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

Self-Organised Criticality (SOC) is a recently developed concept from dynamic systems analysis that aims to investigate the transition trajectories of evolutionary systems. In order to detect one of the most evident characterisations of SOC, viz. the emergence of a power law distribution mapping out the “avalanches” in complex networks, data clustering is necessary. There are, however, several clustering techniques available. In a practical context, the choice of the clustering method adopted is often left to the analyst. Starting from the above considerations, the present chapter aims to investigate a new methodological challenge, by investigating the influence of the specific clustering method adopted on the results representing the SOC state. Our empirical application concerns the developmental patterns of regional labour markets in Germany. The chapter explores the evolutionary dynamics of employment at a district level in West Germany, as well as in the combined West and East German case, by considering different types of clustering methods. The comparative analysis performed on the basis of these distinct clustering methods suggests, in general, the existence of a power law distribution, and hence of a critical state in the network under consideration. It is noteworthy that the evolution of the biggest avalanches shows clear regional differences between the West and East German labour markets.