Rethinking battle commemoration: female letters and the myth of the battle of Las Navas de Tolosa (1212)

This article re-examines epistolary accounts by Berenguela and Blanche of Castile of the Battle of Las Navas de Tolosa. Building on previous assertions that Berenguela’s letter is a forgery, and through close comparison with the three surviving male eyewitness battle accounts, this article argues the letters of Berenguela and Blanche – in the form in which they survive – are confections based on original letters. The Battle of Las Navas de Tolosa’s mythical status and standing as a watershed event within the narrative of Gran Reconquista is understood to have been largely shaped by three male-authored chronicle accounts: Chronica Latina regum Castelle (1239), Chronicon mundi (1239) and De rebus Hispaniae (1243). In asserting these letters are confected accounts, in circulation by the 1220s, this article argues that battle commemoration began in the immediate aftermath of the Christian victory, and that female voices were recognised by contemporaries as a viable tool for commemoration.     

A Comparison of Spatial and Nonspatial Methods in Statistical Modeling of NO2: Prediction Accuracy,Uncertainty Quantification,and Model Interpretation

${\text{NO}}_2$ is a traffic-related air pollutant. Ground ${\text{NO}}_2$ monitoring stations measure ${\text{NO}}_2$ concentrations at certain locations and statistical predictive methods have been developed to predict ${\text{NO}}_2$ as a continuous surface. Among them, ensemble tree-based methods have shown to be powerful in capturing nonlinear relationships between ${\text{NO}}_2$ measurements and geospatial predictors but it is unclear if the spatial structure of ${\text{NO}}_2$ is also captured in the response-covariates relationships. We dive into the comparison between spatial and nonspatial data models accounting for prediction accuracy, model interpretation and uncertainty quantification. Moreover, we implement two new spatial and a nonspatial methods that have not been applied to air pollution mapping. We implemented our study using national ground station measurements of ${\text{NO}}_2$ in Germany and the Netherlands of 2017. Our results indicate heterogeneous levels of importance of modeling the spatial process in different areas. The prediction intervals predicted with ensemble tree-based methods are more satisfactory than the geostatistical methods. The two new methods implemented each obtained better prediction accuracy compared to the original ensemble tree-based and stacking methods. The probabilistic distribution of the spatial random field estimated by the geostatistical methods could provide useful information for analyzing emission sources and the spatial process of observations.