Comparar métodos

	Modelos de Localización-Asignación	Regresión de Poisson y Binomial Negativa
Campo	Análisis espacial	Econometría
Familia	Process / pipeline	Regression model
Año de origen	1963	1998
Autor original	Leon Cooper; S. L. Hakimi	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tipo	Spatial facility-location optimization	Generalized linear model for count data
Fuente seminal	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Relacionados	4	4
Resumen	Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.	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.