Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Multi-kritēriju lēmumu analīze, kas balstīta uz ĢIS (GIS-MCDA)× | Puasona un negatīvās binomiālās regresijas× | |
|---|---|---|
| Nozare≠ | Telpiskā analīze | Ekonometrija |
| Saime≠ | Process / pipeline | Regression model |
| Izcelsmes gads≠ | 2006 | 1998 |
| Autors≠ | Jacek Malczewski (GIS-MCDA synthesis) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Tips≠ | Spatial multi-criteria suitability/decision analysis | Generalized linear model for count data |
| Pirmavots≠ | Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Citi nosaukumi≠ | GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data. | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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