Сравнение методов

	Многокритериальный анализ решений на основе ГИС (GIS-MCDA)	Мультиномиальная логистическая регрессия	Пуассоновская регрессия и регрессия с отрицательным биномиальным распределением
Область	Пространственный анализ	Эконометрика	Эконометрика
Семейство	Process / pipeline	Regression model	Regression model
Год появления	2006	1974	1998
Автор метода	Jacek Malczewski (GIS-MCDA synthesis)	McFadden	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Тип	Spatial multi-criteria suitability/decision analysis	Multinomial logistic regression	Generalized linear model for count data
Основополагающий источник	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Другие названия	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Связанные	4	5	4
Сводка	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.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	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.