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

Jingyi Wang, Chen Weng, Zhen Wang, Chunming Li () and Tingting Wang ()
Additional contact information
Jingyi Wang: Fujian Key Laboratory of Watershed Ecology, Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Chen Weng: Fujian Key Laboratory of Watershed Ecology, Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Zhen Wang: School of Statistics, Huaqiao University, Xiamen 361021, China
Chunming Li: Fujian Key Laboratory of Watershed Ecology, Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Tingting Wang: School of Statistics, Huaqiao University, Xiamen 361021, China

IJERPH, 2022, vol. 19, issue 21, 1-23

Abstract: 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.

Keywords: soundscape; geospatial factors; machine learning; classification prediction; partial dependence analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/21/13913/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/21/13913/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:21:p:13913-:d:953639

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().