Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Universal Kriging (Kriging dengan Tren)× | Regresi Berbobot Geografis (GWR)× | Pembobotan Jarak Terbalik (IDW)× | |
|---|---|---|---|
| Bidang | Analisis Spasial | Analisis Spasial | Analisis Spasial |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 1969 | 2002 | 1968 |
| Pencetus≠ | Georges Matheron | Fotheringham, Brunsdon & Charlton | Donald Shepard |
| Tipe≠ | Geostatistical interpolation with spatial trend | Local spatial regression | Deterministic spatial interpolation |
| Sumber perintis≠ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗ | 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. DOI ↗ |
| Alias | kriging with a trend, kriging with drift, trend kriging, evrensel kriging | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) | IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon |
| Terkait≠ | 3 | 5 | 3 |
| Ringkasan≠ | 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. | 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. |
| ScholarGateSet data ↗ |
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