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A description of methods for deriving air pollution land use regression model predictor variables from remote sensing data in Ulaanbaatar, Mongolia
Author(s) -
Yuchi Weiran,
Knudby Anders,
Cowper Joanna,
Gombojav Enkhjargal,
Amram Ofer,
Walker Blake Byron,
Allen Ryan W.
Publication year - 2016
Publication title -
the canadian geographer / le géographe canadien
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.35
H-Index - 46
eISSN - 1541-0064
pISSN - 0008-3658
DOI - 10.1111/cag.12279
Subject(s) - regression analysis , air pollution , environmental science , regression , satellite imagery , land use , remote sensing , computer science , geography , statistics , machine learning , mathematics , engineering , civil engineering , chemistry , organic chemistry
Key Messages Air pollution is a leading risk factor for death and disease globally. Land use regression (LUR) modelling is a commonly used exposure assessment tool, but spatially resolved data are required to generate predictor variables in LUR models. LUR model predictors can be derived at little or no cost from satellite data and high‐resolution imagery.