Generalized Additive Models versus Geographically Weighted Regression: a comparative analysis

Generalized Additive Models versus Geographically Weighted Regression: a comparative analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Generalized Additive Models versus Geographically Weighted Regression: a comparative analysis Francisco de Asis Lopez Alvarez, Celestino Ordoñez, Javier Roca Pardiñas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3869416/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Regression models for spatial data have attracted the attention of researchers from different fields given their widespread application. In this work we analyze the utility of generalized additive models (GAMs) as a regression method with spatially-dependent coefficients and compare them with geographically weighted regression (GWR), a popular method that performs a local linear regression in the vicinity of each point. The comparison was carried out using both simulated and real data. The comparison results on the simulated data showed better fit and lower uncertainty in the GAM approximation. The comparison using real data showed quite similar results for both methods, although the error in GAM was slightly lower for the GAM approach. Two other important aspects of spatial regression were also tackled in this study: 1) testing the spatial heterogeneity of the data, 2) selecting the significant covariates. The first aspect was addressed using both bootstrapping and the Bayesian information criterion (BIC), while only BIC was used for the second aspect. Spatial regression bootstrapping variable selection spatial heterogeneity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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