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Knowledge based analysis of continental population and migration dynamics

•Continental migration model is developed using knowledge-based world population analysis.•Trends in historical continental migration patterns are discussed.•Trends in future continental migration patterns are analyzed. Continental migration studies range from elaborate recording of periodic migrati...

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Bibliographic Details
Published in:Technological forecasting & social change 2020-02, Vol.151, p.119848, Article 119848
Main Author: Aral, Mustafa M.
Format: Article
Language:English
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Summary:•Continental migration model is developed using knowledge-based world population analysis.•Trends in historical continental migration patterns are discussed.•Trends in future continental migration patterns are analyzed. Continental migration studies range from elaborate recording of periodic migration data to regression analysis to statistical models that may combine several sources of quantitative and qualitative data. It is the writer's observation that continuous mathematical modeling has not been attempted to estimate continental migration because of the complexity of the governing mathematical models. In this study the writer proposes a continuous mathematical model to estimate continental population and migration trends. The proposed model is based on knowledge-based population dynamics model developed earlier which was used to estimate global population levels and stability of world population under various stress scenarios. In this study one of these models are extended to include continental population and continental migration concepts. Resulting mathematical model is calibrated using historical continental population data which is a reliable data source. If historical continental migration is a zero-sum process, the outcome of the calibrated model yields continental population growth and intercontinental migration estimates. Results obtained are in line with global projections that are made in other studies. The proposed model is also used for future projections.
ISSN:0040-1625
1873-5509
DOI:10.1016/j.techfore.2019.119848