Scale and correlation in multiscale geographically weighted regression (MGWR)

Wei Kang () and Taylor M. Oshan
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Wei Kang: University of California Riverside
Taylor M. Oshan: University of Maryland, College Park

Journal of Geographical Systems, 2025, vol. 27, issue 3, No 4, 399-424

Abstract: Abstract Multiscale geographically weighted regression (MGWR) extends geographically weighted regression (GWR) by allowing process heterogeneity to be modeled at different spatial scales. While MGWR improves parameter estimates compared to GWR, the relationship between spatial scale and correlations within and among covariates—specifically spatial autocorrelation and collinearity—has not been systematically explored. This study investigates these relationships through controlled simulation experiments. Results indicate that spatial autocorrelation and collinearity affect specific model components rather than the entire model. Their impacts are cumulative but remain minimal unless they become very strong. MGWR effectively mitigates local multicollinearity issues by applying varying bandwidths across parameter surfaces. However, high levels of spatial autocorrelation and collinearity can lead to bandwidth underestimation for global processes, potentially producing false local effects. Additionally, strong collinearity may cause bandwidths to be overestimated for some processes, which helps mitigate collinearity but may obscure local effects. These findings suggest that while MGWR offers greater robustness against multicollinearity compared to GWR, bandwidth estimates should be interpreted with caution, as they can be influenced by strong spatial autocorrelation and collinearity. These results have important implications for empirical applications of MGWR.

Keywords: Spatial scale; Spatial autocorrelation; Multicollinearity; MGWR; Spatial heterogeneity (search for similar items in EconPapers)
JEL-codes: C18 C31 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10109-025-00468-1

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