Research on the Spatial Connection of Guangdong–Hong Kong–Macao Greater Bay Area Based on Big data of AutoNavi Travel
Bibliographic record
Abstract
Traffic flow is the main carrier and manifestation of the flow of elements such as people and goods, and it can enrich the understanding of urban network and spatial structure. Based on the AutoNavi travel big data of 2020, this study uses methods such as data modeling, spatial analysis, and complex network analysis to analyse the spatial connection of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The results have shown that the interurban connection intensity on the east coast of the GBA is significantly greater than that on the west coast, and cross-city commuting is mainly concentrated in the Guangzhou, Shenzhen and Hong Kong metropolitan circles. The urban network centrality has the characteristics of polarization, and the centrality of Guangzhou, Shenzhen, Foshan and Dongguan is higher than other cities. The diversity of urban network is quite different, and the diversity of Shenzhen, Macau and Hong Kong is low due to the influence of traffic location and geographical location. Generally speaking, the GBA has not yet formed a wide-ranging communication chain. It is recommended to strengthen the construction of the transportation on the west coast, promote the linkage between the East and West Coasts, and provide support for the integrated development of the GBA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".