Research on Visualization Application System of Intelligent Data Fusion in Green Infrastructure Planning

This study proposes a visualization application system based on data fusion, which aims to optimize the decision-making process of green infrastructure planning. The system uses the Geographically Weighted Regression (GWR) algorithm to realize data fusion analysis to dynamically capture the characteristics of spatial heterogeneity, and establishes a data fusion model for different types of infrastructure. The system can make predictions and plans more accurately based on the optimization of spatial attribute weight distribution of the GWR algorithm. To verify the effectiveness of the model, the experiment simulated and analyzed the planning schemes under various scenarios. The results show that the weights of the model change significantly in different regions and scenarios, and the contribution of various influencing factors to the weights varies from place to place. The system achieved an average 8 % improvement in planning accuracy in high-density areas and reduced planning errors by about 10 % in high-pollution areas. In addition, error correction and correlation analysis ensured the scientific nature of the data and the stability of the model. Finally, the visualization module of the system presents complex planning data in a multi-dimensional graphical way, enabling planners to intuitively obtain analysis results, providing a reliable support tool for the scientific planning of green infrastructure.