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Urban Dynamics and Simulation Models 1st ed. 2017 [Kõva köide]

  • Formaat: Hardback, 123 pages, kõrgus x laius: 235x155 mm, kaal: 3495 g, 27 Illustrations, color; 13 Illustrations, black and white; XXII, 123 p. 40 illus., 27 illus. in color., 1 Hardback
  • Sari: Lecture Notes in Morphogenesis
  • Ilmumisaeg: 24-Jan-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319464957
  • ISBN-13: 9783319464954
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  • Formaat: Hardback, 123 pages, kõrgus x laius: 235x155 mm, kaal: 3495 g, 27 Illustrations, color; 13 Illustrations, black and white; XXII, 123 p. 40 illus., 27 illus. in color., 1 Hardback
  • Sari: Lecture Notes in Morphogenesis
  • Ilmumisaeg: 24-Jan-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319464957
  • ISBN-13: 9783319464954
This monograph presents urban simulation methods that help in better understanding urban dynamics. Over historical times, cities have progressively absorbed a larger part of human population and will concentrate three quarters of humankind before the end of the century. This "urban transition" that has totally transformed the way we inhabit the planet is globally understood in its socio-economic rationales but is less frequently questioned as a spatio-temporal process. However, the cities, because they are intrinsically linked in a game of competition for resources and development, self organize in "systems of cities" where their future becomes more and more interdependent. The high frequency and intensity of interactions between cities explain that urban systems all over the world exhibit large similarities in their hierarchical and functional structure and rather regular dynamics. They are complex systems whose emergence, structure and further evolution are widely governed by th

e multiple kinds of interaction that link the various actors and institutions investing in cities their efforts, capital, knowledge and intelligence. Simulation models that reconstruct this dynamics may help in better understanding it and exploring future plausible evolutions of urban systems. This would provide better insight about how societies can manage the ecological transition at local, regional and global scales. The author has developed a series of instruments that greatly improve the techniques of validation for such models of social sciences that can be submitted to many applications in a variety of geographical situations. Examples are given for several BRICS countries, Europe and United States. The target audience primarily comprises research experts in the field of urban dynamics, but the book may also be beneficial for graduate students.

Is urban future predictable - The SimpopLocal Model.- Evaluation of the SimpopLocal Model (or: A new reproducible methodology for validating simulation models).- An incremental multi-modeling method to simulate systems of cities" evolution.- Using models to explore possible futures (contingency and complexity).- An open innovative and open toolbox.
1 Is Urban Future Predictable?
1(20)
1.1 Emergence
4(2)
1.2 Generic Dynamic Features of Systems of Cities
6(4)
1.2.1 The Hierarchical Differentiation of City Sizes
6(1)
1.2.2 The Meta-Stability of Urban Hierarchies
7(1)
1.2.3 A Regular Quasi-stochastic Process of Growth
8(1)
1.2.4 Hierarchical Diffusion of Innovation Waves and Functional Specializations
9(1)
1.3 Variety in the Evolution of Urban Systems
10(4)
1.3.1 A Simplified Typology of Systems of Cities
12(1)
1.3.2 Systematic Variations in the Rhythm of Urban Growth
13(1)
1.4 Urban Future: Models and Scenarios
14(7)
1.4.1 Challenges in Building Scenarios About Urban Evolution
14(2)
1.4.2 Challenges in Model Validation
16(1)
References
17(4)
2 The SimpopLocal Model
21(16)
2.1 Introduction
21(1)
2.2 Purpose of SimpopLocal
21(1)
2.3 Entities, State Variables and Scales
22(1)
2.4 Processes Overview and Scheduling
23(5)
2.4.1 Population Growth Mechanism
23(1)
2.4.2 Apply Innovation Mechanism
24(1)
2.4.3 Create and Diffuse Innovation Mechanisms
25(3)
2.5 Initial Conditions
28(1)
2.6 Input
29(2)
2.7 Running the Model for Parameter Estimates: Calibration
31(1)
2.8 Simulation Results and Return on Observations
32(5)
References
34(3)
3 Evaluation of the SimpopLocal Model
37(20)
3.1 Quantitative Evaluation
37(3)
3.1.1 Stopping Criterion
37(1)
3.1.2 Expectations
38(1)
3.1.3 Handling the Stochasticity
39(1)
3.2 Automated Calibration
40(7)
3.2.1 Optimization Heuristic
40(2)
3.2.2 Adaptation of NSGA2 to a Stochastic Model
42(3)
3.2.3 Experimental Setup
45(1)
3.2.4 Results
46(1)
3.3 Calibration Profiles
47(8)
3.3.1 Algorithm
48(3)
3.3.2 Guide of Interpretation
51(1)
3.3.3 Result Analysis
52(3)
3.4 Conclusion
55(2)
References
55(2)
4 An Incremental Multi-Modelling Method to Simulate Systems of Cities' Evolution
57(24)
4.1 Introduction
57(1)
4.2 Methodological and Technical Framework for Multi-modelling Systems of Cities
58(5)
4.2.1 Complementary and Competing Theories
58(1)
4.2.2 A Methodology for Implementing Multi-models
59(1)
4.2.3 Exploiting the Results of a Family of Models
60(3)
4.3 A Family of Models of (Post-) Soviet Cities: MARIUS
63(4)
4.3.1 Ordering Possible Causes of Evolution from the Most Generic to the Most Specific
63(2)
4.3.2 Implementing Modular Mechanisms
65(2)
4.4 Geographical Insights on (Post-) Soviet City Growth from Multi-modelling
67(6)
4.4.1 Mechanisms' Performance
68(1)
4.4.2 Parameter Values
69(2)
4.4.3 Residual Trajectories
71(2)
4.5 VARIUS: A Visual Aid to Model Composition and Interpretation
73(4)
4.5.1 Building the Model Online
74(2)
4.5.2 Running the Model Online
76(1)
4.5.3 Analyzing Results Online or `How Close Are We?'
76(1)
4.6 Conclusion
77(4)
References
78(3)
5 Using Models to Explore Possible Futures (Contingency and Complexity)
81(16)
5.1 Models as Artefacts of Historically Contingent Complex Systems
82(2)
5.2 A Method to Foster Diversity in a Model Outcomes
84(4)
5.2.1 The Pattern Space Exploration Algorithm: Principles and Implementation
84(1)
5.2.2 Evolutionary Methods for Parameter Space Exploration
85(1)
5.2.3 Novelty Search
86(1)
5.2.4 PSE Algorithm
86(2)
5.3 Application to Systems of Cities
88(5)
5.3.1 Order Parameters from Empirical Observation of Urban Systems Evolution Over Time
89(1)
5.3.2 Parameter Space and Pattern Space
90(1)
5.3.3 Results
91(2)
5.4 Conclusion: Acknowledging Historical Contingency for the Prediction of Potential Urban Futures
93(4)
References
94(3)
6 An Innovative and Open Toolbox
97(22)
6.1 Introduction
97(1)
6.2 The Ant Model
98(1)
6.3 Embed the Model in OpenMOLE
99(3)
6.4 Do Repetitions
102(1)
6.5 Automatic Workload Distribution
103(1)
6.6 Expose the Variability of the Model
103(1)
6.7 Aggregate the Results
104(2)
6.8 Explore the Space of Parameters
106(4)
6.9 Optimization with Genetic Algorithms
110(2)
6.10 Sensitivity Analysis with the Profiles Method
112(3)
6.11 Validation, Testing Output Diversity
115(4)
References
117(2)
Knowledge Accelerator' in Geography and Social Sciences: Further and Faster, but Also Deeper and Wider 119