IDEAS home Printed from https://ideas.repec.org/a/ids/ijitma/v21y2022i1p1-12.html
   My bibliography  Save this article

Construction of an urban multi variable geospatial information integration model based on distributed artificial intelligence

Author

Listed:
  • Lede Niu
  • Mei Pan
  • Yan Zhou

Abstract

In order to improve the ability of urban multi-spatial information integration, a multi-spatial information integration model based on artificial intelligence is proposed. The empirical analysis model of urban multivariate geospatial information integration is established, and the statistical characteristics are analysed according to the information distribution. The multi-resolution clustering model of urban multi-spatial information is established by using the multi-regression parameter method. The methods of fuzzy feature extraction and associated feature mining were used to extract information features, and the constraint parameters of urban multivariate geospatial information integration were analysed by GIS parameter estimation and statistical analysis. The parameter optimisation model of urban multivariate geospatial information integration is established and the artificial intelligence learning algorithm is used to integrate urban multivariate geospatial information. The simulation results show that this method has a good adaptability to the integration of urban multi-spatial information, and is helpful to the spatial distribution planning of geographic information, and improves the ability of accurate mining and scheduling of urban multi-spatial information.

Suggested Citation

  • Lede Niu & Mei Pan & Yan Zhou, 2022. "Construction of an urban multi variable geospatial information integration model based on distributed artificial intelligence," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 21(1), pages 1-12.
    • Handle: RePEc:ids:ijitma:v:21:y:2022:i:1:p:1-12
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=121333
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijitma:v:21:y:2022:i:1:p:1-12. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=18 .

    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.