GIS Based Surveillance of Road Traffic Accidents (RTA) Risk for Rawalpindi City: A Geostatistical Approach
Bibliographic record
Abstract
Identification of previously existing traffic accident hotspots is the first step to ensure future road safety. The present study focused on providing GIS based geostatistical surveillance for the Road Traffic Accidents (RTA) in Rawalpindi for five years (2009-2013) to determine the high risk areas or hotspots. For this purpose, spatial autocorrelation (Moran’s I test), Standard Deviational Ellipse (SDE) and hotspot (Getis-Ord Gi*) analyses were performed on the data obtained from Punjab Emergency Service Department (Rescue 1122). Spatial clusters and hotspots identified during the research lied mostly in the Northern and Northeastern part of the study area encompassing both commercial and residential areas of the city with majority of accident hotspots being near schools, hospitals, airport and highways. The study proposed that serious steps should be taken to improve the road safety conditions in these areas and focus of Emergency Response Providers (ERPs) should be directed there. Furthermore, the integration of GIS based expertise in the Emergency department should be ensured for regular surveillance of shifts in hotspots.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".