เปรียบเทียบวิธี

	Geographically Weighted Regression (GWR)	การถ่วงน้ำหนักด้วยส่วนกลับของระยะทาง (IDW)	Universal Kriging (Kriging ที่มีแนวโน้ม)
สาขาวิชา	การวิเคราะห์เชิงพื้นที่	การวิเคราะห์เชิงพื้นที่	การวิเคราะห์เชิงพื้นที่
ตระกูล	Regression model	Regression model	Regression model
ปีกำเนิด	2002	1968	1969
ผู้ริเริ่ม	Fotheringham, Brunsdon & Charlton	Donald Shepard	Georges Matheron
ประเภท	Local spatial regression	Deterministic spatial interpolation	Geostatistical interpolation with spatial trend
แหล่งต้นตำรับ	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168	Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
ชื่อเรียกอื่น	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)	IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
ที่เกี่ยวข้อง	5	3	3
สรุป	Geographically 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.	Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances.