Small-scale parks contain diverse functional zones within limited space, yet most spatial analysis methods focus on movement rather than dwelling activities, and the cognitive relevance of activity network centrality has been little studied. This study developed Park Functional Morphology Graph Analysis (PFMGA), employing graph theory to construct spatial networks that relate objective morphology with human activities. This method addresses the lack of quantitative approaches for small-scale parks and advances theoretical understanding of visitor perception. Based on a survey of 464 visitors from four Chinese community parks, multilevel models tested relationships between centrality metrics and spatial perception. Three key findings are: (1) PFMGA captured spatial centrality hierarchies consistent with functional roles, with circulation zones showing highest values and resting zones lowest; (2) High correlations and Principal Component Analysis first component explaining 91.1% variance indicated that the four centrality metrics represented a single dimension; (3) Only betweenness centrality significantly predicted perceived spatial centrality (β = 0.36), while degree, closeness and eigenvector centralities were not significant. The findings suggest that in small-scale parks, visitors’ perceptions of spatial importance are primarily shaped by a zone’s bridging role between other areas. This study provides a standardised method for quantifying park morphology, enabling planners to efficiently allocate resources by targeting zones with high betweenness centrality. • Graph-based functional zoning method developed for replicable analysis. • Four network centrality metrics load onto a single component in small-scale parks. • Only betweenness centrality shows a robust association with spatial perception. • The method helps planners prioritise key spatial connectors under limited resources.
Wang et al. (2026) studied this question.