Riedl / Kainz / Elmes Progress in Spatial Data Handling

12th International Symposium on Spatial Data Handling
2006
ISBN: 978-3-540-35589-2
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

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.

Andreas Riedl holds a graduate degree and a PhD in geography and cartography from the University of Vienna. He has been active in the field of cartography and GIS in Austria, Canada, Germany and the Netherlands. His research interests are in multimedia and geocommunication, applied GIS, animation, virtual reality and hyperglobes. Currently he is an assistant professor at the University of Vienna. Wolfgang Kainz holds a graduate degree and a PhD in technical mathematics from the Graz University of Technology, Austria. Since 1981 he has been active in the field of GIS and spatial data handling in various functions at universities and research institutions in Austria, Brazil, Kuwait, Qatar, the USA, and the Netherlands. His research interest is in spatial databases, fuzzy logic and geodata policies. He is Professor of Cartography and Geoinformation at the University of Vienna. Gregory Elmes holds a graduate degree in Geographic Information Systems from Edinburgh University and a PhD in Geography from the Pennsylvania State University. He is Professor of Geography at West Virginia University and has been active in GIS at universities in Sweden and Italy. His current research interests include the incorporation of spatio-temporal information in health and forensic science, and the implications of geographic information technologies in society at local and global scales.

Autoren/Hrsg.

Riedl, Andreas

Kainz, Wolfgang

Elmes, Gregory A.

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