Reggiani / Nijkamp Complexity and Spatial Networks

In Search of Simplicity
1. Auflage 2009
ISBN: 978-3-642-01554-0
Verlag: Springer
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 284 Seiten

Complex systems analysis has become a fascinating topic in modern research on non-linear dynamics, not only in the physical sciences but also in the life sciences and the social sciences. After the era of bifurcation theory, chaos theory, syn- getics, resilience analysis, network dynamics and evolutionary thinking, currently we observe an increasing interest in critical transitions of dynamic real-world systems in many disciplines, such as demography, biology, psychology, economics, earth sciences, geology, seismology, medical sciences, and so on. The relevance of this approach is clearly re?ected in such phenomena as traf?c congestion, ?nancial crisis, ethnic con?icts, eco-system breakdown, health failures, etc. This has prompted a world-wide interest in complex systems. Geographical space is one of the playgrounds for complex dynamics, as is witnessed by population movements, transport ?ows, retail developments, urban expansion, lowland ?ooding and so forth. All such dynamic phenomena have one feature in common: the low predictability of uncertain interrelated events occurring at different interconnected spatio-temporal scale levels and often originating from different disciplinary backgrounds. The study of the associated non-linear (fast and slow) dynamic transition paths calls for a joint research effort of scientists from different disciplines in order to understand the nature, the roots and the con- quences of unexpected or unpredictable changes in complex spatial systems.

Autoren/Hrsg.

Reggiani, Aura

Nijkamp, Peter

Inhaltsverzeichnis

1;Copyright page;5
2;Preface;6
3;Contents;6
4;Contributors;11
5;Chapter 1: Simplicity in Complex Spatial Systems;15
5.1;Moving Worlds;15
5.2;Outline of the Book;17
5.3;References;21
6;Part A: Complexity, Evolution, and Simplicity in Space2;22
6.1;Chapter 2: The ``Thermodynamics´´ of the City;23
6.1.1;Introduction;23
6.1.2;The Thermodynamics of Spatial Interaction;24
6.1.2.1;Introduction;24
6.1.2.2;The Archetypal Submodels;25
6.1.2.3;The Transport Model;25
6.1.2.4;Retail Systems: Interaction Models as Location Models;28
6.1.2.5;Deepening the Thermodynamic Analogy;30
6.1.3;Urban Structure and its Evolution;35
6.1.3.1;The Model;35
6.1.3.2;The Thermodynamics of Structural Change;36
6.1.3.3;An Alternative Thermodynamic Formulation for the {Wj};39
6.1.4;Ongoing Challenges;40
6.1.4.1;Introduction;40
6.1.4.2;Spatial Interaction;41
6.1.4.3;Development and Evolution;41
6.1.4.4;The ``New´´ Thermodynamics and Statistical Mechanics;41
6.1.4.5;Models in Planning;42
6.1.4.6;Concluding Comments;42
6.1.5;References;43
6.2;Chapter 3: Macro and Micro Dynamics of the City Size Distribution;44
6.2.1;Introduction;44
6.2.2;A New Exponent;46
6.2.3;The Cities of Israel;47
6.2.4;The Model;48
6.2.5;Application of the Model to Israeli Cities;50
6.2.6;Macro Analysis of the Model: The Rank Size Curve and the Number of Cities;52
6.2.7;Microdynamics: The Rank-Clock Analysis;55
6.2.8;Conclusions;59
6.2.9;References;60
6.3;Chapter 4: A Morphogenetic Perspective on Spatial Complexity;61
6.3.1;Introduction;61
6.3.2;Transport and Urban Morphology;63
6.3.3;Numerical Solutions;65
6.3.4;Conclusion;68
6.3.5;References;69
6.4;Chapter 5: Algorithmic Complexity and Spatial Simplicity;71
6.4.1;Introduction;71
6.4.2;Background;72
6.4.3;Complexity to Simplicity;72
6.4.4;Kolmogorov Complexity;73
6.4.4.1;Formal Descriptive Language;73
6.4.5;Complexity and Spatially Distributed Phenomena;77
6.4.6;Inferences from above Examples;80
6.4.7;Conclusions and Future Research;81
6.4.8;Appendix;82
6.4.9;References;83
6.5;Chapter 6: Polyplexity;84
6.5.1;6.1Introduction;84
6.5.2;6.2Complex Time, Complex Space;86
6.5.2.1;6.2.1Complex Time;87
6.5.2.2;6.2.2Complex Space;89
6.5.3;6.3Prior Structure, Determination, and Hierarchical Spatio-Temporal Ontologies;92
6.5.4;6.4Some Concluding Thoughts;94
6.5.5;References;96
7;Part B: Evolutionary Networks in a Socio-Economic Context2;98
7.1;Chapter 7: Complexity, Evolution and Learning ;99
7.1.1;Introduction;99
7.1.2;An Asset Pricing Model with Heterogeneous Beliefs;100
7.1.3;Empirical Validation;102
7.1.4;Learning to Forecast Experiments;105
7.1.5;Concluding Remarks;111
7.1.6;References;111
7.2;Chapter 8: Homophily, Conformity, and Noise in the (Co-)Evolution of Complex Social Networks;113
7.2.1;Introduction;113
7.2.2;The Model;115
7.2.3;Analysis;116
7.2.4;Conclusion;121
7.2.5;References;122
7.3;Chapter 9: Complex Evolution and Learning;124
7.3.1;A Short Introduction;124
7.3.2;The Evolution of Technological Capabilities;125
7.3.2.1;Search for Information and Knowledge Building;126
7.3.2.2;Spillovers Through an Average Parameter;128
7.3.2.3;Heterogeneous Knowledge;131
7.3.3;Shaping the Cardinality of Inward Neighbourhoods;133
7.3.4;Some Short Conclusions;136
7.3.5;References;138
7.4;Chapter 10: Proximity, Social Capital and the Simon Model of Stochastic Growth;139
7.4.1;Introduction;139
7.4.2;Industrial Dynamics and Urban Growth;140
7.4.3;Proximity;142
7.4.4;Social Capital;143
7.4.5;Concluding Remarks;145
7.4.6;References;145
7.5;Chapter 11: Evolutionary and Preferential Attachment Models of Demand Growth;147
7.5.1;Introduction;147
7.5.2;Dynamic User Equilibrium;147
7.5.3;Demand Dynamics;149
7.5.3.1;The Dual Time Scale Model;149
7.5.3.2;An Ad Hoc Model of Demand Growth;150
7.5.3.3;Replicator Dynamics for Demand Growth;150
7.5.4;Demand Dynamics Based on Preferential Attachment;152
7.5.5;Demand Learning via the Kalman Filter;153
7.5.6;Conclusions;155
7.5.6.1;Numerical Experiments;155
7.5.6.2;Other Network Growth Processes;156
7.5.7;References;156
7.6;Chapter 12: Modelling the Economy as an Evolving Space of Flows;157
7.6.1; Introduction;157
7.6.2; Methodological Challenges;158
7.6.3; Modelling the Evolution of Commodity-Flow Patterns in the Midwest United States;160
7.6.4; Conclusions;162
7.6.5;Appendix. ADynamic Commodity Flow Model of Donaghy et al. (2006);163
7.6.6;References;169
8;Part C: Empirical Aspects of Network Complexity in the Space-Economy2;171
8.1;Chapter 13: Effects of a Simple Mode Choice Model in a Large-Scale Agent-Based Transport Simulation;172
8.1.1;Introduction;172
8.1.2;Simulation Structure;174
8.1.2.1;Overview;174
8.1.2.2;Activity Time Allocation Module;175
8.1.2.3;Router;176
8.1.2.4;Traffic Flow Simulation;176
8.1.2.5;Agent Database: Feedback;177
8.1.2.6;Scores (= Utilities) for Plans;177
8.1.2.7;Discussion of the Scoring Function;179
8.1.3;Mode Choice Model;180
8.1.3.1;Extension of Scoring Function;181
8.1.3.2;Generating non-Car Plans;181
8.1.4;Zurich Scenario;182
8.1.4.1;Network;182
8.1.4.2;Population, Initial Demand;183
8.1.4.3;Simulation Runs and Base Case;184
8.1.4.4;Sensitivity;185
8.1.4.5;Winner-Loser Analysis;186
8.1.5;Conclusion;188
8.1.6;References;189
8.2;Chapter 14: Complex, Adaptive Systems, Through Time and Across Space;192
8.2.1;Introduction;192
8.2.2;Part 1: Complexity Defined;193
8.2.2.1;Algorithmic Complexity;193
8.2.2.2;Deterministic Complexity;195
8.2.2.3;Aggregate Complexity;198
8.2.3;Part 2: Complexity Applied;198
8.2.3.1;Modelling Complexity through Time and Across Space: A Case Study of the Deregulated Alberta Electrical Power Generation Indust;198
8.2.3.1.1;Modelling Approaches: Privileging Space or Privileging Time;198
8.2.3.1.2;A System Dynamics Simulation Model of the Deregulated Alberta Electrical Market;200
8.2.3.2;Modelling Investor Behaviour;206
8.2.3.2.1;Results of the Simulations;207
8.2.3.3;Discussion;208
8.2.4;Conclusion;209
8.2.5;References;209
8.3;Chapter 15: Measuring and Visualizing Urban Network Dynamics;213
8.3.1;Introduction;213
8.3.2;Overview of Methodology;214
8.3.3;Case Studies;216
8.3.3.1;Comparison of Population Backbones Over Time;216
8.3.3.1.1;Cedar Rapids;216
8.3.3.1.2;Chicago;217
8.3.3.1.3;Phoenix;218
8.3.3.2;Comparison of Population and Employment Backbones;218
8.3.3.2.1;Cedar Rapids;219
8.3.3.2.2;Chicago;220
8.3.3.2.3;Phoenix;220
8.3.3.2.4;Measures of Correlation for the Components and Backbones;221
8.3.4;Directions for Future Research;222
8.3.5;References;222
8.4;Chapter 16: Spatial Autocorrelation in Spatial Interaction;224
8.4.1;Introduction;224
8.4.2;Conceptual Foundation;225
8.4.3;An Empirical Grounding;227
8.4.4;Spatial Autocorrelation in the Texas Journey-to-Work Flows Data;229
8.4.5;Some Features of Complexity in Spatial Flows;232
8.4.6;Additional Features of Complexity in Spatial Flows;233
8.4.7;Implications and Concluding Comments;237
8.4.8;Future Research;238
8.4.9;References;239
8.5;Chapter 17: Complex Networks Analysis of Commuting;241
8.5.1;Introduction;241
8.5.2;Complex Network Analysis Applied to Commuting: A Review of Recent Advances;242
8.5.2.1;The Sardinian Inter-Municipal Commuting Network (SMCN);242
8.5.2.2;The Sicilian vs. The Sardinian Inter-Municipal Commuting Network;244
8.5.2.3;The Influence of Space on Commuter Networks;246
8.5.3;Complex Network Analysis Applied to Commuter Systems: A Research Agenda;250
8.5.3.1;Integration between GIS and CNA;250
8.5.3.2;Time Evolution of Complex Networks;252
8.5.3.3;Comparative Analysis through Complex Network Theory;253
8.5.3.4;Community Detection on Commuter Networks;254
8.5.4;Conclusion and Outlook Remarks;255
8.5.5;References;255
8.6;Chapter 18: Spatial and Commuting Networks;258
8.6.1;Introduction;258
8.6.2;Spatial and Network Analysis: Recent Perspectives;260
8.6.3;Case Study: Dynamics of German Commuting;261
8.6.3.1;Spatial Analysis: The ``Open Cities´´;261
8.6.3.2;Network Analysis: The ``Connected´´ Cities;263
8.6.3.2.1;Connectivity Distribution;263
8.6.3.2.2;Network Indices;264
8.6.4;Multidimensional Synthesis: The Network of the ``Open´´ and Connected Cities;267
8.6.5;Conclusions;270
8.6.6;References;271
9;Part D: Epilogue2;273
9.1;Chapter 19: From Complexity to Simplicity;274
9.1.1;Conceptual Background;274
9.1.2;Spatial Economic Analysis and Network Analysis: A Dual Perspective;276
9.1.3;Towards a Research Agenda;280
9.1.3.1;Methodological Issues;280
9.1.3.2;Policy Issues;281
9.1.4;References;282