A land use regression variable generation, modelling and prediction tool for air pollution exposure assessment. (July 2018) - Morley, David W.; Gulliver, John
Abstract: Land use regression (LUR) is commonly used to estimate air pollution exposures for epidemiological studies. By statistically relating a set of geolocated measured pollutant values with explanatory variables defining sources and modifiers of air pollution patterns, such as land cover characteristics, traffic flow and intensity, it is possible to predict pollution levels at unsampled locations. LUR utilises simple linear regression, but the generation of predictor variables, application of the model and the supervised iterative approach to model development means an analyst must be a competent user of both GIS and statistical packages. Here we present an application to simplify the LUR modelling process for exposure scientists and environmental epidemiologists. RLUR is a user-friendly application built using the statistical and GIS capabilities of the R programming language. The main aim of this software is to provide an introduction to the LUR process without the need for specific GIS or statistical expertise. Graphical abstract: Image 1 Highlights: Easily develop land use regression (LUR) models for pollution exposure estimation. Combined Statistical and GIS functionality in a single application. Open-source code available for customisation. User-friendly interface with visualisation tools.