Land use regression (LUR) is a widely used method to develop prediction models in environmental sciences. However, the process of creating and applying LUR models is repetitive and time-consuming. The XLUR tool was developed to automate this process, while at the same time providing a detailed log of the model building process for reproducibility, and providing evaluation metrics to assess model quality. The aim of this research is to provide a technical demonstration of the use of XLUR in two scenarios.

We demonstrate the use of the XLUR tool to build models for predicting PM₁₀ concentrations in Greater Manchester and intestinal enterococci along the Northwest coast of England. The examples show how the tool facilitates (a) model building using standard published protocols and (b) assessment of prediction quality. As is common with LUR approaches, prediction quality is reliant on data and the characteristics of the phenomena being modelled.

土地利用回归 (LUR) 是一种广泛用于开发环境科学预测模型的方法。但是，创建和应用 LUR 模型的过程是重复且耗时的。开发 XLUR 工具是为了自动化这个过程，同时提供模型构建过程的详细日志以实现可重复性，并提供评估指标来评估模型质量。本研究的目的是提供 XLUR 在两种情况下使用的技术演示。

我们展示了使用 XLUR 工具来建立模型来预测大曼彻斯特的PM ₁₀浓度和英格兰西北海岸的肠道肠球菌。这些示例展示了该工具如何促进 (a) 使用标准发布的协议构建模型和 (b) 预测质量评估。与 LUR 方法一样，预测质量依赖于数据和正在建模的现象的特征。