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The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels

Author

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  • Zhaoxin Dai

    () (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yunfeng Hu

    () (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China)

  • Guanhua Zhao

    () (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Nighttime light data offer a unique view of the Earth’s surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area.

Suggested Citation

  • Zhaoxin Dai & Yunfeng Hu & Guanhua Zhao, 2017. "The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels," Sustainability, MDPI, Open Access Journal, vol. 9(2), pages 1-15, February.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:2:p:305-:d:90802
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    References listed on IDEAS

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    Cited by:

    1. Lionel Roger, 2018. "Blinded by the light? Heterogeneity in the luminosity-growth nexus and the African growth miracle," Discussion Papers 2018-04, University of Nottingham, CREDIT.

    More about this item

    Keywords

    NPP/VIIRS; DMSP/OLS; GDP; spatial scale suitability; regression model suitability; regional suitability;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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