Spatially non-stationarity relationships between high-density built environment and waterlogging disaster: Insights from xiamen island, china

Abstract

A profound comprehension of the relationship between the built environment and waterlogging disasters in high-density urban areas is essential to improve urban resilience and achieve safe and sustainable urban living environments. Based on multi-source data, this study employs a spatial autocorrelation model, geodetector model, and multi-scale geographically weighted regression model to construct a comprehensive method. This has led to the formulation of a more effective integration method for investigating the spatial non-stationary relationships between the high-density built environment and waterlogging disasters. The results show that: (1) The spatial distribution of waterlogging events displays strong spatial dependence. (2) Floor area ratio, building structure index, slope, water surface rate, etc., exhibit limited independent explanatory capability concerning the distribution density of waterlogging events. However, their nonlinear augmentation effect becomes noteworthy upon amalgamation with other factors, especially in combination with the characteristics of the number of rain-sewage mixing nodes. (3) It is more reliable and efficient to reduce the risk of waterlogging disasters by optimizing the rainwater sewage diversion system and reducing the number of rain-sewage mixing nodes instead of modifying the underlying surface and controlling the morphological structure of the impervious ground. (4) Where the built environment demonstrates intricate and diverse attributes, the emergence of waterlogging events displays heightened sensitivity to changes in the built environment. This study provides a novel perspective for understanding the formation mechanisms and spatial heterogeneity characteristics of urban waterlogging disasters.

Keywords

• High-Density
• Built Environment
• Waterlogging
• Geodetector Model
• MGWR Model
• Non-Stationary Relationship