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Navigating the methodological landscape in spatial analysis: a comment on ‘A Route Map for Successful Applications of Geographically-Weighted Regression’

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  • Oshan, Taylor M.

Abstract

The development of ‘route maps’ for spatial analytical methods is a pursuit with important ramifications. Comber et al. (2022) propose a route map to guide applications of geographically weighted regression (GWR) consisting of a 3-step primary pathway and a series of secondary arterials. This comment first highlights some concerns about the underlying ‘map’ (i.e., experimental setup and assumptions) and then with the proposed ‘route’ (i.e., core decisions and evaluation criteria). It closes by suggesting a more general focus on identifying modeling issues with the highest impact and facilitating consensus-building, which could improve the future production of route maps for navigating the methodological landscape in spatial analysis.

Suggested Citation

  • Oshan, Taylor M., 2022. "Navigating the methodological landscape in spatial analysis: a comment on ‘A Route Map for Successful Applications of Geographically-Weighted Regression’," OSF Preprints rckzj, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:rckzj
    DOI: 10.31219/osf.io/rckzj
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    References listed on IDEAS

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    1. Oshan, Taylor M. & Smith, Jordan & Fotheringham, Alexander Stewart, 2020. "Targeting the spatial context of obesity determinants via multiscale geographically weighted regression," OSF Preprints u7j29, Center for Open Science.
    2. Ziqi Li & A. Stewart Fotheringham & Taylor M. Oshan & Levi John Wolf, 2020. "Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(5), pages 1500-1520, September.
    3. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    4. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
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