A Robust Improved GTWR Framework for Spatiotemporal Heterogeneity and Outlier Effects: Evidence from Simulation and Applied Case Studies

A Robust Improved GTWR Framework for Spatiotemporal Heterogeneity and Outlier Effects: Evidence from Simulation and Applied Case Studies | 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 A Robust Improved GTWR Framework for Spatiotemporal Heterogeneity and Outlier Effects: Evidence from Simulation and Applied Case Studies Luri Zahara, I GEDE NYOMAN MINDRA JAYA, DEFI YUSTI FAIDAH This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8526387/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 Spatiotemporal data often exhibit strong heterogeneity, where relationships between predictors and responses vary across locations and time. Standard regression frameworks struggle with this non-stationarity, and although the Improved Geographically and Temporally Weighted Regression (I-GTWR) addresses spatiotemporal heterogeneity through localized weighting, its reliance on Least Squares makes it highly sensitive to outliers. Contaminated observations propagate through the kernel weighting structure, leading to biased parameter estimates and distorted coefficient surfaces. To address this weakness, this study proposes a Robust I-GTWR framework that integrates M, S, and MM-estimators to downweight extreme values while retaining the model’s adaptive spatiotemporal structure. A controlled simulation was designed to generate heterogeneous spatiotemporal coefficient fields and introduce high-leverage contamination at selected locations and years. Model performance was assessed using bias and RMSE. Results show that robust variants consistently produce lower error and narrower uncertainty distributions than standard I-GTWR, particularly under moderate-to-severe contamination. The method was further applied to tuberculosis incidence and six covariates across 27 regencies/cities in West Java from 2020–2024. Model comparison using AIC selected the M-estimator as the optimal specification. The resulting coefficient surfaces reveal persistent spatiotemporal heterogeneity, effects of population density, HIV prevalence, and sanitation vary substantially across regencies/cities and evolve over time. These findings confirm that robust estimation enhances stability without sacrificing the ability to detect non-stationary processes, making Robust I-GTWR a more reliable approach for contaminated spatiotemporal data and geographically targeted disease control strategies. Improved Geographically and Temporally Weighted Regression (I-GTWR) Robust Regression Tuberculosis 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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Studies","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Improved Geographically and Temporally Weighted Regression (I-GTWR), Robust Regression, 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Standard regression frameworks struggle with this non-stationarity, and although the Improved Geographically and Temporally Weighted Regression (I-GTWR) addresses spatiotemporal heterogeneity through localized weighting, its reliance on Least Squares makes it highly sensitive to outliers. Contaminated observations propagate through the kernel weighting structure, leading to biased parameter estimates and distorted coefficient surfaces. To address this weakness, this study proposes a Robust I-GTWR framework that integrates M, S, and MM-estimators to downweight extreme values while retaining the model\u0026rsquo;s adaptive spatiotemporal structure. A controlled simulation was designed to generate heterogeneous spatiotemporal coefficient fields and introduce high-leverage contamination at selected locations and years. Model performance was assessed using bias and RMSE. Results show that robust variants consistently produce lower error and narrower uncertainty distributions than standard I-GTWR, particularly under moderate-to-severe contamination. The method was further applied to tuberculosis incidence and six covariates across 27 regencies/cities in West Java from 2020\u0026ndash;2024. Model comparison using AIC selected the M-estimator as the optimal specification. The resulting coefficient surfaces reveal persistent spatiotemporal heterogeneity, effects of population density, HIV prevalence, and sanitation vary substantially across regencies/cities and evolve over time. These findings confirm that robust estimation enhances stability without sacrificing the ability to detect non-stationary processes, making Robust I-GTWR a more reliable approach for contaminated spatiotemporal data and geographically targeted disease control strategies.\u003c/p\u003e","manuscriptTitle":"A Robust Improved GTWR Framework for Spatiotemporal Heterogeneity and Outlier Effects: Evidence from Simulation and Applied Case Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 23:10:04","doi":"10.21203/rs.3.rs-8526387/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c7ed436-68de-4826-a1dc-295026a22e4d","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T07:27:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 23:10:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8526387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8526387","identity":"rs-8526387","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}