An Across-Country Comparison Of The Hierarchical Spatial Structures Of Cities
Bibliographic record
Abstract
This paper investigates the hierarchical structures of 29 selected European countries from the perspective of blocks and natural cities and makes an across-country comparison among the countries. Blocks are minimum cycles consisting of road segments in the road network of a whole country; natural cities are defined as the aggregations of small blocks. We test the size distributions of blocks and natural cities at the country level and find that both exhibit heavy-tailed distributions. The power law distribution of city sizes indicates the presence of the scaling property. Therefore, the cities in a country can be repeatedly grouped into a similar two-tier structure of head and tail via the head/tail division rule. The ascending tiers represent the small, medium, large and mega cities. Accordingly, a simple model is developed to evaluate and cross compare the degree of similarity and stability of the scaling properties and hierarchical structures of cities. Moreover, cities and blocks are the functional units of a country, and the correlation coefficient values between city sizes/number of blocks and economic factors (i.e., gross domestic product and population) are up to 0.87. We further conjecture that the compared results of hierarchies can serve as an indicator to assess a country's economic system.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".