পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| অবস্থান-বরাদ্দ মডেল× | পয়সোঁ ও নেগেটিভ বাইনোমিয়াল রিগ্রেশন× | |
|---|---|---|
| ক্ষেত্র≠ | স্থানিক বিশ্লেষণ | অর্থমিতি |
| পরিবার≠ | Process / pipeline | Regression model |
| উদ্ভবের বছর≠ | 1963 | 1998 |
| প্রবর্তক≠ | Leon Cooper; S. L. Hakimi | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| ধরন≠ | Spatial facility-location optimization | Generalized linear model for count data |
| মৌলিক উৎস≠ | Cooper, 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 ↗ |
| অপর নাম | 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 |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | 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. |
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