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

Land Surface Temperature (LST), particularly for the urban environment, is a crucial indicator of heat emission, climate, global environmental change, and human-environment interactions. In present study, spatial distribution mean LST was evaluated in association with 16 explanatory indices at neighbourhood's level Mashhad City, Iran. To identify main variables contributing to variations, Principal Components Analysis (PCA), Ordinary Least Square (OLS) Geographically Weighted Regression (GWR) models were employed explore spatially varying relationships model's efficiency neighborhood's scale. Our findings showed five most important components variances, explaining 86.2 % variability. The negative relationship observed between morphological features neighborhoods (PC3). contrast, landscape configuration green patches (PC5) exhibited lowest impacts on changes. Moreover, road traffic density characteristics (PC4) only effective alert average positively. With R2 = 0.687, AICc 1545.848, Moran's I 0.021, results revealed that GWR model had better than corresponding non-spatial OLS terms goodness fits. It suggests has more ability one predict intensities characterize non-stationary. Therefore, it can be applied adapt strategies planning designing mitigation adverse effects.