ABSTRACT:Due to urbanization and changes in the urban thermal environment and because the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. In this regard, due to the unique properties of spatial data, in this study, a geographically weighted regression (GWR) was used to identify effective spatial factors. The GWR is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, the Landsat 8 satellite data on 18 August 2014 and Tehran land use data in 2006 was used for determining the land surface temperature and its effective factors. As a result, R 2 value of 0.765983 was obtained by taking the Gaussian kernel. The results showed that the industrial, military, transportation, and roads areas have the highest surface temperature.

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Due to urbanization and changes in the urban thermal environment and since the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. Hence, by identifying these factors, preventing this phenomenon become possible using general education, inserting rules and also retaining efficient management policies and more monitoring to counter the stimulating factors of increasing land surface temperature. The goal of this research is to identify the effective factors on land surface temperature in Tehran. In this regard, a geographically weighted regression (GWR) was used to identify the effective factors and a genetic algorithm (GA) was employed to select the best combination of these factors. The recommended combination method is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, land surface temperature data