विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| GIS-MCDA× | पॉइसन और ऋणात्मक द्विपद प्रतिगमन (Poisson and Negative Binomial Regression)× | |
|---|---|---|
| क्षेत्र≠ | स्थानिक विश्लेषण | अर्थमिति |
| परिवार≠ | Process / pipeline | Regression model |
| उद्भव वर्ष≠ | 2006 | 1998 |
| प्रवर्तक≠ | Jacek Malczewski (GIS-MCDA synthesis) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| प्रकार≠ | Spatial multi-criteria suitability/decision analysis | Generalized linear model for count data |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम≠ | GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| संबंधित | 4 | 4 |
| सारांश≠ | 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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