Noise (Comparison of land use regression and...
URL: https://canue.ca/wp-content/uploads/2021/05/Liu-et-al-Noise-2019.pdf
Estimated noise levels in five Canadian cities were produced by a national team of researchers at the University of Montreal, Ryerson University, Dalhousie University, University of Toronto, the University of British Columbia, Public Health Ontario, the Montreal Regional Department of Public Health and the Department of Public Health of Monteregie, Longueuil. Two approaches were used: land use regression (LUR) and random forest (RF) models. Geographic predictor variables (e.g., proximity to airports, railways and traffic, population density, vegetation, etc.) around noise monitoring locations are used in both approaches to predict the measured level of noise. Results for each method are included in the dataset, although the research team notes that the RF model performed better than the LUR model. See details in the Supporting Documentation. Estimates are available for Vancouver (circa 2003), Toronto (circa 2016-2018), Montreal (circa 2010-2014, Longueuil (circa 2017), and Halifax (circa 2010). The research team used the model results to produce noise level estimates for postal code locations in each city. CANUE staff linked the estimates to annual postal code files from 1991 to 2019. IMPORTANT NOTE: The researchers report that estimated levels best represent the spatial pattern of noise within each city, rather than an accurate measure that would be suitable for analysis of recommended noise level thresholds. Also, the estimated levels represent different time periods in each city. As such, data users should consider using categories of exposure based on the data, as well as the estimated levels provided. Data users should also consider the time periods of the city-specific model results and how these relate to their study approach.
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Additional Information
| Field | Value |
|---|---|
| Data last updated | September 18, 2023 |
| Metadata last updated | October 9, 2023 |
| Created | September 18, 2023 |
| Format | |
| License | License not specified |
| Has views | False |
| Id | 89608e23-c18e-409e-9c3a-0592f67bc888 |
| Mimetype | application/pdf |
| Package id | ef06db83-ff85-4982-ae8b-9b6d67b68979 |
| Position | 0 |
| State | active |