পদ্ধতির তুলনা করুন

	স্থানিক মিথস্ক্রিয়া (মহাকর্ষ) মডেল	অবস্থান-বরাদ্দ মডেল	মাল্টিনোমিয়াল লজিস্টিক রিগ্রেশন	পয়সোঁ ও নেগেটিভ বাইনোমিয়াল রিগ্রেশন
ক্ষেত্র	স্থানিক বিশ্লেষণ	স্থানিক বিশ্লেষণ	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Process / pipeline	Regression model	Regression model
উদ্ভবের বছর	1971	1963	1974	1998
প্রবর্তক	Alan Wilson (entropy-maximizing family)	Leon Cooper; S. L. Hakimi	McFadden	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
ধরন	Model of flows between spatial origins and destinations	Spatial facility-location optimization	Multinomial logistic regression	Generalized linear model for count data
মৌলিক উৎস	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.	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.
অপর নাম	gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri	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
সম্পর্কিত	4	4	5	4
সারসংক্ষেপ	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.	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.