IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i2p305-d90802.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/2/305/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/2/305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benu Bidani & Niels-Hugo Blunch & Chor-Ching Goh & Christopher O'Leary, 2009. "Evaluating job training in two Chinese cities," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 7(1), pages 77-94.
    2. Kent Matthews & Zhiguo Xiao & Xu Zhang, 2009. "Rational Cost Inefficiency in Chinese Banks," Working Papers 292009, Hong Kong Institute for Monetary Research.
    3. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    4. Niklas Ahlgren & Bo Sjo & Jianhua Zhang, 2009. "Panel cointegration of Chinese A and B shares," Applied Financial Economics, Taylor & Francis Journals, vol. 19(23), pages 1859-1871.
    5. Christopher D. Elvidge & Daniel Ziskin & Kimberly E. Baugh & Benjamin T. Tuttle & Tilottama Ghosh & Dee W. Pack & Edward H. Erwin & Mikhail Zhizhin, 2009. "A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data," Energies, MDPI, vol. 2(3), pages 1-28, August.
    6. Kevin Honglin Zhang, 2009. "Rise of Chinese Multinational Firms," Chinese Economy, Taylor & Francis Journals, vol. 42(6), pages 81-96, November.
    7. Jiang, J. & Li, W. & Cai, X. & Wang, Qiuping A., 2009. "Empirical study of recent Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(9), pages 1893-1907.
    8. Doll, Christopher N.H. & Muller, Jan-Peter & Morley, Jeremy G., 2006. "Mapping regional economic activity from night-time light satellite imagery," Ecological Economics, Elsevier, vol. 57(1), pages 75-92, April.
    9. Zhang, Ying & Filippov, Sergey, 2009. "Internationalization of Chinese firms in Europe," MERIT Working Papers 2009-041, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    Full references (including those not matched with items on IDEAS)

    Citations

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


    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.
    2. 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.
    3. 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.
    4. 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.
    5. Yunfeng Hu & Yunzhi Zhang, 2020. "Global Nighttime Light Change from 1992 to 2017: Brighter and More Uniform," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    6. Robert Duerler & Chunxiang Cao & Bo Xie & Zhibin Huang & Yiyu Chen & Kaimin Wang & Min Xu & Yilin Lu, 2023. "Cross Reference of GDP Decrease with Nighttime Light Data via Remote Sensing Diagnosis," Sustainability, MDPI, vol. 15(8), pages 1-13, April.
    7. Jingtao Wang & Haibin Liu & Di Peng & Qian Lv & Yu Sun & Hui Huang & Hao Liu, 2021. "The County-Scale Economic Spatial Pattern and Influencing Factors of Seven Urban Agglomerations in the Yellow River Basin—A Study Based on the Integrated Nighttime Light Data," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    8. Yuantong Jiang & Shoukai Sun & Shuanning Zheng, 2019. "Exploring Urban Expansion and Socioeconomic Vitality Using NPP-VIIRS Data in Xia-Zhang-Quan, China," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Luyao Wang & Hong Fan & Yankun Wang, 2018. "Estimation of consumption potentiality using VIIRS night-time light data," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    18. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. ZhongXiang Zhang, 2012. "Climate Change Meets Trade in Promoting Green Growth: Potential Conflicts and Synergies," Chapters, in: Chin Hee Hahn & Sang-Hyop Lee & Kyoung-Soo Yoon (ed.), Responding to Climate Change, chapter 6, Edward Elgar Publishing.
    2. Boslett, Andrew & Hill, Elaine & Ma, Lala & Zhang, Lujia, 2021. "Rural light pollution from shale gas development and associated sleep and subjective well-being," Resource and Energy Economics, Elsevier, vol. 64(C).
    3. Felbermayr, Gabriel & Gröschl, Jasmin & Sanders, Mark & Schippers, Vincent & Steinwachs, Thomas, 2018. "Shedding Light on the Spatial Diffusion of Disasters," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181556, Verein für Socialpolitik / German Economic Association.
    4. Jaqueson K. Galimberti, 2020. "Forecasting GDP Growth from Outer Space," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 697-722, August.
    5. Juan Jose Miranda & Oscar A. Ishizawa & Hongrui Zhang, 2020. "Understanding the Impact Dynamics of Windstorms on Short-Term Economic Activity from Night Lights in Central America," Economics of Disasters and Climate Change, Springer, vol. 4(3), pages 657-698, October.
    6. Nguyen, Cuong & Noy, Ilan, 2018. "Measuring the impact of insurance on urban recovery with light: The 2011 New Zealand earthquake," Working Paper Series 6955, Victoria University of Wellington, School of Economics and Finance.
    7. Addison,Douglas M. & Stewart,Benjamin P., 2015. "Nighttime lights revisited : the use of nighttime lights data as a proxy for economic variables," Policy Research Working Paper Series 7496, The World Bank.
    8. Ilari Määttä & Thomas Ferreira & Christian Leßmann, 2022. "Nighttime lights and wealth in very small areas: [Nachtlichter und Wohlstand in Kleinräumigen Daten:]," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(2), pages 161-190, August.
    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. Felbermayr, Gabriel & Gröschl, Jasmin & Sanders, Mark & Schippers, Vincent & Steinwachs, Thomas, 2022. "The economic impact of weather anomalies," World Development, Elsevier, vol. 151(C).
    11. Tilottama Ghosh & Sharolyn J. Anderson & Christopher D. Elvidge & Paul C. Sutton, 2013. "Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being," Sustainability, MDPI, vol. 5(12), pages 1-32, November.
    12. Jeremy Proville & Daniel Zavala-Araiza & Gernot Wagner, 2017. "Night-time lights: A global, long term look at links to socio-economic trends," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-12, March.
    13. Thomas Steinwachs, 2019. "Geography Matters: Spatial Dimensions of Trade, Migration and Growth," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 81.
    14. Meng, Lina & Graus, Wina & Worrell, Ernst & Huang, Bo, 2014. "Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a ," Energy, Elsevier, vol. 71(C), pages 468-478.
    15. Nguyen, Cuong & Noy, Ilan, 2018. "Measuring the impact of insurance on urban recovery with light: The 2011 New Zealand earthquake," Working Paper Series 20316, Victoria University of Wellington, School of Economics and Finance.
    16. World Bank, 2015. "Socioeconomic Impact of Mining on Local Communities in Africa," World Bank Publications - Reports 22489, The World Bank Group.
    17. Punam Chuhan-Pole & Andrew L. Dabalen & Bryan Christopher Land, 2017. "Mining in Africa [L'exploitation minière en Afrique]," World Bank Publications - Books, The World Bank Group, number 26110, December.
    18. Hoang Ha Nguyen Thi & Alfons Weichenrieder, 2023. "Tax Haven Welfare and the Crackdown on Secrecy: Evidence from Night Light Emissions," CESifo Working Paper Series 10721, CESifo.
    19. Cuong Nguyen & Ilan Noy, 2018. "Measuring the Impact of Insurance on Urban Recovery with Light: The 2010-2011 New Zealand Earthquakes," CESifo Working Paper Series 7031, CESifo.
    20. Corral, Leonardo R. & Schling, Maja, 2017. "The impact of shoreline stabilization on economic growth in small island developing states," Journal of Environmental Economics and Management, Elsevier, vol. 86(C), pages 210-228.

    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:gam:jsusta:v:9:y:2017:i:2:p:305-:d:90802. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.