The Analysis of Spatial Pattern and Hotspots of Aviation Accident and Ranking the Potential Risk Airports Based on GIS Platform
Bibliographic record
Abstract
Aviation accident analysis is an important task to ensure aviation safety. The existing researches mainly focus on the analysis of aviation accident time characteristics and accident causes and less analysis of the spatial characteristics of aviation accidents. The spatial characteristics analysis of aviation accidents can identify hot spots of aviation accidents, improve the accuracy of aviation accident emergency management, and provide decision support for airport route planning. This study established the severity index of aviation accident based on aviation accident data, using GIS spatial analysis methods to study the spatial distribution characteristics of aviation accidents. The hot spots were identified in the aviation accidents. Finally, airports around the accident hot spots were ranked to obtain the airports with high potential aviation risks based on RI, taking Florida as an example. It was found that in the Florida aviation accident, general aviation accidents accounted for the majority, but the aviation accident severity index for air route flight was far greater than general aviation accidents. From the spatial distribution point of view, accidents with high severity index were distributed around large international airports. The Density Center for Aviation Accidents was located in Tampa, Miami, and some airports link areas in Florida. In terms of the Moran’s I index, the distribution of aviation accidents tended to aggregate in the region as a whole. However, aviation accident severity index was randomly distributed for each year separately. At the level of significance of 0.01, there were a total of 75 accident hotspots in the Florida region, mainly in the north and southwest. Airports with high RI in the Florida area were mainly concentrated in the Miami area and the Tampa Bay area, and Orlando Airport was ranked outside the top 10.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".