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.

热浪是过去几十年欧洲最致命的极端天气事件。由于城市热岛 (UHI) 效应，居住在城市的人们特别容易发生此类事件，这会增加城市环境中的热应力，尤其是在平静、稳定和辐射密集的天气情况下。由于城市中的官方测量站稀缺，对 UHI 空间格局的研究通常依赖于卫星数据、业余气象学家的数据或模型输出。此外，使用基于点的测量分析空间 UHI 模式需要足够且具有成本效益的空间插值方法。在这项研究中，由 60 个低成本测量设备 (LCD) 检索到的气温数据用于在瑞士伯尔尼通过土地利用回归 (LUR) 方法对 UHI 的空间模式进行建模。为此，计算了 14 个具有不同缓冲半径的空间变量，以评估它们对 UHI 的影响并插入气温数据。结果，开发了三个模型，涵盖了夜间三种不同的热浪。鉴于在不同场景中的良好模型性能，这里介绍的研究证明了通过基于城市内可公开访问的空间信息的 LUR 建模成功插值低成本温度数据。