Increases in noise complaints during the COVID-19 lockdown in Spring 2020: a case study in Greater London, UK
TL;DR: In this article, the authors examined how noise complaints changed during the first stages of the lockdown implementation, during Spring 2020, both locally and at city scale, and how urban factors may have been influencing them.
About: This article is published in Science of The Total Environment.The article was published on 2021-09-01 and is currently open access. It has received 43 citations till now. The article focuses on the topics: Noise regulation & Environmental noise.
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TL;DR: In this paper , the spatial and temporal distribution characteristics of noise complaints in urban blocks were analyzed to explore the relationship between noise complaint behaviours and The Point of Interest (POI) distribution.
25 citations
TL;DR: In this paper, the authors investigated the impact of acoustical, building, urban and person-related factors on soundscape dimensions and well-being, and found that perceived dominance of neighbours' noises during relaxation, moderated by noise sensitivity, and the number of people at home were common factors negatively affecting both comfort and wellbeing.
22 citations
TL;DR: It is proved that there were differences of noise pollution before and during COVID-19 in Taiwan, and the decreased noise pollution in Taiwan can improve quality of life.
Abstract: Background and objectives: The impacts of COVID-19 are like two sides of one coin. During 2020, there were many research papers that proved our environmental and climate conditions were improving due to lockdown or large-scale restriction regulations. In contrast, the economic conditions deteriorated due to disruption in industry business activities and most people stayed at home and worked from home, which probably reduced the noise pollution. Methods: To assess whether there were differences in noise pollution before and during COVID-19. In this paper, we use various statistical methods following odds ratios, Wilcoxon and Fisher’s tests and Bayesian Markov chain Monte Carlo (MCMC) with various comparisons of prior selection. The outcome of interest for a parameter in Bayesian inference is complete posterior distribution. Roughly, the mean of the posterior will be clear with point approximation. That being said, the median is an available choice. Findings: To make the Bayesian MCMC work, we ran the sampling from the conditional posterior distributions. It is straightforward to draw random samples from these distributions if they have regular shapes using MCMC. The case of over-standard noise per time frame, number of noise petition cases, number of industry petition cases, number of motorcycles, number of cars and density of vehicles are significant at α = 5%. In line with this, we prove that there were differences of noise pollution before and during COVID-19 in Taiwan. Meanwhile, the decreased noise pollution in Taiwan can improve quality of life.
11 citations
TL;DR: In this article , a review study examines evidence of the COVID-19 crisis impacts on soundscapes and quantifies the prevalence of unprecedented changes in acoustic environments and proposes soundscape materiality, its nexus with related SDGs, and prospective approaches to support resilient soundscape during and after the pandemic, which should be achieved to enhance healthy living and human wellbeing.
9 citations
TL;DR: In this article, the authors quantified census tract-level socioeconomic disparities in noise complaints since 2010 and examined how such disparities changed during the COVID-19 pandemic by using linear mixed-effects models.
8 citations
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Journal Article•
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
47,974 citations
"Increases in noise complaints durin..." refers methods in this paper
...The whole process was implemented using the sci-kit learn 0.19.1 in Python 3.7.0 (Pedregosa et al., 2011)....
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Posted Content•
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.
28,898 citations
TL;DR: The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.
Abstract: Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.
2,782 citations
"Increases in noise complaints durin..." refers methods in this paper
...The variables in this study are not normally distributed, according to the Shapiro-Wilk test (Ghasemi and Zahediasl, 2012; Yap and Sim, 2011)....
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TL;DR: Spearman's rank correlation coecient (RS) as mentioned in this paper is a nonparametric (distribution-free) rank statistic proposed by Charles Spearman as a measure of the strength of the associations between two variables.
Abstract: Spearman’s rank correlation coecient (denoted here by rs) is a nonparametric (distribution-free) rank statistic proposed by Charles Spearman as a measure of the strength of the associations between two variables. It is a measure of a monotone association that is used when the distribution of the data makes Pearson’s correlation coecient undesirable or misleading. Spearman’s coecient is not a measure of the linear relationship between two variables, as some ”statisticians” declare. It assesses how well an arbitrary monotonic function can describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. Unlike Pearson’s product-moment correlation coecient, it does not require the assumption that the relationship between the variables is linear, nor does it require the variables to be measured on interval scales; it can be used for variables measured at the ordinal level. In principle, rs is simply a special case of Pearson’s product-moment coecient
1,249 citations
"Increases in noise complaints durin..." refers methods in this paper
...Therefore, Spearman's rho, as a nonparametric test which does not assume normal distributions (Hauke and Kossowski, 2011), was applied to measure the correlations between the urban factors and noise complaints....
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