Estimating GDP by Fusing Nighttime Light and Land Cover Data
Bibliographic record
Abstract
Accurate information on gross domestic product (GDP) is essential for better understanding the dynamics of regional economies and urbanization processes. Satellite based nighttime light datasets can well track GDP in urban areas, however, they are difficult to be used in suburban, rural and sparsely populated areas. Thus, this study explored the potential of GlobeLand30 and relief degree for improving the ability of VIIRS Nighttime Light data of GDP estimation. Firstly, we calculated the Moran’s Index (Moran’s I) to measure spatial auto-correlation of GDP. At provincial level, Moran’s I Index of GDP is 0.14, Z value is 2.29. While at the prefecture city level, it is 0.11 and 14.54, respectively. Then, we compared the results derived from geographically weighted regression (GWR) and OLS models (i.e., R2, root mean square error, corrected Akaike information criteria and residuals). Both models suggest that land cover information can significantly improve GDP estimation performance, and total nighttime light (TNL) is the most important economic indicator for estimating GDP. The coefficients of the GWR model for TNL at the provincial and prefecture levels are 1.75 and 1.19, respectively, which are significantly larger than the coefficients for other factors such as land cover and terrain undulation. In addition, the GWR model performed better than OLS model in GDP estimation at both provincial and prefecture levels, and prefecture-level models can better depict the spatial variation in detail. In provincial-level models, GWR could account for 93% of economic development, while OLS could only reflect 82%. Likewise, in prefecture-level models, R2 of GWR model improved almost 50% compared with that of OLS model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".