PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

• Published in: Frontiers of environmental science & engineering 2020-06, Vol.14 (3), p.44-44, Article 44
• Main Authors: Ma, Xuying, Longley, Ian, Salmond, Jennifer, Gao, Jay
• Format: Article
• Language: English
• Subjects: air; Air pollution; Air pollution modelling; computer software; Data processing; Earth and Environmental Science; Environment; GDAL/OGR Python; Geographic information systems; GIS spatial analysis; Land use; LUR; Mapping; Model accuracy; model validation; Modelling; Modules; Pollutant concentration mapping; Pollutants; prediction; Python; Regression analysis; Regression models; Research Article; Software; Spatial distribution; statistics
• ISSN: 2095-2201; 2095-221X
• DOI: 10.1007/s11783-020-1221-5

* PyLUR comprises four modules for developing and applying a LUR model. * It considers both conventional and novel potential predictor variables. * GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction. * Developed on Python platform, PyLUR is rather efficient in data processing. Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.