Spatial Analysis of Road Traffic Crashes in Oyo State of Nigeria
Bibliographic record
Abstract
Road Traffic Crashes (RTC) are a global concern because of the frequencies of deaths, injuries and material losses experienced by countries as a result of the menace. The significance of the effect of RTC to individuals, societies and nations at large call for investigation into the pattern of the menace across neighbourhoods. This paper examined the characteristics, spatial pattern and concentrations of RTC in Nigeria, Oyo state and across Local Government Areas (LGA). Data on RTC were obtained from Federal Road Safety Commission (FRSC). The longitudes and latitudes of RTC locations were collected based on landmarks provided by FRSC. RTC cases were found to pose a greater risk of deaths than most of the diseases that is the focus of individuals, government, nongovernmental organizations and international bodies in Nigeria. The analysis showed that there was less than 1% likelihood that the observed clustering pattern in RTC could be a result of random chance. The Unique RTC center was found to be Akinyele LGA. The standard deviational ellipse was found to be a more elegant measure of spatial concentration than the standard distance deviation. The black spots include Oyo West, Oyo East, Afijio, Akinyele, Lagelu, Egbeda, Ona Ara, Oluyole, Ido, Ibadan North, Ibadan North East, Ibadan North West, Ibadan South East and Ibadan South West LGA. The results should enable the orientation of safety and injury prevention policies targeted towards reducing the frequency of RTC and deaths of young adults in the state.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".