[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v13y2024i12p664-d1541014.html
   My bibliography  Save this article

Migration and Segregated Spaces: Analysis of Qualitative Sources Such as Wikipedia Using Artificial Intelligence

Author

Listed:
  • Javier López-Otero

    (Facultad de Letras y de la Educación, Universidad Internacional de La Rioja, Avenida de la Paz 137, 26006 Logroño, La Rioja, Spain)

  • Ángel Obregón-Sierra

    (Facultad de Letras y de la Educación, Universidad Internacional de La Rioja, Avenida de la Paz 137, 26006 Logroño, La Rioja, Spain)

  • Antonio Gavira-Narváez

    (Departament of Ciencias Sociales, Filosofía, Geografía y Traducción e Interpretación, Universidad de Córdoba, Square of Cardenal Salazar, 14003 Córdoba, Andalusia, Spain)

Abstract
The scientific literature on residential segregation in large metropolitan areas highlights various explanatory factors, including economic, social, political, landscape, and cultural elements related to both migrant and local populations. This paper contrasts the impact of these factors individually, such as the immigrant rate and neighborhood segregation. To achieve this, a machine learning analysis was conducted on a sample of neighborhoods in the main Spanish metropolitan areas (Madrid and Barcelona), using a database created from a combination of official statistical sources and textual sources, such as Wikipedia. These texts were transformed into indexes using Natural Language Processing (NLP) and other artificial intelligence algorithms capable of interpreting images and converting them into indexes. The results indicate that the factors influencing immigrant concentration and segregation differ significantly, with crucial roles played by the urban landscape, population size, and geographic origin. While land prices showed a relationship with immigrant concentration, their effect on segregation was mediated by factors such as overcrowding, social support networks, and landscape degradation. The novel application of AI and big data, particularly through ChatGPT and Google Street View, has enhanced model predictability, contributing to the scientific literature on segregated spaces.

Suggested Citation

  • Javier López-Otero & Ángel Obregón-Sierra & Antonio Gavira-Narváez, 2024. "Migration and Segregated Spaces: Analysis of Qualitative Sources Such as Wikipedia Using Artificial Intelligence," Social Sciences, MDPI, vol. 13(12), pages 1-21, December.
  • Handle: RePEc:gam:jscscx:v:13:y:2024:i:12:p:664-:d:1541014
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/13/12/664/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2076-0760/13/12/664/
    Download Restriction: no
    ---><---

    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:gam:jscscx:v:13:y:2024:i:12:p:664-:d:1541014. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.