Geographically Weighted Regression Modeling for Multiple Outcomes

Vivian Yi Ju Chen, Tse Chuan Yang, Hong Lian Jian

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Geographically weighted regression (GWR) has been a popular tool applied in various disciplines to explore spatial nonstationarity for georeferenced data. Such a technique, however, typically restricts the analysis to a single outcome variable and a set of explanatory variables. When analyzing multiple interrelated response variables, GWR fails to provide sufficient information about the data because it only allows separate modeling for each response variable. This study attempts to address this gap by introducing a geographically weighted multivariate multiple regression (GWMMR) technique that not only explores spatial nonstationarity but also accounts for correlations across multivariate responses. We first present the model specification of the proposed method and then draw the associated statistical inferences. Several modeling issues are discussed. We also examine finite sample properties of GWMMR using simulation. For an empirical illustration, the new technique is applied to the stop-and-frisk data published by the New York Police Department. The results demonstrate the usefulness of the GWMMR.

Original languageEnglish (US)
Pages (from-to)1278-1295
Number of pages18
JournalAnnals of the American Association of Geographers
Issue number5
StatePublished - Jul 4 2022


  • geographically weighted regression
  • multiple outcomes
  • multivariate multiple regression
  • spatial nonstationarity

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes


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