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Predicting subnational GDP in Vietnam with remote sensing data: a machine learning approach

Author

Listed:
  • Hussein Suleiman

    (Nagoya University)

  • Minh-Thu Thi Nguyen

    (Nagoya University)

  • Carlos Mendez

    (Nagoya University)

Abstract

Official subnational Gross Domestic Product (GDP) data in Vietnam has been available only since 2010, hindering the analysis of long-term dynamics of local development. Based on remote sensing data and machine learning methods, we construct a subnational GDP indicator for the 63 Vietnamese provinces from 1992 to 2009. Specifically, we rely on nighttime lights (NTL), agricultural land, and climate datasets and employ six machine learning algorithms to construct the GDP dataset. We compare the accuracy of several machine learning algorithms and compare the predicted subnational GDP of the best-performing algorithm using two nighttime lights datasets. We show consistent predictions using both datasets, and construct the subnational GDP dataset using the NTL data with the longer temporal coverage. This new dataset allows researchers and policymakers to analyze long-term economic trends at the subnational level in Vietnam, filling a critical gap in historical economic data.

Suggested Citation

  • Hussein Suleiman & Minh-Thu Thi Nguyen & Carlos Mendez, 2025. "Predicting subnational GDP in Vietnam with remote sensing data: a machine learning approach," Letters in Spatial and Resource Sciences, Springer, vol. 18(1), pages 1-12, December.
    • Handle: RePEc:spr:lsprsc:v:18:y:2025:i:1:d:10.1007_s12076-025-00397-z
    DOI: 10.1007/s12076-025-00397-z
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    References listed on IDEAS

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    1. Nguyen Viet Cuong & Tran Ngoc Truong & Roy Van Der Weide, 2010. "Poverty and Inequality Maps in Rural Vietnam: An Application of Small Area Estimation," Asian Economic Journal, East Asian Economic Association, vol. 24(4), pages 355-390, December.
    2. Peter Lanjouw & Marleen Marra & Cuong Nguyen, 2017. "Vietnam’s Evolving Poverty Index Map: Patterns and Implications for Policy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 93-118, August.
    3. Gibson, John & Olivia, Susan & Boe-Gibson, Geua & Li, Chao, 2021. "Which night lights data should we use in economics, and where?," Journal of Development Economics, Elsevier, vol. 149(C).
    4. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    5. Lanjouw, P. & Marra, M.R., 2018. "Urban poverty across the spectrum of Vietnam’s towns and cities," World Development, Elsevier, vol. 110(C), pages 295-306.
    6. Minh-Thu Thi Nguyen, 2024. "Provincial income convergence in Vietnam: spatio-temporal dynamics and conditioning factors," Asia-Pacific Journal of Regional Science, Springer, vol. 8(2), pages 429-460, June.
    7. William Nordhaus & Xi Chen, 2015. "A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics," Journal of Economic Geography, Oxford University Press, vol. 15(1), pages 217-246.
    8. Cuong, Nguyen Viet & Truong, Tran Ngoc & van der Weide, Roy, 2010. "Poverty and inequality maps for rural Vietnam: an application of small area estimation," Policy Research Working Paper Series 5443, The World Bank.
    9. 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.
    10. Nattapong Puttanapong & Nutchapon Prasertsoong & Wichaya Peechapat, 2023. "Predicting Provincial Gross Domestic Product Using Satellite Data and Machine Learning Methods: A Case Study of Thailand," Asian Development Review (ADR), World Scientific Publishing Co. Pte. Ltd., vol. 40(02), pages 39-85, September.
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    More about this item

    Keywords

    Remote sensing; Nighttime lights; Machine learning; Vietnam;
    All these keywords.

    JEL classification:

    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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