A comment on geographically weighted regression with parameter-specific distance metrics |
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| Authors: | Taylor Oshan Levi John Wolf Wei Kang Ziqi Li Hanchen Yu |
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| Institution: | 1. Center for Geospatial Information Science, Department of Geographical Sciences, University of Maryland, College Park, MD, USA;2. School of Geographical Sciences, University of Bristol, Bristol, UK;3. Center for Geospatial Sciences, School of Public Policy, University of California, Riverside, CA, USA;4. Spatial Analysis Research Center, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, USA;5. School of Government, Peking University, Beijing, PRC |
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| Abstract: | A recent paper in this journal proposed a form of geographically weighted regression (GWR) that is termed parameter-specific distance metric geographically weighted regression (PSDM GWR). The central focus of the PSDM generalization of the GWR framework is that it allows the kernel function that weights nearby data to be specified with a distinct distance metric. As with the recent paper on Multiscale GWR (MGWR), the PSDM framework presents a form of GWR that also allows for parameter-specific bandwidths to be computed. As a result, a secondary focus of the PSDM GWR framework is to reduce the computational overhead associated with searching a massive parameter space to find a set of optimal parameter-specific bandwidths and parameter-specific distance metrics. In this comment, we discuss several concerns with the PSDM GWR framework in terms of model interpretability, complexity, and computational efficiency. We also recommend some best practices when using these models, suggest how to more holistically assess model variations, and set out an agenda to constructively focus future research endeavors. |
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| Keywords: | Multiscale geographically weighted regression big models spatial analysis spatial statistics |
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