IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0264223.html
   My bibliography  Save this article

Modeling human migration driven by changing mindset, agglomeration, social ties, and the environment

Author

Listed:
  • Gonzalo Suarez
  • Rachata Muneepeerakul

Abstract

Migration is an adaptation strategy to unfavorable conditions and is governed by a complex set of socio-economic and environmental drivers. Here we identified important drivers relatively underrepresented in many migration models—CHanging mindset, Agglomeration, Social ties, and the Environment (CHASE)—and asked: How does the interplay between these drivers influence transient dynamics and long-term outcomes of migration? We addressed this question by developing and analyzing a parsimonious Markov chain model. Our findings suggest that these drivers interact in nonlinear and complex ways. The system exhibits legacy effects, highlighting the importance of including migrants’ changing priorities. The increased characteristic population size of the system counter-intuitively leads to fewer surviving cities, and this effect is mediated by how fast migrants change their mindsets and how strong the social ties are. Strong social ties result in less diverse populations across cities, but this effect is influenced by how many cities remain. To our knowledge, this is the first time that these drivers are incorporated in one coherent, mechanistic, parsimonious model and the effects of their interplay on migration systematically studied. The complex interplay underscores the need to incorporate these drivers into mechanistic migration models and implement such models for real-world cases.

Suggested Citation

  • Gonzalo Suarez & Rachata Muneepeerakul, 2022. "Modeling human migration driven by changing mindset, agglomeration, social ties, and the environment," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-11, February.
  • Handle: RePEc:plo:pone00:0264223
    DOI: 10.1371/journal.pone.0264223
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0264223
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0264223&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0264223?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    2. Ryuichi Kitamura & Cynthia Chen & Ram Pendyala & Ravi Narayanan, 2000. "Micro-simulation of daily activity-travel patterns for travel demand forecasting," Transportation, Springer, vol. 27(1), pages 25-51, February.
    3. Siaw Akwawua & James A. Pooler, 2001. "The development of an intervening opportunities model with spatial dominance effects," Journal of Geographical Systems, Springer, vol. 3(1), pages 69-86, May.
    4. Diana Suleimenova & Derek Groen, 2020. "How Policy Decisions Affect Refugee Journeys in South Sudan: A Study Using Automated Ensemble Simulations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-2.
    5. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    6. Hassani-Mahmooei, Behrooz & Parris, Brett W., 2012. "Climate change and internal migration patterns in Bangladesh: an agent-based model," Environment and Development Economics, Cambridge University Press, vol. 17(6), pages 763-780, December.
    7. Govert E. Bijwaard, 2008. "Modeling Migration Dynamics of Immigrants," Tinbergen Institute Discussion Papers 08-070/4, Tinbergen Institute.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
    2. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.
    3. Chaogui Kang & Yu Liu & Diansheng Guo & Kun Qin, 2015. "A Generalized Radiation Model for Human Mobility: Spatial Scale, Searching Direction and Trip Constraint," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-11, November.
    4. Cai, Hua & Zhan, Xiaowei & Zhu, Ji & Jia, Xiaoping & Chiu, Anthony S.F. & Xu, Ming, 2016. "Understanding taxi travel patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 590-597.
    5. Alejandro Llorente & Manuel Garcia-Herranz & Manuel Cebrian & Esteban Moro, 2015. "Social Media Fingerprints of Unemployment," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    6. Fagiolo, Giorgio & Santoni, Gianluca, 2015. "Human-mobility networks, country income, and labor productivity," Network Science, Cambridge University Press, vol. 3(3), pages 377-407, September.
    7. Jungmin Kim & Juyong Park & Wonjae Lee, 2018. "Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    8. Raja Jurdak, 2013. "The Impact of Cost and Network Topology on Urban Mobility: A Study of Public Bicycle Usage in 2 U.S. Cities," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-6, November.
    9. Daniel Austin & Robin M Cross & Tamara Hayes & Jeffrey Kaye, 2014. "Regularity and Predictability of Human Mobility in Personal Space," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.
    10. Fernando Santa & Roberto Henriques & Joaquín Torres-Sospedra & Edzer Pebesma, 2019. "A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments," Sustainability, MDPI, vol. 11(3), pages 1-29, January.
    11. Huang, Jinyu & Chen, Chao, 2022. "Metapopulation epidemic models with a universal mobility pattern on interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    12. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    13. Andrés Leiva-Araos & Héctor Allende-Cid, 2021. "A Hierarchical Fuzzy-Based Correction Algorithm for the Neighboring Network Hit Problem," Mathematics, MDPI, vol. 9(4), pages 1-36, February.
    14. Lenormand, Maxime & Bassolas, Aleix & Ramasco, José J., 2016. "Systematic comparison of trip distribution laws and models," Journal of Transport Geography, Elsevier, vol. 51(C), pages 158-169.
    15. Siqin Wang & Mengxi Zhang & Tao Hu & Xiaokang Fu & Zhe Gao & Briana Halloran & Yan Liu, 2021. "A Bibliometric Analysis and Network Visualisation of Human Mobility Studies from 1990 to 2020: Emerging Trends and Future Research Directions," Sustainability, MDPI, vol. 13(10), pages 1-22, May.
    16. Nimrod Serok & Efrat Blumenfeld-Lieberthal, 2015. "A Simulation Model for Intra-Urban Movements," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-15, July.
    17. Bassel Daher & Silva Hamie & Konstantinos Pappas & Mohammad Nahidul Karim & Tessa Thomas, 2021. "Toward Resilient Water-Energy-Food Systems under Shocks: Understanding the Impact of Migration, Pandemics, and Natural Disasters," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    18. Erlström, Andreas & Grillitsch, Markus & Hall, Ola, 2020. "The Geography of Connectivity: Trails of Mobile Phone Data," Papers in Innovation Studies 2020/6, Lund University, CIRCLE - Centre for Innovation Research.
    19. Huang, Zhiren & Wang, Pu & Zhang, Fan & Gao, Jianxi & Schich, Maximilian, 2018. "A mobility network approach to identify and anticipate large crowd gatherings," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 147-170.
    20. Sanja Šćepanović & Igor Mishkovski & Pan Hui & Jukka K Nurminen & Antti Ylä-Jääski, 2015. "Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0264223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.