Spatial strategies for urban data centres

This study examines the geography of urban data centres (DCs) and develops a spatial strategy framework that explains the concentration of DCs and the infrastructural conditions that support these clusters. The paper addresses the lack of integrated, spatially specific site models by considering DC location as a decision-making problem influenced by power and network infrastructure, environmental aspects, and urban factors. Four metropolitan case studies: Paris (Île-de-France), Ashburn (Northern Virginia), Beijing, and Riyadh are examined as hubs in the centre network. The methodology combines DBSCAN clustering and kernel density estimation with a GIS-based multi-criteria suitability analysis. Six factors (power-grid substations, fibre hubs, telecom hubs, rivers/water sources, major roads and central business districts) are transformed and scaled into 1–5 suitability scores and integrated through a weighted overlay. Results show that DCs are strongly clustered rather than randomly distributed in all four cities, with distinct morphologies in each case. Clustered facilities consistently exhibit higher suitability scores than non-clustered sites (0.7–1.7 on a 1–5 scale). Regression models demonstrate strong correlations between cluster membership and suitability ( R = 0.71–0.90) and contribute 50% to over 80% of the variance in suitability (R 2 = 0.50–0.81, all p < 0.001 ), confirming the significant influence of proximity to high-capacity power and fibre infrastructure, enhanced by road access and urban demand. The framework provides planners and policymakers with a useful tool to identify current and prospective DC corridors, align grid and fibre investments with land-use planning, and predict the challenges associated with the rapid growth of digital infrastructure.