A Modular Modelling Framework for Hypotheses Testing in the Simulation of Urbanisation

Abstract

In this paper, we present a modelling experiment developed to study systems of cities and processes of urbanisation in large territories over long time spans. Building on geographical theories of urban evolution, we rely on agent-based models to 1) formalise complementary and alternative hypotheses of urbanisation and 2) explore their ability to simulate observed patterns in a virtual laboratory. The paper is therefore divided into two sections : an overview of the mechanisms implemented to represent competing hypotheses used to simulate urban evolution; and an evaluation of the resulting model structures in their ability to simulate-efficiently and parsimoniously-a system of cities (between 1000 and 2000 cities in the Former Soviet Union) over several periods of time (before and after the crash of the USSR). We do so using a modular framework of model-building and evolutionary algorithms for the calibration of several model structures. This project aims at tackling equifinality in systems dynamics by confronting different mechanisms with similar evaluation criteria. It enables the identification of the best-performing models with respect to the chosen criteria by scanning automatically the parameter space along with the space of model structures (the different combinations of mechanisms).

The Old and the New: Qualifying City Systems in the World with Classical Models and New Data

“…This explanation could account for the “short‐ and long‐term memories in the social system” as well as for the “spatial coherence” in urban growth detected in their spatio‐temporal analysis of U.S. cities from 1900 to 2000 by Hernando et al (). Indeed, the observed spatial and temporal fluctuations of urban growth rates reflect a very complex pattern of urban co‐evolution (Paulus ) which requires a more disaggregate type of generative modeling than the statistical exercise we are doing here (see e.g., the agent‐based model MARIUS simulating the urban growth patterns in former USSR by Cottineau et al ).…”

Peut-on estimer la singularité des villes (post-)soviétiques ?

“…Cette dialectique, couplée à une connaissance locale et fine des différents terrains d'étude, permet d'appréhender avec plus de matière les défis urbains à venir, dont les causes et les facteurs sont de différentes natures et d'échelles géographiques diverses (déclin démographique, globalisation, métropolisation, etc.). Couplée à un type de modélisation générative (c'est-à-dire mettant en oeuvre la simulation de mécanismes causaux d'interaction entre villes), elle permet d'envisager des scénarios d'évolution et de politique publique, et c'est en ce sens que nous poursuivons nos recherches [Chérel et al, 2015, Cottineau et al, 2015a, Cottineau et al, 2015b.…”

The Old and the New: Qualifying City Systems in the World with Classical Models and New Data

“…This could provide an explanation to the "short-and long-term memories in the social system" as well as the "spatial coherence" in urban growth detected in their spatio-temporal analysis of US cities from 1900 to 2000 by Hernando et al (2014). Indeed, the observed spatial and temporal fluctuations of urban growth rates reflects a very complex pattern of urban co-evolution (Paulus, 2004) that requires a more disaggregated type of generative modeling than the statistical exercise we are doing here (see for instance the agent-based model MARIUS simulating the urban growth patterns in former USSR by Cottineau et al, 2015).…”