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مدل‌های مکان‌یابی-تخصیص×رگرسیون پواسون و دوجمله‌ای منفی×
حوزهتحلیل فضاییاقتصادسنجی
خانوادهProcess / pipelineRegression model
سال پیدایش19631998
پدیدآورLeon Cooper; S. L. HakimiCameron & Trivedi (textbook treatment); Hilbe (negative binomial)
نوعSpatial facility-location optimizationGeneralized linear model for count data
منبع بنیادینCooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343. DOI ↗Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗
نام‌های دیگرfacility location, p-median problem, maximal covering location problem, yer-tahsis modellericount regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
مرتبط44
خلاصهLocation-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.
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  3. PUBLISHED

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ScholarGateمقایسهٔ روش‌ها: Location-Allocation · Poisson Regression. بازیابی‌شده در 2026-06-17 از https://scholargate.app/fa/compare