Noise and the City: Leveraging crowdsourced big data to examine the spatio-temporal relationship between urban development and noise annoyance
Bibliographic record
Abstract
Noise is one of the most frequently complained nuisances and public health hazards. While traffic-related noise has been studied extensively, research on construction noise has been lacking. In this study, we examined the relationship between construction activities and noise annoyance and tested whether this relationship is stronger during after-hours. Data were drawn from a historical inventory of major development projects and crowdsourced citizen complaints data (311 calls) in Vancouver, Canada from 2011 to 2016. Mixed effects models were developed with an interaction between construction activities and after-hours report. Results show that neighborhood noise complaints were significantly associated with major constructions (IRR = 1.062, 95% CI = 1.024–1.097). A significant interaction effect was also found between construction activities and after-hours reporting (IRR = 1.050 CI = 1.012–1.087). To our knowledge, this is one of the first studies to empirically show adverse effects of urban development on noise annoyance. Results imply that existing noise bylaws may not be effective in restricting construction activities at night and during sleeping hours that may cause adverse health effects.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".