Identifying Urban Functional Zones and Multi-Dimensional Flood Risk Connectivity: Integrating POI Semantic Representation and Spatial Network Analysis

Refined urban flood risk assessment requires a paradigm beyond traditional single-scale analyses. Existing approaches often overlook fine-scale functional differentiation and cascading risk connectivity across urban systems. This study proposes an integrated framework combining semantic–spatial representation of Point-of-Interest (POI) data, hierarchical feature integration, and a hydrological–functional–road tripartite potential flood risk connectivity network. A semantic–spatial dual-branch autoencoder extracts POI features for functional zone identification, while hazard, exposure, and coping capacity indicators are integrated to quantify block-scale flood risk. Hydrological connectivity, functional coupling, and road cascading failure models are further combined to characterize multi-path flood risk connectivity patterns. Results show that (1) functional zones were derived from dominant POI categories. The semantic–spatial clustering achieved a silhouette coefficient of 0.564 with 0.2% noise blocks. The spatial-only configuration achieved 0.554. This suggests that semantic information provided complementary but limited refinement. (2) A total of 302 high-risk blocks (20.5%) were identified, while the median-based classification showed that public service zones had the highest proportion of the high-risk group (61.3%). (3) The coupled flood risk connectivity network covers 99.2% of blocks with significant spatial dependence (Moran’s I = 0.703). Connectivity intensity varied among functional zones, but most pairwise differences were not significant after Bonferroni correction. Network centrality showed significant associations with connectivity intensity and range. (4) The mean standardized functional mixture degree was 0.629. Geographically weighted regression (GWR) revealed spatial variation in the local association between functional mixture and physical flood susceptibility, but the Monte Carlo test did not provide evidence of statistically significant global spatial non-stationarity (p = 0.960). This framework provides a function-oriented perspective for understanding urban flood resilience by integrating functional structure, multidimensional flood risk, and multi-path risk connectivity, and supports the characterization of potential flood-sensitive areas through function-based flood risk assessment.