Spatio-temporal dynamics of European innovation - An exploratory approach via multivariate functional data cluster analysis

Abstract

We apply a functional data approach for mixture model-based multivariate innovation clustering toidentify different regional innovation portfolios in Europe. Innovation concentration is considered aspattern of specialization among innovation types. We examine patent registration data and combine themwith other innovation and economic data across 225 regions, 13 years and 8 patent classes. This allows usto identify innovation clusters that are supported by several innovation- and economy-related variables.We are able to form several regional clusters according to their specific innovation types. The regionalinnovation cluster solutions for IPC classes for ‘fixed constructions’ and ‘mechanical engineering’ arevery comparable, and relatively less comparable for ‘chemistry and metallurgy’. The clusters for innovationsin ‘physics’ and ‘chemistry and metallurgy’ are similar; innovations in ‘electricity’ and ‘physics’ showsimilar temporal dynamics. For all other innovation types, the regional clustering is different andinnovation concentrations in the respective regions are unique within clusters. By taking regional profiles,strengths and developments into account, options for improved efficiency of location-based regionalinnovation policy in order to promote tailored and efficient innovation-promoting programs can be derived