Joint-likelihood Bayesian model for urban heat island mapping with two crowdsourced datasets
开发了一个贝叶斯分层模型，融合车载温度计和公民气象站两类众包数据，以百米分辨率绘制城市逐时气温图，在法国第戎的验证中，联合似然模型均方根误差低于1°C的图占比超75%，优于单一数据源模型。
Abstract Heat stress is a growing public health concern in cities as urban dwellers are exposed to combined effect of global warming and urban heat islands. We develop a cutting-edge Bayesian Hierarchical Model that incorporates data from connected vehicles and citizen weather stations to draw hourly urban air temperature maps at hectometric resolution. To overpass the uncertainty of opportunistic observations, we set priors on the measurement error into two separate likelihoods, one per each data source. The model with joint-likelihoods is compared to two single models, one for the on-board thermometers and the other for citizen weather stations. All models, inferred with the INLA-SPDE approach, are evaluated against an independent professional network in the French city of Dijon. The maps are consistent with the reference network. The performance of the joint-likelihood model with both data sources exceeds the other two. Its Root Mean Square Error is less than 1∘C for more than 75% of the 714 hourly maps. These results open up new perspectives in urban climatology and for the post-processing of numerical weather forecasts on cities. They will also support research on urban heat exposure and all actors involved in sustainable urban planning.