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

H. Suleiman et al.

Letters in Spatial and Resource Sciences2025https://doi.org/10.1007/s12076-025-00397-zarticle
AJG 1
Weight
0.41

What the paper says

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.

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https://doi.org/https://doi.org/10.1007/s12076-025-00397-z

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@article{h.2025,
  title        = {{Predicting subnational GDP in Vietnam with remote sensing data: a machine learning approach}},
  author       = {H. Suleiman et al.},
  journal      = {Letters in Spatial and Resource Sciences},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s12076-025-00397-z},
}

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

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.