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Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.

Geografisch Gewogen Regressie (GWR)×Omgekeerde Afstandsweging (IDW)×
VakgebiedRuimtelijke analyseRuimtelijke analyse
FamilieRegression modelRegression model
Jaar van ontstaan20021968
GrondleggerFotheringham, Brunsdon & CharltonDonald Shepard
TypeLocal spatial regressionDeterministic spatial interpolation
Oorspronkelijke bronFotheringham, 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 ↗
AliassenGWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon
Verwant53
SamenvattingGeographically 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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ScholarGateMethoden vergelijken: Geographically Weighted Regression · Inverse Distance Weighting. Geraadpleegd op 2026-06-20 via https://scholargate.app/nl/compare