Noise and the City: Leveraging crowdsourced 311 data to examine the spatio-temporal relationship between urban development and noise annoyance
Bibliographic record
Abstract
This study investigates the spatio-temporal relationship between urban development and noise annoyance in Vancouver, Canada. Noise is one of the most frequently complained nuisances and public health hazards in many cities. Chronic exposure to noise is known to increase stress levels and decrease work productivity. While traffic-related noise has been studied extensively, research on other sources of noise has been lacking. Using a historical inventory of major development projects and novel crowdsourced citizen report data from 2011 to 2016, this study finds that neighborhood noise complaints are significantly associated with year (IRR = 1.074, 95% CI = 1.053–1.098) and counts of major construction (IRR = 1.059, 95% CI = 1.026–1.093), while controlling for neighborhood-level confounders. To our knowledge, this is one of the first studies to empirically show adverse effects of urban development on neighborhood wellbeing with respect to noise. Results inform urban planning policies and decisions for determining how and where to target more concerted effort to mitigate chronic noise problems in rapidly growing cities.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".