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Rapid urbanization has altered the natural surface properties and spatial patterns, increasing the risk of urban waterlogging. Assessing the probability of urban waterlogging risk is crucial for preventing and mitigating the environmental risks associated with urban waterlogging. This study aims to evaluate the impact of different urban spatial morphologies on the probability of urban waterlogging risk. The proposed assessment framework was demonstrated in Guangzhou, a high-density city in China. Firstly, a spatial weight naive Bayes model was employed to map the probability of waterlogging risk in Guangzhou. Secondly, the World Urban Database and Access Portal Tools (WUDAPT)-based method was used to create a local climate zone (LCZ) map of Guangzhou. Then, the range of waterlogging risk and the proportion of risk levels were analyzed across different LCZs. Finally, the Theil index was used to measure the disparity in waterlogging risk exposure among urban residents. The results indicate that 16.29% of the area in Guangzhou is at risk of waterlogging. Specifically, 13.06% of the area in LCZ 2 is classified as high risk, followed by LCZ 1, LCZ 8, and LCZ 10, with area proportions of 11.42%, 8.37%, and 6.26%, respectively. Liwan District has the highest flood exposure level at 0.975, followed by Haizhu, Yuexiu, and Baiyun. The overall disparity in waterlogging exposure in Guangzhou is 0.30, with the difference between administrative districts (0.13) being smaller than the difference within the administrative districts (0.17). These findings provide valuable insights for future flood risk mitigation and help in adopting effective risk reduction strategies at urban planning level. [ABSTRACT FROM AUTHOR]

Urbanization and climate change have led to rising urban temperatures, increasing heat-related health risks. Assessing urban heat risk is crucial for understanding and mitigating these risks. Many studies often overlook the impact of block types on heat risk, which limits the development of mitigation strategies during urban planning. This study aims to investigate the influence of various spatial factors on the heat risk at the block scale. Firstly, a GIS approach was used to generate a Local Climate Zones (LCZ) map, which represents different block types. Secondly, a heat risk assessment model was developed using hazard, exposure, and vulnerability indicators. Thirdly, the risk model was demonstrated in Guangzhou, a high-density city in China, to investigate the distribution of heat risk among different block types. An XGBoost model was used to analyze the impact of various urban spatial factors on heat risk. Results revealed significant variations in heat risk susceptibility among different block types. Specifically, 33.9% of LCZ 1–4 areas were classified as being at a high-risk level, while only 23.8% of LCZ 6–9 areas fell into this level. In addition, the pervious surface fraction (PSF) had the strongest influence on heat risk level, followed by the height of roughness elements (HRE), building surface fraction (BSF), and sky view factor (SVF). SVF and PSF had a negative impact on heat risk, while HRE and BSF had a positive effect. The heat risk assessment model provides valuable insights into the spatial characteristics of heat risk influenced by different urban morphologies. This study will assist in formulating reasonable risk mitigation measures at the planning level in the future. [ABSTRACT FROM AUTHOR]