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Advances in Cartography and GIScience

E-BookPDF1 - PDF WatermarkE-Book
542 Seiten
Englisch
Springer International Publishingerschienen am30.05.20171st ed. 2017
This book presents a selection of manuscripts submitted to the 2017 International Cartographic Conference held in Washington, DC at the beginning of July and made available at the conference. These manuscripts have been selected by the Scientific Program Committee and represent the wide-range of research that is done in the discipline. It also forms an important international collection representing research from at least 30-40 countries.mehr
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Produkt

KlappentextThis book presents a selection of manuscripts submitted to the 2017 International Cartographic Conference held in Washington, DC at the beginning of July and made available at the conference. These manuscripts have been selected by the Scientific Program Committee and represent the wide-range of research that is done in the discipline. It also forms an important international collection representing research from at least 30-40 countries.
Details
Weitere ISBN/GTIN9783319573366
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2017
Erscheinungsdatum30.05.2017
Auflage1st ed. 2017
ReihePublications of the International Cartographic Association (ICA)
Seiten542 Seiten
SpracheEnglisch
IllustrationenXVI, 542 p. 218 illus., 197 illus. in color.
Artikel-Nr.2396632
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;6
2;Acknowledgements;8
3;Scientific Program Committee;9
4;Contents;12
5;The Span of Cartography;16
6;1 Cartographic Memory Preservation of the Petrópolis City in Brazil: Koeler Map Scanning Using Photographic Survey;17
6.1;Abstract;17
6.2;1 Introduction;18
6.3;2 Materials and Methods;21
6.4;3 Results and Discussion;24
6.5;4 Conclusions;32
6.6;Acknowledgements;32
6.7;References;32
7;2 Location Spoofing in a Location-Based Game: A Case Study of Pokémon Go;34
7.1;Abstract;34
7.2;1 Introduction;35
7.3;2 Related Works;35
7.3.1;2.1 Location-Based Game;36
7.3.2;2.2 Actor-Network Theory;36
7.4;3 Location Spoofing in Pokémon Go;37
7.5;4 Technical Nuisance or Intentional Plot?;39
7.6;5 Generative Mechanisms for Spoofing;40
7.6.1;5.1 Uneven Distribution of Pokémons;40
7.6.2;5.2 Individual Motivations;42
7.7;6 Concluding Remarks;44
7.8;References;44
8;Crowdsourcing and Data;46
9;3 Educational Aspects of Crowdsourced Noise Mapping;47
9.1;Abstract;47
9.2;1 Introduction;48
9.2.1;1.1 The Study Area;49
9.3;2 Method;50
9.3.1;2.1 Project Planning, Database Modelling and Fieldwork;50
9.3.2;2.2 Data Processing and Analyses;50
9.3.3;2.3 Visualization;51
9.4;3 Results;53
9.5;4 Conclusion;57
9.6;Acknowledgements;57
9.7;References;57
10;4 Crowd and Community Sourced Data Quality Assessment;59
10.1;Abstract;59
10.2;1 Introduction;59
10.3;2 State of the Art;60
10.4;3 Report Platform Description;61
10.5;4 Reports Reliability Assessment Methodology;62
10.5.1;4.1 Data Quality Assessment Workflow;62
10.5.2;4.2 Data Quality Indicators;63
10.6;5 Reports Description and Results;64
10.6.1;5.1 Reports Data Description;64
10.6.2;5.2 Contributors Description;66
10.6.3;5.3 Topographic Database;67
10.6.4;5.4 Reliability Assessment Results;68
10.7;6 Conclusions;70
10.8;Acknowledgements;71
10.9;References;71
11;5 Crowdsourcing Mapping and Participatory Planning Support System: Case Study of Brno, Czechia;73
11.1;Abstract;73
11.2;1 Introduction;73
11.3;2 Methods, Case Study Location and Data;74
11.4;3 Results;77
11.4.1;3.1 Demographics of the Respondents;77
11.4.2;3.2 Spatial Distribution of Marked Points;78
11.4.3;3.3 Why Were Certain Locations Marked?;79
11.5;4 Conclusions;82
11.6;Acknowledgements;82
11.7;References;83
12;6 A Framework for Enhancing Real-Time Social Media Data to Improve the Disaster Management Process;86
12.1;Abstract;86
12.2;1 Introduction;87
12.3;2 Social Media in Relation with Disaster Management;88
12.4;3 Proposed Research Framework;89
12.4.1;3.1 Designed Web User API Component;90
12.4.2;3.2 Social Media System Component;91
12.4.2.1;3.2.1 Data Capture;91
12.4.2.2;3.2.2 Verification;93
12.4.2.3;3.2.3 Language Recognition;93
12.4.2.4;3.2.4 Metadata Extraction;93
12.4.2.5;3.2.5 Geotagging;94
12.4.2.6;3.2.6 Text Classification;94
12.5;4 Conclusion;94
12.6;References;95
13;7 Building a Real-Time Geo-Targeted Event Observation (Geo) Viewer for Disaster Management and Situation Awareness;96
13.1;Abstract;96
13.2;1 Introduction;97
13.3;2 The Design of GeoViewer System Architecture;99
13.4;3 User Interface Design and Key Functions;100
13.4.1;3.1 Real-Time Display of Geo-Tagged Tweets;101
13.4.2;3.2 Interactive Mapping Functions for Geovisualization;102
13.4.3;3.3 Spatial, Text, and Temporal Search;103
13.4.4;3.4 Labeling and Text-Tagging Function;104
13.5;4 Nepal Earthquake Case Study;105
13.6;5 Conclusion;107
13.7;Acknowledgements;108
13.8;References;108
14;8 The Academic SDI-Towards Understanding Spatial Data Infrastructures for Research and Education;110
14.1;Abstract;110
14.2;1 Introduction;112
14.3;2 Demand for SDIs at Universities and Research Institutes;112
14.4;3 The ICA s SDI Model;113
14.5;4 SDI Implementations at Universities and Research Institutes;114
14.5.1;4.1 University of Twente, The Netherlands;114
14.5.2;4.2 University of Groningen, the Netherlands;114
14.5.3;4.3 VSB-Technical University of Ostrava, Czechia;116
14.5.4;4.4 CSIR, South Africa;117
14.5.5;4.5 Research Centre for Sustainable Urban Development (CEDEUS), Chile;117
14.5.6;4.6 University of the Witwatersrand (Wits), Johannesburg, South Africa;118
14.5.7;4.7 Academic Geo Hub Platform, Wroclaw University of Environmental and Life Sciences (Poland);119
14.6;5 The Academic SDI;120
14.7;6 Discussion and Conclusion;122
14.8;Acknowledgements;123
14.9;References;123
15;Map Design;125
16;9 Introducing Leader Lines into Scale-Aware Consistent Labeling;126
16.1;Abstract;126
16.2;1 Introduction;126
16.3;2 Related Work;129
16.3.1;2.1 Static Map Labeling;129
16.3.2;2.2 Dynamic Map Labeling;129
16.4;3 Design Principles for Label Placement;130
16.5;4 Consistent Label Placement with Leader Lines;132
16.5.1;4.1 Genetic-Based Optimization of Active Ranges;132
16.5.2;4.2 Occlusion-Free Placement of Labels and Leader Lines;133
16.5.3;4.3 Fitness Evaluation of the Label Placement;133
16.6;5 Results;134
16.7;6 Conclusion and Future Work;137
16.8;Acknowledgements;138
16.9;References;138
17;10 On the Way to Create Individualized Cartographic Images for Online Maps Using Free and Open Source Tools;140
17.1;Abstract;140
17.2;1 Introduction;141
17.3;2 Data Sources;142
17.4;3 Processing the OpenStreetMap Data;143
17.5;4 Creating Different Cartographic Images;145
17.6;5 Setting Up Maps Online;150
17.7;6 Discussion;150
17.8;Acknowledgements;151
17.9;References;151
18;11 Hebrus Valles-The Mars Exploration Zone Map;154
18.1;Abstract;154
18.2;1 Introduction-Basic Information About Hebrus Valles;155
18.3;2 Motivation and Goals;155
18.4;3 Exploration Zone Criteria;156
18.5;4 Exploration Zone Map Symbology;156
18.6;5 Paths (Traverses);159
18.7;6 Hebrus Valles Exploration Zone Map Detailed Description;159
18.8;7 Creation of the Map, Methodology;161
18.9;8 Map Format and Adapting to New Technologies;163
18.10;9 Summary;165
18.11;Acknowledgements;166
18.12;References;166
19;12 XY Domain: A Sound Map Artwork for Communicating Big Data Characteristics;168
19.1;Abstract;168
19.2;1 Introduction;168
19.3;2 Background;170
19.3.1;2.1 Transduction, Energy and Humans;170
19.3.2;2.2 Big Data, Cartography and Art;171
19.3.3;2.3 Cartography and Sound;172
19.4;3 Context and Construction;173
19.4.1;3.1 Context;173
19.4.2;3.2 Idea Development;173
19.4.3;3.3 Construction of the Sound Map;175
19.4.4;3.4 Construction of the Visual Map;176
19.5;4 Explanation and Reaction;177
19.6;5 Conclusion;178
19.7;Acknowledgements;180
19.8;References;180
20;13 Reproducible Cartography;182
20.1;Abstract;182
20.2;1 Introduction;182
20.3;2 From GUI to Script;183
20.3.1;2.1 A Step Backward?;183
20.3.2;2.2 R as a Go-To Tool for Integrated Analysis;184
20.4;3 The Cartography Package;185
20.4.1;3.1 Design;185
20.4.2;3.2 Main Features;186
20.5;4 Conclusion;191
20.6;References;192
21;Evaluating Map Quality;193
22;14 Effectiveness and Efficiency of Using Different Types of Rectangular Treemap as Diagrams in Cartography;194
22.1;Abstract;194
22.2;1 Introduction;195
22.3;2 Treemap: A Brief Review;195
22.4;3 Data Types Represented by Treemaps;197
22.5;4 User Study Design;197
22.5.1;4.1 Visual Tasks for Treemap Cartography;198
22.5.2;4.2 Questionnaire Design;199
22.5.2.1;4.2.1 Dataset and Test Material;199
22.5.2.2;4.2.2 Questions;201
22.5.3;4.3 Procedure;203
22.5.4;4.4 Subjects;204
22.6;5 Results and Discussion;205
22.7;6 Conclusion;211
22.8;Acknowledgements;212
22.9;References;212
23;15 The Usability of a GeoVisual Analytics Environment for the Exploration and Analysis of Different Datasets;214
23.1;Abstract;214
23.2;1 Introduction;215
23.3;2 Usability of GeoVisual Analytics Environments;215
23.4;3 Experiment Design;216
23.4.1;3.1 The Use Case Studies;216
23.4.2;3.2 Experimental GVA Environment;217
23.4.3;3.3 User Tasks;219
23.4.4;3.4 Test Participants;220
23.4.5;3.5 Experiment;220
23.5;4 Results;220
23.5.1;4.1 Locate the Map;221
23.5.2;4.2 Identify Time;222
23.5.3;4.3 Compare Differences;223
23.5.4;4.4 Characterize Change;223
23.5.5;4.5 User Satisfaction;224
23.5.6;4.6 Task Performance;224
23.5.7;4.7 Use of the Visual Representations in the GVA Environment;226
23.6;5 Conclusions;227
23.7;References;227
24;16 Characterizing Maps from Visual Properties;229
24.1;Abstract;229
24.2;1 Introduction;229
24.3;2 Approach to Make Custom-Made Maps;231
24.4;3 Visual Properties of Sample Maps;232
24.5;4 Test Protocol;233
24.5.1;4.1 Research Hypotheses About Database Design and Test;233
24.5.2;4.2 Sample Map Database;233
24.5.3;4.3 Implementation of the Test;234
24.6;5 Characterizing Maps with Visual Properties from the User Test;234
24.6.1;5.1 Typical Property per Map;234
24.6.2;5.2 Extreme Property(-ies) per Map;235
24.6.3;5.3 Unanimous Property(-ies) per Map;236
24.7;6 Analysis of Visual Properties Through Statistical Features;236
24.7.1;6.1 Statistical Feature: Typical;236
24.7.2;6.2 Statistical Feature: Extreme;237
24.7.3;6.3 Statistical Feature: Unanimous;239
24.7.4;6.4 Correlations Among Properties and Among Statistical Features;240
24.8;7 Exploring and Increasing the Sample Map Database;241
24.9;8 Conclusions and Perspectives;242
24.10;References;243
25;17 How Hard Is It to Design Maps for Beginners, Intermediates and Experts?;245
25.1;Abstract;245
25.2;1 Introduction;246
25.3;2 Thoughts of the Map Maker and the Map Reader;246
25.4;3 What Questions Can Be Answered with the Experiment?;247
25.5;4 Categorization of Map Readers;248
25.6;5 Differently Designed Cartographic Images and the Test Questions;248
25.7;6 Database-Sampling and Weighting;253
25.8;7 Proportion of Good Answers;253
25.9;8 Completion Time;255
25.10;9 Map Scale;256
25.11;10 Summary;257
25.12;Acknowledgements;258
25.13;References;258
26;18 Interaction Problems Found Through Usability Testing on Interactive Maps;260
26.1;Abstract;260
26.2;1 Introduction;260
26.3;2 Theoretical References;261
26.3.1;2.1 Interactive Maps;261
26.3.2;2.2 Usability and Evaluation of Interfaces;262
26.4;3 Methodology;263
26.4.1;3.1 Participants;263
26.4.2;3.2 Stimuli and Apparatus;263
26.4.3;3.3 Procedures;264
26.5;4 Results and Discussion;266
26.6;5 Conclusions;271
26.7;References;272
27;19 The Apprehension of Overlaid Information in a Web Map;274
27.1;Abstract;274
27.2;1 Introduction;274
27.3;2 Background and Related Work;275
27.4;3 Web-Based Experiment;277
27.5;4 Results;280
27.6;5 Discussion and Conclusions;283
27.7;References;285
28;20 Visualization of Environment-related Information in Augmented Reality: Analysis of User Needs;287
28.1;Abstract;287
28.2;1 Introduction;288
28.3;2 Background;288
28.4;3 Methods;290
28.5;4 Results;290
28.5.1;4.1 General Background;291
28.5.2;4.2 Areas of Application of Augmented Reality in Paragliding;291
28.5.3;4.3 User Test;293
28.6;5 Discussion;294
28.7;6 Conclusions;294
28.8;Acknowledgements;295
28.9;References;295
29;Geographic Analysis;297
30;21 Analysis and Visualization of the Urban Residents Income-Related Happiness Index in China;298
30.1;Abstract;298
30.2;1 Introduction;299
30.3;2 Research Region and Data Sources;299
30.4;3 Research Method;299
30.4.1;3.1 Analysis of the Current Situation;299
30.4.2;3.2 Analysis of Regional Disparities;300
30.4.3;3.3 Analysis of the Spatial-Temporal Variations;301
30.4.3.1;3.3.1 Analysis of the Temporal Variation;301
30.4.3.2;3.3.2 Average Annual Growth Rate;301
30.4.4;3.4 Analysis of Indicator Correlations;302
30.5;4 Analysis and Expression;303
30.5.1;4.1 Current Situation of the Urban Residents Income-Related Happiness Index;303
30.5.2;4.2 Regional Disparities in the Urban Residents Income-Related Happiness Index;304
30.5.2.1;4.2.1 Multi-level Disparities;304
30.5.2.2;4.2.2 Between-Province Disparities;305
30.5.2.3;4.2.3 Within-Province Disparities;305
30.5.3;4.3 Analysis of the Spatial-Temporal Changes in the Urban Residents Income-Related Happiness Index;306
30.5.3.1;4.3.1 Temporal Changes;306
30.5.3.2;4.3.2 Characteristics of the Spatial Distribution of the Annual Growth Rate;307
30.5.4;4.4 Correlation Analysis of Indicators;307
30.6;5 Conclusions;309
30.7;Acknowledgements;310
31;22 Displaying Voter Gains and Losses: Local Government Elections in South Africa for 2011 and 2016;311
31.1;Abstract;311
31.2;1 Introduction;311
31.3;2 Mapping Political Voting Results;312
31.4;3 Methodology;315
31.4.1;3.1 Cartograms;315
31.4.2;3.2 Three-Dimensional (3D) Mapping;316
31.4.3;3.3 Thematic Map Combined with Cartogram;317
31.5;4 Discussions;318
31.6;5 Conclusions;323
31.7;Acknowledgements;324
31.8;References;324
32;23 Mapping Community Vulnerability to Poaching: A Whole-of-Society Approach;326
32.1;Abstract;326
32.2;1 Introduction;327
32.3;2 Whole-of-Society;327
32.4;3 Drivers of Vulnerability to Becoming Involved in Wildlife Crime;331
32.5;4 Methodology;332
32.6;5 Results and Discussions;334
32.6.1;5.1 The Four Groups;336
32.6.2;5.2 The Socio-Economic Indicators;336
32.6.3;5.3 Crime Risk Indicators;338
32.7;6 Conclusions and Future Research;339
32.8;Acknowledgements;340
32.9;References;340
33;24 Mapping Urban Landscapes Along Streets Using Google Street View;342
33.1;Abstract;342
33.2;1 Introduction;343
33.3;2 Google Street View (GSV) Data Collection;344
33.3.1;2.1 Collecting Static GSV Images;344
33.3.2;2.2 Collecting GSV Panoramas;344
33.4;3 Urban Landscape Quantification and Mapping;346
33.4.1;3.1 Mapping the Visibility of Street Greenery;346
33.4.2;3.2 Mapping the Openness of Street Canyons;349
33.5;4 Discussion and Conclusions;353
33.6;References;355
34;Numerical Analysis;358
35;25 Cross-Scale Analysis of Sub-pixel Variations in Digital Elevation Models;359
35.1;Abstract;359
35.2;1 Introduction;360
35.3;2 Dataset and Study Area;361
35.4;3 Methods;362
35.4.1;3.1 Interpolation Methods;363
35.4.2;3.2 Contiguity Configuration;366
35.4.3;3.3 Workflow and Processing;367
35.4.4;3.4 Accuracy Assessment;367
35.5;4 Results and Discussion;368
35.5.1;4.1 Analysis of Residuals;368
35.5.2;4.2 Optimal Configuration for Weighted Average Interpolator;369
35.5.3;4.3 Optimal Configuration for Best Fitting Polynomials;370
35.5.4;4.4 Comparing Surface-Adjusted Elevations with the Rigid Pixel Paradigm;371
35.6;5 Summary;372
35.7;Acknowledgements;372
35.8;References;372
36;26 Extraction of Ridge Lines from Grid DEMs with the Steepest Ascent Method Based on Constrained Direction;374
36.1;Abstract;374
36.2;1 Introduction;375
36.3;2 Related Works;376
36.3.1;2.1 The Steepest Ascent Method;376
36.3.2;2.2 The Method of Overland Flow Simulation;376
36.4;3 The Steepest Ascent Method Based on Constrained Direction (SAMBCD);377
36.4.1;3.1 The Algorithm of SAMBCD;377
36.4.2;3.2 The Implementation of SAMBCD;378
36.4.2.1;3.2.1 The Major Ridge Lines;378
36.4.2.2;3.2.2 The Minor Ridge Lines;381
36.5;4 Comparison and Analysis;382
36.6;5 Conclusion;384
36.7;Acknowledgements;385
36.8;References;385
37;Using the A Algorithm to Find Optimal Sequences for Area Aggregation;387
37.1;1 Introduction;387
37.2;2 Preliminaries;389
37.3;3 Using the A Algorithm;391
37.3.1;3.1 Formalizing Area Aggregation as a Pathfinding Problem;391
37.3.2;3.2 Cost Functions;391
37.3.3;3.3 Estimating the Cost of Type Change;393
37.3.4;3.4 Estimating the Cost of Shape;394
37.3.5;3.5 Overestimation;396
37.3.6;3.6 Integrating Aggregation Sequences of Different Regions;396
37.4;4 Case Study;396
37.5;5 Conclusions;401
37.6;References;402
38;28 Quantitative Expressions of Spatial Similarity in Multi-scale Map Spaces;403
38.1;Abstract;403
38.2;1 Origination and Significance;404
38.3;2 Literature Review: Features of Similarity Relations;406
38.3.1;2.1 Similarity Relations in Computer Sciences;406
38.3.2;2.2 Similarity Relations in Psychology;407
38.3.3;2.3 Similarity Relations in Geography;408
38.4;3 Features and Their Mathematical Expressions;409
38.5;4 Conclusions;412
38.6;Acknowledgements;412
38.7;References;412
39;29 Balanced Allocation of Multi-criteria Geographic Areas by a Genetic Algorithm;415
39.1;Abstract;415
39.2;1 Introduction;416
39.3;2 Territory Design by a Multi-objective Genetic Algorithm;417
39.3.1;2.1 Usability of Graphs in Order to Simulate the Problem;417
39.3.2;2.2 Mapping the Territory Design Problem into a Graph Model;417
39.3.3;2.3 The Core of the GA;419
39.4;3 Case Study: Sales Territory Planning;421
39.5;4 Results;422
39.6;5 Tuning of the GA Parameters;423
39.7;6 Location-Allocation and Initializing the Territories;423
39.7.1;6.1 Evaluating the Balance of Workload;425
39.7.2;6.2 Evaluating the Travel Time Improvement;428
39.7.3;6.3 Evaluating the Contiguity and Compactness;429
39.8;7 Conclusions;429
39.9;Acknowledgements;430
39.10;References;430
40;30 Rethinking the Buffering Approach for Assessing OpenStreetMap Positional Accuracy;432
40.1;Abstract;432
40.2;1 Introduction;432
40.3;2 Theoretical Analysis of the Buffering Approach;434
40.3.1;2.1 Principles of the Buffering Approach;434
40.3.2;2.2 Limitations of the Buffering Approach to Assessing the Positional Accuracy;436
40.4;3 Design of Experiments for Evaluating the Buffering Approach;436
40.4.1;3.1 Experimental Data;436
40.4.2;3.2 Approaches, Steps and Implementation for Assessing the Positional Accuracy;437
40.4.3;3.3 Evaluation of the Buffering Approach;438
40.4.3.1;3.3.1 Quantitative Assessment;438
40.4.3.2;3.3.2 Visual Inspection;438
40.5;4 Results and Analyses;439
40.5.1;4.1 Quantitative Analysis;439
40.5.2;4.2 Visual Inspection;442
40.6;5 Reasons for Inconsistency Between the OSM Road Network and Reference Road Network;443
40.7;6 Conclusions;444
40.8;Acknowledgements;444
40.9;References;444
41;31 Data Classification for Highlighting Polygons with Local Extreme Values in Choropleth Maps;446
41.1;Abstract;446
41.2;1 Introduction;446
41.3;2 Previous Work;447
41.3.1;2.1 Task-Oriented Approach;447
41.3.2;2.2 Data Classification;448
41.4;3 PLEX Method;449
41.4.1;3.1 Definition of Local Extreme Values;449
41.4.2;3.2 Partitioning;449
41.5;4 Example;451
41.5.1;4.1 Data Sets;451
41.5.2;4.2 Usage of Conventional Methods;451
41.5.3;4.3 Usage of PLEX Method;455
41.6;5 Conclusions and Outlook;455
41.7;References;456
42;Routing;457
43;32 A Confidence-Based Approach for the Assessment of Accessibility of Pedestrian Network for Manual Wheelchair Users;458
43.1;Abstract;458
43.2;1 Introduction;459
43.3;2 Pedestrian Network Database;461
43.3.1;2.1 Determining the Most Important Environmental Criteria for Enabling the Mobility of Persons with Manual Wheelchairs;461
43.3.2;2.2 Pedestrian Network Segmentation;463
43.4;3 Evaluating the Accessibility of Segments;465
43.4.1;3.1 Aggregation of Confidence Levels;466
43.5;4 Cases Study;468
43.6;5 Conclusion and Future Work;470
43.7;Acknowledgements;471
43.8;References;471
44;33 Accessibility in Pedestrian Routing;473
44.1;Abstract;473
44.2;1 Introduction;474
44.3;2 Related Work;474
44.4;3 Methods;476
44.4.1;3.1 Personalization;476
44.4.2;3.2 Routing Implementation;478
44.4.3;3.3 Interface Design;478
44.5;4 Challenges and Discussion;481
44.6;5 Conclusions;484
44.7;Acknowledgements;485
44.8;References;485
45;34 Visualization of Traffic Bottlenecks: Combining Traffic Congestion with Complicated Crossings;487
45.1;Abstract;487
45.2;1 Introduction;488
45.3;2 State of the Art;488
45.3.1;2.1 Vehicle Tracking: The Floating Car Data (FCD) Method;488
45.3.2;2.2 Traffic Congestion Detection;488
45.3.3;2.3 Complexity of Urban Transportation Infrastructure;489
45.3.4;2.4 Traffic Data Visualization;490
45.4;3 Test Data for Applying the Cartographic Traffic Bottleneck Visualization Method;491
45.4.1;3.1 Shanghai Floating Taxi Data from 2007;491
45.4.2;3.2 OSM Road Network of Shanghai;491
45.5;4 Method for Detecting and Visualizing Vehicle Traffic Bottlenecks;492
45.5.1;4.1 Detection and Classification of Complicated Crossings;493
45.5.2;4.2 Computation of Traffic Congestion and Bottlenecks Based on Floating Taxi Data;493
45.5.3;4.3 Cartographic Representation of Vehicle Traffic Bottlenecks;494
45.6;5 Results;494
45.7;6 Conclusion;496
45.8;Acknowledgements;497
45.9;References;497
46;35 Psychogeography in the Age of the Quantified Self-Mental Map Modelling with Georeferenced Personal Activity Data;500
46.1;Abstract;500
46.2;1 Introduction;500
46.3;2 Personal (Mental) Maps;501
46.3.1;2.1 Academic Perspectives: Cartography and Cognitive Sciences;501
46.3.2;2.2 Psychogeography as Political Practice;502
46.3.3;2.3 Duality;503
46.3.4;2.4 Explorative Tools for Personal Spatial Data Analysis;503
46.4;3 Algorithmic Approach;505
46.4.1;3.1 Data Aggregation, Cleaning and Organisation;506
46.4.2;3.2 Temporal Data Clustering and Network Analysis;507
46.4.3;3.3 Evaluation of Clusters and Model;509
46.5;4 Reflective Practices;512
46.6;5 Applications and Future Works;513
46.7;6 Conclusion;513
46.8;Acknowledgements;514
46.9;References;514
47;Final Reflections;516
48;36 In Search of the Essence of Cartography;517
48.1;Abstract;517
48.2;1 Introduction;517
48.3;2 Significance of Maps and Cartography;518
48.4;3 Cartography in Philosophical Context;520
48.5;4 Properties of Cartographic Modelling;522
48.6;5 The Case of Cartographic and Art Models;525
48.7;6 Conclusions;526
48.8;References;527
49;Author Index;529
50;Subject Index;531mehr