Table of Contents

Spatial Dynamics, Networks and Modelling

Spatial Dynamics, Networks and Modelling

New Horizons in Regional Science series

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.

Chapter 4: Spatial Data Clustering and Self-Organized Criticality: Empirical Experiments on Regional Labour Market Dynamics

Aura Reggiani, Christian Ventrucci and Peter Nijkamp

Subjects: economics and finance, regional economics, urban and regional studies, regional economics

Extract

Aura Reggiani, Christian Ventrucci, Peter Nijkamp and Giovanni Russo 4.1 THE SELF-ORGANIZED CRITICALITY ISSUE 4.1.1 The SOC Principle: Prefatory Remarks The analysis of dynamic systems and of critical paths has inspired much research on complex phenomena. Self-organized criticality (SOC) is based on the idea that complex behavioural systems can spontaneously develop, that is, they auto-organize themselves into a state characterized by a complex structure, where small shocks can provoke chain reactions in all constituting elements (Bak et al. 1987; Bak and Chen 1991). In other words, SOC shows that several dynamic systems may evolve, in a natural way, into a ‘critical state’ without any spatial or temporal influences (Jensen 1998), according to a self-organized process (Nicolis and Prigogine 1977) and without any adjustments of the systems’ parameters. Thus, the SOC concept can capture phenomena like catastrophes and avalanches1 – either positive or negative – which are usually considered to be the result of aggregate shocks. Such critical shocks in a complex system emerge from the interactive behaviour of small units, but may alter the constellation of the entire complex system. It should be noted here that in the scientific discussion centring around the SOC concept, there is a clear lack of a well-defined theoretical and operational testing procedure. Despite this general drawback, the generally accepted feature of a critical SOC state is a statistical feature, namely, the existence of a power-law distribution of the ‘avalanches’ (Bak 1996; Jensen 1998; Zhao and Chen 2000). Bak and his co-authors used the example of the...

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