Comparer des méthodes

	Modèles d'interaction spatiale (gravitationnelle)	Régression de Poisson et binomiale négative
Domaine	Analyse spatiale	Économétrie
Famille	Regression model	Regression model
Année d'origine	1971	1998
Auteur d'origine	Alan Wilson (entropy-maximizing family)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Model of flows between spatial origins and destinations	Generalized linear model for count data
Source fondatrice	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	4	4
Résumé	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.	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.