Effects of urban form on the urban heat island effect based on spatial regression model

The urban heat island (UHI) effect is becoming more of a concern with the accelerated process of urbanization. However, few studies have examined the effect of urban form on land surface temperature (LST) especially from an urban planning perspective. This paper used spatial regression model to investigate the effects of both land use composition and urban form on LST in Wuhan City, China, based on the regulatory planning management unit. Landsat ETM+ image data was used to estimate LST. Land use composition was calculated by impervious surface area proportion, vegetated area proportion, and water proportion, while urban form indicators included sky view factor (SVF), building density, and floor area ratio (FAR). We first tested for spatial autocorrelation of urban LST, which confirmed that a traditional regression method would be invalid. A spatial error model (SEM) was chosen because its parameters were better than a spatial lag model (SLM). The results showed that urban form metrics should be the focus for mitigation efforts of UHI effects. In addition, analysis of the relationship between urban form and UHI effect based on the regulatory planning management unit was helpful for promoting corresponding UHI effect mitigation rules in practice. Finally, the spatial regression model was recommended to be an appropriate method for dealing with problems related to the urban thermal environment. Results suggested that the impact of urbanization on the UHI effect can be mitigated not only by balancing various land use types, but also by optimizing urban form, which is even more effective. This research expands the scientific understanding of effects of urban form on UHI by explicitly analyzing indicators closely related to urban detailed planning at the level of regulatory planning management unit. In addition, it may provide important insights and effective regulation measures for urban planners to mitigate future UHI effects. (C) 2018 Elsevier B.V. All rights reserved.