Skip to content

Inference in Multi-Scale Geographically Weighted Regression

Research output: Contribution to journalArticle

  • Hanchen Yu
  • AS Fotheringham
  • Taylor Oshan
  • Wei Kang
  • Levi Wolf
Original languageEnglish
JournalGeographical Analysis
DateSubmitted - 2 May 2018


A recent paper (Fotheringham et al. 2017) expands the well-known Geographically-Weighted Regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model - a framework referred to as Multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, until now, no inference about the local parameter estimates was possible. Formally, the so-called "hat matrix," which projects the observed response vector into the predicted response vector, was available in GWR but not in MGWR. This paper addresses this limitation by reframing GWR as a Generalized Additive Model (GAM), extending this framework to MGWR and then deriving standard errors for the local parameters in MGWR. In addition, we also demonstrate how the effective number of parameters (ENP) can be obtained for the overall fit of an MGWR model and for each of the covariates within a model. The statistic is essential for comparing model fit between MGWR, GWR, and traditional global models, as well as adjusting for multiple-hypothesis tests. We demonstrate these advances to the MGWR framework with both a simulated data set and a real-world data set.


View research connections

Related faculties, schools or groups