Linganisha mbinu

	Mifumo ya Mwingiliano wa Anga (Mvuto)	Usajili wa Lojistiki wa Kimultinomia	Uchanganuzi wa Poisson na Negative Binomial
Nyanja	Uchanganuzi wa Kimaeneo	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	1971	1974	1998
Mwanzilishi	Alan Wilson (entropy-maximizing family)	McFadden	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Aina	Model of flows between spatial origins and destinations	Multinomial logistic regression	Generalized linear model for count data
Chanzo asilia	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.	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.
Majina mbadala	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli	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
Zinazohusiana	4	5	4
Muhtasari	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.	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.