Analysis of Hotspots in and outside School Zones: A Case Study of Seoul
Bibliographic record
Abstract
With growing social concern on pedestrian accidents involving children, the Korean government announced a plan to decrease the number of child deaths due to traffic accidents by 2026. Therefore, policymakers should consider various measures for school zones because a safe school walkway is essential for preventing traffic accidents around schools. Some parts of the roads within a radius of 300 m from elementary school and kindergarten entrances are designated as school zones. Certain roads experience frequent accidents within the school zone, while others experience frequent accidents outside the school zone. Hence, this study aimed to provide school zone types in Seoul by noting different occurrence accidents within and outside each school zone and suggest proper countermeasure by type. After selecting a 300 m radius analysis unit from the school zones, a distinction was made between the school zones and outside for each analysis unit. After verifying the spatial autocorrelation in each unit, hotspot analysis identified four types based on the presence or absence of hotspots in each unit. Types were defined as follows: Type A—no hotspots in school zones or outside the school zones; Type B—hotspots only outside the school zones; Type C—hotspots only the school zones; and Type D—hotspots both in school zones and outside the school zones. Subsequently, a case study was conducted to validate the types. For Types B and C, the results revealed differences in the installation of traffic safety facilities and the environment between within and outside the school zones. Therefore, Type B requires improving safety outside the school zones by expanding school zones to match the safety level within. For Type C, it implies the need to strengthen safety measures in the school zones. Lastly, for Type D, improvement projects for a safe walking environment should be implemented in primarily by conducting separate inspections.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".