Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data

Vivian Yi Ju Chen, Wen Shuenn Deng, Tse Chuan Yang, Stephen A. Matthews

Research output: Contribution to journalArticlepeer-review

50 Scopus citations


In recent years, techniques have been developed to explore spatial nonstationarity 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, respectively, and then outlines their synergy and a new approach, GWQR. The estimations of GWQR parameters and their standard errors, the cross-validation bandwidth selection criterion, and the nonstationarity test are discussed. We apply GWQR to U.S. county data as an example, with mortality as the dependent variable and five social determinants as explanatory covariates. Maps summarize analytic results at the 5, 25, 50, 75, and 95 percentiles. We found that the associations between mortality and determinants vary not only spatially, but also simultaneously across the distribution of mortality. These new findings provide insights into the mortality literature, and are relevant to public policy and health promotion. Our GWQR approach bridges two important statistical approaches, and facilitates spatial quantile-based statistical analyses.

Original languageEnglish (US)
Pages (from-to)134-150
Number of pages17
JournalGeographical Analysis
Issue number2
StatePublished - Apr 2012

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Earth-Surface Processes


Dive into the research topics of 'Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data'. Together they form a unique fingerprint.

Cite this