## 4 Discussion
### Clustering Analysis for Identifying GSI Demand Areas in the GBA  
As shown in Figures 7-10, districts with demand primarily driven by population vulnerability and flood risk probability (i.e., Clusters II and III) tend to exhibit more severe imbalances between GSI supply and demand when compared with those driven by social infrastructure and economic indicators (i.e., Clusters I and IV). This suggests that Clusters II and III are more exposed to urban flood risks and possess weaker adaptive capacity.  
The root causes of this disparity may lie in the historical neglect of population dynamics and environmental vulnerability in the urban planning and development phases of Clusters II and III [54]. For example, the rapid pace of urbanization has led to surging population growth, rising socio-economic inequality, intensified urban heat island effects, insufficient wastewater treatment, and overstressed public infrastructure--all of which increase vulnerability to flooding [55; 56]. Additionally, accelerated urban expansion has been shown to significantly reduce soil water retention in various districts [57], compounding stormwater management challenges.  
To address these imbalances, this study introduces the use of reference network maps as a tool to guide planners toward more equitable and adaptive GSI deployment strategies. These maps serve as graphical frameworks that allow planners to identify \"reference\" districts with similar demand characteristics and better-balanced supply-demand coordination. The concept is akin to those applied in various fields--such as social mapping [58], academic influence mapping [59], healthcare accessibility [60], and financial risk networks [22].  
Each of the four demand-oriented clusters is linked to a representative district that can serve as a reference point for planning. Specifically, Cluster I is anchored by Wuhe District, Cluster II by Luzhou District, Cluster III by Baipenzhu District, and Cluster IV by Gaotan District. These reference districts are located near the geometric centers of their respective clusters, reflecting relatively balanced GSI supply-demand relationships. Planners can look to these benchmark areas when designing infrastructure interventions for districts with similar urban characteristics.  
Figures 7-10 display the proximity-based reference network maps for each cluster. In these diagrams, districts within the inner circle are strongly associated with the reference district based on high proximity scores, indicating similar vulnerability patterns and planning contexts. For instance, in Wuhe Township (Figure 7), high-probability reference relationships are established with Dong District (0.75), Shenshuipo District (0.80), and Huawangtong District (0.75). In Luzhou Town (Figure 8), similar patterns are observed with Hualong Town (0.70), Huicheng Street (0.83), and Zengjiang Street (0.70). In contrast, districts located in the outer circle of the reference network maps indicate weaker coordination between GSI supply and demand. These areas could benefit from adaptive strategies drawn from their inner circle counterparts or from potential cross-district collaborations to improve collective resilience. In some cases, districts with limited resources or fragmented infrastructure may be considered for integration or cooperative planning with neighboring municipalities.  
Reference network maps have shown great potential in stormwater management, particularly when integrated with advanced techniques. For example, [PERSON] et al. [61] employed reference networks in combination with deep learning and image recognition to estimate urban flood depth, eliminating the need for additional hydrological sensors while improving real-time flood response.