Relationship between contextual perceptions and soundscape evaluations based on the structural equation modelling approach

Zhao, Wei; Kang, Jian; Xu, Hongpeng; Zhang, Yifei

doi:10.1016/j.scs.2021.103192

Cited by 22 publications

(8 citation statements)

References 56 publications

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“…However, several auditory indicators have emerged as a part of the soundscape concept, providing a broader understanding of improvements in acoustic environments in residential settings (Aletta et al, 2016). There is a growing body of literature using this framework to investigate how contextual indicators (person-related characteristics and non-auditory contextual indicators) influence both auditory indicators (i.e., perceived sources and sound indicators) and soundscape factors (Hong and Jeon, 2015;Liu et al, 2019;Zhao et al, 2021;Hasegawa and Lau, 2022). The notion of auditory factors, especially pleasantness, has "proved particularly relevant to assess the quality of the sound environment" (Aumond et al, 2017, pp.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…A systematic process to interpret the relationships amongst the perceptual factors and their corresponding indicators has required the application of sophisticated analytical tools, including advanced machine learning techniques, but the focus has been on the use of multivariate techniques such as the Structural Equation Models (SEM) approach. Applied complex models using SEM have involved perceptual factors of interest (i.e., auditory and visual factors), and include mediating effects through the specification of quantitative attributes (Hong and Jeon, 2015;Liu et al, 2019;Zhao et al, 2021). However, we are not aware of prior statistical estimations using a combined specification between Pleasantness and Liveability in a residential environment, when exploring direct and indirect effects amongst objective attributes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the relationships between auditory and visual factors in a residential environment context: A SEM approach

Garzón

Bravo-Moncayo²,

Arellana³

et al. 2023

Front. Psychol.

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To understand the effects of specific elements that may enhance or detract residents’ well-being, it is important to explore the relationships between auditory and visual factors, based on people’s sensory experiences. Although residential environments provide natural experimental conditions to observe these relationships, the complexity of measuring sensory perceptions and their subsequent interpretation constitutes a challenge. This study aims to identify the influence of socio-demographics and residential location characteristics on three latent variables: noise-Sensitivity, sound-Pleasantness, and visual-Liveability in a Latin American city. The methodology is replicable and relies on a digital survey that displays environments in 360-format video and uses sound immersion techniques; it was applied to a sample of household heads in Quito, Ecuador. Based on an efficient experimental design, we selected different residential environments according to acoustic-visual attributes and the proximity to residential, commercial, and recreational land uses. Structural Equation Models (SEM) were estimated using mediating variables. Our results reveal the influence of noise-Sensitivity on sound-Pleasantness and, indirectly, on visual-Liveability. Further analysis shows that the impact of sound and visual perception changes with different socio-demographics and residential location characteristics.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the relationships between auditory and visual factors in a residential environment context: A SEM approach

Garzón

Bravo-Moncayo²,

Arellana³

et al. 2023

Front. Psychol.

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show abstract

“…If e i assumes normally distributed with mean 0 and variance σ 2 i . Weighted mean of effect size estimation y in the fixed effect model is [4]…”

Section: Fixed Effect Meta-analysis Modelmentioning

confidence: 99%

“…Currently, SEM is a widely used method in quantitative research. Many studies apply SEM model in various fields, for example in the fields of economic and social [3,4], education and health [5,6], engineering [7,8], and many others. The previous studies using Structural Equation Modeling (SEM) technique were integrated with the meta-analysis based on the effect size correlation.…”

Section: Introductionmentioning

confidence: 99%

Fixed Effect Meta-Analytic Structural Equation Modeling (MASEM) Estimation Using Generalized Method of Moments (GMM)

2021

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The fixed effect meta-analytic structural equation modeling (MASEM) model assumes that the population effect is homogeneous across studies. It was first developed analytically using Generalized Least Squares (GLS) and computationally using Weighted Least Square (WLS) methods. The MASEM fixed effect was not estimated analytically using the estimation method based on moment. One of the classic estimation methods based on moment is the Generalized Method of Moments (GMM), whereas GMM can possibly estimate the data whose studies has parameter uncertainty problems, it also has a high accuracy on data heterogeneity. Therefore, this study estimates the fixed effect MASEM model using GMM. The symmetry of this research is based on the proof goodness of the estimator and the performance that it is analytical and numerical. The estimation results were proven to be the goodness of the estimator, unbiased and consistent. To show the performance of the obtained estimator, a comparison was carried out on the same data as the MASEM using GLS. The results show that the estimation of MASEM using GMM yields the SE value in each coefficient is smaller than the estimation of MASEM using GLS. Interactive GMM for the determination of the optimal weight on GMM in this study gave better results and therefore needs to be developed in order to obtain a Random Model MASEM estimator using GMM that is much more reliable and accurate in performance.

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“…Results indicate that the application of a combination of these two factors could be a promising approach for developing soundscape prediction models. Zhao et al [ 16 ] constructed a structural equation model for the influence of urban contextual factors (namely, urban management, natural and urban conditions, and the physical environment) on soundscape evaluation (including pleasantness and eventfulness) based on the positive relationship being found. However, the previous researches are restricted to a single or small-scale study area, and the limited influence factors impede the application and promotion of conclusions.…”

Section: Introductionmentioning

confidence: 99%

What Constitutes the High-Quality Soundscape in Human Habitats? Utilizing a Random Forest Model to Explore Soundscape and Its Geospatial Factors Behind

Wang

Weng

Wang

et al. 2022

IJERPH

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Soundscape is the production of sounds and the acoustic environment, and it emphasizes peoples’ perceiving and experiencing process in the context. To this end, this paper focuses on the Pearl River Delta in China, and implements an empirical study based on the soundscape evaluation data from the Participatory Soundscape Sensing (PSS) system, and the geospatial data from multiple sources. The optimal variable set with 24 features are successfully used to establish a random forest model to predict the soundscape comfort of a new site (F1 = 0.61). Results show that the acoustic factors are most important to successfully classify soundscape comfort (averaged relative importance of 17.45), subsequently ranking by built environment elements (11.28), temporal factors (9.59), and demographic factors (9.14), while landscape index (8.60) and land cover type (7.71) seem to have unclear importance. Furthermore, the partial dependence analysis provides the answers about the appropriate threshold or category of various variables to quantitatively or qualitatively specify the necessary management and control metrics for maintaining soundscape quality. These findings suggest that mainstreaming the soundscape in the coupled natural–human systems and clarifying the mechanisms between soundscape perception and geospatial factors can be beneficial to create a high-quality soundscape in human habitats.

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Relationship between contextual perceptions and soundscape evaluations based on the structural equation modelling approach

Cited by 22 publications

References 56 publications

On the relationships between auditory and visual factors in a residential environment context: A SEM approach

On the relationships between auditory and visual factors in a residential environment context: A SEM approach

Fixed Effect Meta-Analytic Structural Equation Modeling (MASEM) Estimation Using Generalized Method of Moments (GMM)

What Constitutes the High-Quality Soundscape in Human Habitats? Utilizing a Random Forest Model to Explore Soundscape and Its Geospatial Factors Behind

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