Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

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dc.contributor.authorOshan, Taylor M.
dc.contributor.authorSmith, Jordan P.
dc.contributor.authorFotheringham, A. Stewart
dc.date.accessioned2021-05-13T17:43:45Z
dc.date.available2021-05-13T17:43:45Z
dc.date.issued2020-04-05
dc.description.abstractObesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR). This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study. Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori. In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts. The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.en_US
dc.description.urihttps://doi.org/10.1186/s12942-020-00204-6
dc.identifierhttps://doi.org/10.13016/zzse-zgtm
dc.identifier.citationOshan, T.M., Smith, J.P. & Fotheringham, A.S. Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. Int J Health Geogr 19, 11 (2020).en_US
dc.identifier.urihttp://hdl.handle.net/1903/27049
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtGeologyen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.titleTargeting the spatial context of obesity determinants via multiscale geographically weighted regressionen_US
dc.typeArticleen_US

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