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| Τοπική Γεωγραφικά Σταθμισμένη Παλινδρόμηση (GWR)× | Τοπική Χωρική Αυτοσυσχέτιση× | |
|---|---|---|
| Πεδίο | Χωρική Ανάλυση | Χωρική Ανάλυση |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 1996 | 1995 |
| Δημιουργός≠ | Brunsdon, Fotheringham & Charlton | Luc Anselin |
| Τύπος≠ | Spatially varying coefficient regression | Spatial association analysis |
| Θεμελιώδης πηγή≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Εναλλακτικές ονομασίες | GWR, geographically weighted regression, local spatial regression, spatially varying coefficient model | local spatial association, local SA, LISA methods, local spatial clustering |
| Συναφείς≠ | 5 | 6 |
| Σύνοψη≠ | Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data. | Local Spatial Autocorrelation methods decompose global spatial clustering into location-specific statistics, revealing where in a study area significant clustering or dispersion occurs. Each observation receives its own association score and significance value, enabling the detection of spatial hot spots, cold spots, and spatial outliers rather than reporting a single summary statistic. |
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