Salīdzināt metodes

	Telpiskās mijiedarbības (gravitācijas) modeļi	Location-Allocation	Puasona un negatīvās binomiālās regresijas
Nozare	Telpiskā analīze	Telpiskā analīze	Ekonometrija
Saime	Regression model	Process / pipeline	Regression model
Izcelsmes gads	1971	1963	1998
Autors	Alan Wilson (entropy-maximizing family)	Leon Cooper; S. L. Hakimi	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tips	Model of flows between spatial origins and destinations	Spatial facility-location optimization	Generalized linear model for count data
Pirmavots	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.	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.
Citi nosaukumi	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli	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
Saistītās	4	4	4
Kopsavilkums	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.	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.