পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| স্থানিক মিথস্ক্রিয়া (মহাকর্ষ) মডেল× | পয়সোঁ ও নেগেটিভ বাইনোমিয়াল রিগ্রেশন× | |
|---|---|---|
| ক্ষেত্র≠ | স্থানিক বিশ্লেষণ | অর্থমিতি |
| পরিবার | Regression model | Regression model |
| উদ্ভবের বছর≠ | 1971 | 1998 |
| প্রবর্তক≠ | Alan Wilson (entropy-maximizing family) | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| ধরন≠ | Model of flows between spatial origins and destinations | 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. DOI ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| অপর নাম | gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| সম্পর্কিত | 4 | 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. | 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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