Unveiling intra-urban complexity and identifying urban cores through the lens of living structure using point-of-interest data
Bibliographic record
Abstract
The intra-urban space is essentially an organized structure of complexity that consists of centers at different hierarchical levels or scales. This kind of complexity can be measured from the perspective of living structure inspired by Christopher Alexander’s organic view of space. Previous studies have revealed that the living structure can be used to characterize the structural complexity of photos, satellite images and urban systems. However, its potential to measure intra-urban complexity using massive point-based datasets remains underexplored. This study introduces a recursive method to analyze intra-urban complexity using massive point-of-interest (POI) data. By recursively decomposing urban substructures, we quantified structural complexity based on the livingness of substructures using a unified criterion. Our findings indicate that cities or intra-urban areas with higher livingness exhibit greater structural complexity. The resulting substructures exhibit power-law distributions and align closely with human activity patterns across multiple spatial scales in four large cities in China. Remarkably, intra-urban structures can be effectively understood with no more than four levels of recursive decomposition. Furthermore, we found that the urban centers or core areas can be effectively located using the proposed method. These insights underscore the potential of living structure as a framework for understanding and measuring the organized complexity of intra-urban spaces.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".