방법 비교

	공간 상호작용 (중력) 모형	지리정보시스템 기반 다기준 의사결정 분석 (GIS-MCDA)	포아송 및 음이항 회귀분석
분야	공간분석	공간분석	계량경제학
계열	Regression model	Process / pipeline	Regression model
기원 연도	1971	2006	1998
창시자	Alan Wilson (entropy-maximizing family)	Jacek Malczewski (GIS-MCDA synthesis)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
유형	Model of flows between spatial origins and destinations	Spatial multi-criteria suitability/decision analysis	Generalized linear model for count data
원전	Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
별칭	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
관련	4	4	4
요약	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.	GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data.	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.