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The problem of urban waterlogging has consistently affected areas of southern China, and has generated widespread concerns among the public and professionals. The geographically weighted regression model (GWR) is widely used to reflect the spatial non-stationarity of parameters in different locations, with the relationship between variables able to change with spatial position. In this research, Shenzhen City, which has a serious waterlogging problem, was used as a case study. Several key results were obtained. (1) The spatial autocorrelation of flood spot density in Shenzhen was significant at the 5% level, but because the Z value was not large it was not very obvious. (2) The degree of impact on flood disasters from large to small was: Built up_ DIVISION > SHDI > Built up_ COHESION > CONTAG > Built up_ LPI. (3) The degree of waterlogging disasters in higher altitude regions was less affected by the landscape pattern. The results of this study highlight the important role of the landscape pattern on waterlogging disasters and also indicate the different impacts of different regional landscape patterns on waterlogging disasters, which provides useful information for planning the landscape pattern and controlling waterlogging.

The spatial non-stationary effect of urban landscape pattern on urban waterlogging: a case study of Shenzhen City