IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7000390.html
   My bibliography  Save this article

Correlation between Population Structure and Regional Innovation Ability Based on Big Data Analysis

Author

Listed:
  • Lulu Qin
  • Ruofei Lin
  • Rongwei Gao
  • Han Lin
  • Ning Cao

Abstract

With the continuous advancement of the urbanization process, the scale of cities has expanded rapidly, and the amount of floating population has grown rapidly. Big data not only brings changes in thinking, technology, and management but also promotes the renewal and development of social governance concepts, technologies, methods, and models, which creates new opportunities for urban floating population governance innovation. City governments at all levels are facing enormous management pressure and urgently need to promote innovative reforms in the governance of urban floating population. The development of information technology has developed into the era of big data, and strong data collection and processing capabilities can greatly improve the scientific level of urban floating population management. In the research of spatial correlation of innovation ability, the research of domestic and foreign scholars has been very mature, and the methods used are more comprehensive, combining big data with urban floating population governance, placing urban floating population governance in the context of big data research and analysis, exploring the channels for the government to use big data to optimize urban floating population governance, and putting forward specific and feasible countermeasures and suggestions. After a series of tests, the double-fixed-effects spatial Dubin model is used to explore the influence of population structure on regional innovation ability from five aspects: population urban-rural structure, population industrial structure, population education structure, population age structure, and population density. The results show that the population structure of higher education has the most significant role in promoting regional innovation capability, followed by urban population, population density, and secondary industry population structure; the working-age population structure has no significant impact on regional innovation capability; provincial innovation capability has a significant effect positive spatial spillover effect.

Suggested Citation

  • Lulu Qin & Ruofei Lin & Rongwei Gao & Han Lin & Ning Cao, 2022. "Correlation between Population Structure and Regional Innovation Ability Based on Big Data Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, August.
  • Handle: RePEc:hin:jnlmpe:7000390
    DOI: 10.1155/2022/7000390
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/7000390.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/mpe/2022/7000390.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/7000390?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
    ---><---

    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:hin:jnlmpe:7000390. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.