Módszerek összehasonlítása

	Mixed Logit Modell	Térbeli interakciós (gravitációs) modellek
Tudományterület	Ökonometria	Térbeli elemzés
Módszercsalád	Regression model	Regression model
Keletkezés éve	2000	1971
Megalkotó	Daniel McFadden & Kenneth Train	Alan Wilson (entropy-maximizing family)
Típus	Random-parameters discrete choice model	Model of flows between spatial origins and destinations
Alapmű	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.
Alternatív nevek	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli
Kapcsolódó	3	4
Összefoglaló	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.	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.