Heatwaves have been the deadliest weather extreme events in Europe in the last decades. People living in cities are especially prone to such events due to the urban heat island (UHI) effect which increases the heat stress in urban surroundings especially during calm, steady, and radiation intensive synoptic situations. Since official measurement stations in cities are scarce, studies on spatial patterns of UHIs often rely on satellite data, hobby meteorologists' data, or on model outputs. Additionally, analyses of spatial UHI patterns using point-based measurements need adequate and cost-effective methods for spatial interpolation. In this study, air temperature data retrieved by 60 low cost measurement devices (LCD) are used to model the spatial pattern of the UHI with a land use regression (LUR) approach in Bern, Switzerland. For this purpose, 14 spatial variables with different buffer radii were calculated to evaluate their effect on the UHI and to interpolate the air temperature data. As a result, three models covering three different heatwaves at nighttime were developed. Given good model performance throughout the different scenarios, the here presented study demonstrates the successful interpolation of low cost temperature data by LUR modelling based on publicly accessible spatial information within a city.