IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9266705.html
   My bibliography  Save this article

Inversion of Regional Economic Trend from NPP-VIIRS Nighttime Light Data Based on Adaptive Clustering Algorithm

Author

Listed:
  • Hao Lu
  • Wenqiang Qu
  • Shengnan Min
  • Jiaqi Chen
  • Eric Lefevre

Abstract

Night lighting is closely related to social and economic development. Inversion of socioeconomic parameters using nighttime light (NTL) remote sensing data is a research hot spot currently. In this paper, a calibration method based on adaptive clustering algorithm for the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data was proposed to remove background noise in the original imagery. The validity of the calibration method was evaluated through comparing the correlation between the corrected NTL data and the regional economic data. The result indicated that the NTL data obtained by this calibration method have higher correlation with the regional GDP data, and the values of R2 and the root mean square error (RMSE) were 0.8531 and 133.18, respectively. On this basis, the total nighttime light (TNL)-gross domestic product (GDP) regression model obtained from this paper was used to invert the GDP of Liaoning Province from 2012 to 2016. Using the TNL-GDP regression model established in high-quality regions to verify the fraudulent economic statistics of Liaoning Province, it can be proved that NTL data can be a reliable reference for reflecting regional economic development trends.

Suggested Citation

  • Hao Lu & Wenqiang Qu & Shengnan Min & Jiaqi Chen & Eric Lefevre, 2022. "Inversion of Regional Economic Trend from NPP-VIIRS Nighttime Light Data Based on Adaptive Clustering Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, August.
    • Handle: RePEc:hin:jnlmpe:9266705
    DOI: 10.1155/2022/9266705
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/9266705.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/9266705.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/9266705?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rao, Yanchun & Wang, Xiuli & Li, Hengkai, 2024. "Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data of NPP-VIIRS-like nighttime light," Energy, Elsevier, vol. 294(C).

    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:9266705. 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.