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Research on urban polycentricity has long been hampered by fragmented case studies, inconsistent standardized criteria, and methodological misuse, complicating cross-study comparisons and limiting a nuanced understanding of complex spatial structures. To systematically reveal the combined effects of center definition methods and measurement indicators on polycentricity assessment, this study takes Hangzhou, Wuhan, and Nanning as comparative cases. We integrate four employment center identification methods and five polycentricity measurement indicators to conduct a comprehensive assessment from both morphological and functional dimensions. A Linear Mixed-effects Model (LMM) is employed to quantify the influence of method choices, indicator properties, and their interactions on the measurement results. The findings indicate that: (1) There are systematic differences between morphological and functional polycentricity. Morphological polycentricity is insensitive to the choice of center definition methods, whereas functional polycentricity is significantly constrained by the identification method used. (2) A significant “method-indicator” interaction effect exists in functional polycentricity. The LMM reveals that specific “method-indicator” combinations systematically lead to substantial variations in measurement results, suggesting that functional polycentricity is a “dynamic process” co-constructed by methodological choices rather than a purely objective reality. (3) Inter-city comparisons reveal diverse pathways of polycentric development: Hangzhou exhibits a “mature-type” structure characterized by the synergy between morphological and functional polycentricity; Wuhan demonstrates a transitional pattern of “morphologically monocentric but functionally networked”; while Nanning represents a policy-driven emerging polycentric city. This study argues that methodological choices profoundly shape the conclusions of polycentricity research. Future studies and practices should emphasize methodological transparency and sensitivity analysis, promoting a paradigm shift from “seeking the single truth” to “understanding sources of uncertainty,” thereby providing more reliable foundations for scientific urban planning.
Zhu et al. (Mon,) studied this question.