Objects
161(1)
6.2.2 Individual Linear Objects
161(2)
6.2.3 Individual Areal Objects
163(1)
6.2.4 Point Clouds
163(1)
6.2.5 Parallel Line Clusters
164(2)
6.2.6 Intersected Line Networks
166(1)
6.2.7 Tree-Like Networks
167(2)
6.2.8 Discrete Polygon Groups
169(1)
6.2.9 Connected Polygon Groups
170(1)
6.2.10 Maps
170(1)
6.3 Discussion About the Formulae
171(2)
6.4 Approach to Automatically Terminate a Procedure in Map Generalization
173(2)
6.5 Calculation of the Distance Tolerance in the Douglas--Peucker Algorithm
175(6)
6.5.1 The Douglas--Peucker Algorithm and Its Disadvantages
175(2)
6.5.2 Approach to Calculating the Distance Tolerance for the Douglas--Peucker Algorithm
177(2)
6.5.3 An Example for Testing the Approach
179(2)
6.6
Chapter Summary
181(2)
References
181(2)
7 Conclusions
183(4)
7.1 Overall Summary
183(1)
7.2 Contributions
184(1)
7.3 Limitations
185(1)
7.4 Recommendations for Further Research
186(1)
Appendix 187
Dr. Haowen Yan obtained his Bachelors, Masters, and Doctors degrees in Cartography and Geographic Information Engineering from Wuhan University, China. He has worked at the Hong Kong Polytechnic University as a research assistant, at the University of Zurich as a senior researcher, and at the University of Waterloo as a research fellow. He is currently a visiting professor at the University of Waterloo and professor of Geomatics in the Lanzhou Jiaotong University, China.

Jonathan Li is a professor at the Department of Geography and Environmental Management at the University of Waterloo. Dr. Li's research interests are mainly in the areas of remote sensing and geographic information science, including high-resolution satellite mapping, airborne and terrestrial mobile LIDAR mapping, earth observation of global change, remote sensing of inland and coastal waters, remote sensing of renewable energy potential, mapping of climate-induced hazards, Internet GIS and Web Mapping, Terrain Analysis in Hydrogeography, geospatial sensor network, and geospatial information technologies for emergency response and disaster management.