|
Preface |
6 |
|
|
Acknowledgements |
8 |
|
|
Scientific Program Committee |
9 |
|
|
Contents |
12 |
|
|
The Span of Cartography |
16 |
|
|
1 Cartographic Memory Preservation of the Petrópolis City in Brazil: Koeler Map Scanning Using Photographic Survey |
17 |
|
|
Abstract |
17 |
|
|
1 Introduction |
18 |
|
|
2 Materials and Methods |
21 |
|
|
3 Results and Discussion |
24 |
|
|
4 Conclusions |
32 |
|
|
Acknowledgements |
32 |
|
|
References |
32 |
|
|
2 Location Spoofing in a Location-Based Game: A Case Study of Pokémon Go |
34 |
|
|
Abstract |
34 |
|
|
1 Introduction |
35 |
|
|
2 Related Works |
35 |
|
|
2.1 Location-Based Game |
36 |
|
|
2.2 Actor-Network Theory |
36 |
|
|
3 Location Spoofing in Pokémon Go |
37 |
|
|
4 Technical Nuisance or Intentional Plot? |
39 |
|
|
5 Generative Mechanisms for Spoofing |
40 |
|
|
5.1 Uneven Distribution of Pokémons |
40 |
|
|
5.2 Individual Motivations |
42 |
|
|
6 Concluding Remarks |
44 |
|
|
References |
44 |
|
|
Crowdsourcing and Data |
46 |
|
|
3 Educational Aspects of Crowdsourced Noise Mapping |
47 |
|
|
Abstract |
47 |
|
|
1 Introduction |
48 |
|
|
1.1 The Study Area |
49 |
|
|
2 Method |
50 |
|
|
2.1 Project Planning, Database Modelling and Fieldwork |
50 |
|
|
2.2 Data Processing and Analyses |
50 |
|
|
2.3 Visualization |
51 |
|
|
3 Results |
53 |
|
|
4 Conclusion |
57 |
|
|
Acknowledgements |
57 |
|
|
References |
57 |
|
|
4 Crowd and Community Sourced Data Quality Assessment |
59 |
|
|
Abstract |
59 |
|
|
1 Introduction |
59 |
|
|
2 State of the Art |
60 |
|
|
3 Report Platform Description |
61 |
|
|
4 Reports Reliability Assessment Methodology |
62 |
|
|
4.1 Data Quality Assessment Workflow |
62 |
|
|
4.2 Data Quality Indicators |
63 |
|
|
5 Reports Description and Results |
64 |
|
|
5.1 Reports Data Description |
64 |
|
|
5.2 Contributors Description |
66 |
|
|
5.3 Topographic Database |
67 |
|
|
5.4 Reliability Assessment Results |
68 |
|
|
6 Conclusions |
70 |
|
|
Acknowledgements |
71 |
|
|
References |
71 |
|
|
5 Crowdsourcing Mapping and Participatory Planning Support System: Case Study of Brno, Czechia |
73 |
|
|
Abstract |
73 |
|
|
1 Introduction |
73 |
|
|
2 Methods, Case Study Location and Data |
74 |
|
|
3 Results |
77 |
|
|
3.1 Demographics of the Respondents |
77 |
|
|
3.2 Spatial Distribution of Marked Points |
78 |
|
|
3.3 Why Were Certain Locations Marked? |
79 |
|
|
4 Conclusions |
82 |
|
|
Acknowledgements |
82 |
|
|
References |
83 |
|
|
6 A Framework for Enhancing Real-Time Social Media Data to Improve the Disaster Management Process |
86 |
|
|
Abstract |
86 |
|
|
1 Introduction |
87 |
|
|
2 Social Media in Relation with Disaster Management |
88 |
|
|
3 Proposed Research Framework |
89 |
|
|
3.1 Designed Web User API Component |
90 |
|
|
3.2 Social Media System Component |
91 |
|
|
3.2.1 Data Capture |
91 |
|
|
3.2.2 Verification |
93 |
|
|
3.2.3 Language Recognition |
93 |
|
|
3.2.4 Metadata Extraction |
93 |
|
|
3.2.5 Geotagging |
94 |
|
|
3.2.6 Text Classification |
94 |
|
|
4 Conclusion |
94 |
|
|
References |
95 |
|
|
7 Building a Real-Time Geo-Targeted Event Observation (Geo) Viewer for Disaster Management and Situation Awareness |
96 |
|
|
Abstract |
96 |
|
|
1 Introduction |
97 |
|
|
2 The Design of GeoViewer System Architecture |
99 |
|
|
3 User Interface Design and Key Functions |
100 |
|
|
3.1 Real-Time Display of Geo-Tagged Tweets |
101 |
|
|
3.2 Interactive Mapping Functions for Geovisualization |
102 |
|
|
3.3 Spatial, Text, and Temporal Search |
103 |
|
|
3.4 Labeling and Text-Tagging Function |
104 |
|
|
4 Nepal Earthquake Case Study |
105 |
|
|
5 Conclusion |
107 |
|
|
Acknowledgements |
108 |
|
|
References |
108 |
|
|
8 The Academic SDI—Towards Understanding Spatial Data Infrastructures for Research and Education |
110 |
|
|
Abstract |
110 |
|
|
1 Introduction |
112 |
|
|
2 Demand for SDIs at Universities and Research Institutes |
112 |
|
|
3 The ICA’s SDI Model |
113 |
|
|
4 SDI Implementations at Universities and Research Institutes |
114 |
|
|
4.1 University of Twente, The Netherlands |
114 |
|
|
4.2 University of Groningen, the Netherlands |
114 |
|
|
4.3 VSB—Technical University of Ostrava, Czechia |
116 |
|
|
4.4 CSIR, South Africa |
117 |
|
|
4.5 Research Centre for Sustainable Urban Development (CEDEUS), Chile |
117 |
|
|
4.6 University of the Witwatersrand (Wits), Johannesburg, South Africa |
118 |
|
|
4.7 Academic Geo Hub Platform, Wroclaw University of Environmental and Life Sciences (Poland) |
119 |
|
|
5 The Academic SDI |
120 |
|
|
6 Discussion and Conclusion |
122 |
|
|
Acknowledgements |
123 |
|
|
References |
123 |
|
|
Map Design |
125 |
|
|
9 Introducing Leader Lines into Scale-Aware Consistent Labeling |
126 |
|
|
Abstract |
126 |
|
|
1 Introduction |
126 |
|
|
2 Related Work |
129 |
|
|
2.1 Static Map Labeling |
129 |
|
|
2.2 Dynamic Map Labeling |
129 |
|
|
3 Design Principles for Label Placement |
130 |
|
|
4 Consistent Label Placement with Leader Lines |
132 |
|
|
4.1 Genetic-Based Optimization of Active Ranges |
132 |
|
|
4.2 Occlusion-Free Placement of Labels and Leader Lines |
133 |
|
|
4.3 Fitness Evaluation of the Label Placement |
133 |
|
|
5 Results |
134 |
|
|
6 Conclusion and Future Work |
137 |
|
|
Acknowledgements |
138 |
|
|
References |
138 |
|
|
10 On the Way to Create Individualized Cartographic Images for Online Maps Using Free and Open Source Tools |
140 |
|
|
Abstract |
140 |
|
|
1 Introduction |
141 |
|
|
2 Data Sources |
142 |
|
|
3 Processing the OpenStreetMap Data |
143 |
|
|
4 Creating Different Cartographic Images |
145 |
|
|
5 Setting Up Maps Online |
150 |
|
|
6 Discussion |
150 |
|
|
Acknowledgements |
151 |
|
|
References |
151 |
|
|
11 Hebrus Valles—The Mars Exploration Zone Map |
154 |
|
|
Abstract |
154 |
|
|
1 Introduction—Basic Information About Hebrus Valles |
155 |
|
|
2 Motivation and Goals |
155 |
|
|
3 Exploration Zone Criteria |
156 |
|
|
4 Exploration Zone Map Symbology |
156 |
|
|
5 Paths (Traverses) |
159 |
|
|
6 Hebrus Valles Exploration Zone Map Detailed Description |
159 |
|
|
7 Creation of the Map, Methodology |
161 |
|
|
8 Map Format and Adapting to New Technologies |
163 |
|
|
9 Summary |
165 |
|
|
Acknowledgements |
166 |
|
|
References |
166 |
|
|
12 XY Domain: A Sound Map Artwork for Communicating Big Data Characteristics |
168 |
|
|
Abstract |
168 |
|
|
1 Introduction |
168 |
|
|
2 Background |
170 |
|
|
2.1 Transduction, Energy and Humans |
170 |
|
|
2.2 Big Data, Cartography and Art |
171 |
|
|
2.3 Cartography and Sound |
172 |
|
|
3 Context and Construction |
173 |
|
|
3.1 Context |
173 |
|
|
3.2 Idea Development |
173 |
|
|
3.3 Construction of the Sound Map |
175 |
|
|
3.4 Construction of the Visual Map |
176 |
|
|
4 Explanation and Reaction |
177 |
|
|
5 Conclusion |
178 |
|
|
Acknowledgements |
180 |
|
|
References |
180 |
|
|
13 Reproducible Cartography |
182 |
|
|
Abstract |
182 |
|
|
1 Introduction |
182 |
|
|
2 From GUI to Script |
183 |
|
|
2.1 A Step Backward? |
183 |
|
|
2.2 R as a Go-To Tool for Integrated Analysis |
184 |
|
|
3 The Cartography Package |
185 |
|
|
3.1 Design |
185 |
|
|
3.2 Main Features |
186 |
|
|
4 Conclusion |
191 |
|
|
References |
192 |
|
|
Evaluating Map Quality |
193 |
|
|
14 Effectiveness and Efficiency of Using Different Types of Rectangular Treemap as Diagrams in Cartography |
194 |
|
|
Abstract |
194 |
|
|
1 Introduction |
195 |
|
|
2 Treemap: A Brief Review |
195 |
|
|
3 Data Types Represented by Treemaps |
197 |
|
|
4 User Study Design |
197 |
|
|
4.1 Visual Tasks for Treemap Cartography |
198 |
|
|
4.2 Questionnaire Design |
199 |
|
|
4.2.1 Dataset and Test Material |
199 |
|
|
4.2.2 Questions |
201 |
|
|
4.3 Procedure |
203 |
|
|
4.4 Subjects |
204 |
|
|
5 Results and Discussion |
205 |
|
|
6 Conclusion |
211 |
|
|
Acknowledgements |
212 |
|
|
References |
212 |
|
|
15 The Usability of a GeoVisual Analytics Environment for the Exploration and Analysis of Different Datasets |
214 |
|
|
Abstract |
214 |
|
|
1 Introduction |
215 |
|
|
2 Usability of GeoVisual Analytics Environments |
215 |
|
|
3 Experiment Design |
216 |
|
|
3.1 The Use Case Studies |
216 |
|
|
3.2 Experimental GVA Environment |
217 |
|
|
3.3 User Tasks |
219 |
|
|
3.4 Test Participants |
220 |
|
|
3.5 Experiment |
220 |
|
|
4 Results |
220 |
|
|
4.1 Locate the Map |
221 |
|
|
4.2 Identify Time |
222 |
|
|
4.3 Compare Differences |
223 |
|
|
4.4 Characterize Change |
223 |
|
|
4.5 User Satisfaction |
224 |
|
|
4.6 Task Performance |
224 |
|
|
4.7 Use of the Visual Representations in the GVA Environment |
226 |
|
|
5 Conclusions |
227 |
|
|
References |
227 |
|
|
16 Characterizing Maps from Visual Properties |
229 |
|
|
Abstract |
229 |
|
|
1 Introduction |
229 |
|
|
2 Approach to Make Custom-Made Maps |
231 |
|
|
3 Visual Properties of Sample Maps |
232 |
|
|
4 Test Protocol |
233 |
|
|
4.1 Research Hypotheses About Database Design and Test |
233 |
|
|
4.2 Sample Map Database |
233 |
|
|
4.3 Implementation of the Test |
234 |
|
|
5 Characterizing Maps with Visual Properties from the User Test |
234 |
|
|
5.1 Typical Property per Map |
234 |
|
|
5.2 Extreme Property(-ies) per Map |
235 |
|
|
5.3 Unanimous Property(-ies) per Map |
236 |
|
|
6 Analysis of Visual Properties Through Statistical Features |
236 |
|
|
6.1 Statistical Feature: Typical |
236 |
|
|
6.2 Statistical Feature: Extreme |
237 |
|
|
6.3 Statistical Feature: Unanimous |
239 |
|
|
6.4 Correlations Among Properties and Among Statistical Features |
240 |
|
|
7 Exploring and Increasing the Sample Map Database |
241 |
|
|
8 Conclusions and Perspectives |
242 |
|
|
References |
243 |
|
|
17 How Hard Is It to Design Maps for Beginners, Intermediates and Experts? |
245 |
|
|
Abstract |
245 |
|
|
1 Introduction |
246 |
|
|
2 Thoughts of the Map Maker and the Map Reader |
246 |
|
|
3 What Questions Can Be Answered with the Experiment? |
247 |
|
|
4 Categorization of Map Readers |
248 |
|
|
5 Differently Designed Cartographic Images and the Test Questions |
248 |
|
|
6 Database—Sampling and Weighting |
253 |
|
|
7 Proportion of Good Answers |
253 |
|
|
8 Completion Time |
255 |
|
|
9 Map Scale |
256 |
|
|
10 Summary |
257 |
|
|
Acknowledgements |
258 |
|
|
References |
258 |
|
|
18 Interaction Problems Found Through Usability Testing on Interactive Maps |
260 |
|
|
Abstract |
260 |
|
|
1 Introduction |
260 |
|
|
2 Theoretical References |
261 |
|
|
2.1 Interactive Maps |
261 |
|
|
2.2 Usability and Evaluation of Interfaces |
262 |
|
|
3 Methodology |
263 |
|
|
3.1 Participants |
263 |
|
|
3.2 Stimuli and Apparatus |
263 |
|
|
3.3 Procedures |
264 |
|
|
4 Results and Discussion |
266 |
|
|
5 Conclusions |
271 |
|
|
References |
272 |
|
|
19 The Apprehension of Overlaid Information in a Web Map |
274 |
|
|
Abstract |
274 |
|
|
1 Introduction |
274 |
|
|
2 Background and Related Work |
275 |
|
|
3 Web-Based Experiment |
277 |
|
|
4 Results |
280 |
|
|
5 Discussion and Conclusions |
283 |
|
|
References |
285 |
|
|
20 Visualization of Environment-related Information in Augmented Reality: Analysis of User Needs |
287 |
|
|
Abstract |
287 |
|
|
1 Introduction |
288 |
|
|
2 Background |
288 |
|
|
3 Methods |
290 |
|
|
4 Results |
290 |
|
|
4.1 General Background |
291 |
|
|
4.2 Areas of Application of Augmented Reality in Paragliding |
291 |
|
|
4.3 User Test |
293 |
|
|
5 Discussion |
294 |
|
|
6 Conclusions |
294 |
|
|
Acknowledgements |
295 |
|
|
References |
295 |
|
|
Geographic Analysis |
297 |
|
|
21 Analysis and Visualization of the Urban Residents’ Income-Related Happiness Index in China |
298 |
|
|
Abstract |
298 |
|
|
1 Introduction |
299 |
|
|
2 Research Region and Data Sources |
299 |
|
|
3 Research Method |
299 |
|
|
3.1 Analysis of the Current Situation |
299 |
|
|
3.2 Analysis of Regional Disparities |
300 |
|
|
3.3 Analysis of the Spatial–Temporal Variations |
301 |
|
|
3.3.1 Analysis of the Temporal Variation |
301 |
|
|
3.3.2 Average Annual Growth Rate |
301 |
|
|
3.4 Analysis of Indicator Correlations |
302 |
|
|
4 Analysis and Expression |
303 |
|
|
4.1 Current Situation of the Urban Residents’ Income-Related Happiness Index |
303 |
|
|
4.2 Regional Disparities in the Urban Residents’ Income-Related Happiness Index |
304 |
|
|
4.2.1 Multi-level Disparities |
304 |
|
|
4.2.2 Between-Province Disparities |
305 |
|
|
4.2.3 Within-Province Disparities |
305 |
|
|
4.3 Analysis of the Spatial–Temporal Changes in the Urban Residents’ Income-Related Happiness Index |
306 |
|
|
4.3.1 Temporal Changes |
306 |
|
|
4.3.2 Characteristics of the Spatial Distribution of the Annual Growth Rate |
307 |
|
|
4.4 Correlation Analysis of Indicators |
307 |
|
|
5 Conclusions |
309 |
|
|
Acknowledgements |
310 |
|
|
22 Displaying Voter Gains and Losses: Local Government Elections in South Africa for 2011 and 2016 |
311 |
|
|
Abstract |
311 |
|
|
1 Introduction |
311 |
|
|
2 Mapping Political Voting Results |
312 |
|
|
3 Methodology |
315 |
|
|
3.1 Cartograms |
315 |
|
|
3.2 Three-Dimensional (3D) Mapping |
316 |
|
|
3.3 Thematic Map Combined with Cartogram |
317 |
|
|
4 Discussions |
318 |
|
|
5 Conclusions |
323 |
|
|
Acknowledgements |
324 |
|
|
References |
324 |
|
|
23 Mapping Community Vulnerability to Poaching: A Whole-of-Society Approach |
326 |
|
|
Abstract |
326 |
|
|
1 Introduction |
327 |
|
|
2 Whole-of-Society |
327 |
|
|
3 Drivers of Vulnerability to Becoming Involved in Wildlife Crime |
331 |
|
|
4 Methodology |
332 |
|
|
5 Results and Discussions |
334 |
|
|
5.1 The Four Groups |
336 |
|
|
5.2 The Socio-Economic Indicators |
336 |
|
|
5.3 Crime Risk Indicators |
338 |
|
|
6 Conclusions and Future Research |
339 |
|
|
Acknowledgements |
340 |
|
|
References |
340 |
|
|
24 Mapping Urban Landscapes Along Streets Using Google Street View |
342 |
|
|
Abstract |
342 |
|
|
1 Introduction |
343 |
|
|
2 Google Street View (GSV) Data Collection |
344 |
|
|
2.1 Collecting Static GSV Images |
344 |
|
|
2.2 Collecting GSV Panoramas |
344 |
|
|
3 Urban Landscape Quantification and Mapping |
346 |
|
|
3.1 Mapping the Visibility of Street Greenery |
346 |
|
|
3.2 Mapping the Openness of Street Canyons |
349 |
|
|
4 Discussion and Conclusions |
353 |
|
|
References |
355 |
|
|
Numerical Analysis |
358 |
|
|
25 Cross-Scale Analysis of Sub-pixel Variations in Digital Elevation Models |
359 |
|
|
Abstract |
359 |
|
|
1 Introduction |
360 |
|
|
2 Dataset and Study Area |
361 |
|
|
3 Methods |
362 |
|
|
3.1 Interpolation Methods |
363 |
|
|
3.2 Contiguity Configuration |
366 |
|
|
3.3 Workflow and Processing |
367 |
|
|
3.4 Accuracy Assessment |
367 |
|
|
4 Results and Discussion |
368 |
|
|
4.1 Analysis of Residuals |
368 |
|
|
4.2 Optimal Configuration for Weighted Average Interpolator |
369 |
|
|
4.3 Optimal Configuration for Best Fitting Polynomials |
370 |
|
|
4.4 Comparing Surface-Adjusted Elevations with the Rigid Pixel Paradigm |
371 |
|
|
5 Summary |
372 |
|
|
Acknowledgements |
372 |
|
|
References |
372 |
|
|
26 Extraction of Ridge Lines from Grid DEMs with the Steepest Ascent Method Based on Constrained Direction |
374 |
|
|
Abstract |
374 |
|
|
1 Introduction |
375 |
|
|
2 Related Works |
376 |
|
|
2.1 The Steepest Ascent Method |
376 |
|
|
2.2 The Method of Overland Flow Simulation |
376 |
|
|
3 The Steepest Ascent Method Based on Constrained Direction (SAMBCD) |
377 |
|
|
3.1 The Algorithm of SAMBCD |
377 |
|
|
3.2 The Implementation of SAMBCD |
378 |
|
|
3.2.1 The Major Ridge Lines |
378 |
|
|
3.2.2 The Minor Ridge Lines |
381 |
|
|
4 Comparison and Analysis |
382 |
|
|
5 Conclusion |
384 |
|
|
Acknowledgements |
385 |
|
|
References |
385 |
|
|
Using the A Algorithm to Find Optimal Sequences for Area Aggregation |
387 |
|
|
1 Introduction |
387 |
|
|
2 Preliminaries |
389 |
|
|
3 Using the A Algorithm |
391 |
|
|
3.1 Formalizing Area Aggregation as a Pathfinding Problem |
391 |
|
|
3.2 Cost Functions |
391 |
|
|
3.3 Estimating the Cost of Type Change |
393 |
|
|
3.4 Estimating the Cost of Shape |
394 |
|
|
3.5 Overestimation |
396 |
|
|
3.6 Integrating Aggregation Sequences of Different Regions |
396 |
|
|
4 Case Study |
396 |
|
|
5 Conclusions |
401 |
|
|
References |
402 |
|
|
28 Quantitative Expressions of Spatial Similarity in Multi-scale Map Spaces |
403 |
|
|
Abstract |
403 |
|
|
1 Origination and Significance |
404 |
|
|
2 Literature Review: Features of Similarity Relations |
406 |
|
|
2.1 Similarity Relations in Computer Sciences |
406 |
|
|
2.2 Similarity Relations in Psychology |
407 |
|
|
2.3 Similarity Relations in Geography |
408 |
|
|
3 Features and Their Mathematical Expressions |
409 |
|
|
4 Conclusions |
412 |
|
|
Acknowledgements |
412 |
|
|
References |
412 |
|
|
29 Balanced Allocation of Multi-criteria Geographic Areas by a Genetic Algorithm |
415 |
|
|
Abstract |
415 |
|
|
1 Introduction |
416 |
|
|
2 Territory Design by a Multi-objective Genetic Algorithm |
417 |
|
|
2.1 Usability of Graphs in Order to Simulate the Problem |
417 |
|
|
2.2 Mapping the Territory Design Problem into a Graph Model |
417 |
|
|
2.3 The Core of the GA |
419 |
|
|
3 Case Study: Sales Territory Planning |
421 |
|
|
4 Results |
422 |
|
|
5 Tuning of the GA Parameters |
423 |
|
|
6 Location-Allocation and Initializing the Territories |
423 |
|
|
6.1 Evaluating the Balance of Workload |
425 |
|
|
6.2 Evaluating the Travel Time Improvement |
428 |
|
|
6.3 Evaluating the Contiguity and Compactness |
429 |
|
|
7 Conclusions |
429 |
|
|
Acknowledgements |
430 |
|
|
References |
430 |
|
|
30 Rethinking the Buffering Approach for Assessing OpenStreetMap Positional Accuracy |
432 |
|
|
Abstract |
432 |
|
|
1 Introduction |
432 |
|
|
2 Theoretical Analysis of the Buffering Approach |
434 |
|
|
2.1 Principles of the Buffering Approach |
434 |
|
|
2.2 Limitations of the Buffering Approach to Assessing the Positional Accuracy |
436 |
|
|
3 Design of Experiments for Evaluating the Buffering Approach |
436 |
|
|
3.1 Experimental Data |
436 |
|
|
3.2 Approaches, Steps and Implementation for Assessing the Positional Accuracy |
437 |
|
|
3.3 Evaluation of the Buffering Approach |
438 |
|
|
3.3.1 Quantitative Assessment |
438 |
|
|
3.3.2 Visual Inspection |
438 |
|
|
4 Results and Analyses |
439 |
|
|
4.1 Quantitative Analysis |
439 |
|
|
4.2 Visual Inspection |
442 |
|
|
5 Reasons for Inconsistency Between the OSM Road Network and Reference Road Network |
443 |
|
|
6 Conclusions |
444 |
|
|
Acknowledgements |
444 |
|
|
References |
444 |
|
|
31 Data Classification for Highlighting Polygons with Local Extreme Values in Choropleth Maps |
446 |
|
|
Abstract |
446 |
|
|
1 Introduction |
446 |
|
|
2 Previous Work |
447 |
|
|
2.1 Task-Oriented Approach |
447 |
|
|
2.2 Data Classification |
448 |
|
|
3 PLEX Method |
449 |
|
|
3.1 Definition of Local Extreme Values |
449 |
|
|
3.2 Partitioning |
449 |
|
|
4 Example |
451 |
|
|
4.1 Data Sets |
451 |
|
|
4.2 Usage of Conventional Methods |
451 |
|
|
4.3 Usage of PLEX Method |
455 |
|
|
5 Conclusions and Outlook |
455 |
|
|
References |
456 |
|
|
Routing |
457 |
|
|
32 A Confidence-Based Approach for the Assessment of Accessibility of Pedestrian Network for Manual Wheelchair Users |
458 |
|
|
Abstract |
458 |
|
|
1 Introduction |
459 |
|
|
2 Pedestrian Network Database |
461 |
|
|
2.1 Determining the Most Important Environmental Criteria for Enabling the Mobility of Persons with Manual Wheelchairs |
461 |
|
|
2.2 Pedestrian Network Segmentation |
463 |
|
|
3 Evaluating the Accessibility of Segments |
465 |
|
|
3.1 Aggregation of Confidence Levels |
466 |
|
|
4 Cases Study |
468 |
|
|
5 Conclusion and Future Work |
470 |
|
|
Acknowledgements |
471 |
|
|
References |
471 |
|
|
33 Accessibility in Pedestrian Routing |
473 |
|
|
Abstract |
473 |
|
|
1 Introduction |
474 |
|
|
2 Related Work |
474 |
|
|
3 Methods |
476 |
|
|
3.1 Personalization |
476 |
|
|
3.2 Routing Implementation |
478 |
|
|
3.3 Interface Design |
478 |
|
|
4 Challenges and Discussion |
481 |
|
|
5 Conclusions |
484 |
|
|
Acknowledgements |
485 |
|
|
References |
485 |
|
|
34 Visualization of Traffic Bottlenecks: Combining Traffic Congestion with Complicated Crossings |
487 |
|
|
Abstract |
487 |
|
|
1 Introduction |
488 |
|
|
2 State of the Art |
488 |
|
|
2.1 Vehicle Tracking: The Floating Car Data (FCD) Method |
488 |
|
|
2.2 Traffic Congestion Detection |
488 |
|
|
2.3 Complexity of Urban Transportation Infrastructure |
489 |
|
|
2.4 Traffic Data Visualization |
490 |
|
|
3 Test Data for Applying the Cartographic Traffic Bottleneck Visualization Method |
491 |
|
|
3.1 Shanghai Floating Taxi Data from 2007 |
491 |
|
|
3.2 OSM Road Network of Shanghai |
491 |
|
|
4 Method for Detecting and Visualizing Vehicle Traffic Bottlenecks |
492 |
|
|
4.1 Detection and Classification of Complicated Crossings |
493 |
|
|
4.2 Computation of Traffic Congestion and Bottlenecks Based on Floating Taxi Data |
493 |
|
|
4.3 Cartographic Representation of Vehicle Traffic Bottlenecks |
494 |
|
|
5 Results |
494 |
|
|
6 Conclusion |
496 |
|
|
Acknowledgements |
497 |
|
|
References |
497 |
|
|
35 Psychogeography in the Age of the Quantified Self—Mental Map Modelling with Georeferenced Personal Activity Data |
500 |
|
|
Abstract |
500 |
|
|
1 Introduction |
500 |
|
|
2 Personal (Mental) Maps |
501 |
|
|
2.1 Academic Perspectives: Cartography and Cognitive Sciences |
501 |
|
|
2.2 Psychogeography as Political Practice |
502 |
|
|
2.3 Duality |
503 |
|
|
2.4 Explorative Tools for Personal Spatial Data Analysis |
503 |
|
|
3 Algorithmic Approach |
505 |
|
|
3.1 Data Aggregation, Cleaning and Organisation |
506 |
|
|
3.2 Temporal Data Clustering and Network Analysis |
507 |
|
|
3.3 Evaluation of Clusters and Model |
509 |
|
|
4 Reflective Practices |
512 |
|
|
5 Applications and Future Works |
513 |
|
|
6 Conclusion |
513 |
|
|
Acknowledgements |
514 |
|
|
References |
514 |
|
|
Final Reflections |
516 |
|
|
36 In Search of the Essence of Cartography |
517 |
|
|
Abstract |
517 |
|
|
1 Introduction |
517 |
|
|
2 Significance of Maps and Cartography |
518 |
|
|
3 Cartography in Philosophical Context |
520 |
|
|
4 Properties of Cartographic Modelling |
522 |
|
|
5 The Case of Cartographic and Art Models |
525 |
|
|
6 Conclusions |
526 |
|
|
References |
527 |
|
|
Author Index |
529 |
|
|
Subject Index |
531 |
|