New Explanatory Models for Analysing Spatial Innovation: A Comparative Investigation
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Innovation research has become an important topic in regional science analysis. Yet the modelling base of much innovation research is still feeble. This paper aims to map out the research potential of recent approaches in quantitative complexity analysis, in particular Neural Networks (NNs) analysis, from the perspective of their operational applicability in the space-economy. The urban context of European innovation processes is used as an empirical background. The paper addresses also the issue of space-time transferability of the tools employed.The first part of the paper is devoted to a concise conceptual overview and illustration of the innovation process, which is conceived of as a self-organising system. The second part presents empirical results on innovation processes in Europe. In this framework a comparative analysis is conducted between NN models and a conventional tool often used in spatial economics studies, viz. (non)linear regression analysis. The sensitivity of the various results, - by using 'transferability' experiments - is also examined. The empirical experiments underline the advantages and limitations of these approaches from a methodological as well as an empirical viewpoint. They appear to offer a plausible range of values of empirical outcomes, which may highlight an acceptable degree of variation in spatial innovation processes.