Analysis of spatially varying relationships between urban environment factors and land surface temperature in Mashhad city, Iran

Land Surface Temperature (LST), in particular for the urban environment, is a key indicator to characterize urban heat changes, urban climate, global environmental change, and human-environment interactions. However, due to differences in the local spatial variations of LST and the related influence factors, few studies have discussed the spatial non-stationarity and spatial scale effects within urban areas. Moreover, in cities such as Mashhad, which are located in a hot and dry climate, have been less studied of the relationship between LST and urban influencing factors on a neighborhood scale. In the present study, the spatial distribution of the mean LST was evaluated in association with the 16 explanatory indices at the neighborhood's level in Mashhad City, Iran, as a case study. To assess the main components contributing to the LST variations, Principal Components Analysis (PCA) was employed in this study. Additionally, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were used to explore the spatially varying relationships and identify the model's efficiency at the neighborhood's scale. Our findings showed the five most important components contributing to LST variances, explaining 86.2% of the variability. The most negative relationship was observed between LST and the morphological features of neighborhoods (PC3). In contrast, the landscape composition of the green patches (PC2) exhibited the lowest negative impacts on LST changes. Moreover, road and traffic density characteristics of the neighborhoods (PC4) were the only effective components to alert the average LST positively. With R2= 0.678, AIC c= 2125.6, and Moran's I= 0.018, the results revealed that the GWR model had better efficiency than the corresponding non-spatial OLS model in terms of the goodness of fits. It suggests that the GWR model has more ability than the OLS one to predict LST intensities and characterize spatial non-stationary. Therefore, it can be applied to adapt more effective strategies in planning and designing the urban neighborhoods for mitigation of the adverse heat effects.