Urban Dynamics and Simulation Models

136 Pages·2017·4.539 MB·English

by  Denise Pumain, Romain Reuillon (auth.)

Lecture Notes in Morphogenesis
Series Editor: Alessandro Sarti
Denise Pumain
Romain Reuillon
Urban
Dynamics and
Simulation
Models
Lecture Notes in Morphogenesis
Series editor
Alessandro Sarti, CAMS Center for Mathematics, CNRS-EHESS, Paris, France
e-mail: [email protected]
More information about this series athttp://www.springer.com/series/11247
Denise Pumain Romain Reuillon
(cid:129)
Urban Dynamics
and Simulation Models
é (cid:1) é
Guillaume Ch rel Cl mentine Cottineau
(cid:1)
Robin Cura Mathieu Leclaire
é
S bastien Rey-Coyrehourcq
(cid:1) fi
Clara Schmitt El e Swerts
123
Denise Pumain Romain Reuillon
UniversitéParis I Panthéon-Sorbonne CNRS, UMR Géographie-Cités
andUMRGéographie-Cités andInstitut des Systèmes Complexes
Paris Paris-Ile-de-France
France Paris
France
ISSN 2195-1934 ISSN 2195-1942 (electronic)
Lecture Notesin Morphogenesis
ISBN978-3-319-46495-4 ISBN978-3-319-46497-8 (eBook)
DOI 10.1007/978-3-319-46497-8
LibraryofCongressControlNumber:2016956834
©SpringerInternationalPublishingAG2017
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Acknowledgements
Thatresearchwas supportedbytheadvanced grantGeoDiverCityoftheEuropean
Research Council.
v
Contents
1 Is Urban Future Predictable?... .... .... .... .... .... ..... .... 1
1.1 Emergence .... .... ..... .... .... .... .... .... ..... .... 4
1.2 Generic Dynamic Features of Systems of Cities. .... ..... .... 6
1.2.1 The Hierarchical Differentiation of City Sizes. ..... .... 6
1.2.2 The Meta-Stability of Urban Hierarchies. .... ..... .... 7
1.2.3 A Regular Quasi-stochastic Process of Growth..... .... 8
1.2.4 Hierarchical Diffusion of Innovation Waves and
Functional Specializations.... .... .... .... ..... .... 9
1.3 Variety in the Evolution of Urban Systems .... .... ..... .... 10
1.3.1 A Simplified Typology of Systems of Cities.. ..... .... 12
1.3.2 Systematic Variations in the Rhythm
of Urban Growth... .... .... .... .... .... ..... .... 13
1.4 Urban Future: Models and Scenarios . .... .... .... ..... .... 14
1.4.1 Challenges in Building Scenarios About Urban
Evolution ... ..... .... .... .... .... .... ..... .... 14
1.4.2 Challenges in Model Validation ... .... .... ..... .... 16
References.. .... .... .... ..... .... .... .... .... .... ..... .... 17
2 The SimpopLocal Model.. ..... .... .... .... .... .... ..... .... 21
2.1 Introduction ... .... ..... .... .... .... .... .... ..... .... 21
2.2 Purpose of SimpopLocal... .... .... .... .... .... ..... .... 21
2.3 Entities, State Variables and Scales .. .... .... .... ..... .... 22
2.4 Processes Overview and Scheduling.. .... .... .... ..... .... 23
2.4.1 Population Growth Mechanism.... .... .... ..... .... 23
2.4.2 Apply Innovation Mechanism. .... .... .... ..... .... 24
2.4.3 Create and Diffuse Innovation Mechanisms .. ..... .... 25
2.5 Initial Conditions ... ..... .... .... .... .... .... ..... .... 28
2.6 Input. .... .... .... ..... .... .... .... .... .... ..... .... 29
2.7 Running the Model for Parameter Estimates: Calibration... .... 31
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2.8 Simulation Results and Return on Observations. .... ..... .... 32
References.. .... .... .... ..... .... .... .... .... .... ..... .... 34
3 Evaluation of the SimpopLocal Model.... .... .... .... ..... .... 37
3.1 Quantitative Evaluation.... .... .... .... .... .... ..... .... 37
3.1.1 Stopping Criterion.. .... .... .... .... .... ..... .... 37
3.1.2 Expectations . ..... .... .... .... .... .... ..... .... 38
3.1.3 Handling the Stochasticity.... .... .... .... ..... .... 39
3.2 Automated Calibration .... .... .... .... .... .... ..... .... 40
3.2.1 Optimization Heuristic .. .... .... .... .... ..... .... 40
3.2.2 Adaptation of NSGA2 to a Stochastic Model. ..... .... 42
3.2.3 Experimental Setup. .... .... .... .... .... ..... .... 45
3.2.4 Results . .... ..... .... .... .... .... .... ..... .... 46
3.3 Calibration Profiles.. ..... .... .... .... .... .... ..... .... 47
3.3.1 Algorithm ... ..... .... .... .... .... .... ..... .... 48
3.3.2 Guide of Interpretation .. .... .... .... .... ..... .... 51
3.3.3 Result Analysis.... .... .... .... .... .... ..... .... 52
3.4 Conclusion .... .... ..... .... .... .... .... .... ..... .... 55
References.. .... .... .... ..... .... .... .... .... .... ..... .... 55
4 An Incremental Multi-Modelling Method to Simulate Systems
of Cities’ Evolution.. .... ..... .... .... .... .... .... ..... .... 57
4.1 Introduction ... .... ..... .... .... .... .... .... ..... .... 57
4.2 Methodological and Technical Framework
for Multi-modelling Systems of Cities .... .... .... ..... .... 58
4.2.1 Complementary and Competing Theories .... ..... .... 58
4.2.2 A Methodology for Implementing Multi-models.... .... 59
4.2.3 Exploiting the Results of a Family of Models. ..... .... 60
4.3 A Family of Models of (Post-) Soviet Cities: MARIUS.... .... 63
4.3.1 Ordering Possible Causes of Evolution
from the Most Generic to the Most Specific.. ..... .... 63
4.3.2 Implementing Modular Mechanisms.... .... ..... .... 65
4.4 Geographical Insights on (Post-) Soviet City Growth
from Multi-modelling..... .... .... .... .... .... ..... .... 67
4.4.1 Mechanisms’ Performance.... .... .... .... ..... .... 68
4.4.2 Parameter Values .. .... .... .... .... .... ..... .... 69
4.4.3 Residual Trajectories.... .... .... .... .... ..... .... 71
4.5 VARIUS: A Visual Aid to Model Composition
and Interpretation ... ..... .... .... .... .... .... ..... .... 73
4.5.1 Building the Model Online... .... .... .... ..... .... 74
4.5.2 Running the Model Online ... .... .... .... ..... .... 76
4.5.3 Analyzing Results Online or ‘How Close Are We?’. .... 76
4.6 Conclusion .... .... ..... .... .... .... .... .... ..... .... 77
References.. .... .... .... ..... .... .... .... .... .... ..... .... 78
Contents ix
5 Using Models to Explore Possible Futures
(Contingency and Complexity).. .... .... .... .... .... ..... .... 81
5.1 Models as Artefacts of Historically Contingent Complex
Systems .. .... .... ..... .... .... .... .... .... ..... .... 82
5.2 A Method to Foster Diversity in a Model Outcomes . ..... .... 84
5.2.1 The Pattern Space Exploration Algorithm:
Principles and Implementation .... .... .... ..... .... 84
5.2.2 Evolutionary Methods for Parameter Space
Exploration.. ..... .... .... .... .... .... ..... .... 85
5.2.3 Novelty Search .... .... .... .... .... .... ..... .... 86
5.2.4 PSE Algorithm .... .... .... .... .... .... ..... .... 86
5.3 Application to Systems of Cities. .... .... .... .... ..... .... 88
5.3.1 Order Parameters from Empirical Observation
of Urban Systems Evolution Over Time. .... ..... .... 89
5.3.2 Parameter Space and Pattern Space. .... .... ..... .... 90
5.3.3 Results . .... ..... .... .... .... .... .... ..... .... 91
5.4 Conclusion: Acknowledging Historical Contingency
for the Prediction of Potential Urban Futures... .... ..... .... 93
References.. .... .... .... ..... .... .... .... .... .... ..... .... 94
6 An Innovative and Open Toolbox ... .... .... .... .... ..... .... 97
6.1 Introduction ... .... ..... .... .... .... .... .... ..... .... 97
6.2 The Ant Model. .... ..... .... .... .... .... .... ..... .... 98
6.3 Embed the Model in OpenMOLE.... .... .... .... ..... .... 99
6.4 Do Repetitions . .... ..... .... .... .... .... .... ..... .... 102
6.5 Automatic Workload Distribution.... .... .... .... ..... .... 103
6.6 Expose the Variability of the Model.. .... .... .... ..... .... 103
6.7 Aggregate the Results..... .... .... .... .... .... ..... .... 104
6.8 Explore the Space of Parameters .... .... .... .... ..... .... 106
6.9 Optimization with Genetic Algorithms.... .... .... ..... .... 110
6.10 Sensitivity Analysis with the Profiles Method... .... ..... .... 112
6.11 Validation, Testing Output Diversity . .... .... .... ..... .... 115
References.. .... .... .... ..... .... .... .... .... .... ..... .... 117
Knowledge Accelerator’ in Geography and Social Sciences:
Further and Faster, but Also Deeper and Wider .. .... .... ..... .... 119
Introduction
Since the turn of twenty-first century, half of the population of the world lives in
cities.Thisisbynomeansarevolutionoradecisiveinflexionasitwassometimes
suggested by the media, because the world trend in increasing the share of urban
population (that ratio is technically called the ‘urbanization rate’) has been a con-
tinuous one for at least two centuries. The major significance of crossing the
symbolic50%thresholdisthatcitieshavebecometheplaceswherethemajorityof
people will henceforth make their living and invent their ways of life. This has
already been the case for 50 years for the population of the more developed
countries. It is becoming a reality for more and more inhabitants of the world.
Beforetheendofthiscenturymorethanthreequartersofsomeninebillionhumans
willliveincities.Theconsequenceisthatmostsocietalchallengeshavenowtobe
confronted and solved within urban environments. Whatever the topic: trans-
portation, pollution, production, consumption, social segregation, poverty, culture,
architecture, and esthetics, it must now be thought of as an urban problem, to be
managedinanurbanmilieuandsolutionshavetobeadaptedtothespecificplaces.
Althoughcitiesareobviouslypartofmanyproblems(climatechange,inequality,
etc.), they also represent the only viable solution to such problems (Carter et al.
2015), and they already have started solving them. Indeed, although they were
never coordinated by any dedicated institution in a global and continuous way,
cities have self-organized throughout the course of history into systems of cities.
Becausetheyareconnectedbymultiplenetworksforexchangingmaterialgoods
aswellasimmaterialinformation,systemsofcitiescouldfigureprominentlyamong
themostingeniousinventionofhumansocieties.Ifonewouldconceivethemasan
institution produced intentionally, systems of cities would be thought of as suc-
cessful tools designed for reducing uncertainties of terrestrial environments using
distantresourcesandmultiplyinginnovationfromnetworkinteractions.Fromthese
repeated and increased exchanges, the systems of cities have spontaneously gen-
erated significant co-evolution processes between more and more distant cities in
expanding political, trade and cultural networks.
Thisevolutionisnotlinearandinvolvesmanydiscontinuitiesinspaceandtime:
unequalurbandevelopmentisaruleandbarrierstotradeandnewbordershavebeen
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