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In recent years, techniques have been developed to explore spatial non-stationarity and to model the entire distribution of a regressand. The former is mainly addressed by geographically weighted regression (GWR), and the latter by quantile regression (QR). However, little attention has been paid to combining these analytical techniques. The goal of this article is to fill this gap by introducing geographically weighted quantile regression (GWQR). This study briefly reviews GWR and QR,doi:10.1111/j.1538-4632.2012.00841.x pmid:25342860 pmcid:PMC4204738 fatcat:o3cpqxquuvhufphrum76zqncyy