Digital economy development assessment and spatiotemporal evolution at the urban level in China based on the NTL&POI fusion index
Bibliographic record
Abstract
Precisely measuring digital economy development is essential for optimizing regional spatial layouts and promoting coordinated growth. To address the limitations of traditional data sources and the weak integration of industrial structure in single remote sensing indicators, this study constructs a fusion index combining nighttime light (NTL) and point of interest (POI) data. Coupled with an ε-support vector regression (ε-SVR) model, this NTL&POI fusion index was used to estimate the digital economy index (DEI) for 367 Chinese cities during 2018–2023. The results reveal a marked improvement in urban digital economies: the proportion of starting-stage cities declined from 75 % to 58 %, while leading-stage cities more than doubled from 6 to 13. Spatially, an increasingly clustered distribution around core cities is indicated by the rise of global Moran’s I from 0.235 to 0.381. Spatial imbalance remains significant, with an average annual Gini coefficient of 0.108, and between-region differences accounting for 40.60 % of the total variation. High-value clusters are concentrated in eastern coastal areas, whereas western regions lag behind. Increased spatial concentration is also evident from a contracting standard deviation ellipse, with the center of gravity remaining stable in Nanyang, Henan Province. This study proposes a novel spatial assessment framework for the digital economy using fused multi-source geospatial data as proxies. The findings offer empirical insights into the spatial dynamics and geographic restructuring of the digital economy in China and provide a foundation for targeted policymaking to foster cross-regional coordination, phased development, and integrated digital growth. • The NTL&POI fusion index provides a new perspective for measuring the digital economy. • The digital economy development level in Chinese cities has shown remarkable temporal advancement. • The digital economy of Chinese cities has generated spatial spillover effects in agglomeration. • Significant spatial imbalances characterize digital economy development in Chinese cities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".