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2017 | Buch

Urban Dynamics and Simulation Models

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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 the 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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Is Urban Future Predictable?
Abstract
Despite uncertainties linked to the increasing speed of technological and societal evolution, important features of future urbanism can be predicted at the regional and global levels and even sometimes for local situations. Comparative urban studies have brought results about universal processes and typical trajectories in the history of urban systems. This analytic description provides the basis for designing robust dynamic models as well as realistic scenarios for exploring a diversity of possible urban futures.
Denise Pumain, Romain Reuillon
Chapter 2. The SimpopLocal Model
Abstract
The model is described in detail in order to explain how to implement the stylized facts that are theoretically essential for representing the emergence of cities into a multi-agent system. The model activates rather simple feedback loops between settlement size, innovation and resources enabling settlement growth from local innovation creation and inter-settlement diffusion. The necessary attributes, processes and parameters of the model, that were identified according to a rule of parsimony, are described and their estimated values after simulations are presented.
Denise Pumain, Romain Reuillon
Chapter 3. Evaluation of the SimpopLocal Model
Abstract
The SimpopLocal model exposes 6 free parameters that cannot be set using empirical data. This chapter presents how to evaluate SimpopLocal in spite of these degrees of freedom. A first evaluation establishes whether the model has the capacity to produce acceptable dynamics. To achieve this evaluation, the quality of the simulated dynamics is made explicit using a quantitative analysis. Based on this quantitative evaluation, an automated calibration algorithm is designed using a state-of-the-art multi-objective genetic algorithm. The results show that the model is able to produce acceptable dynamics. A second evaluation exposes the contribution of each free parameter to the capacity of the model to produce these acceptable dynamics. A novel sensitivity analysis algorithm called calibration profile is then applied. The results of this analysis show that the model can be simplified by removing one superfluous mechanism and one superfluous parameter and that all the remaining mechanisms are mandatory in the model and all the remaining parameters can be better constrained by narrowing down their definition domains.
Denise Pumain, Romain Reuillon
Chapter 4. An Incremental Multi-Modelling Method to Simulate Systems of Cities’ Evolution
Abstract
Explaining the evolution of urban systems at large spatio-temporal scales is uneasy. Processes are frequently unobserved empirically and equifinality is a challenge for any generative explanation models. We try to address the causation challenge in urban modelling by proposing a multi-modelling framework for the comparison of several model structures. Each structure represents a combination of mechanisms translating alternative or complementary hypotheses about the processes at play. This approach implies that the conception, implementation and evaluation of the model(s) integrate a diversity of mechanisms. Their contribution to the explanation of urbanization is evaluated in time and space by confronting models to empirical data through an interactive visualization platform. We argue that multi-modelling can provide an alternative way to account for the possible causes generating observed patterns, between traditional approaches such as 1/simple models focusing on a single cause (as is often the case for proving a theory) or 2/very complex models including all possible mechanisms at once (as it might prevent from distinguishing their individual contribution).
Denise Pumain, Romain Reuillon
Chapter 5. Using Models to Explore Possible Futures (Contingency and Complexity)
Abstract
This chapter considers models of urban systems as virtual laboratories to explore possible trajectories that a ‘real’ geographical system could have taken instead of the evolution path that is observed historically. It therefore builds on the concept of historical contingency, and regards simulation modelling as an opportunity to explore historical contingency in silico. This approach is illustrated by an experiment performed on a model of systems of cities applied to the urbanization of the (Former) Soviet Union, MARIUS, using a new algorithm seeking to maximize diversity in a model’s outcomes. The discovery of possible trajectories of the target system through the model provides insight into the singularity of the realized trajectory, and can be used for prediction to define a range of possible outcomes resulting from a simulation of the model, not based on predefined scenarios but on the maximum diversity allowed by the model within reasonable parameter bounds.
Guillaume Chérel, Clémentine Cottineau, Romain Reuillon
Chapter 6. An Innovative and Open Toolbox
Abstract
This chapters introduces the platform OpenMOLE. It is a generic tool used to run the different methods presented in details in the previous chapters. To simplify the comprehension, we focus on a simple model, but which does not concern the city modelling. However the principles are the same.
Denise Pumain, Romain Reuillon
Erratum to: Urban Dynamics and Simulation Models
Denise Pumain, Romain Reuillon
Backmatter
Metadaten
Titel
Urban Dynamics and Simulation Models
verfasst von
Denise Pumain
Romain Reuillon
Copyright-Jahr
2017
Electronic ISBN
978-3-319-46497-8
Print ISBN
978-3-319-46495-4
DOI
https://doi.org/10.1007/978-3-319-46497-8