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| Multiregional Demography× | إسقاط السكان بالنهج القائم على الأجيال والمكونات× | |
|---|---|---|
| المجال | علم السكان | علم السكان |
| العائلة | Process / pipeline | Process / pipeline |
| سنة النشأة≠ | 1975 | 2001 |
| صاحب الطريقة≠ | Andrei Rogers | Preston, Heuveline & Guillot |
| النوع≠ | Matrix framework for multiregional population dynamics with migration | Demographic projection pipeline |
| المصدر التأسيسي≠ | Rogers, A. (1975). Introduction to Multiregional Mathematical Demography. John Wiley & Sons, New York. ISBN: 9780471730354 | Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell. ISBN: 978-1-557-86451-2 |
| الأسماء البديلة≠ | Multiregional Population Analysis, Multiregional Life Table, Rogers Multiregional Model | Cohort-Component Method, Component Method of Population Projection, Age-Sex-Specific Population Projection, Kohort-Bileşen Projeksiyonu |
| ذات صلة≠ | 4 | 3 |
| الملخص≠ | Multiregional demography extends the classical tools of mathematical demography — the life table, the Leslie matrix, and stable-population theory — from a single closed population to a system of interconnected regions linked by migration. Developed by Andrei Rogers, it tracks people not only by age but by region of residence, modeling birth, death, and interregional movement simultaneously. The result is a unified matrix framework that yields multiregional life tables, projections, and stable regional population shares, making it the foundation for analyzing how migration shapes the size and distribution of populations across space. | Cohort-Component Projection is the standard demographic method for forecasting future population size and age-sex structure by explicitly tracking births, deaths, and migration for each age-sex cohort across discrete time steps. Systematically formalized in the textbook literature by Preston, Heuveline, and Guillot (2001), the method builds on foundational actuarial and demographic work dating to the early twentieth century and remains the workhorse technique used by national statistical offices and international organizations worldwide. |
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