Riedl / Kainz Progress in Spatial Data Handling

12th International Symposium on Spatial Data Handling
1. Auflage 2006
ISBN: 978-3-540-35589-2
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 942 Seiten

Since the first symposium in 1984 the International Symposia on Spatial Data Handling (SDH) has become a major resource for recent advances in GIS research. The International Symposium on Spatial Data Handling is regarded as a premier international research forum for GIS. All papers are fully reviewed by an international program committee composed of experts in the field.

Autoren/Hrsg.

Riedl, Andreas

Kainz, Wolfgang

Inhaltsverzeichnis

1;The Devil is still in the Data: Persistent Spatial Data Handling Challenges in Grassroots GIS;19
1.1;1 Introduction1;19
1.2;2 The Humboldt Park GIS Project;21
1.3;3 Geospatial Data in Grassroots GIS;22
1.4;4 Intersecting with GIScience Research on Spatial Data Handling;28
1.5;5 Conclusions;31
1.6;References;33
2;Physical vs. Web Space – Similarities and Differences;35
2.1;Abstract;35
2.2;1 Introduction;35
2.3;2 Concepts of Space;36
2.4;3 Physical and Web Space;37
2.5;4 Geometric Aspects of Interest;38
2.6;5 Application of Geometry to Physical and Web Space;40
2.7;6 Conclusions and Future Research;43
2.8;References;43
3;Utilization of Qualitative Spatial Reasoning in Geographic Information Systems;45
3.1;Abstract;45
3.2;1 Introduction;46
3.3;2 Qualitative Proximity Formalism;48
3.4;3 GIS Interface and Usability;50
3.5;4 TreeSap – Qualitative Reasoning GIS;51
3.6;5 Qualitative Querying;52
3.7;6 Qualitative Visualization;54
3.8;7 Relative Features;57
3.9;8 Future Works;58
3.10;9 Conclusions;59
3.11;References;60
4;QACHE: Query Caching in Location- Based Services;115
4.1;Abstract;115
4.2;1 Introduction;116
4.3;2 Related Work;119
4.4;3 Overview of QACHE;120
4.5;4 Design and Implementation of QACHE;124
4.6;5 Performance Evaluation;128
4.7;6 Conclusions;131
4.8;References;132
5;Modeling Geometric Rules in Object Based Models: An XML / GML Approach;148
5.1;Abstract;148
5.2;1 Introduction;149
5.3;2 Rule Representation in Spatial Data;149
5.4;3 Road Markings and Rules: The Application Domain;153
5.5;4 Object Based Approaches to Modeling Traffic Features;154
5.6;5 Summary;162
5.7;References;162
6;Exploring Geographical Data with Spatio-Visual Data Mining;164
6.1;Abstract;164
6.2;1 Introduction;164
6.3;2 Exploration Data;166
6.4;3 The Spatio- Visual Exploration Method;167
6.5;4 Results of Data Exploration;173
6.6;5 Conclusions and Future Work;177
6.7;Acknowledgements;179
6.8;Contributors;179
6.9;References;179
7;Continuous Wavelet Transformations for Hyperspectral Feature Detection;182
7.1;Abstract;182
7.2;1 Introduction;182
7.3;2 Materials and Methods;186
7.4;3 Results;188
7.5;4 Discussion;190
7.6;Acknowledgements;192
7.7;References;192
8;Measuring Linear Complexity with Wavelets;194
8.1;Abstract;194
8.2;1 Introduction;194
8.3;2 Terminology;195
8.4;3 Wavelet History;195
8.5;4 Wavelets in One Dimension;197
8.6;5 Wavelets with Spatial Data;201
8.7;6 Wavelets and Linear Complexity;205
8.8;7 Technical Implementation;210
8.9;8 Conclusions;211
8.10;Acknowledgements;211
8.11;References;211
9;Expert Knowledge and Embedded Knowledge: Or Why Long Rambling Class Descriptions are Useful;212
9.1;Abstract;212
9.2;1 Introduction;213
9.3;2 Data Used;215
9.4;3 Proposed Method;216
9.5;4 Results;221
9.6;5 Discussion and Conclusion;226
9.7;Acknowledgements;227
9.8;References;227
10;Preference Based Retrieval of Information Elements;229
10.1;Abstract;229
10.2;1 Introduction;230
10.3;2 Decision Making Processes;231
10.4;3 Framework for a User Model;234
10.5;4 Direct Manipulation Interface – A Dynamic Approach;235
10.6;5 Modeling the Dynamic Interaction Process;235
10.7;6 Conclusions and Future Work;240
10.8;Acknowledgement;241
10.9;Reference;241
11;Spatiotemporal Event Detection and Analysis over Multiple Granularities;243
11.1;Abstract;243
11.2;1 Introduction;244
11.3;2 The Spatiotemporal Helix;246
11.4;3 Scale Space Analysis;247
11.5;4 An Example: Spatiotemporal Hurricane Data Clustering;254
11.6;5 Conclusions;257
11.7;Acknowledgements;258
11.8;References;258
12;Reduced Data Model for Storing and Retrieving Geographic Data;260
12.1;Abstract;260
12.2;1 Introduction;260
12.3;2 Issues with the Current Technology;262
12.4;3 Concepts to Retain;266
12.5;4 Description of the Solution;267
12.6;5 Example: How to Use RelDB;268
12.7;6 Conclusions;272
12.8;Acknowledgements;273
12.9;References;273
13;Filling the Gaps in Keyword-Based Query Expansion for Geodata Retrieval;276
13.1;Abstract;276
13.2;1 Introduction;276
13.3;2 Creating the Reference Matrix;278
13.4;3 Matrices and Graphs;280
13.5;4 Matrix Structures;281
13.6;5 Algorithms to Fill the Gaps;284
13.7;6 Simulation and Results;286
13.8;7 Discussions and Outlook;289
13.9;Acknowledgements;290
13.10;References;291
14;Using Metadata to Link Uncertainty and Data Quality Assessments;292
14.1;1 Introduction;292
14.2;2 Uncertainty, Data Quality and Metadata;293
14.3;3 Geographic Information – Origins and Use;297
14.4;4 Linking Uncertainty, Data Quality and GI;299
14.5;5 Recommendations and Conclusions;302
14.6;Acknowledgements;303
14.7;References;304
15;An Evaluation Method for Determining Map-Quality;306
15.1;Abstract;306
15.2;1 Introduction;306
15.3;2 A cartographic Communication in Detail;307
15.4;3 Aspects of Map-Quality;310
15.5;4 Determining Simple Model Parameters;312
15.6;5 Evaluation Tool;313
15.7;6 Conclusions;315
15.8;Acknowledgement;316
15.9;References;316
16;Implementation of a Prototype Toolbox for Communicating Spatial Data Quality and Uncertainty Using a Wildfire Risk Example;334
16.1;Abstract;334
16.2;1 Introduction;334
16.3;2 Key Concepts of a Data Quality Communication Toolbox;335
16.4;3 Four Levels of Communicating Data Quality Information;337
16.5;4 The Map Thesaurus;338
16.6;5 Visualization of a Wildfire Risk Example;341
16.7;6 Conclusions;349
16.8;References;349
17;Changes in Topological Relations when Splitting and Merging Regions;351
17.1;Abstract;351
17.2;1 Introduction;351
17.3;2 Binary Topological Relations between Regions;353
17.4;3 Splitting a Region into Two Regions;354
17.5;4 Potential Splitting Configurations;355
17.6;5 Feasible Splitting Configurations;356
17.7;6 Achievable Splitting Configurations;361
17.8;7 Conclusions;363
17.9;Acknowledgments;364
17.10;References;364
18;Integrating 2D Topographic Vector Data with a Digital Terrain Model – a Consistent and Semantically Correct Approach;365
18.1;Abstract;365
18.2;1 Introduction;365
18.3;2 An Algorithm for Consistent and Semantically Correct Integration;367
18.4;3 Results;373
18.5;4 Conclusions and Outlook;375
18.6;Acknowledgement;376
18.7;References;376
19;A Hierarchical Approach to the Line- Line Topological Relations;377
19.1;Abstract;377
19.2;1 Introduction;377
19.3;2 A Line of Thought for Topological Relations between Two Lines;380
19.4;3 Hierarchical Descriptions for Basic (Elementary) Relations between Lines;382
19.5;4 Compound Line-line Relation Model;386
19.6;5 An Example of Application;390
19.7;6 Conclusions;392
19.8;Acknowledgements;393
19.9;References;393
20;Coastline Matching Process Based on the Discrete Fréchet Distance;395
20.1;Abstract;395
20.2;1 Introduction;395
20.3;2 Discrete Fréchet Distance;397
20.4;3 Average Fréchet Distance;400
20.5;4 Global Process;403
20.6;5 Example of Coastline Matching Process;405
20.7;6 Discussions;410
20.8;7 Conclusions;411
20.9;References;411
21;Characterizing Land Cover Structure with Semantic Variograms;413
21.1;Abstract;413
21.2;1 Introduction;414
21.3;2 Variograms and Semantic Distances;415
21.4;3 Experimental Data and Methods;419
21.5;4 Results;422
21.6;5 Discussion / Conclusions;425
21.7;Acknowledgements;426
21.8;References;426
22;Semantic Similarity Measures within theSemantic Framework of the Universal Ontology of Geographical Space;428
22.1;Abstract;428
22.2;1 Introduction;429
22.3;2 Universal Ontology of Geographical Space (UOGS) and Semantic Measures;429
22.4;3 Estimation of Semantic Similarity among the Concepts in Spatial Databases;433
22.5;4 Implementing UOGS and its Searching Capabilities;438
22.6;5 Conclusions;442
22.7;Acknowledgements;444
22.8;References;444
23;A Quantitative Similarity Measure for Maps;446
23.1;Abstract;446
23.2;1 Introduction;446
23.3;2 Related Work;448
23.4;3 Our Approach;449
23.5;4 Experimental Results;458
23.6;5 Conclusions;460
23.7;References;460
24;A Semantic- based Approach to the Representation of Network-Constrained Trajectory Data;462
24.1;Abstract;462
24.2;1 Introduction;463
24.3;2 Network-Based modeling;464
24.4;3 Semantic-Based Approach;465
24.5;4 Prototyping;469
24.6;5 Conclusions;473
24.7;References;474
25;Towards an Ontologically- driven GIS to Characterize Spatial Data Uncertainty;476
25.1;Abstract;476
25.2;1 Introduction;477
25.3;2 Metadata, Fitness, and Propagation;478
25.4;3 Ontology-driven GIS;480
25.5;4 Tying Uncertainty Models to ODGIS;482
25.6;5 Implementation and Evaluation;483
25.7;6 Conclusions;484
25.8;References;485
26;Structuring Kinetic Maps;488
26.1;Abstract;488
26.2;1 Introduction;488
26.3;2 The Dynamic Point VD and its Dual DT;491
26.4;3 The Moving- Point VD or DT;492
26.5;4 The Kinetic Constrained DT;493
26.6;5 The Kinetic Line- Segment VD;496
26.7;6 Circumcircle;498
26.8;7 Robustness;500
26.9;8 Applications;500
26.10;9 Conclusions;501
26.11;Acknowledgements;503
26.12;References;503
27;A Tangible Augmented Reality Interface to Tiled Street Maps and its Usability Testing;521
27.1;Abstract;521
27.2;1 Introduction;522
27.3;2 Tangible Augmented Reality Street Map (TASM);524
27.4;3 Usability Testing;528
27.5;4 Results;530
27.6;5 Discussion;533
27.7;6 Conclusions;535
27.8;Acknowledgements;536
27.9;References;536
28;Automated Construction of Urban Terrain Models;557
28.1;Abstract;557
28.2;1 Introduction;558
28.3;2 Smart Terrain Models;561
28.4;3 Case Study;569
28.5;4 Conclusions and Future Work;570
28.6;Acknowledgements;571
28.7;References;571
29;A Tetrahedronized Irregular Network Based DBMS Approach for 3D Topographic Data Modeling;590
29.1;Abstract;590
29.2;1 Introduction;590
29.3;2 3D Topographic Data Modeling in a TEN Data Structure;592
29.4;3 Incorporating the TEN Structure in a Spatial DBMS;597
29.5;4 Implementation: First Experiences;602
29.6;5 Conclusions and Further Research;603
29.7;Acknowledgements;604
29.8;References;604
30;3D Analysis with High- Level Primitives: A Crystallographic Approach;608
30.1;Abstract;608
30.2;1 Introduction;608
30.3;2 A Plea for the Third Dimension in GIS;610
30.4;3 Striving for 3D Models Suited to Geographical Information Analysis;613
30.5;4 Object Description with Crystallography;615
30.6;5 Outlook of the 3D GIS with Crystallographic Primitives;621
30.7;6 Conclusions;623
30.8;References;624
31;The Hierarchical Watershed Partitioning and Data Simplification of River Network;626
31.1;Abstract;626
31.2;1 Introduction;627
31.3;2 Watershed Partitioning;628
31.4;3 Parameter Computation;634
31.5;4 River Network Generalization;636
31.6;5 Conclusions;638
31.7;Acknowledgements;639
31.8;References;640
32;Grid Typification;642
32.1;Abstract;642
32.2;1 Introduction;642
32.3;2 Grid Detection;645
32.4;3 Grid Adjustment;647
32.5;4 Grid Reduction;649
32.6;5 Conclusions and Outlook;650
32.7;References;651
33;Conflict Identification and Representation for Roads Based on a Skeleton;668
33.1;Abstract;668
33.2;1 Introduction;668
33.3;2 Why Use the Skeleton to Represent Inter Feature Distances?;669
33.4;3 Creating Partial Skeletons;672
33.5;4 Creating Conflict Lines;676
33.6;5 Classifying the Skeleton Graph;678
33.7;6 Utilizing the Conflict Line Features;683
33.8;7 Conclusions;687
33.9;References;688
34;The Stroke Concept in Geographic Network Generalization and Analysis;690
34.1;Abstract;690
34.2;1 Introduction;690
34.3;2 Perceptual Grouping;692
34.4;3 Stroke-based Generalization;694
34.5;4 Strokes and Space Syntax;700
34.6;5 Conclusions;703
34.7;Acknowledgments;704
34.8;References;704
35;An Integrated Cloud Model for Measurement Errors and Fuzziness;707
35.1;Abstract;707
35.2;1 Introduction;708
35.3;2 Fundamentals of Cloud Model Theory;710
35.4;3 An Integrated Model for Measurement Errors and Fuzziness;713
35.5;4 Uncertainty Reasoning Based Upon the Cloud Model;717
35.6;5 Conclusions;723
35.7;Acknowledgements;724
35.8;References;724
36;The Influence of Uncertainty Visualization on Decision Making: An Empirical Evaluation;727
36.1;Abstract;727
36.2;1 Introduction;727
36.3;2 Background;728
36.4;3 Methods;731
36.5;4 Pilot Study Analysis and Results;735
36.6;5 Summary of Results;743
36.7;6 Discussion;744
36.8;7 Conclusions;745
36.9;References;745
37;Modeling Uncertainty in Knowledge Discovery for Classifying Geographic Entities with Fuzzy Boundaries;747
37.1;Abstract;747
37.2;1 Introduction;747
37.3;2 Uncertainty in Classifying Geographic Entities with Fuzzy Boundaries;748
37.4;3 Modeling Uncertainty Through Boosting;751
37.5;4 Case Study;754
37.6;5 Conclusions and Future Work;760
37.7;Reference;761
38;Capturing and Representing Conceptualization Uncertainty Interactively using Object- Fields;763
38.1;Abstract;763
38.2;1 Introduction;763
38.3;2 Conceptualizing Uncertainty;764
38.4;3 Object-Field Representation and Metadata;768
38.5;4 Case Study: Demonstrating the Application Using Land Cover Data;771
38.6;5 Discussion and Conclusions;776
38.7;References;777
39;Use of Plan Curvature Variations for the Identification of Ridges and Channels on DEM;797
39.1;Abstract;797
39.2;1 Introduction;798
39.3;2 Methodology;801
39.4;3 Results;805
39.5;4 Conclusions;811
39.6;Acknowledgements;811
39.7;References;811
40;An Evaluation of Spatial Interpolation Accuracy of Elevation Data;813
40.1;Abstract;813
40.2;1 Introduction;814
40.3;2 The Spatial Interpolation Algorithms;814
40.4;3 Data and Methods;818
40.5;4 Results;820
40.6;5 Discussion and Conclusions;826
40.7;References;830
41;Development Density-Based Optimization Modeling of Sustainable Land Use Patterns;887
41.1;Abstract;887
41.2;1 Introduction: Sustainable Land Use Patterns;887
41.3;2 Optimization Techniques for Land Use Allocation;889
41.4;3 A Multiobjective Model for Sustainable Land Allocation;891
41.5;4 Model Evaluation;895
41.6;5 Future Work;899
41.7;6 Conclusions;900
41.8;References;900
42;Building an Integrated Cadastral Fabric for Higher Resolution Socioeconomic Spatial Data Analysis;903
42.1;1 Introduction;903
42.2;2 Looking for High Resolution Data in the Literature;905
42.3;3 Generating Socioeconomic Surfaces: Review of Previous Methodologies;908
42.4;4 The Rise of the Household: Why We Should Use Cadastral Data for Socioeconomic Analysis;910
42.5;5 Methodology, Protocol by Protocol;911
42.6;6 Conclusion: Moving Beyond the Census;921
42.7;References;923
43;Analysis of Cross Country Trafficability;927
43.1;Abstract;927
43.2;1 Cross Country Trafficability;928
43.3;2 Previous Studies and Theoretical Framework;929
43.4;3 Methods and Techniques;934
43.5;4 Results;939
43.6;5 Discussion;943
43.7;Acknowledgements;945
43.8;References;945