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

Arbeitspapier

We apply a functional data approach for mixture model-based multivariate innovation clustering to identify different regional innovation portfolios in Europe. Innovation concentration is considered as pattern of specialization among innovation types. We examine patent registration data and combine them with other innovation and economic data across 225 regions, 13 years and 8 patent classes. This allows us to 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 regional innovation cluster solutions for IPC classes for 'fixed constructions' and 'mechanical engineering' are very comparable, and relatively less comparable for 'chemistry and metallurgy'. The clusters for innovations in 'physics' and 'chemistry and metallurgy' are similar; innovations in 'electricity' and 'physics' show similar temporal dynamics. For all other innovation types, the regional clustering is different and innovation 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 regional innovation policy in order to promote tailored and efficient innovation-promoting programs can be derived.

Object Details

• ISBN: 978-3-96973-084-3
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Ruhr Economic Papers ; No. 926

Classification and Topics

• Classification: Wirtschaft ; Technological Change: Choices and Consequences; Diffusion Processes ; Size and Spatial Distributions of Regional Economic Activity ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
• Subject: Functional Data Analysis (FDA) ; innovation concentration ; spatio-temporal cluster modeling ; multivariate cluster analysis ; European innovation ; cluster algorithm

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Rhoden, Imke ; Weller, Daniel ; Voit, Ann-Katrin
• Event: Veröffentlichung
• (who): RWI - Leibniz-Institut für Wirtschaftsforschung
• (where): Essen
• (when): 2021

Further information

• DOI: doi:10.4419/96973084
• Handle: http://hdl.handle.net/10419/248742
• Last update: 10.03.2025, 11:41 AM CET