Griffith / Chun / Dean Advances in Geocomputation

Geocomputation 2015--The 13th International Conference
1. Auflage 2017
ISBN: 978-3-319-22786-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 436 Seiten, eBook

Reihe: Advances in Geographic Information Science

This book contains refereed papers from the 13th International Conference on GeoComputation held at the University of Texas, Dallas, May 20-23, 2015. Since 1996, the members of the GeoComputation (the art and science of solving complex spatial problems with computers) community have joined together to develop a series of conferences in the United Kingdom, New Zealand, Australia, Ireland and the United States of America. The conference encourages diverse topics related to novel methodologies and technologies to enrich the future development of GeoComputation research.

Zielgruppe

Research

Autoren/Hrsg.

Griffith, Daniel A.

Chun, Yongwan

Dean, Denis J.

Inhaltsverzeichnis

1;Preface;6
1.1;The Symposium Series, the Venue, and the Conference Program;7
2;Contents;12
3;1 Introduction;16
3.1;1 Concluding Comments;17
4;2 The Nexus of Food, Energy, and Water Resources: Visions and Challenges in Spatial Computing;19
4.1;Abstract;19
4.2;1 Introduction;20
4.3;2 A Spatial Computing Vision;23
4.3.1;2.1 FEW Observations;24
4.3.2;2.2 FEW Data Management;24
4.3.3;2.3 FEW Data Mining;25
4.3.4;2.4 Decision Support;26
4.3.5;2.5 FEW Data Visualization;27
4.4;3 Spatial Computing Challenges;27
4.4.1;3.1 FEW Observation Challenges;27
4.4.2;3.2 FEW Data Management Challenges;28
4.4.3;3.3 FEW Data-Mining Challenges;29
4.4.4;3.4 FEW Decision Support Challenges;30
4.4.5;3.5 FEW Data Visualization Challenges;31
4.5;4 Summary;31
4.6;Acknowledgements;32
4.7;References;32
5;3 The Bird’s-Eye View from a Worm’s-Eye Perspective;35
5.1;Abstract;35
5.2;1 Introduction;35
5.3;2 Ups and Downs;37
5.4;3 Ins and Outs;39
5.5;4 Before and After;39
5.6;5 Here and There;42
5.7;6 Corners and Curves;43
5.8;7 Conclusion;44
5.9;References;45
6;Spatial Data: Construction, Representation, and Visualization;46
7;High-Resolution Population Grids for the Entire Conterminous United States;47
7.1;1 Introduction;47
7.2;2 Data and Methods;49
7.2.1;2.1 The Gen-1 Disaggregation Method;50
7.2.2;2.2 The Gen-2 Disaggregation Method;50
7.3;3 Results;51
7.4;4 Conclusions;56
7.5;References;57
8;5 A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling;59
8.1;Abstract;59
8.2;1 Introduction;60
8.3;2 Related Work;60
8.4;3 Methodology;61
8.5;4 Application and Results;63
8.5.1;4.1 Datasets;63
8.5.2;4.2 Results and Discussion;64
8.6;5 Conclusion;68
8.7;Acknowledgements;69
8.8;References;69
9;6 Can Social Media Play a Role in the Development of Building Occupancy Curves?;71
9.1;Abstract;71
9.2;1 Introduction;72
9.3;2 Unit Occupancy;73
9.4;3 Social Media Unit Occupancy;74
9.5;4 Results;75
9.6;5 A Model-Based Research Agenda;75
9.7;Acknowledgment;77
9.8;References;77
10;7 Application of Social Media Data to High-Resolution Mapping of a Special Event Population;79
10.1;Abstract;79
10.2;1 Introduction;80
10.3;2 Methods and Results;80
10.4;3 Discussion;85
10.5;Acknowledgements;85
10.6;References;85
11;8 Animating Maps: Visual Analytics Meets GeoWeb 2.0;87
11.1;Abstract;87
11.2;1 Introduction;87
11.3;2 Literature Review;88
11.4;3 System Design;89
11.4.1;3.1 Mode of Operation;89
11.4.1.1;3.1.1 Automatic Mode;89
11.4.1.2;3.1.2 User Customization Mode;91
11.4.2;3.2 System Architecture;91
11.4.2.1;3.2.1 User Interface;92
11.4.2.2;3.2.2 Server;93
11.4.2.3;3.2.3 Map API;93
11.4.2.4;3.2.4 Database;93
11.5;4 Results;93
11.6;5 Conclusion;95
11.7;References;95
12;9 Atvis: A New Transit Visualization System;97
12.1;Abstract;97
12.2;1 Introduction;97
12.3;2 Atvis Visualization Model;99
12.3.1;2.1 Goals and Objectives;99
12.3.2;2.2 Atvis Model Design;99
12.4;3 An Atvis Visualization Demonstration Program;100
12.4.1;3.1 Data Description;101
12.4.2;3.2 Backend System;101
12.4.3;3.3 The Frontend System;102
12.4.4;3.4 Visualization Methodologies;106
12.4.4.1;3.4.1 The Display Method;106
12.4.4.2;3.4.2 The Arc Normalization Method;106
12.4.4.3;3.4.3 The Arc Scaling Algorithm;106
12.5;4 Discussion/Conclusion;107
12.6;References;107
13;10 Mapping Spatiotemporal Patterns of Disabled People: The Case of the St. Jude’s Storm Emergency;109
13.1;Abstract;109
13.2;1 Introduction;110
13.3;2 Data;111
13.3.1;2.1 The Oyster Card System;111
13.3.2;2.2 A Case Study;112
13.3.3;2.3 Choosing Covariates;113
13.3.3.1;2.3.1 Opportunities/Destinations;113
13.3.3.2;2.3.2 PTAL;114
13.4;3 Methods;115
13.4.1;3.1 Data Preparation;115
13.4.2;3.2 Defining the Spatial Neighborhood;116
13.4.3;3.3 Modeling;117
13.5;4 Results;119
13.6;5 Conclusions;121
13.7;6 Limitations and Future Work;122
13.8;References;123
14;11 Terra Populus: Challenges and Opportunities with Heterogeneous Big Spatial Data;126
14.1;Abstract;126
14.2;1 Introduction;126
14.3;2 Terra Populus;127
14.4;3 Terra Populus User Interface;128
14.5;4 Terra Populus’s High-Performance Architecture;129
14.5.1;4.1 Microdata Integration;129
14.5.2;4.2 High-Performance Computation of Vector and Raster Data;130
14.6;5 Conclusion;131
14.7;References;132
15;Spatial Analysis: Methods and Applications;133
16;12 A Deviation Flow Refueling Location Model for Continuous Space: A Commercial Drone Delivery System for Urban Areas;135
16.1;Abstract;135
16.2;1 Introduction;136
16.3;2 Route Derivation: A Convex Path Algorithm;136
16.4;3 Distance-Restricted Maximal Coverage Location Model;138
16.5;4 A Heuristic Solution Technique: Simulated Annealing with a Greedy Algorithm;139
16.6;5 Application Results;140
16.7;6 Conclusions;141
16.8;References;141
17;13 Exploring the Spatial Decay Effect in Mass Media and Location-Based Social Media: A Case Study of China;143
17.1;Abstract;143
17.2;1 Introduction;143
17.3;2 Datasets;144
17.3.1;2.1 The Main Dataset: GDELT;145
17.3.2;2.2 Complementary Datasets;146
17.4;3 Methodology and Preliminary Results;147
17.4.1;3.1 Data Preprocessing;147
17.4.2;3.2 Model Construction;147
17.5;4 Conclusion;150
17.6;References;151
18;14 Uncovering the Digital Divide and the Physical Divide in Senegal Using Mobile Phone Data;153
18.1;Abstract;153
18.2;1 Introduction;153
18.3;2 Methods;154
18.4;3 Results;156
18.4.1;3.1 The Digital Divide;156
18.4.2;3.2 The Physical Divide;157
18.5;4 Conclusions;160
18.6;References;161
19;15 Application of Spatio-Temporal Clustering For Predicting Ground-Level Ozone Pollution;162
19.1;Abstract;162
19.2;1 Introduction;163
19.3;2 Method;163
19.4;3 Dataset;164
19.5;4 Data Mining;167
19.6;5 Ozone Forecasting;172
19.7;6 Conclusion;175
19.8;References;175
20;16 Does the Location of Amerindian Communities Provide Signals About the Spatial Distribution of Tree and Palm Species?;177
20.1;Abstract;177
20.2;1 Introduction;178
20.3;2 Methodology;179
20.3.1;2.1 The Study Area;179
20.3.2;2.2 Collection of Spatial and Attribute Data About Multiple-Use Plants;180
20.3.3;2.3 Designing the Spatial Dataset;182
20.4;3 Results;182
20.5;4 Discussion/Conclusions;185
20.6;5 Future Work;186
20.7;Acknowledgements;186
20.8;References;187
21;World Climate Search and Classification Using a Dynamic Time Warping Similarity Function;188
21.1;1 Introduction;188
21.2;2 Data and Methods;189
21.2.1;2.1 Data Source;190
21.2.2;2.2 Data Preprocessing;190
21.2.3;2.3 Variables and Their Normalization;190
21.2.4;2.4 Dissimilarity Measure;192
21.2.5;2.5 Clustering Methods and CCs Comparisons;194
21.3;3 Climate Classifications;195
21.4;4 Climate Search;198
21.5;5 Conclusions;201
21.6;References;202
22;18 Attribute Portfolio Distance: A Dynamic Time Warping-Based Approach to Comparing and Detecting Common Spatiotemporal Patterns Among Multiattribute Data Portfolios;203
22.1;Abstract;203
22.2;1 Introduction;204
22.3;2 Dynamic Time Warping;204
22.4;3 Attribute Portfolio Distance;206
22.5;4 Trend Only Attribute Portfolio Distance;206
22.6;5 Application and Results;207
22.7;6 Summary;209
22.8;Acknowledgements;210
22.9;References;211
23;19 When Space Beats Time: A Proof of Concept with Hurricane Dean;212
23.1;Abstract;212
23.2;1 Introduction;213
23.3;2 A Case Study: The Yucatan Peninsula—NDVI Before and After Hurricane Dean;214
23.4;3 Methods and Data;214
23.4.1;3.1 Data;215
23.4.2;3.2 Methods: Temporal and Spatial Models;216
23.4.3;3.3 Model Performance Assessment;216
23.5;4 Results;217
23.6;5 Conclusion and Discussion;219
23.7;References;220
24;20 Using Soft Computing Logic and the Logic Scoring of Preference Method for Agricultural Land Suitability Evaluation;221
24.1;Abstract;221
24.2;1 Introduction;222
24.3;2 Context of the Case Study;222
24.4;3 The Logic Scoring of Preference Method;223
24.5;4 LSP Land Suitability Maps;227
24.6;5 Conclusion;229
24.7;Acknowledgements;230
24.8;References;230
25;21 Surgical Phase Recognition using Movement Data from Video Imagery and Location Sensor Data;232
25.1;Abstract;232
25.2;1 Introduction;233
25.3;2 Data Collection;234
25.3.1;2.1 Video Imagery;234
25.3.2;2.2 Ultrasonic Location Aware System;234
25.4;3 Methods;235
25.4.1;3.1 Tag Movements;235
25.4.2;3.2 Optical Flow;236
25.4.3;3.3 Trajectory Clustering;236
25.5;4 Results;237
25.6;5 Discussion;238
25.7;Acknowledgements;239
25.8;References;239
26;Spatial Statistical and Geostatistical Modeling;241
27;22 Respondent-Driven Sampling and Spatial Autocorrelation;242
27.1;Abstract;242
27.2;1 Introduction;243
27.3;2 Data;243
27.3.1;2.1 Network;243
27.3.2;2.2 Demographics;244
27.3.3;2.3 Transformation and Mapping;245
27.3.4;2.4 Spatial Autocorrelation;245
27.4;3 Methodology;248
27.4.1;3.1 Network Chains;248
27.4.2;3.2 Simulation Design;248
27.5;4 Anticipated Results;248
27.6;Acknowledgements;249
27.7;Appendix;249
27.8;References;251
28;23 The Moran Coefficient and the Geary Ratio: Some Mathematical and Numerical Comparisons;253
28.1;Abstract;253
28.2;1 Introduction;253
28.3;2 The Relationship Between the MC and GR;254
28.4;3 Derivation of the MC and GR Asymptotic Variances;255
28.5;4 Efficiency Analysis;257
28.5.1;4.1 Normal Variance Ratios;259
28.5.2;4.2 Uniform Variance Ratios;260
28.5.3;4.3 Beta Variance Ratios;262
28.5.4;4.4 Exponential Variance Ratios;262
28.5.5;4.5 Variance Ratio Convergence;264
28.6;5 A Power Comparison;265
28.6.1;5.1 Establishing Statistical Power;265
28.6.2;5.2 Theoretical Evaluation;267
28.7;6 Conclusions;268
28.8;References;269
29;24 A Variance-Stabilizing Transformation to Mitigate Biased Variogram Estimation in Heterogeneous Surfaces with Clustered Samples;270
29.1;Abstract;270
29.2;1 Introduction;270
29.3;2 Methodology;272
29.3.1;2.1 Data;272
29.3.2;2.2 The Box–Cox Transformation and Kriging Prediction;273
29.4;3 Results;277
29.5;4 Conclusions;278
29.6;References;279
30;Estimating a Variance Function of a Nonstationary Process;280
30.1;1 Introduction;280
30.2;2 Data Model and Variance Function Estimator;281
30.2.1;2.1 Data Model;281
30.2.2;2.2 Notation and Definitions;282
30.2.3;2.3 A Variance Function Estimator;284
30.3;3 Exploring Filter Options and an Application;285
30.3.1;3.1 The Filter Configuration and Weights;285
30.3.2;3.2 Simulation Set-up;286
30.3.3;3.3 Results and Recommendations;287
30.3.4;3.4 An Empirical Example;290
30.4;4 Conclusions;291
30.5;References;292
31;26 The Statistical Distribution of Coefficients for Constructing Eigenvector Spatial Filters;293
31.1;Abstract;293
31.2;1 Introduction;293
31.3;2 Eigenvector Spatial Filtering;294
31.4;3 Methodology;294
31.5;4 A Simulation Experiment;295
31.6;5 Results;296
31.7;6 Implications;299
31.8;Acknowledgements;300
31.9;References;300
32;27 Spatial Data Analysis Uncertainties Introduced by Selected Sources of Error;301
32.1;Abstract;301
32.2;1 Introduction;301
32.3;2 Literature Review;302
32.4;3 Data and Simulation Experiments;303
32.4.1;3.1 Location Error Simulation Experiment Design;303
32.4.2;3.2 Measurement Error Simulation Experiment Design;305
32.5;4 Results;306
32.5.1;4.1 Location Error;306
32.5.2;4.2 Measurement Error;306
32.6;5 Findings and Future Research;308
32.7;Acknowledgements;310
32.8;References;310
33;28 Spatiotemporal Epidemic Modeling with libSpatialSEIR: Specification, Fitting, Selection, and Prediction;312
33.1;Abstract;312
33.2;1 Introduction;312
33.3;2 Stochastic Compartmental Models;313
33.4;3 Software;315
33.5;4 Analysis;315
33.6;5 Impact;319
33.7;References;319
34;29 Geostatistical Models for the Spatial Distribution of Uranium in the Continental United States;321
34.1;Abstract;321
34.2;1 Introduction;321
34.3;2 Methods;323
34.4;3 Results;324
34.5;4 Conclusions;329
34.6;Acknowledgements;330
34.7;References;330
35;30 Modeling Land Use Change Using an Eigenvector Spatial Filtering Model Specification for Discrete Responses;331
35.1;Abstract;331
35.2;1 Introduction;331
35.3;2 Multinomial Autologistic Regression for Land Suitability Analysis;332
35.4;3 Estimation Method;333
35.5;4 Study Area and Data;334
35.6;5 Results;336
35.6.1;5.1 The Nonspatial MNL Model;337
35.6.2;5.2 The Spatial MNL Model;338
35.7;6 Conclusion;339
35.8;References;339
36;Computational Challenges and Advances in Geocomputation: High-Performance Computation and Dynamic Simulation;341
37;31 From Everywhere to Everywhere (FETE): Adaptation of a Pedestrian Movement Network Model to a Hybrid Parallel Environment;342
37.1;Abstract;342
37.2;1 Introduction;342
37.3;2 Proposed Solution;344
37.4;3 Results;345
37.5;4 Conclusions;347
37.6;Acknowledgements;347
37.7;References;348
38;32 Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data;349
38.1;Abstract;349
38.2;1 Introduction;349
38.3;2 The Affinity Propagation Program;351
38.4;3 Computation Constraints in the AP Program;353
38.5;4 Parallelization of the AP Program;354
38.6;5 Implementation of the Parallelized AP Program with the GPU;355
38.7;6 Conclusion;357
38.8;Acknowledgments;358
38.9;Appendix 1;358
38.10;Appendix 2;361
38.11;References;362
39;33 A Web-Based Geographic Information Platform to Support Urban Adaptation to Climate Change;364
39.1;Abstract;364
39.2;1 Introduction;365
39.3;2 The Urban-CAT Framework;366
39.3.1;2.1 Framework;367
39.3.2;2.2 Methods;368
39.4;3 Some Initial Results;371
39.5;4 Conclusion;372
39.6;Acknowledgements;373
39.7;References;373
40;34 A Fully Automated High-Performance Image Registration Workflow to Support Precision Geolocation for Imagery Collected by Airborne and Spaceborne Sensors;375
40.1;Abstract;375
40.2;1 Introduction;375
40.3;2 Core Development Concepts;376
40.4;3 Registration Workflow;376
40.4.1;3.1 Preprocessing;377
40.4.2;3.2 Trusted Source Selection;379
40.4.3;3.3 Global Localization;379
40.4.4;3.4 Image Registration;380
40.4.5;3.5 Sensor Model Resection and Uncertainty Propagation;382
40.4.6;3.6 A Note About Spatial Uncertainty;382
40.4.7;3.7 Enhanced Metadata Generation;383
40.5;4 Initial System Performance Metrics;384
40.6;5 Conclusion;385
40.7;Acknowledgements;385
40.8;References;385
41;35 MIRAGE: A Framework for Data-Driven Collaborative High-Resolution Simulation;387
41.1;Abstract;387
41.2;1 Introduction;388
41.3;2 Methods and Data;388
41.4;3 Model Execution and Work in Progress;392
41.5;4 Conclusion and the Next Steps;394
41.6;Acknowledgements;394
41.7;References;394
42;36 A Graph-Based Locality-Aware Approach to Scalable Parallel Agent-Based Models of Spatial Interaction;396
42.1;Abstract;396
42.2;1 Introduction;397
42.3;2 Literature Review;397
42.3.1;2.1 Spatially Explicit Agent-Based Models;397
42.3.2;2.2 Locality of Reference;398
42.4;3 A Locality-Aware Approach;400
42.4.1;3.1 The Locality Principle;400
42.4.2;3.2 Locality-Aware Computational Domain;400
42.5;4 Design and Experimentation of Parallel SE-ABMs;402
42.5.1;4.1 Agent-Based Spatial Interaction Model;402
42.5.2;4.2 Homogeneous Neighborhoods;403
42.5.3;4.3 Heterogeneous Neighborhoods;404
42.5.4;4.4 Locality-Aware Parallel Models on Shared-Memory Platforms;405
42.6;5 Results and Discussion;407
42.6.1;5.1 Homogeneous Interaction;407
42.6.2;5.2 Heterogeneous Interaction;410
42.7;6 Conclusions and Future Work;411
42.8;References;412
43;37 Simulation of Human Wayfinding Uncertainties: Operationalizing a Wandering Disutility Function;415
43.1;Abstract;415
43.2;1 Introduction;415
43.3;2 Definitions;416
43.4;3 The Research Problem;416
43.5;4 Background;417
43.5.1;4.1 Quantifying Dementia;417
43.5.2;4.2 Spatial Orientation and Human Wayfinding;418
43.5.3;4.3 Wandering Behavior;419
43.5.4;4.4 Observation and Simulation of Human Movement and Wandering;419
43.6;5 Methods;420
43.7;6 Expected Results;422
43.8;7 Conclusion;422
43.9;References;424
44;38 Design and Validation of Dynamic Hierarchies and Adaptive Layouts Using Spatial Graph Grammars;426
44.1;Abstract;426
44.2;1 Introduction;427
44.3;2 Theory and Methodology;427
44.3.1;2.1 Dynamic Hierarchies with Emergence;428
44.3.2;2.2 Modeling with Multiple Representations;429
44.3.3;2.3 Adapting to Layout Context with Spatial Semantics;429
44.4;3 Implementation;430
44.5;4 Conclusion and Discussion;433
44.6;Acknowledgements;434
44.7;References;434

Über Autor(innen)

Dr. Daniel A. Griffith is an Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas, chair of the International Spatial Accuracy Research Association (ISARA) Steering Committee, a Committee member of the International Geographical Union (IGU) Commission on Modeling Geographical Systems Steering, an elected Regional Science Association International (RSAI) Councilor, a two-appointment Fulbright Senior Specialist, and an elected fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), the Spatial Econometrics Association, the RSAI, and the New York Academy of Sciences. He also is a former Guggenheim fellow, and has been awarded distinguish scholarship honors by the Association of American Geographers. Dr. Griffith has published nearly two dozen books and over 200 papers, and is a previous editor of Geographical Analysis.

Dr. Yongwan Chun is an associate professor of Geospatial Information Sciences at the University of Texas at Dallas. His research interests lie in spatial statistics and GIS, focusing on urban issues concerning population movement, environment, health, and crime. His research has been supported by the US National Science Foundation and the US National Institutes of Health, among others. He has over 50 publications, including books, journal articles, book chapters, conference proceedings, and encyclopedia entries. He served on the Geocomputation (2015) and Spatial Statistics (2013) international conference organizing committees.

Dr. Denis J. Dean is the Dean of the School of Economic, Political and Policy Sciences and a former Head of the Geospatial Information Sciences program at the University of Texas at Dallas.  He has taught courses in spatial optimization, geospatial modeling and analysis,remote sensing, cartography, geodesy and other aspects of geospatial information sciences to students in North and South America, Europe and Asia.  He has published over 75 papers on spatial optimization, accuracy assessment of common spatial analysis operators, and other areas of geospatial information science.