Single and Multiscale Models of Process Spatial Heterogeneity

Levi John Wolf, Taylor M. Oshan, Stewart Fotheringham

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


Recent work in local spatial modeling has affirmed and broadened interest in multivariate local spatial analysis. Two broad approaches have emerged: Geographically Weighted Regression (GWR) which follows a frequentist perspective and Bayesian Spatially Varying Coefficients models. Although several comparisons between the two approaches exist, recent developments, particularly in GWR, mean that these are incomplete and missing some important axes of comparison. Consequently, there is a need for a more thorough comparison of the two families of local estimators, including recent developments in multiscale variants and their relative performance under controlled conditions. We find that while both types of local models generally perform similarly on a series of criteria, some interesting and important differences exist.

Original languageEnglish (US)
Pages (from-to)223-246
Number of pages24
JournalGeographical Analysis
Issue number3
StatePublished - Jul 2018

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes


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