Abstract
Though one of the most commonly employed analysis techniques in the leisure literature, multiple regression and, in particular, the ordinary least squares (OLS) approach are subject to a number of critical assumptions, violation of which threaten the efficiency and validity of OLS findings. This article demonstrates the utility of an alternative approach, geographically weighted regression (GWR), a local form of linear regression that can be used to model spatially varying relationships and accounts for the spatial effects of heterogeneity (nonstationarity) and dependence (autocorrelation) in data. The small number of leisure studies that have employed GWR is reviewed, with a focus on the relative performance of the two approaches; GWR is shown to be superior to OLS in every case where the appropriate comparison was conducted. Other areas to which GWR could usefully be applied are suggested, and limitations of GWR are acknowledged.
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