方法对比

	空间交互（引力）模型	区位-分配模型	泊松回归与负二项回归
领域	空间分析	空间分析	计量经济学
方法族	Regression model	Process / pipeline	Regression model
起源年份	1971	1963	1998
提出者	Alan Wilson (entropy-maximizing family)	Leon Cooper; S. L. Hakimi	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
类型	Model of flows between spatial origins and destinations	Spatial facility-location optimization	Generalized linear model for count data
开创性文献	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.
别名	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
相关	4	4	4
摘要	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.