Learning from architectural theory about how cities work as complex and evolving spatial systems.

This article focuses on advancing the methodological framework of evolutionary economic geography (EEG) by integrating a relational and networked conceptualization of urban space, drawing particularly on architectural theory and urban data science, notably Bill Hillier's space syntax. It argues that EEG has traditionally relied on conventional Euclidean spatial concepts—such as distance, size, and gravitational pull—while neglecting the topological and configurational properties of urban built environments that shape cities as complex evolving systems. Using a case study of Greater Manchester, the article demonstrates how the multiscale connectivity of street networks influences economic interdependencies and agglomeration economies, and how disruptions to these spatial networks through planning interventions may contribute to economic decline. The study suggests that combining spatial network analysis with economic complexity approaches can enrich understanding of urban economic evolution and inform planning to support city adaptability and innovation.