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