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, R² 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.