Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Getis-Ord Gi* karstā analīze× | Telpiskās nobīdes modelis (SAR / Telpiskais autoregresīvais)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1992 | 1988 |
| Autors≠ | Arthur Getis and J. Keith Ord | Anselin (textbook formalisation); LeSage & Pace |
| Tips≠ | Local spatial statistic | Spatial autoregressive regression |
| Pirmavots≠ | Getis, A. & Ord, J.K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206. DOI ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| Citi nosaukumi≠ | hot spot analysis, cold spot analysis, Gi* statistic, local Gi statistic | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Getis-Ord Gi* is a local spatial statistic, introduced by Getis and Ord in 1992 and refined in 1995, that compares the value at each location and its neighbours against the global mean to identify statistically significant clusters of high values (hot spots) and low values (cold spots). | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. |
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