2020
DOI: 10.1016/j.landurbplan.2020.103932
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Combining visual and noise characteristics of a neighborhood environment to model residential satisfaction: An application using GIS-based metrics

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Cited by 23 publications
(10 citation statements)
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“…Housing comfort is related to the size and performance of the living space 57 . In a residential neighborhood, it is related to the availability of open space and environmental problems such as noise and vibration 58,59 .…”
Section: ) Amenitymentioning
confidence: 99%
“…Housing comfort is related to the size and performance of the living space 57 . In a residential neighborhood, it is related to the availability of open space and environmental problems such as noise and vibration 58,59 .…”
Section: ) Amenitymentioning
confidence: 99%
“…Such 2.5D analyses are preferable to 2D analyses, which result in visible area overestimation when 2D viewsheds are used as field of view (FoV) surrogates [46]. Accordingly, 2.5D tangential view analyses have been successfully implemented in several studies [47][48][49][50][51][52] and were thus applied in this experiment (described in Section 2.5).…”
Section: Visibility Metrics For Visual Pollution Assessmentmentioning
confidence: 99%
“…Previous studies on the public resources of residential communities have primarily focused on residential activities, age-friendly transformations, resilience improvement, and other aspects [19][20][21][22], among which the investigation and analysis of residents' satisfaction have played an important role. The impact of residential public resource factors on the evaluation of residents' satisfaction was examined via partial least squares (PLS) path modeling [22], multigroup structural equation modeling (MSEM) [23], logistic regression [24], and multiple regression [25].…”
Section: Introductionmentioning
confidence: 99%
“…Previous studies on the public resources of residential communities have primarily focused on residential activities, age-friendly transformations, resilience improvement, and other aspects [19][20][21][22], among which the investigation and analysis of residents' satisfaction have played an important role. The impact of residential public resource factors on the evaluation of residents' satisfaction was examined via partial least squares (PLS) path modeling [22], multigroup structural equation modeling (MSEM) [23], logistic regression [24], and multiple regression [25]. From the perspective of the composition of evaluation factors, factors such as residential location [23], social class, age, gender, and income [26] have been proven to have an impact on residents' satisfaction, and residents' subjective cognition often plays a greater role than the objective material conditions.…”
Section: Introductionmentioning
confidence: 99%