Geospatial modelling and socioeconomic inequalities of transportation, human, and nature-based sounds in Accra, Ghana
Bibliographic record
Abstract
BACKGROUND AND AIM: Urban environments are filled with mixtures of mechanical, human, and nature-based sounds, which can be detrimental, neutral, or even beneficial to physical and psychological health. The majority of epidemiological studies have focused on the harmful effects of transportation noise exposure, and there is a major gap in city-scale information on the distribution of other types of sounds which may impact health and wellbeing. METHOD: Within a large-scale multi-pollutant measurement campaign in Accra, Ghana, we collected a novel dataset of audio recordings at 146 locations over 1yr. The recordings were processed with a pre-trained neural network to classify different types of sounds and then modelled with Random Forest land use regression (LUR) to make predictions across the city with both day and night-time models. We also investigated possible socioeconomic (SES) inequalities by linking the sound predictions to small area data on household consumption, higher educational attainment, and unemployment. RESULTS: LUR model performance, assessed with 10fold cross-validation, was good for road-transport and animal sounds (R2 range 0.37 – 0.70), moderate for human speech and music (R2 range 0.21-0.42), and poor for nature sounds (R2 range: 0 – 0.10). The prevalence of road-traffic sounds was highest in the urban core and along major motorways (median: 63%-42%), human speech and music in low-income high-density residential neighbourhoods (median: 9%-23%), and animal sounds in peri-urban areas (median: 64-71%). There was a weak inverse association between road-traffic sounds and SES (Pearson correlation (r): -0.16 to -0.20). Conversely, animal sounds (like birds) are often described in soundscape surveys as ‘calming’ and ‘pleasant’ and were more prevalent in wealthier neighbourhoods (r: 0.37). CONCLUSIONS: We have illustrated a high-resolution approach to characterising the abundance of a variety of sound types at city scale, which can be incorporated into epidemiological investigations of the direct and interactive effects of harmful and/or potentially beneficial sound exposures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".