Сравнение на методи

	Пространствени модели на взаимодействието (гравитационни)	Мултикритериална анализ на решенията, базиран на ГИС (GIS-MCDA)	Мултиномиална логистична регресия
Област	Пространствен анализ	Пространствен анализ	Иконометрия
Семейство	Regression model	Process / pipeline	Regression model
Година на възникване	1971	2006	1974
Създател	Alan Wilson (entropy-maximizing family)	Jacek Malczewski (GIS-MCDA synthesis)	McFadden
Тип	Model of flows between spatial origins and destinations	Spatial multi-criteria suitability/decision analysis	Multinomial logistic regression
Основополагащ източник	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.	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
Други названия	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon
Свързани	4	4	5
Резюме	Spatial interaction models predict the volume of flows — migrants, commuters, shoppers, trade, trips — between origins and destinations as a function of the size of each place and the distance or cost separating them. By analogy to Newton's gravity, interaction rises with the 'mass' of origin and destination and falls with separation, and Wilson's 1971 entropy-maximizing family put these models on a rigorous footing for transport, migration, and retail analysis.	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.