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Computer Research and Modeling, 2021, Volume 13, Issue 6, Pages 1205–1232
DOI: https://doi.org/10.20537/2076-7633-2021-13-6-1205-1232
(Mi crm945)
 

This article is cited in 3 scientific papers (total in 3 papers)

MODELS OF ECONOMIC AND SOCIAL SYSTEMS

Migration processes modelling: methods and tools (overview)

E. B. Oleynik, N. V. Ivashina, Yu. D. Shmidt

Far Eastern Federal University, 8, Sukhanova st., Vladivostok, 690091, Russia
Full-text PDF (465 kB) Citations (3)
References:
Abstract: Migration has a significant impact on the shaping of the demographic structure of the territories population, the state of regional and local labour markets. As a rule, rapid change in the working-age population of any territory due to migration processes results in an imbalance in supply and demand on labour markets and a change in the demographic structure of the population. Migration is also to a large extent a reflection of socio-economic processes taking place in the society. Hence, the issues related to the study of migration factors, the direction, intensity and structure of migration flows, and the prediction of their magnitude are becoming topical issues these days.
Mathematical tools are often used to analyze, predict migration processes and assess their consequences, allowing for essentially accurate modelling of migration processes for different territories on the basis of the available statistical data. In recent years, quite a number of scientific papers on modelling internal and external migration flows using mathematical methods have appeared both in Russia and in foreign countries in recent years. Consequently, there has been a need to systematize the currently most commonly used methods and tools applied in migration modelling to form a coherent picture of the main trends and research directions in this field.
The presented review considers the main approaches to migration modelling and the main components of migration modelling methodology, i. e. stages, methods, models and model classification. Their comparative analysis was also conducted and general recommendations on the choice of mathematical tools for modelling were developed. The review contains two sections: migration modelling methods and migration models. The first section describes the main methods used in the model development process — econometric, cellular automata, system-dynamic, probabilistic, balance, optimization and cluster analysis. Based on the analysis of modern domestic and foreign publications on migration, the most common classes of models — regression, agent-based, simulation, optimization, probabilistic, balance, dynamic and combined — were identified and described. The features, advantages and disadvantages of different types of migration process models were considered.
Keywords: migration, migration processes, migration models, methods, regression models, cellular automata, agent-based models, balance models, dynamic models.
Funding agency Grant number
Russian Foundation for Basic Research 20-110-50093
The work was supported by Foundation for basic research (project no. 20-110-50093).
Received: 22.09.2021
Revised: 14.10.2021
Accepted: 18.10.2021
Document Type: Article
UDC: 314.7: 51-77
Language: Russian
Citation: E. B. Oleynik, N. V. Ivashina, Yu. D. Shmidt, “Migration processes modelling: methods and tools (overview)”, Computer Research and Modeling, 13:6 (2021), 1205–1232
Citation in format AMSBIB
\Bibitem{OleIvaShm21}
\by E.~B.~Oleynik, N.~V.~Ivashina, Yu.~D.~Shmidt
\paper Migration processes modelling: methods and tools (overview)
\jour Computer Research and Modeling
\yr 2021
\vol 13
\issue 6
\pages 1205--1232
\mathnet{http://mi.mathnet.ru/crm945}
\crossref{https://doi.org/10.20537/2076-7633-2021-13-6-1205-1232}
Linking options:
  • https://www.mathnet.ru/eng/crm945
  • https://www.mathnet.ru/eng/crm/v13/i6/p1205
  • This publication is cited in the following 3 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Computer Research and Modeling
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    Abstract page:172
    Full-text PDF :86
    References:27
     
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