Study on the spatiotemporal evolution of urban spatial structure in Nanjing's main urban area: A coupling study of POI and nighttime light data
Bibliographic record
Abstract
The urban spatial structure reflects a city's development history, cultural heritage, and socio-economic conditions. A rational urban spatial structure is crucial for urban development. This study focuses on the main urban area of Nanjing, analyzing POI and nighttime light data from 2016 to 2020. Utilizing kernel density estimation and coupling coordination models, it explores the temporal and spatial evolution characteristics of Nanjing's urban spatial structure. Geographic detectors are employed to assess the impact of various factors on this structure. The findings indicate that: (1) Nanjing's urban spatial structure displays a pattern of central aggregation and peripheral expansion, with high brightness concentrated in the urban center and a significant increase in peripheral brightness, signaling initial success in establishing urban subcenters; (2) The coupling relationship between nighttime light brightness and POI density has strengthened, suggesting improved coordination of the urban spatial structure; (3) The evolution of Nanjing's urban spatial structure results from the combined effects of multiple factors, including economic level, population distribution, transportation conditions, and policy planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".