Complex artificial environments: simulation, cognition and VR in the study and planning of cities
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| Other Authors: | |
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| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2006
|
| Series: | Springer complexity
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XVI, 322 S. Ill., graph. Darst., Kt. |
| ISBN: | 9783540259176 3540259171 |
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| adam_text | Contents
Introduction 1
Juval Portugali
Part one: General aspects of Complex Artificial Environments
The Scope of Complex Artificial Environments 9
Juval Portugali
1. Introduction 9
2. The second urban revolution 9
3. What is a city? 11
4. Self organization and the city 13
5. The science of cities 14
6. SIRN cities 16
7. On cities and languages 17
8. Forms of planning 20
9. PSS Planning Support Systems 21
10. Planning and the Prediction Paradox 22
11. Three dimensional urban simulation models 23
12. VR Cities 24
13. Concluding notes 25
References 28
Recognition of Natural and Artificial Environments by Computers:
Commonalities and Differences 31
Hermann Haken
1. Use of Computers 31
2. Typical computer procedures 32
3. Natural vs. Artificial 33
4. Pattern recognition 35
5. Pattern recognition vs. pattern formation 36
5.1. Synergetics 36
5.2. Fractal Theory 37
6. VR and its inverse 38
7. Semantics of the City 39
8. Semantic networks 40
VI Contents
9. By means of associations and analogies to a new insight 45
Acknowledgement 48
References 48
Visualization in Spatial Modeling 49
Michael Batty, Philip Steadman, and Yichun Xie
Abstract 49
1. Defining visualization 49
2. Defining spatial modeling 51
3. The space of visualization 54
4. Visualizing theoretical explanations: the von Thiinen model
of market land rent and transport 57
5. Visualizing model exploration and calibration: urban development
using cellular automata 61
6. Visualizing model operation with stakeholder involvement:
pedestrian movement and public safety 65
7. Next steps: a paradigm for visual modeling 69
References 69
Transferring Concepts for Urban Modeling: Capture or Exchange? 71
Denise Pumain
1. Introduction 71
2. Complexity: a reversal in the dominant scientific paradigm? 72
3. Quality of ontology and measurement for social systems 74
4. Cumulativity of knowledge 78
5. Conclusion: organizing a more symmetrical trans disciplinary
communication 80
References 81
Part two: Specific experiences
Design Issues to be Considered for Development of an Object Oriented
System for 3D Geovisualization: The Aalborg Experience 85
Lars Bodum
1. Introduction 85
2. 3D Geovisualization 86
2.1 Technological progress 87
2.2 Integrated database concept 87
3. Outline of GRIFINOR 90
3.1 System architecture 90
3.2 Semi automatic construction of objects 91
3.3 The object database (ODB) 92
3.4 3D viewer 92
3.5 Applications 93
Contents VII
4. Conclusion 93
Acknowledgements 93
References 94
Complex Artificial Environments ESLab s Experience 95
Juval Portugali and the ESLab team
Abstract 95
1. Introduction 95
2. The ESLab GPSS (General Purpose Support System) 96
2.1 The two VR simulators 96
2.2 The family of urban simulation models 100
2.3 Geographical Information Systems 104
3. Some products of ESLab s RSS (Research Support System) 106
3.1 InfoCity 106
3.2 Cognition in real and virtual environments 108
4. Some products of ESLab s PSS (Planning Support System) 109
4.1 PlanCity 109
5. Some products of the ESLab s CSS (Community Support System) 111
5.1 AccessCity 111
5.2 CommunCity 112
Concluding notes 113
References 114
Part three: Urban simulation models
Geosimulation and its Application to Urban Growth Modeling 119
Paul M. Torrens
1. Introduction 119
2. Geosimulation as a new trend in spatial simulation 120
3. Defining geosimulation 121
4. Automata as the favored geosimulation methodology 122
5. Why we need geographic automata 123
6. Geographic Automata Systems 124
7. An application to simulating sprawl 127
8. Model description 128
9. Simulating urban growth 132
10. Conclusions 134
References 134
Geographic Automata Systems and the OBEUS Software
for Their Implementation 137
Itzhak Benenson, Slava Birfur, Vlad Kharbash
1. From arbitrary spatial units to geographic objects 137
2. Geographic Automata System (GAS): a short introduction 139
VIII Contents
3. From a Geographic Automata System to software 140
3.1 Automata of a given type kGK —» Instances of population class.. 140
3.2 Individual automata of type k — Class of Objects of a type k 140
3.3 Relationships between automata — Class of relationships 141
3.4 Limitations of relationships in OBEUS 142
3.5 Location and movement rules are nothing but relationship
transition rules 143
3.6 State and relationship transition rules Ts and RN — Automata
behavior and assessment rules 144
4. Beyond GAS features of OBEUS 144
4.1 Patterns in OBEUS 144
4.2 Population time versus unitary objects time 145
4.3 Synchronization of events in OBEUS 145
5. User s view of OBEUS 146
5.1 Building a model tree 146
5.2 Defining behavioral rules 148
5.3 Building a synchronization chart 149
6. What we get and will get with OBEUS? 150
References 152
The CityDev Project: An Interactive Multi agent Urban Model
on the Web 155
Ferdinando Semboloni
1. Introduction 155
2. Agents strategies and goods 156
2.2. Producers 159
2.3. Developers and the building process 160
3. General aspects 161
4. Results 162
5. Discussion 162
6. Conclusion 163
Acknowledgments 163
References 163
Modeling Multi scale Processes in a Cellular Automata Framework 165
Roger White
1. Introduction 165
2. Regionalized constrained CA—the problems 166
3. A Variable grid CA 168
3.1. Definition of the cell neighbourhood 168
3.2. Cell states and activities 169
3.3. Calculating cell state transitions and activity allocation 170
3.4. Calculating distances 173
4. Application to the dublin region 173
5. Conclusion 176
References 176
Contents IX
Part four: Cognition and VR
Multi Agent Models of Spatial Cognition, Learning and Complex Choice
Behavior in Urban Environments 181
Theo Arentze and Harry Timmermans
1. Introduction 181
2. A multi agent model of activity travel behavior 182
3. A multi agent model to simulate learning and adaptation behavior
in non stationary urban environments under condition of uncertainty
and information search 188
4. A computational model linking cognition, choice set formation,
activity travel behavior and land use dynamics 193
4.1. The system design 193
4.2. Demand side agents 195
4.3. Supply side agents 196
5. Conclusions and discussion 199
References 200
Cognition and Decision in Multi agent Modeling of Spatial Entities
at Different Geographical Scales 201
Lena Sanders
1. Introduction 201
2. Agents for modeling the dynamics of a settlement system 202
2.1. Agents for simulating individuals decisions in matter
of spatial choice 203
2.2. Agents for simulating the dynamics of spatial entities 205
2.3. Where are the driving forces of change in a settlement system? ... 211
3. Different ways for building a hybrid model combining micro
and meso level approaches 213
3.1. Introducing spatial rules in microsimulation 214
3.2. Introducing cognition in spatial MAS 215
References 217
Cognitive Modeling of Urban Complexity 219
Sylvie Occelli and Giovanni A. Rabino
1. Introduction 219
2. Modeling as an ALC agent 220
3. The ALC agent in action 224
4. Concluding remarks: what an ALC model can teach us in dealing
with city complexities 231
References 232
Navigation in Electronic Environments 235
Stephen C. Hirtle
1. Introduction 235
2. A View from the real world 236
X Contents
3. Application I: hypertext navigation 237
3.1. Graph theoretic approach 238
3.2. Cognitive map approach 238
4. Application II: spatial information kiosks 240
5. Summary 242
Acknowledgments 242
References 243
Enhancing the Legibility of Virtual Cities by Means of Residents Urban
Image: a Wayfinding Support System 245
Itzhak Omer, Ran Goldblatt, Karin Talmor, AsafRoz
Abstract 245
1. Introduction 245
2. Principles and tools for supporting VE wayfinding tasks 246
3. WSS for the virtual model of Tel Aviv: the rationale and methodology... 248
3.1. What is the appropriate design for enhancing wayfinding
in a virtual model of a real city? 248
3.2. The structure of urban image 250
4. WSS for the virtual model of Tel Aviv: 252
4.1. The operation principles of the WSS 252
4.2. The implementation of WSS for navigational tasks: illustration... 253
5. Discussion 255
References 256
Small World Modeling for Complex Geographic Environments 259
Bin Jiang
1. Introduction 259
2. Small world networks and models 260
2.1. Small world networks 260
2.2. Rewiring simulation (W S model) 261
2.3. Efficient behaviour of small world networks (M L model) 262
2.4. Scale free property of small world networks (B A model) 262
2.5. Directed search (W D N model) 263
3. Geographic environments as small worlds 263
3.1. Small world properties of geographic environments 263
3.2. Efficiency distribution for geographic environments 265
3.3. Re examination of scale free property 266
3.4. Directed search in geographic environments 267
4. Discussion and conclusion 268
Acknowledgements 270
References 270
Contents XI
Part five: Planning
Planning and Designing with People 275
Michael Kwartler
1. Overview 275
2. Baltimore Region 278
2.1 Vision 2030: Shaping our Region s Future Together 278
2.2 The regional workshop ( where to grow ) 278
2.3 The Regional Public Meetings ( how to grow ) 280
3. Southwest Santa Fe 288
3.1 Southwest Santa Fe City/County Master Planning Initiative 288
3.2 Methodology/process 288
4. Near Northside, Houston 296
4.1 Near Northside Economic Revitalization Planning Process 296
4.2 Organizational structure 297
4.3 Methodology 297
4.4 Focus on simulation / visualization 299
5. Conclusion 304
References 305
Notes 305
Planning Support Systems Evolving: When the Rubber Hits the Road 307
Richard K. Brail
1. Introduction 307
2. UTMS: a historical perspective 309
3. PSS evaluation framework 311
3.1. Data 311
3.2. Acceptance and support 312
3.3. Ease of use 313
3.4. Appropriate and useful output 314
4. Conclusion do we need those shades? 315
References 316
Index 549
|
| adam_txt |
Contents
Introduction 1
Juval Portugali
Part one: General aspects of Complex Artificial Environments
The Scope of Complex Artificial Environments 9
Juval Portugali
1. Introduction 9
2. The second urban revolution 9
3. What is a city? 11
4. Self organization and the city 13
5. The science of cities 14
6. SIRN cities 16
7. On cities and languages 17
8. Forms of planning 20
9. PSS Planning Support Systems 21
10. Planning and the Prediction Paradox 22
11. Three dimensional urban simulation models 23
12. VR Cities 24
13. Concluding notes 25
References 28
Recognition of Natural and Artificial Environments by Computers:
Commonalities and Differences 31
Hermann Haken
1. Use of Computers 31
2. Typical computer procedures 32
3. Natural vs. Artificial 33
4. Pattern recognition 35
5. Pattern recognition vs. pattern formation 36
5.1. Synergetics 36
5.2. Fractal Theory 37
6. VR and its inverse 38
7. Semantics of the City 39
8. Semantic networks 40
VI Contents
9. By means of associations and analogies to a new insight 45
Acknowledgement 48
References 48
Visualization in Spatial Modeling 49
Michael Batty, Philip Steadman, and Yichun Xie
Abstract 49
1. Defining visualization 49
2. Defining spatial modeling 51
3. The space of visualization 54
4. Visualizing theoretical explanations: the von Thiinen model
of market land rent and transport 57
5. Visualizing model exploration and calibration: urban development
using cellular automata 61
6. Visualizing model operation with stakeholder involvement:
pedestrian movement and public safety 65
7. Next steps: a paradigm for visual modeling 69
References 69
Transferring Concepts for Urban Modeling: Capture or Exchange? 71
Denise Pumain
1. Introduction 71
2. Complexity: a reversal in the dominant scientific paradigm? 72
3. Quality of ontology and measurement for social systems 74
4. Cumulativity of knowledge 78
5. Conclusion: organizing a more symmetrical trans disciplinary
communication 80
References 81
Part two: Specific experiences
Design Issues to be Considered for Development of an Object Oriented
System for 3D Geovisualization: The Aalborg Experience 85
Lars Bodum
1. Introduction 85
2. 3D Geovisualization 86
2.1 Technological progress 87
2.2 Integrated database concept 87
3. Outline of GRIFINOR 90
3.1 System architecture 90
3.2 Semi automatic construction of objects 91
3.3 The object database (ODB) 92
3.4 3D viewer 92
3.5 Applications 93
Contents VII
4. Conclusion 93
Acknowledgements 93
References 94
Complex Artificial Environments ESLab's Experience 95
Juval Portugali and the ESLab team
Abstract 95
1. Introduction 95
2. The ESLab GPSS (General Purpose Support System) 96
2.1 The two VR simulators 96
2.2 The family of urban simulation models 100
2.3 Geographical Information Systems 104
3. Some products of ESLab's RSS (Research Support System) 106
3.1 InfoCity 106
3.2 Cognition in real and virtual environments 108
4. Some products of ESLab's PSS (Planning Support System) 109
4.1 PlanCity 109
5. Some products of the ESLab's CSS (Community Support System) 111
5.1 AccessCity 111
5.2 CommunCity 112
Concluding notes 113
References 114
Part three: Urban simulation models
Geosimulation and its Application to Urban Growth Modeling 119
Paul M. Torrens
1. Introduction 119
2. Geosimulation as a new trend in spatial simulation 120
3. Defining geosimulation 121
4. Automata as the favored geosimulation methodology 122
5. Why we need geographic automata 123
6. Geographic Automata Systems 124
7. An application to simulating sprawl 127
8. Model description 128
9. Simulating urban growth 132
10. Conclusions 134
References 134
Geographic Automata Systems and the OBEUS Software
for Their Implementation 137
Itzhak Benenson, Slava Birfur, Vlad Kharbash
1. From arbitrary spatial units to geographic objects 137
2. Geographic Automata System (GAS): a short introduction 139
VIII Contents
3. From a Geographic Automata System to software 140
3.1 Automata of a given type kGK —» Instances of population class. 140
3.2 Individual automata of type k — Class of Objects of a type k 140
3.3 Relationships between automata — Class of relationships 141
3.4 Limitations of relationships in OBEUS 142
3.5 Location and movement rules are nothing but relationship
transition rules 143
3.6 State and relationship transition rules Ts and RN — Automata
behavior and assessment rules 144
4. Beyond GAS features of OBEUS 144
4.1 Patterns in OBEUS 144
4.2 Population time versus unitary objects time 145
4.3 Synchronization of events in OBEUS 145
5. User's view of OBEUS 146
5.1 Building a model tree 146
5.2 Defining behavioral rules 148
5.3 Building a synchronization chart 149
6. What we get and will get with OBEUS? 150
References 152
The CityDev Project: An Interactive Multi agent Urban Model
on the Web 155
Ferdinando Semboloni
1. Introduction 155
2. Agents' strategies and goods 156
2.2. Producers 159
2.3. Developers and the building process 160
3. General aspects 161
4. Results 162
5. Discussion 162
6. Conclusion 163
Acknowledgments 163
References 163
Modeling Multi scale Processes in a Cellular Automata Framework 165
Roger White
1. Introduction 165
2. Regionalized constrained CA—the problems 166
3. A Variable grid CA 168
3.1. Definition of the cell neighbourhood 168
3.2. Cell states and activities 169
3.3. Calculating cell state transitions and activity allocation 170
3.4. Calculating distances 173
4. Application to the dublin region 173
5. Conclusion 176
References 176
Contents IX
Part four: Cognition and VR
Multi Agent Models of Spatial Cognition, Learning and Complex Choice
Behavior in Urban Environments 181
Theo Arentze and Harry Timmermans
1. Introduction 181
2. A multi agent model of activity travel behavior 182
3. A multi agent model to simulate learning and adaptation behavior
in non stationary urban environments under condition of uncertainty
and information search 188
4. A computational model linking cognition, choice set formation,
activity travel behavior and land use dynamics 193
4.1. The system design 193
4.2. Demand side agents 195
4.3. Supply side agents 196
5. Conclusions and discussion 199
References 200
Cognition and Decision in Multi agent Modeling of Spatial Entities
at Different Geographical Scales 201
Lena Sanders
1. Introduction 201
2. Agents for modeling the dynamics of a settlement system 202
2.1. Agents for simulating individuals' decisions in matter
of spatial choice 203
2.2. Agents for simulating the dynamics of spatial entities 205
2.3. Where are the driving forces of change in a settlement system? . 211
3. Different ways for building a hybrid model combining micro
and meso level approaches 213
3.1. Introducing spatial rules in microsimulation 214
3.2. Introducing cognition in spatial MAS 215
References 217
Cognitive Modeling of Urban Complexity 219
Sylvie Occelli and Giovanni A. Rabino
1. Introduction 219
2. Modeling as an ALC agent 220
3. The ALC agent in action 224
4. Concluding remarks: what an ALC model can teach us in dealing
with city complexities 231
References 232
Navigation in Electronic Environments 235
Stephen C. Hirtle
1. Introduction 235
2. A View from the real world 236
X Contents
3. Application I: hypertext navigation 237
3.1. Graph theoretic approach 238
3.2. Cognitive map approach 238
4. Application II: spatial information kiosks 240
5. Summary 242
Acknowledgments 242
References 243
Enhancing the Legibility of Virtual Cities by Means of Residents' Urban
Image: a Wayfinding Support System 245
Itzhak Omer, Ran Goldblatt, Karin Talmor, AsafRoz
Abstract 245
1. Introduction 245
2. Principles and tools for supporting VE wayfinding tasks 246
3. WSS for the virtual model of Tel Aviv: the rationale and methodology. 248
3.1. What is the appropriate design for enhancing wayfinding
in a virtual model of a real city? 248
3.2. The structure of urban image 250
4. WSS for the virtual model of Tel Aviv: 252
4.1. The operation principles of the WSS 252
4.2. The implementation of WSS for navigational tasks: illustration. 253
5. Discussion 255
References 256
Small World Modeling for Complex Geographic Environments 259
Bin Jiang
1. Introduction 259
2. Small world networks and models 260
2.1. Small world networks 260
2.2. Rewiring simulation (W S model) 261
2.3. Efficient behaviour of small world networks (M L model) 262
2.4. Scale free property of small world networks (B A model) 262
2.5. Directed search (W D N model) 263
3. Geographic environments as small worlds 263
3.1. Small world properties of geographic environments 263
3.2. Efficiency distribution for geographic environments 265
3.3. Re examination of scale free property 266
3.4. Directed search in geographic environments 267
4. Discussion and conclusion 268
Acknowledgements 270
References 270
Contents XI
Part five: Planning
Planning and Designing with People 275
Michael Kwartler
1. Overview 275
2. Baltimore Region 278
2.1 Vision 2030: Shaping our Region's Future Together 278
2.2 The regional workshop ("where to grow") 278
2.3 The Regional Public Meetings ("how to grow") 280
3. Southwest Santa Fe 288
3.1 Southwest Santa Fe City/County Master Planning Initiative 288
3.2 Methodology/process 288
4. Near Northside, Houston 296
4.1 Near Northside Economic Revitalization Planning Process 296
4.2 Organizational structure 297
4.3 Methodology 297
4.4 Focus on simulation / visualization 299
5. Conclusion 304
References 305
Notes 305
Planning Support Systems Evolving: When the Rubber Hits the Road 307
Richard K. Brail
1. Introduction 307
2. UTMS: a historical perspective 309
3. PSS evaluation framework 311
3.1. Data 311
3.2. Acceptance and support 312
3.3. Ease of use 313
3.4. Appropriate and useful output 314
4. Conclusion do we need those shades? 315
References 316
Index 549 |
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| genre | (DE-588)1071861417 Konferenzschrift 2004 Venedig gnd-content |
| genre_facet | Konferenzschrift 2004 Venedig |
| id | DE-604.BV021320379 |
| illustrated | Illustrated |
| index_date | 2024-07-02T13:58:43Z |
| indexdate | 2024-07-09T20:35:35Z |
| institution | BVB |
| isbn | 9783540259176 3540259171 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014640790 |
| oclc_num | 62363503 |
| open_access_boolean | |
| owner | DE-384 |
| owner_facet | DE-384 |
| physical | XVI, 322 S. Ill., graph. Darst., Kt. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer complexity |
| spelling | Complex artificial environments simulation, cognition and VR in the study and planning of cities J. Portugali (ed.) Berlin [u.a.] Springer 2006 XVI, 322 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Springer complexity Literaturangaben Mathematisches Modell Stadtplanung City planning Computer simulation Congresses City planning Mathematical models Congresses City planning Simulation methods Congresses Stadtentwicklung (DE-588)4056730-8 gnd rswk-swf Virtuelle Realität (DE-588)4399931-1 gnd rswk-swf Stadtplanung (DE-588)4056754-0 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2004 Venedig gnd-content Stadtentwicklung (DE-588)4056730-8 s Computersimulation (DE-588)4148259-1 s Virtuelle Realität (DE-588)4399931-1 s DE-604 Stadtplanung (DE-588)4056754-0 s Porṭugali, Yuval 1941- (DE-588)121165469 edt text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2623274&prov=M&dok_var=1&dok_ext=htm Inhaltstext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014640790&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Complex artificial environments simulation, cognition and VR in the study and planning of cities Mathematisches Modell Stadtplanung City planning Computer simulation Congresses City planning Mathematical models Congresses City planning Simulation methods Congresses Stadtentwicklung (DE-588)4056730-8 gnd Virtuelle Realität (DE-588)4399931-1 gnd Stadtplanung (DE-588)4056754-0 gnd Computersimulation (DE-588)4148259-1 gnd |
| subject_GND | (DE-588)4056730-8 (DE-588)4399931-1 (DE-588)4056754-0 (DE-588)4148259-1 (DE-588)1071861417 |
| title | Complex artificial environments simulation, cognition and VR in the study and planning of cities |
| title_auth | Complex artificial environments simulation, cognition and VR in the study and planning of cities |
| title_exact_search | Complex artificial environments simulation, cognition and VR in the study and planning of cities |
| title_exact_search_txtP | Complex artificial environments simulation, cognition and VR in the study and planning of cities |
| title_full | Complex artificial environments simulation, cognition and VR in the study and planning of cities J. Portugali (ed.) |
| title_fullStr | Complex artificial environments simulation, cognition and VR in the study and planning of cities J. Portugali (ed.) |
| title_full_unstemmed | Complex artificial environments simulation, cognition and VR in the study and planning of cities J. Portugali (ed.) |
| title_short | Complex artificial environments |
| title_sort | complex artificial environments simulation cognition and vr in the study and planning of cities |
| title_sub | simulation, cognition and VR in the study and planning of cities |
| topic | Mathematisches Modell Stadtplanung City planning Computer simulation Congresses City planning Mathematical models Congresses City planning Simulation methods Congresses Stadtentwicklung (DE-588)4056730-8 gnd Virtuelle Realität (DE-588)4399931-1 gnd Stadtplanung (DE-588)4056754-0 gnd Computersimulation (DE-588)4148259-1 gnd |
| topic_facet | Mathematisches Modell Stadtplanung City planning Computer simulation Congresses City planning Mathematical models Congresses City planning Simulation methods Congresses Stadtentwicklung Virtuelle Realität Computersimulation Konferenzschrift 2004 Venedig |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2623274&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014640790&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT portugaliyuval complexartificialenvironmentssimulationcognitionandvrinthestudyandplanningofcities |