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

Abstract This article seeks to advance a key element of the methodological framework for evolutionary economic geography (EEG)—the conceptualisation ‘urban space’. Drawing on recent theories from architecture and urban data science, it argues that networked topological qualities built environments have been neglected in EEG discipline, yet they play an important role how cities work as ‘complex evolving systems’. The explores different ways which spatial configuration influences evolution, drawing Bill Hillier’s theory space syntax case study Greater Manchester. It concludes when street systems are disrupted this can potentially become one contributing factor why places economically ‘left behind’.