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| Projecció per Cohorts i Components× | Models d'interacció espacial (de gravetat)× | |
|---|---|---|
| Camp≠ | Demografia | Anàlisi espacial |
| Família≠ | Process / pipeline | Regression model |
| Any d'origen≠ | 2001 | 1971 |
| Autor original≠ | Preston, Heuveline & Guillot | Alan Wilson (entropy-maximizing family) |
| Tipus≠ | Demographic projection pipeline | Model of flows between spatial origins and destinations |
| Font seminal≠ | Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell. ISBN: 978-1-557-86451-2 | Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32. DOI ↗ |
| Àlies | Cohort-Component Method, Component Method of Population Projection, Age-Sex-Specific Population Projection, Kohort-Bileşen Projeksiyonu | gravity model, spatial interaction model, competing destinations model, mekânsal etkileşim modeli |
| Relacionats≠ | 3 | 4 |
| Resum≠ | 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. | 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. |
| ScholarGateConjunt de dades ↗ |
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