IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8062-d1147679.html
   My bibliography  Save this article

Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model

Author

Listed:
  • Fuliang Deng

    (School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Luwei Cao

    (School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Fangzhou Li

    (Development Research Center for Surveying and Mapping, Ministry of Natural Resources of the People’s Republic of China, Beijing 100830, China)

  • Lanhui Li

    (School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Wang Man

    (School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Yijian Chen

    (School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Wenfeng Liu

    (School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Chaofeng Peng

    (School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China)

Abstract

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km 2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km 2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km 2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.

Suggested Citation

  • Fuliang Deng & Luwei Cao & Fangzhou Li & Lanhui Li & Wang Man & Yijian Chen & Wenfeng Liu & Chaofeng Peng, 2023. "Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8062-:d:1147679
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8062/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/8062/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van der Laan Mark J., 2006. "Statistical Inference for Variable Importance," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-33, February.
    2. 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.
    3. Achten, Sandra & Lessmann, Christian, 2020. "Spatial inequality, geography and economic activity," World Development, Elsevier, vol. 136(C).
    4. 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.
    5. Sutton, Paul C. & Costanza, Robert, 2002. "Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation," Ecological Economics, Elsevier, vol. 41(3), pages 509-527, June.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. De Luca, Giacomo & Hodler, Roland & Raschky, Paul A. & Valsecchi, Michele, 2018. "Ethnic favoritism: An axiom of politics?," Journal of Development Economics, Elsevier, vol. 132(C), pages 115-129.
    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. Anna Bruederle & Roland Hodler, 2018. "Nighttime lights as a proxy for human development at the local level," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-22, September.
    5. Keyang Zhou & Yutian Liang & Chen Zhong & Jiaqi Zeng & Zhengke Zhou, 2022. "Spatial Features of Urban Expansion in Vietnam Based on Long-Term Nighttime Lights Data," Land, MDPI, vol. 11(5), pages 1-12, April.
    6. 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.
    7. Ackermann, Klaus & Awaworyi Churchill, Sefa & Smyth, Russell, 2021. "Mobile phone coverage and violent conflict," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 269-287.
    8. 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.
    9. Konno, Akio & Kato, Hironori & Takeuchi, Wataru & Kiguchi, Riku, 2021. "Global evidence on productivity effects of road infrastructure incorporating spatial spillover effects," Transport Policy, Elsevier, vol. 103(C), pages 167-182.
    10. Leonardo Corral & Maja Schling & Cassandra Rogers & Janice Cumberbatch & Fabian Hinds & Naijun Zhou & Michele H. Lemay, 2016. "The Impact of Coastal Infrastructure Improvements on Economic Growth: Evidence from Barbados," IDB Publications (Working Papers) 95978, Inter-American Development Bank.
    11. 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.
    12. Axel Dreher & Steffen Lohmann, 2015. "Aid and growth at the regional level," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 31(3-4), pages 420-446.
    13. 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.
    14. Idriss Fontaine & Sabine Garabedian & Hélène Vérèmes, 2022. "The current and future costs of tropical cyclones: A case study of La Réunion," TEPP Working Paper 2022-10, TEPP.
    15. 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.
    16. Zhang, Ping & Shi, XunPeng & Sun, YongPing & Cui, Jingbo & Shao, Shuai, 2019. "Have China's provinces achieved their targets of energy intensity reduction? Reassessment based on nighttime lighting data," Energy Policy, Elsevier, vol. 128(C), pages 276-283.
    17. Corral, Leonardo & Schling, Maja & Rogers, Cassandra & Cumberbatch, Janice & Hinds, Fabian & Zhou, Naijun & Lemay, Michele H., 2016. "The Impact of Coastal Infrastructure Improvements on Economic Growth: Evidence from Barbados," IDB Publications (Working Papers) 7860, Inter-American Development Bank.
    18. Kiyoyasu Tanaka & Souknilanh Keola, 2017. "Shedding Light on the Shadow Economy: A Nighttime Light Approach," Journal of Development Studies, Taylor & Francis Journals, vol. 53(1), pages 32-48, January.
    19. Keola, Souknilanh & Andersson, Magnus & Hall, Ola, 2015. "Monitoring Economic Development from Space: Using Nighttime Light and Land Cover Data to Measure Economic Growth," World Development, Elsevier, vol. 66(C), pages 322-334.
    20. Patrick Lehnert & Michael Niederberger & Uschi Backes-Gellner & Eric Bettinger, 2020. "Proxying Economic Activity with Daytime Satellite Imagery: Filling Data Gaps Across Time and Space," Economics of Education Working Paper Series 0165, University of Zurich, Department of Business Administration (IBW), revised Sep 2022.

    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:15:y:2023:i:10:p:8062-:d:1147679. 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.