Urban street clusters: unraveling the associations of street characteristics on urban vibrancy dynamics in age, time, and day
Bibliographic record
Abstract
Abstract Understanding urban vibrancy has been considered crucial to promoting human activities and interactions in public open spaces. Recent advancements in urban big data have facilitated the potential to understand and measure vibrancy patterns throughout cities. While streets are considered the center stage of human activity, previous studies have often overlooked their multifaceted nature and their association with urban vibrancy. In this study, we incorporate multi-source big data and combine a set of features that comprehensively describe the scale, function, and topology of street segments in two Seoul districts: Jung-gu and Gangnam-gu. Using these features, we employ a machine learning clustering technique to classify them into five distinct typologies. Then, with street-level aggregated mobile phone tracking data, we investigate whether street typology characteristics are associated with urban vibrancy with respect to age groups, time of day, and day types (weekends/weekdays). The results show varying relationships between street characteristics with age-, time- and day-vibrancy measures by the identified street typology. Further, we contrast the results of the two districts to evaluate urban vibrancy differences in organic and planned urban layouts. This study enables a more nuanced understanding of urban streets to better comprehend their impact on people’s use of street space. The derived novel insights could assist planners and designers to better pinpoint street management solutions for different age- and time-dependent needs based on the complexities in urban vibrancy dynamics.
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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 | 0.002 | 0.003 |
| 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) | 0.002 | 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".