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Spatial Dynamics, Networks and Modelling

Edited by Aura Reggiani and Peter Nijkamp

This important new book provides a valuable set of studies on spatial dynamics, emerging networks and modelling efforts. It employs interdisciplinary concepts alongside innovative trajectories to highlight recent advances in analysing and modelling the spatial economy, transport networks, industrial dynamics and regional systems. It is argued that modelling network processes at different spatial scales provides critical information for the design of plans and policies. Furthermore, a key issue in the current complex and heterogeneous landscape is the adoption and validation of new approaches, models and methodologies, which are able to grasp the emergent aspects of economic uncertainty and discontinuity, as well as overcome the current difficulties of carrying out appropriate forecasts. In exploring diverse pathways for theoretical, methodological and empirical analysis, this exciting volume offers promising and evolutionary perspectives on the modern spatial network society.
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Chapter 15: Dynamic Effects of Transport Costs on Urban Shape

Francesca Medda, Peter Nijkamp and Piet Rietveld


Francesca Medda, Peter Nijkamp and Piet Rietveld 15.1 INTRODUCTION Urban dynamics, investigated inter alia by means of system dynamics, predator–prey modelling or dynamic simulation of growth processes of the city, has offered analytical instruments to cope with the (socio)-economic dynamics of cities. Much less attention has been paid, however, to the structural shape changes of the urban territory in relation to transport costs from a spatial-dynamic perspective. When we examine urban growth and transport, we often observe a dual relationship between variables that induce growth and variables that halt it (Engle et al. 1992; Sakashita 1995; Brueckner and Lai 1996; Sasaki 1998). This approach of deploying a joint analysis of growth-inducing and growth-inhibiting variables has been used in various dynamic social or ecological systems: for example, to examine the formation of valleys and rivers by erosion, or the developmental patterns of animals such as the hydra. An appropriate analytical tool has been developed to simulate growth through a process of counterbalances among determining variables; it is known as the morphogenetic algorithm, first defined and developed by Turing in 1952. In the present study we model how transport cost, which we assume is a function of distance as well as time, impacts on the growth of the city. To do so, we analyse the problem within a dynamic setting and, in particular, we apply the morphogenetic algorithm as a general mechanism for mapping out space–time dynamics. The approach we propose in this chapter applies the essentials of the...

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