Cultural, creative, and complex: A computational foundation of culture-driven urban governance

Abstract

Culture and creative production have an important but somewhat elusive role in urban development. None of the many conceptual paradigms that have been proposed so far to explain it has turned out entirely satisfactory. We argue that the main reason behind this failure is the implicit linear thinking that informs all these approaches: namely, the idea that a few, major drivers explain urban development through a direct, clearly readable systemic impact. Cities are complex socio-environmental systems whose functioning depends on the concurrent interaction of many different factors which cannot be reduced to the action of a few, simple causal forces. Failing to understand such complexity easily leads to dysfunctional urban governance approaches. In this paper, we analyze a database of 144 European cities as described by 58 variables belonging to different domains, as designed by a preliminary stage of the Cultural and Creative City Monitor (CCCM). Our analysis builds on innovative machine learning techniques (PST) and on the Minimum Spanning Tree (MST) representation to map the structural interdependencies between the cultural and non-cultural sectors in cities with a strong cultural policy orientation. This toolbox carries considerable potential for precision cultural policies and data-driven urban governance strategies of the future.