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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.

Examining the land surface temperature (LST) and its mechanism is very significant for urban planning.The purpose of this study is to determine the factors affecting the surface temperature of urban areas of Shiraz. The OLS and GWR were used o determine the effective factors. Also, satellite data of Landsat 8 for summer 2019 were used to obtain the surface temperature of Shiraz. To this end, the Landsat 8 satellite images of Shiraz urban districts during Summer 2019, were prepared. First, the LST and vegetation was extracted from the images. Before performing the regression model between the LST as the dependent variable and geographical variables such as slope, slope gradient, elevation, distance to rivers, direct and indirect solar radiation, sunshine duration, and Total Solar Irradiance (TSI) as independent variables, the component analysis method was employed to eliminate collinearity and dependence relations among independent variables and reducing variables. Four significant components that had eigenvalues above one and about 86.75% of the variance of the initial variables were selected, which included the significance of (direct, indirect, general) solar radiation, the direction of slope, distance to rivers, vegetation, and slope gradient, respectively. In the next step, the regression analysis was performed between the components and the LST via the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The results showed that GWR has better performance in showing the spatial distribution of LSTs in Shiraz. Shiraz UHIs correspond to the airport and the dirt and barren districts around the city, which are often non-residential.