Developing land use regression models for environmental science research using the XLUR tool – More than a one-trick pony

Abstract 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.