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

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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, 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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    5. E. Ustaoglu & R. Bovkır & A. C. Aydınoglu, 2021. "Spatial distribution of GDP based on integrated NPS-VIIRS nighttime light and MODIS EVI data: a case study of Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10309-10343, July.
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    7. Jesson A. Pagaduan, 2022. "Do higher‐quality nighttime lights and net primary productivity predict subnational GDP in developing countries? Evidence from the Philippines," Asian Economic Journal, East Asian Economic Association, vol. 36(3), pages 288-317, September.
    8. GIBSON, John & ZHANG, Xiaoxuan & PARK, Albert & YI, Jiang & XI, Li, 2024. "Remotely measuring rural economic activity and poverty : Do we just need better sensors?," CEI Working Paper Series 2023-08, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    9. 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.
    10. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    11. Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
    12. Qian Chen & Tingting Ye & Naizhuo Zhao & Mingjun Ding & Zutao Ouyang & Peng Jia & Wenze Yue & Xuchao Yang, 2021. "Mapping China’s regional economic activity by integrating points-of-interest and remote sensing data with random forest," Environment and Planning B, , vol. 48(7), pages 1876-1894, September.
    13. Tümer, Abdullah Erdal & Akkuş, Aytekin, 2018. "Forecasting Gross Domestic Product per Capita Using Artificial Neural Networks with Non-Economical Parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 468-473.
    14. Lin Chu & Tiancheng Sun & Tianwei Wang & Zhaoxia Li & Chongfa Cai, 2018. "Evolution and Prediction of Landscape Pattern and Habitat Quality Based on CA-Markov and InVEST Model in Hubei Section of Three Gorges Reservoir Area (TGRA)," Sustainability, MDPI, vol. 10(11), pages 1-28, October.
    15. Yanjun Wang & Fei Teng & Mengjie Wang & Shaochun Li & Yunhao Lin & Hengfan Cai, 2022. "Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(13), pages 1-29, June.
    16. Tianyi Zeng & Hong Jin & Zhifei Geng & Zihang Kang & Zichen Zhang, 2022. "Urban–Rural Fringe Long-Term Sequence Monitoring Based on a Comparative Study on DMSP-OLS and NPP-VIIRS Nighttime Light Data: A Case Study of Shenyang, China," IJERPH, MDPI, vol. 19(18), pages 1-18, September.
    17. Pengpeng Chang & Xueru Pang & Xiong He & Yiting Zhu & Chunshan Zhou, 2022. "Exploring the Spatial Relationship between Nighttime Light and Tourism Economy: Evidence from 31 Provinces in China," Sustainability, MDPI, vol. 14(12), pages 1-22, June.
    18. Nattapong Puttanapong & Amornrat Luenam & Pit Jongwattanakul, 2022. "Spatial Analysis of Inequality in Thailand: Applications of Satellite Data and Spatial Statistics/Econometrics," Sustainability, MDPI, vol. 14(7), pages 1-25, March.
    19. Deguang Li & Zhicheng Ding & Jianghuan Liu & Qiurui He & Hamad Naeem, 2022. "Exploring Spatiotemporal Dynamics of PM 2.5 Emission Based on Nighttime Light in China from 2012 to 2018," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    20. Majdi Debbich, 2019. "Assessing Oil and Non-Oil GDP Growth from Space: An Application to Yemen 2012-17," IMF Working Papers 2019/221, International Monetary Fund.

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