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الانحدار الموزون جغرافيًا (GWR)×الترجيح بالمسافة العكسية (IDW)×
المجالالتحليل المكانيالتحليل المكاني
العائلةRegression modelRegression model
سنة النشأة20021968
صاحب الطريقةFotheringham, Brunsdon & CharltonDonald Shepard
النوعLocal spatial regressionDeterministic spatial interpolation
المصدر التأسيسيFotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524. DOI ↗
الأسماء البديلةGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon
ذات صلة53
الملخصGeographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.Inverse distance weighting is a simple, deterministic method for estimating values at unsampled locations by taking a weighted average of nearby measured points, where closer points carry more weight. Introduced by Donald Shepard in 1968, it embodies the first law of geography — near things are more related than distant things — and is one of the most widely used interpolation methods in GIS for mapping continuous fields such as rainfall, elevation, or pollution from scattered samples.
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ScholarGateقارن الطرق: Geographically Weighted Regression · Inverse Distance Weighting. استُرجع بتاريخ 2026-06-20 من https://scholargate.app/ar/compare