Abstract

Urban innovation districts (UIDs) provide an important space for innovation-led development. However, their scale and the heterogeneity within the districts are not easy to capture with administrative units, especially in urban centers of great denseness. Therefore, we develop a parcel-based geospatial framework to locate and typify the UIDs in Shanghai using multi-source geospatial data. Morphology parcels are used to measure an innovation asset index, and a hierarchical density-based spatial clustering algorithm (HDBSCAN) is applied to identify functionally coherent innovation agglomerations beyond administrative boundaries. The procedure identifies 18 UIDs; an exploratory stability check further indicates that high-value core parcels are largely retained under moderate density-connectivity perturbations, whereas peripheral boundary parcels are more sensitive. Patent-density and policy-alignment evidence are used as supporting external evidence for the delineated innovation geography and as one part of a convergent evidence check. To describe spatial heterogeneity, we collate 31 conceptual indicators, operationalized as 38 PCA variables after dummy coding, to represent locational features, urban form, function and environmental quality. Principal component analysis reduces these variables to six components explaining 81.59% of the total variance, and hierarchical clustering classifies the districts into five types. A k = 2–8 cluster-quality assessment supports the five-type solution as a balanced and interpretable classification rather than a uniquely optimal statistical partition. The results reveal mismatches between administrative boundaries and functional innovation spaces, as well as systematic built-environment differences across district types.

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