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| Metoda odwrotności odległości (IDW)× | Kokryging (ang. cokriging)× | Regresja geograficznie ważona (GWR)× | |
|---|---|---|---|
| Dziedzina | Analiza przestrzenna | Analiza przestrzenna | Analiza przestrzenna |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 1968 | 1963 | 2002 |
| Twórca≠ | Donald Shepard | Georges Matheron (geostatistics); multivariate extension | Fotheringham, Brunsdon & Charlton |
| Typ≠ | Deterministic spatial interpolation | Multivariate geostatistical interpolation | Local spatial regression |
| Źródło pierwotne≠ | Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524. DOI ↗ | 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 |
| Inne nazwy≠ | IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon | co-kriging, multivariate kriging, ortak kriging | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Pokrewne≠ | 3 | 3 | 5 |
| Podsumowanie≠ | 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. | Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation, yielding more accurate interpolations and prediction variances than kriging the primary variable alone. | 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. |
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