Context sensitivity of surface urban heat island at the local and regional scales

In this study we analysed the multi-annual (2002-2011) average summer surface urban heat island (SUHI) intensity of the 5000 largest urban clusters in Europe. We investigated its relationship with a proposed Gravitational Urban Morphology (GUM) index that can capture the local context sensitivity of SUHI. The GUM index was found to be an effective predictor of SUHI intensity. Together with other urban factors we built different multivariate linear regression models and a climate space based geographically weighted regression (GWR) model that can better predict SUHI intensity. As the GWR model captures the variation of influence from different urban factors on SUHI, it considerably outperformed linear models in predicting SUHI intensity in terms of R2 and other statistical criteria. By investigating the variation of GWR coefficients against background climate factors, we further built a nonlinear regression model that takes into account the sensitivity of SUHI to regional climate context. The nonlinear model showed comparable performance to that of the GWR model and it prevailed against all the linear models. Our work underlines the potential of SUHI reduction through optimising urban morphology, as well as the importance of integrating future urbanisation and climate change into the implementation of urban heat mitigation strategies. Superscript/Subscript Available

Automated summary

The study found the Gravitational Urban Morphology (GUM) index to be an effective predictor of Urban Heat Island (SUHI) intensity, and highlighted the strong performance of the Geographically Weighted Regression (GWR) model in predicting SUHI intensity. The nonlinear regression model showed superior performance, emphasizing the potential of reducing SUHI through optimizing urban morphology.