Single and Multiscale Models of Process Spatial Heterogeneity

Levi John Wolf*, Taylor M. Oshan, A. Stewart Fotheringham

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

18 Citations (Scopus)
135 Downloads (Pure)

Abstract

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
Pages (from-to)223-246
Number of pages24
JournalGeographical Analysis
Volume50
Issue number3
Early online date10 Nov 2017
DOIs
Publication statusPublished - 1 Jul 2018

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  • Prizes

    John Odland Award

    Wolf, Levi J (Recipient), 15 Apr 2018

    Prize: Prizes, Medals, Awards and Grants

  • The Measurement of Scale and Process Heterogeneity Through Local Multivariate Models

    AS Fotheringham (Recipient), Wolf, Levi J (Recipient) & Taylor Oshan (Recipient), 1 Apr 2018

    Prize: Prizes, Medals, Awards and Grants

  • Cite this