Relationship between spatiotemporal variability of soundscape and urban morphology in a multifunctional urban area: A case study in Seoul, Korea

Accepted Manuscript Relationship between spatiotemporal variability of soundscape and urban morphology in a multifunctional urban area: A case study in Seoul, Korea Joo Young Hong, Jin Yong Jeon PII:

S0360-1323(17)30479-1

DOI:

10.1016/j.buildenv.2017.10.021

Reference:

BAE 5134

To appear in:

Building and Environment

Received Date: 1 August 2017 Revised Date:

29 September 2017

Accepted Date: 16 October 2017

Please cite this article as: Hong JY, Jeon JY, Relationship between spatiotemporal variability of soundscape and urban morphology in a multifunctional urban area: A case study in Seoul, Korea, Building and Environment (2017), doi: 10.1016/j.buildenv.2017.10.021. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Relationship between spatiotemporal variability of soundscape and urban morphology

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in a multifunctional urban area: a case study in Seoul, Korea

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4 Joo Young Hong a, b and Jin Yong Jeon a

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Department of Architectural Engineering, Hanyang University, Seoul 04763, Korea

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School of Electrical & Electronic Engineering, Nanyang Technological University, 639798,

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Singapore

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Running title: Spatiotemporal variability of soundscapes

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Send correspondence to: Jin Yong Jeon ([email protected])

Architectural Acoustics Lab (Room 605-1)

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Department of Architectural Engineering

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Hanyang University

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17 Haengdang-dong, Seongdong-gu

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Seoul 04763, Korea

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Phone: +82 2 2220 1795 Fax: +82 2 2220 4794

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Abstract This study presents a new perspective on interrelationships among spatiotemporal patterns

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in soundscape, acoustic and urban morphological indicators. Spatiotemporal characteristics of

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soundscapes in a multifunctional urban area of Seoul, Korea were analyzed. Physical acoustic

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and perceived soundscape data were collected during three different daytime sampling

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periods. Urban morphological indicators representing building, road, open public space, and

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water feature components, were analyzed to quantify urban textures. Spatial and temporal

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factors were found to play critical roles in urban soundscapes. Regarding spatial patterns in

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soundscapes, urban soundscapes were characterized based on the main function of spaces.

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The temporal variability of soundscapes in urban spaces depended on diurnal patterns in

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perceived sound sources. Particularly, the perception of birdsong and sounds from human

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activities showed considerable variation across urban spaces. In addition, significant

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correlations were found among the spatiotemporal patterns in soundscape, acoustic and

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morphological factors. Based on the acoustic and morphological indicators, pleasantness and

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eventfulness models were proposed and the results indicated that the pleasantness model

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explained 50% of the variance, while the eventfulness model only predicted 13% of the

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variance. The results from the present study showed that urban morphological factors could

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be useful indicators for better understanding soundscapes in urban environments.

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Keywords: Soundscape, spatiotemporal variability, perceptual evaluation, urban acoustic

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environment, soundscape map, urban morphology

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1. Introduction The acoustic environment is considered an important factor in creating sustainable and

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healthy cities [1,2] because urban noise generated from transportation or industrial facilities

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can adversely impact human health [3–5]. However, many studies reported that reducing

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physical sound pressure levels (SPLs) does not guarantee desirable acoustic quality in urban

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environments, since SPLs cannot differentiate the type of sound sources that have positive or

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negative values [6]. As an alternative approach to managing sound environments,

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soundscapes, which focus on human perception of acoustic environments, have attracted

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attention [2,6,7].

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A soundscape is defined as the “acoustic environment as perceived or experienced and/or

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understood by a person or people, in context [8]”. Therefore, soundscape studies investigate

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perceptual descriptors of environmental sounds, so called soundscape descriptors, and related

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physical environmental factors in certain contexts of place. Soundscape indicators are

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measures used to predict the value of soundscape descriptors.

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In soundscape studies, investigating the relationship between soundscape descriptors and

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indicators is important [9,10]. Soundscape indicators are required to support not only acoustic

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appraisal but also context appraisal [11]. In previous studies, diverse acoustic parameters

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were used as soundscape indicators for acoustic appraisal [6]. A number of soundscape

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studies have been conducted in various outdoor public spaces including parks [12–14],

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squares [10,15,16], and streets [17–19]. In each urban setting, the perceived acoustic

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environment has been assessed based on soundscape descriptors such as acoustic comfort,

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tranquility, restoration, and appropriateness, and the relationships between the soundscape

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descriptors and the acoustic indicators representing strength and the spectral and temporal

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characteristics of the acoustic environment have then been explored in the previous studies.

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ACCEPTED MANUSCRIPT In particular, combinations of conventional noise indicators based on SPLs and

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psychoacoustic parameters based on critical band have been applied to describe the

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characteristics of soundscapes [20,12,21]. Psychoacoustic parameters (e.g., loudness,

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sharpness, roughness, and fluctuation strength) covering several dimensions of basic auditory

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sensation [22] can provide more accurate information on the relationship between the

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acoustic environment (physical phenomenon) and the soundscape (perceptual construct) than

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conventional SPL based noise indicators [23].

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According to ISO 12913-1, context, which includes all other non-acoustic factors of a place,

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plays an important role in the perception of soundscape [8]. The contexts can be categorized

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into the following four clusters: person, place, person-place interaction, and activity [24].

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Hong and Jeon [25] showed that functions of places and activities might influence perceptual

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construction of soundscapes in urban environments. The results indicate that studies focusing

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on non-acoustic indicators are important to more accurately predict soundscape descriptors.

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Urban morphology affects environmental factors in urban areas [26–31]. In addition, urban

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morphology is related to urban activities [32–34]. In this context, relationships between urban

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morphology and acoustic environment have been explored in previous studies [35–47]. In

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those studies, various morphological parameters for buildings, roads, and green areas (e.g.,

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height, width, distance, density, length, coverage ratio, façade and configuration) have been

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applied to characterize the urban form. Some studies have investigated the effect of urban

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morphology in urban areas on the spatial distribution of traffic sound levels [35–39], aircraft

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[40], wind turbines [41] and birdsong [42] because sound propagation in urban built

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environments is significantly influenced by urban morphology. However, those studies were

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limited mainly to noise sources and their physical sound levels, which may not directly relate

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to soundscape perception in urban areas. Relatively few studies focused on the relationships

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ACCEPTED MANUSCRIPT between perceived acoustic environment and urban morphologies [43–46]. Ge et al. [44]

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collected objective and subjective data on acoustic environment in a case study area in Saga,

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Japan and found significant correlations among soundscape descriptors, SPLs and the

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morphological parameters related to buildings, land use, open space, water and green space;

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however, in that study, temporal variation in soundscape components was not considered. Liu

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et al. [43,45] and Mazaris et al. [46] conducted case studies to observe the spatiotemporal

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patterns of perceived sound sources. They examined the relationships between landscape

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indicators (e.g., density, green, and configuration of landscape) and temporal patterns in

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sound sources concluding that spatiotemporal patterns in sound sources are closely associated

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with landscape indices. However, their study focused on the dominance of various sound

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sources, but not perceived soundscape descriptors such as pleasantness or tranquility.

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Therefore, it is necessary to provide comprehensive views on the interrelationships among

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spatiotemporal variations of soundscape components, acoustic indicators and urban

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morphology.

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The aims of the present study are to explore the spatiotemporal patterns of soundscapes in a

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multifunctional urban area, to investigate interrelationships among soundscape descriptors,

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physical acoustic environment, and urban morphology, and to develop soundscape prediction

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model based on the interrelationships. For these purposes, physical acoustic and subjective

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data on soundscapes were measured in an urban area. Subsequently, various morphological

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indicators were analyzed using a geographic information system (GIS) and the relationships

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with the measured soundscape data were explored.

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2. Methods

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2.1 Case study area 5

ACCEPTED MANUSCRIPT As shown in Fig. 1(a), the study was conducted in the north part of Seoul (Joong-gu and

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Jongro-gu areas, 26,550 m2). The case study area was chosen to include various urban

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settings with considerable variation in urban morphological characteristics. The case study

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areas were classified into the following five groups regarding main function of urban spaces

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and activities similar to a previous study [25]: high-density commercial area (e.g., Myeong-

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dong street), low-density commercial area (e.g., Insa-dong street), residential areas (e.g.,

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Sajik-dong), the central business district (CBD) consisting mainly of office buildings (e.g.,

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Euljiro) and an urban recreation area including urban parks (e.g., Deoksugung park) and city

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streams (e.g., Cheonggyecheon).

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Soundscape data were collected from 122 locations over the study area. To analyze the

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morphological indicators, the case study area was divided based on the sampling locations as

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shown in Fig. 1(b). The grids in the present study were modified to better reflect the

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morphological characteristics from the grids used in a previous study [48]. The different grid

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sizes were applied in the study area to reflect the spatial characteristics of the places; the main

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grid size was 150 m × 150 m (110 meshes), while a 150 m × 75 m grid size was applied for

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the city stream areas (e.g., Cheonggyecheon), which has long and narrow spaces (12 meshes).

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In each grid, one sampling location, which was considered to represent the overall

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soundscape quality of the grid, was chosen for collection of soundscape data.

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2.2 Data collection

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Soundscape data were collected using a questionnaire as shown in Appendix A. The

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dominance of the perceived sound sources were assessed using the question, “To what extent 6

ACCEPTED MANUSCRIPT do you presently hear the following types of sounds?” and the following five-point scale: 1 =

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not heard at all, 2 = heard a little, 3 = heard moderately, 4 = heard a lot and 5 = sound

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dominates completely. Based on previous studies [25,45], the types of sound sources were

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classified into five categories: traffic noise, sounds from human activities, water sounds,

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birdsong and music, and other noises such as construction noise or mechanical noise. The

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perceived affective quality was also assessed using the following eight adjectives: pleasant,

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unpleasant, eventful, uneventful, exciting, monotonous, calm, and chaotic [9,49]. At each

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location, the observers evaluated the degree to which an adjective attribute applied to their

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perception of the soundscape quality with the question, “To what extent do you agree with the

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eight attributes below on how you experience the present surrounding sound environment?”

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and using a five-point scale with the following response alternatives: 1 = strongly disagree, 2

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= slightly disagree, 3 = neither disagree nor agree, 4 = slightly agree, and 5 = strongly agree.

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Soundscape data over the study area were collected by a group of eight observers (6 males,

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2 females, Mage = 25.6 years, SDage = 2.0 years) applying the same method used in a previous

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study [48]. To obtain a homogenous group of observers, undergraduate or graduate students

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from the department of architectural engineering were recruited as the observers. Using the

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same soundscape data-collecting protocol, pilot training sessions in data collection

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procedures were conducted for the observers under laboratory conditions to reduce

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observation bias. Similar to a previous study [45], the observers practiced soundscape

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evaluation methods using urban sound excerpts (1-min) with corresponding visual images.

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Inter-rater variability and intraclass reliability were calculated based on the training results to

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quantify the observation bias. Cronbach’s alpha values and the average measure of intraclass

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correlation were greater than 0.9, indicating good reliability of agreement among observers’

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judgments [50].

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ACCEPTED MANUSCRIPT The observers assessed the soundscape during three different periods (period 1: 09:00–11:00,

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period 2: 13:00–15:00, and period 3: 18:00–20:00) at each location. As the case study area is

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too large to enable the collection of the soundscape data by the observers in a day, the study

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area was divided into eight zones (A to I) as shown in Fig. 1(a). Each zone included

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approximately 11–16 measurement locations. The observers used bicycles to move to each

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measurement point within a zone in order to reduce the moving time. In each zone, the eight

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observers recorded soundscape data across the three sampling periods within a day. Thus, a

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total of 8 days were required to perform the soundscape measurements during clear weather

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on weekdays in May. Weekends were not considered for the data collection in order to

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minimize the errors across the measurement days because the soundscape on weekdays might

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be significantly different from that on the weekend. Within each sampling period, both

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perceptual soundscape data and a 5-min recording of the acoustic environment were

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simultaneously acquired at each evaluation location using a binaural microphone (Type 4101,

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B&K, Denmark) and a digital recorder (Zoom, H4n, Japan).

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In total, 2928 responses (122 points × 8 observers × 3 sampling periods) were obtained from

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each observer for eight measurement days. The responses from the eight observers were

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averaged at each measurement point in order to obtain representative values at each point.

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Thus, 366 datasets (122 points × 3 sampling periods) were used for performing the data

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analyses and mappings in this study.

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2.3 Acoustic indicators

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The physical acoustic indicators are summarized by three aspects: sound strength, the

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spectral content, and the temporal structure of sounds [51]. The acoustic recording samples at

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each grid were used to calculate SPLs and psychoacoustic parameters. A-weighted SPL (LAeq)

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was calculated to represent sound strength of the acoustic environment. Regarding the 8

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spectral contents of acoustic environment, LCeq-Aeq, representing energy at low frequencies,

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was calculated [25,49]. The differences between the 10 and 90 percentile levels were

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calculated (L10-90) to quantify the temporal variability of the sound environment.

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for understanding soundscapes in urban environments among the psychoacoustic parameters.

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Loudness represents the magnitude of sound based on an auditory sensation, and sharpness is

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the sensation value of the amount of high-frequency content in the sound, which can be used

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as a quantitative indicator for the spectral envelope [22]. Thus, among the psychoacoustic

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parameters, the loudness and sharpness of the recording were analyzed using a B&K PULSE

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Sound Quality software (Type 7698, B&K, Denmark) based on 1-min audio recordings; the

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Zwicker’s loudness was calculated according to DIN 45631/ A1 (2010) [52] and the

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sharpness was calculated according to DIN 45692, (2009) [53]

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2.4 Urban morphological indices

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Based on previous studies [36,42], various urban morphological parameters were used to

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quantify the 2D and 3D characteristics of urban morphologies in the case study area. Table 1

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presents descriptions and formulas of 16 morphological indices employed in this study. The

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urban morphological indicators were classified into four groups in terms of variables related

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to the following: 1) buildings, 2) green and open public areas (sum of green, square and city

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stream areas), 3) exposed ground and road surfaces, and 4) water features. The 2D

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characteristics of urban morphologies were quantified based on area and perimeter of the

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features at the ground level. Ratios of the total surface areas of buildings to plan areas were

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used to measure the 3D characteristics of building morphologies. The geographical

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information on area, perimeter and height of buildings in the study area were constructed

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using GIS software, ArcGIS ver. 10.1 (ESRI, USA) and the urban morphology indices were

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calculated in each grid.

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Table 1 2.5 Mapping process

The soundscape data collected in the study area during the three sampling periods were

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visualized using GIS software, ArcGIS ver. 10.1 (ESRI, USA). The inverse distance

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weighted (IDW) surface interpolation method was applied to create the sound source and the

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perceived affective quality maps. IDW interpolation is based on the assumption that each

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measured point has more local weight on the predicted values and the weights diminish as a

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function of distance from the measurement point. In previous studies, IDW has been applied

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because it can emphasize the locality of foreground sounds [46,54]. The collected soundscape

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data obtained from eight observers were averaged at each measured location across the three

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sampling periods for the IDW interpolation. The power value in the weight function was set

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as “2,” and the standard search neighborhood defined by the ellipse parameters such as angle

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and the major and minor semi-axis was used to create the interpolation surfaces.

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3. Results and discussion

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3.1 Spatiotemporal patterns of soundscape

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3.1.1 Perceived sound sources

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Spatial distribution of the perceived sound sources including traffic, human, bird and water

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sounds at three different sampling periods are shown in Fig. 2. In terms of spatial distribution

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of sound sources, spatial patterns of the dominated sound source types differed across the

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study area [48]. Overall, traffic sounds dominated near high-traffic roads. Sounds from 10

ACCEPTED MANUSCRIPT human activities and music were primarily observed in urban parks or commercial areas. In

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terms of natural sounds, birdsong was frequently observed in urban green areas and water

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sounds were mainly heard near the water features such as fountains or city streams. The

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temporal distribution of different types of sound sources also varied across the study area.

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Small temporal variations were observed for traffic and water sounds because the sound

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sources, traffic roads and water features are usually situated in fixed locations. In contrast, the

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temporal distributions of sounds related to human activities and birdsong significantly varied

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in the different sampling periods because the behavioral patterns of people and birds are

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affected by time of day [43,45].

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Figure 2

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The percentage of responses recorded as “heard a lot” or “dominates completely” for a

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given source type were calculated to explore dominant sound sources for the three different

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sampling periods and four categories of urban functions as shown in Fig. 3. The dominant

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sound source varied based on the main functions of spaces and sampling period. Traffic

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sounds were most and least dominant in business districts and recreational areas, respectively.

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As plotted in Fig. 3a, the temporal variation in traffic sounds was relatively smaller than other

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sounds implying that traffic noise was a typical background sound over the case study area.

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Human sounds showed significantly different temporal patterns based on different urban

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functional areas, as shown in Fig. 3b. In business districts, the dominance of human sounds

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gradually increased from the morning to evening hours. Interestingly, in commercial districts,

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different temporal tendencies in the dominance of human sound were observed in high- and

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low-density commercial areas. In the commercial district with a high density of buildings, the

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ACCEPTED MANUSCRIPT prominence of human sounds dramatically increased from morning and peaked at 65.4% in

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sampling period 3, indicating that people usually go to the high-density commercial district

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(Myeong-dong shopping district), Seoul’s premier shopping destination, to enjoy their urban

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lives after working hours, which may increase the sounds from human activities. In contrast,

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the low-density commercial area (Insa-dong street), consisting of stores specializing in a wide

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variety of goods representing Korean traditional culture and crafts, exhibited a sudden

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decrease in the dominance of human sounds in sampling period 3. This result indicates that

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people mainly visited this area during the day rather than evening or nighttime. In

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recreational areas, dominance of the human sounds in urban recreational areas gradually

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decreased from sampling period 1 to period 3. This result indicates that the frequency of

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visitors in the recreational areas in the study area were concentrated during daytime. For

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residential areas, human sounds were frequently heard during the commuting hours in the

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morning (period 1) and evening (period 3). Temporal patterns for music according to main

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space function were similar to the patterns of human sounds (Fig. 3c). These findings implied

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that population flow and temporal visitation patterns differed by time of day depending on the

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socio-spatial characteristics of urban space.

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Bird and water sounds were most dominant in urban recreational areas as shown in Figs. 3d

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and 3e, respectively. The dominance of bird sounds significantly declined in period 3 by

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10.0%, whereas water sounds slightly increased by 23.3%. The observers could better

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perceive water sounds in the evening (period 3) due to decreased human activities in urban

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stream areas.

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3.1.2 Perceived affective quality Principal component analysis (PCA) was conducted based on the responses for eight

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semantic attributes to extract the perceived affective quality of soundscape using varimax

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rotation method. Two components with eigenvalues larger than 1 were obtained. Components

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1 (eventfulness) and 2 (pleasantness) explained 41.3% and 30.8% of the variance in the data

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set, respectively. The Kaiser–Mayer–Olkin (KMO) measure of the sampling adequacy was

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0.77 and the Bartlett's test of sphericity was also significant (χ2 (28) = 1725.53, and p < 0.01),

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which indicates that the data set is appropriate for PCA.

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As listed in Table 2 for the factor loadings of the eight attributes, the PCA results showed

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good agreement with the pleasantness-eventfulness model of soundscape perception proposed

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in a previous study [49]. Pleasantness is associated with hedonic value of sound and

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eventfulness is associated with variety of sounds or temporal structure of a soundscape. This

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2D model of perceived soundscape quality has been widely used in soundscape studies

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because it can provide comprehensive soundscape information for understanding

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soundscapes.

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Table 2

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To calculate the component scores for evaluation locations in the study area, the regression

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method included in IBM SPSS 23 software was used and the component scores for

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pleasantness and eventfulness were mapped over the study area for the three sampling

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periods as shown in Fig. 4. The temporal variation for the pleasantness score (Fig. 4a) was

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smaller than the eventfulness score (Fig. 4b) over the study area. 13

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Figure 4

313 Regarding the functions of urban spaces, higher pleasantness scores were observed in

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recreational and residential areas, while the office district and high-density commercial areas

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showed lower pleasantness scores (Fig. 5a). As shown in Fig. 5b, the high-density

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commercial areas had the highest eventfulness score among the five types of urban functions

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in the three sampling periods and peaked during period 3. The eventfulness scores in the low-

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density commercial areas and urban recreational areas gradually decreased across the

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sampling periods. Overall, the business districts and the residential areas were assessed as

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uneventful soundscape for periods 1 and 2, but eventfulness score for the residential areas

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slightly increased in period 3.

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Figure 5

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Correlation coefficients between PCA component scores and perceived sound source types

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were calculated as shown in Table 3. Perception of traffic sounds had strong and negative

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correlation coefficients with the pleasantness scores for all sampling periods and no

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significant correlations with eventfulness scores. The dominance of human activity sounds

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was not associated with pleasantness score but showed significant and positive correlations

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with eventfulness scores over the sampling periods. In addition, the correlation coefficients

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for the sampling periods 2 (r=0.51, p<0.01) and 3 (r=0.55, p<0.01) were slightly larger than

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for period 1 (r=0.42, p<0.01). Similar to the human sounds, perception of music sounds

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showed positive correlation with eventfulness, but the correlation strength was less than

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human sounds showing correlation coefficients of approximately 0.3 (p<0.01). There were

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ACCEPTED MANUSCRIPT modest correlations between pleasantness scores and perception of water sounds over the

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study area. The perceived dominance of birdsong showed significant correlations with

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pleasantness scores. Interestingly, the correlations became weaker over the periods where the

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highest correlation was 0.61 (p<0.01) in period 1 and the correlation declined by 0.38

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(p<0.01) in period 3. This result was in good agreement with the temporal tendency of bird

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sound dominance over the sampling periods.

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Table 3

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The calculated acoustic parameters in the three sampling periods for different urban spatial

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functions are listed in Table 4. The mean value of LAeq ranged from 62.0 to 69.0 dB over the

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study area, which fell below noise standards set by the Korean Ministry of Environment

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(KMOE). Regarding the loudness of sound environment, the business district and commercial

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areas showed higher LAeq and loudness values than the residential and recreational areas. The

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LAeq and loudness values tended to decrease slightly from period 1 to period 3 except in high-

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density commercial areas. The spectral characteristics of sound environment differed

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according to the functions of space. The sound environment in the business districts and high-

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density commercial areas showed higher LCeq-Aeq and lower sharpness values indicating that

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main sound sources in the areas contained higher energy at low frequencies. In contrast, the

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acoustic environment in the low-density commercial area had greater high-frequency content

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with lower LCeq-Aeq and higher sharpness values. The residential and recreational areas

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showed both relatively higher LCeq-Aeq and sharpness values compared with other urban areas.

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Correlation coefficients among the acoustic parameters and identified sound sources were

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calculated as listed in Table 5. Similar correlation tendencies between acoustic parameters

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and perceived sound source types were found in a previous study [13]. Traffic noise was 15

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ACCEPTED MANUSCRIPT positively correlated with LAeq (r=0.55, p<0.01), loudness (r=0.53, p<0.01) and L10-90 (r=0.37,

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p<0.01), whereas strong and negative relationships were found between traffic sound and

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sharpness. This finding supports that road traffic sounds, containing significant low-

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frequency energy, are the main sound source that increases the background noise level in

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urban areas. The presence of human sounds (r=-0.31, p<0.01) and music (r=-0.26, p<0.01)

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were negatively correlated to LCeq-Aeq indicating that sounds from human activities usually

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consist of higher frequencies. In addition, human sounds showed weak correlation with LAeq

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(r=0.17, p<0.01) and loudness (r=0.11, p<0.05) indicating that human sounds also contribute

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to the increase in background sound levels during a certain period. Perception of bird sounds

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was negatively correlated with overall loudness of the sound environment (LAeq and loudness)

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and positively associated with sharpness. This finding demonstrates that urban green areas

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with low traffic noise level could provide urban habitat for birds [55]. With respect to the

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temporal variation, bird sounds showed large variations in the correlations with the acoustic

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parameters according to sampling period; the correlation coefficients between the bird sounds

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and the acoustic parameters decreased in period 3 due to the decreasing behavioral

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frequencies in the period. This finding of temporal behavioral pattern of birds in urban areas

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corresponds well with results of a previous study [43].

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Table 4 Table 5

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3.2 Relationship between morphological indices and soundscape variables

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3.2.1 Spatial variation of morphological indices

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ACCEPTED MANUSCRIPT PCA was also conducted to characterize the 16 morphological variables. As in the previous

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PCA, varimax rotation was applied to obtain the main components. The KMO measure of the

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sampling adequacy was 0.53, and the Bartlett’s test of sphericity was significant (χ2 (105) =

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3952.25, and p < 0.01), which suggests that PCA with the 16 morphological indices is

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feasible. Four components with Eigenvalues larger than 1 were found, as listed in Table 6.

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The extracted components showed good agreement with our prior classification for 16

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morphological indices. Component 1 (Open space) is highly related to variables representing

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urban green and public areas. Component 2 (Building) represents morphological variables

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associated with building areas and surfaces. Components 3 (Water feature) and 4 (Roads) are

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associated with variables representing urban water features and road surfaces, respectively.

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Table 6

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To understand the morphological characteristics of spaces with different functions, the

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component scores for morphological variables were calculated using regression analysis. The

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range of component scores varied by urban function as shown in Fig. 6. The business and

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commercial areas in the study area can be characterized by higher building density than the

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other urban functional areas. High-density commercial areas obtained higher component 2

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and 3 scores than other areas indicating these areas consisted mainly of buildings and wide

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roads. The low-density commercial areas showed relatively lower component 3 scores

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implying these areas consisted of narrower roads. Residential areas showed small ranges and

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neutral values for all component scores. Recreational areas showed higher values for

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component 1 and lower values for component 2 than the other functional areas, indicating

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recreational areas can be characterized as places with large open areas and low density of

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ACCEPTED MANUSCRIPT 407

buildings. In addition, the range of component 3 values in the recreational areas was the

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largest among the urban functions indicating roads are main morphological components in

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urban recreational areas.

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Figure 6

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3.2.2 Correlation between soundscape variables and morphological indices

The relationship between the identified sound source types and morphological indices were

415

explored by calculating the correlation coefficients (Table 7). As expected, traffic sounds

416

showed significant positive correlations with morphological factors related to road areas

417

(Rd(A) and RAF). Although the correlation was weaker than Rd(A) and RAF, negative

418

relationships were observed between perception of traffic sounds and morphological indices

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for Open space (Op(A), Op(P), and OSR). Statistically significant correlations were not found

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between traffic sounds and building factors.

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Interestingly, human sounds mainly correlated with building-related components only for

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period 3 and no significant relationships were observed for periods 1 and 2. In period 3, as

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the morphological indices associated with the building area and perimeter increased, the

424

dominance of human sounds increased. This finding may be due to human activities

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significantly increasing after working hours, especially in commercial areas, which mainly

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consist of building components. This implies that the relationships between morphological

427

factors and human sounds depend on the temporal behavioral patterns of human activities.

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Unlike human sounds, music sounds are consistently associated with building components

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because music sounds are usually generated by shops in the commercial areas, which are

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relatively independent from human activity.

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ACCEPTED MANUSCRIPT Bird sounds were significantly associated with morphological variables for green and open

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public spaces and had a negative correlation with road-related indices (Rd(A) and RAF).

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Identification of water sounds was significantly correlated with the morphological indicators

434

related to water features (Wt(A) and Wt(P)). In addition, the variable associated with open

435

public space (Op(P) and OSR) strongly correlated with water sounds because water features

436

such as fountains are usually installed in urban public spaces such as urban squares. The

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building-related indicators also had a negative relationship with water sounds; however, this

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should be interpreted as an indirect relationship within the case study area because water

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sounds were directly related to water features.

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3.2.3 Regression models using acoustic and morphological factors

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3.2.3.1 Sound source models

Multiple linear regression analyses were performed to investigate the contributions of the

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acoustic and urban morphological metrics prediction of the perceived sound sources in the

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case study area. For the acoustic indicators, LAeq, LCeq-Aeq, L10-90 and sharpness values were

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selected as independent variables. The loudness value was removed due to multicollinearity

448

with LAeq in the regression models. In terms of urban morphological indicators, four

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component scores including Open public space, Buildings, Roads, and Water features were

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used to reduce the number of morphological variables. The regression models were

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developed using not only the total dataset but also datasets for each sampling period to

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compare the temporal contributions of the input variables.

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The standardized regression coefficients (β) for perceived traffic, human, bird and water

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sounds, and the coefficients of determination for the regression models are listed in Table 8. 19

ACCEPTED MANUSCRIPT In terms of traffic noise, the regression model explained 44% to 55% of the variance in

456

perceived traffic noise. Among the acoustic indicators, the contributions of LAeq were the

457

greatest over the sampling periods. The contributions of the morphological components were

458

relatively smaller than of acoustic indicators. The Water feature component had a negative

459

relationship with traffic noise, whereas the Road component had positive contribution to the

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perceived dominance of traffic noise; however, the strength was weak.

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The regression models for human sounds showed lower R2 values than other types of sounds

462

indicating that further efforts should be taken to develop better acoustic and morphological

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indicators to accurately predict human sounds in urban areas. The regression model of period

464

1 explained approximately 14% of the variance in human sound, while R2 in periods 1 and 3

465

increased by 24% and 25%, respectively. Among the acoustic indicators, LCeq-Aeq,

466

representing the low-frequency sound content, had only a significant negative relationship

467

with human sounds in the sampling periods because human sounds usually contain higher

468

frequency energy. This finding was in good agreement with a previous study showing that

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human-made sounds in urban environments were associated with frequency-related indicators

470

[12]. Regarding the morphological components, the Building component significantly

471

contributed to the prediction of human sounds only in sampling period 3. This is supported by

472

the observation that human activities were dramatically increased in the commercial districts

473

with high density of buildings in sampling period 3 in the case study area (Figs. 3 and 6). In

474

addition, the contribution of the Building component (β=0.35) was greater than LCeq-Aeq (β=-

475

0.26). This result also demonstrated that the contribution of urban morphological factors

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depends on diurnal patterns of human sounds in urban environments.

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With respect to acoustic and morphological factors LAeq and Open space significantly

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contributed to the explanation of perceived bird sounds in urban areas, respectively, in the 20

ACCEPTED MANUSCRIPT sampling periods. In contrast to the regression model for perceived human sounds, the R2

480

values of the regression models for bird sounds gradually decreased from 0.51 to 0.21 across

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the three sampling periods. This result might be associated with the observed temporal

482

behavioral patterns of bird sounds in urban areas implying that temporal factors play

483

important roles in developing the prediction models, especially for biological sounds.

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The regression models for perceived water sounds explain more than 50% of the variance of

485

the identification of water sounds in the case study area. The acoustic indicators including

486

LAeq and sharpness showed significant relationships with perceived water sounds in period 1.

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In terms of morphological factors, Water feature showed the most significant contribution for

488

predicting water sounds for all periods in the case study area. This finding supports the use of

489

morphological indicators for water features as reliable indicators for mapping the water

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soundscape in urban areas [56,57] because in general, water sounds are generated from water

491

features such as fountains, rivers or sea, which are fixed at certain locations in urban areas.

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Table 8

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3.2.3.2 Perceived affective quality models Multiple linear regression analyses were performed to predict pleasantness and eventfulness

497

scores using the acoustic and urban morphological indicators. Regression models for

498

pleasantness and eventfulness were developed and Table 9 summarizes the regression

499

analyses. The four acoustical and four urban morphological indicators were applied in the

500

same manner as in the perceived sound source model in the previous section.

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Regarding the goodness of fit of the models, the pleasantness model showed significantly

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higher R2 than the eventfulness model. The pleasantness model explained approximately 50%

503

of the variance, whereas the eventfulness model only predicted 13% of the variance. For the 21

ACCEPTED MANUSCRIPT pleasantness model, LAeq was the most significant variable in terms of the acoustic

505

parameters. Regarding the morphological factors, the contribution of the water feature was

506

relatively stronger than the other morphological components even though the pleasantness

507

score showed higher correlation with perception of bird sounds than water sounds (Table 2).

508

Open space showed limited correlation with perceived bird sounds in the case study area

509

ranging from 0.32 to 0.40. Relationships between bird sounds and morphological indicators

510

related to urban green areas were also investigated in previous studies [42,43]; however, the

511

suggested indicators are limited to accurately predict birdsong in urban areas. Thus, more

512

reliable morphological indicators should be developed for bird sounds in the future.

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For the eventfulness model, the effects of LAeq and LCeq-Aeq were statistically significant

514

among the acoustic parameters, while the contribution of the Building component was only

515

significant among the morphological components. In general, eventfulness of soundscape was

516

closely associated with presence of human sounds, which was found dependent on temporal

517

patterns of human activity in urban areas. Reportedly, the eventfulness of soundscape plays a

518

vital role in judging appropriateness of soundscape in multifunctional urban spaces [9,25].

519

Thus, in the future, investigating relationships between soundscapes and the indicators

520

representing human activities, spatial configurations or building functions is necessary to

521

establish better eventfulness models.

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Table 9

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Conclusion

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In the present study, the relationships between the acoustic, urban morphological indicators

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and spatiotemporal characteristics of soundscapes were explored in multifunctional urban 22

ACCEPTED MANUSCRIPT areas in order to provide insight for soundscape planning and management in an urban

529

environment. Both physical acoustic data and perceptual soundscape data were collected by

530

observers at 122 locations over the three sampling periods from morning to evening in the

531

case study area. Morphological indicators relating to buildings, roads, open public spaces,

532

and water feature components were calculated in order to measure the urban forms.

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The soundscapes varied spatially and temporally in the urban area throughout a day. In

534

terms of spatial variations, urban soundscapes were influenced by the main function of the

535

spaces. The temporal variability of soundscapes in urban spaces was affected by diurnal

536

patterns in sound sources. Traffic and water sounds had relatively constant correlations with

537

acoustic and morphological indicators over the course of the day as sound sources such as

538

roads and water features are usually fixed in urban areas. In contrast, the dominance of

539

birdsong and sounds from human activities varied throughout the day owing to greater

540

temporal variation in biological and anthropogenic activity in urban areas.. This indicates that

541

temporal patterns in various sound sources can play critical roles in creating soundscapes in

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urban areas.

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Regression models for pleasantness and eventfulness were proposed based on acoustic and

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morphological indicators, and the results revealed that the application of a combination of

545

acoustic and morphological factors could be a good approach for developing soundscape

546

prediction models in urban spaces. Pleasantness model was developed using LAeq, open space

547

and water feature components, which explain approximately 50% of the variance in the

548

dataset. However, the eventfulness model had some limitations in its explanation of the

549

variance of the collected soundscape data in this study, which indicates that further studies

550

will be required to investigate soundscape indicators for eventfulness. In particular,

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soundscape indicators for bird and human activities in urban spaces, which are closely related

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ACCEPTED MANUSCRIPT 552

to the pleasantness and eventfulness of soundscapes, respectively, should be explored in order

553

to improve soundscape prediction models. The present study has some inherent limitations. One limitation is the use of a small number

555

of observers in their 20s, whose observations may not be directly transferable to general

556

results. In order to generalize the results, large soundscape data collection from various age

557

groups is essential. Furthermore, the findings in this study are limited to the case study area

558

of Seoul, Korea. The urban density and morphology of typical western and eastern cities are

559

significantly different. Thus, cross-national comparative studies on relationships between

560

urban morphology and its soundscape should be conducted in the future. Despite these

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limitations, this study has significantly enhanced our understanding of the relationships

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between the urban morphology and soundscapes in urban areas, which will aid in urban

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soundscape planning and design.

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Table 1. Calculation of urban morphological parameters. (A) and (P) denote area and perimeter, respectively. Indicators Definition

Formula

Sum of building area

Bldg (P)

Sum of building perimeter

Bldsf (A)

Sum of building surface area

BPAF

The ratio of the plan area of buildings to the total surface area The summed area of roughness elements and exposed ground divided by the total surface area of the study region

0.00 – 144543.70



0.00 – 4819.35

M AN U

SC



Sum of green area

Gr (P)

Sum of green perimeter

Op (A)

Sum of open public area including urban squares, green and water areas

Op (P)

Sum of open perimeter including urban squares, green and water feature areas

TE D

Gr (A)

The ratio of the open area divided by the total surface area of the study region

Grd (A)

Sum of exposed ground area

Rd (A)

Sum of road area

EP

OSR



0.00 – 10387198

( ) ( )

0.00 – 0.64

( ) ( )

0.22 – 4.82

0.00 – 1575.57







0.00 – 557.03



0.00 – 15754.57





0.00 – 686.20

( ) ( )

0.00 – 0.70 630.29 – 17416.47





Wt (A)

Sum of water feature area

Wt (P)

Sum of water feature perimeter

AC C

RAF

The ratio of the exposed ground area divided by the total surface area of the study region The ratio of the road area to the study region

EGR



RI PT

Bldg (A)

CAR

Range

32.07 – 14765.52

( ) ( )

0.03 – 0.77

( ) ( )

0.00 – 0.66



32

!

0.00 – 2106.92



0.00 – 342.11

ACCEPTED MANUSCRIPT Table 2. Rotated component matrices of the PCA using semantic attribute responses (numbers in parentheses represent explained variance) Component 2 (30.8%)

0.13 0.00 -0.34 0.09

0.88 -0.92 0.80 -0.66

AC C

EP

TE D

M AN U

0.91 -0.84 -0.75 0.89

RI PT

Component 1 (41.3%)

SC

Attributes Pleasantness Pleasant Unpleasant Calm Chaotic Eventfulness Eventful Uneventful Monotonous Exciting

33

0.03 0.11 0.18 0.03

ACCEPTED MANUSCRIPT Table 3. Correlation coefficients between perceived sound sources and perceptual components of soundscapes for the three sampling periods (*p<0.05, **p<0.01) Water 0.17* 0.24** 0.29** 0.23** 0.11 0.08 0.04 0.08

M AN U TE D EP AC C 34

Bird 0.61** 0.54** 0.38** 0.51** -0.12 -0.10 0.06 -0.05

RI PT

Music 0.18* 0.03 -0.01 0.07 0.30** 0.37** 0.36** 0.34**

SC

Period Traffic Human ** Pleasantness P1 -0.65 -0.02 ** P2 -0.64 -0.07 P3 -0.65** -0.08 ** Total -0.64 -0.05 Eventfulness P1 -0.06 0.42** P2 0.01 0.51** P3 -0.07 0.55** Total -0.04 0.49** P1 (09:00-11:00), P2 (13:00-15:00) and P3 (18:00-20:00)

ACCEPTED MANUSCRIPT Table 4. Mean values of acoustic parameters of the three sampling periods

P2 P3 High-density

P1

Commercial P2 P3 Low-density Commercial

P1 P2 P3

Residential

P1

L10-90 [dB]

Loudness [sone]

Sharpness [acum]

Mean

67.88

9.91

6.90

25.03

1.69

SD Mean SD Mean SD

4.47 68.49 4.32 66.50 4.76

2.92 9.98 3.23 9.65 3.04

3.23 7.13 2.76 6.82 3.37

6.63 25.46 6.46 22.67 6.58

0.12 1.70 0.18 1.72 0.15

Mean SD Mean SD Mean SD

69.00 5.47 69.29 4.82 69.72 4.66

8.45 2.62 9.18 2.74 7.47 3.03

6.38 2.64 6.22 2.22 6.42 3.30

26.18 7.69 27.36 8.29 26.66 6.43

1.66 0.11 1.65 0.15 1.66 0.16

Mean SD Mean SD Mean SD

67.44 5.46 67.13 6.24 66.78 4.73

7.58 3.36 6.84 3.22 6.32 2.57

9.12 3.39 8.34 3.28 8.79 4.80

22.48 5.96 22.25 6.85 21.53 6.68

1.85 0.13 1.90 0.19 1.90 0.22

Mean SD Mean SD Mean SD

63.20 6.29 63.73 5.41 61.95 5.66

9.81 2.55 10.27 3.44 8.69 3.00

8.17 3.27 7.40 3.40 8.04 3.16

19.09 8.17 18.58 7.45 16.97 6.06

1.93 0.25 1.92 0.30 1.97 0.25

6.38 3.31 5.36 2.49 5.19 3.28

22.24 9.10 20.19 6.97 18.67 7.34

1.86 0.27 1.83 0.25 1.93 0.31

EP

P2

LCeq-Aeq [dB]

AC C

P3 Recreational

RI PT

P1

LAeq [dB]

SC

Business

Statistics

M AN U

Period

TE D

Functions

P1

Mean 65.62 9.59 SD 6.13 3.62 P2 Mean 64.08 9.36 SD 5.62 3.15 P3 Mean 62.96 8.43 SD 6.71 2.88 P1 (09:00-11:00), P2 (13:00-15:00) and P3 (18:00-20:00)

35

ACCEPTED MANUSCRIPT Table 5. Correlation coefficients between perceived sound sources and acoustic parameters (*p<0.05, **p<0.01)

M AN U

TE D

EP AC C 36

Sharpness -0.48** -0.52** -0.56** -0.52** 0.07 -0.11 -0.22* -0.09 0.00 -0.04 0.00 -0.01 0.58** 0.58** 0.30** 0.46** 0.34** 0.20* 0.14 0.21**

RI PT

Loudness 0.52** 0.56** 0.51** 0.53** 0.02 0.19* 0.13 0.11* -0.07 0.08 0.17 0.06 -0.54** -0.53** -0.31** -0.44** 0.10 0.11 -0.02 0.07

SC

Period LAeq LCeq-Aeq L10-90 ** Traffic P1 0.55 -0.13 0.34** ** P2 0.58 0.03 0.38** ** P3 0.52 0.16 0.41** Total 0.55** 0.03 0.37** Human P1 0.11 -0.34** 0.11 * P2 0.22 -0.35** 0.04 * ** P3 0.19 -0.24 0.04 Total 0.17** -0.31** 0.06 ** Music P1 0.02 -0.29 -0.03 P2 0.10 -0.25** -0.09 ** P3 0.15 -0.25 -0.08 Total 0.09 -0.26** -0.07 ** Bird P1 -0.55 0.12 -0.04 P2 -0.59** 0.01 -0.20* P3 -0.36** 0.13 -0.11 Total -0.47** 0.10 -0.10 Water P1 0.03 0.01 -0.28** P2 0.08 -0.08 -0.23* P3 -0.09 -0.06 -0.25** Total 0.01 -0.04 -0.25** P1 (09:00-11:00), P2 (13:00-15:00) and P3 (18:00-20:00)

ACCEPTED MANUSCRIPT Table 6. Rotated component matrices of the PCA using morphological indices (numbers in parentheses represent explained variance) 2 (19.44%)

3 (16.81%)

4 (10.54%)

0.89 0.84 0.90 0.77 0.89

-0.11 -0.16 -0.29 -0.40 -0.32

-0.04 -0.10 0.10 0.08 0.11

-0.09 -0.09 0.04 0.31 0.17

-0.18 -0.30 -0.09 -0.18 -0.36

0.91 0.87 0.75 0.94 0.81

-0.10 0.06 0.06 -0.11 -0.06

-0.19 -0.09 -0.14 -0.13 -0.01

-0.20 -0.21

0.10 0.09

0.96 0.96

-0.18 -0.17 -0.27 -0.25

0.90 -0.79 0.92 -0.85

-0.16 -0.30 0.12 -0.14

TE D

-0.23 -0.39 -0.22 -0.42

SC

M AN U

0.04 0.02

RI PT

1 (41.29%)

AC C

EP

Component Open space Gr (A) Gr (P) Op (A) Op (P) OSR Building Bldg (A) Bldsf (A) Bldg (P) BPAF CAR Water feature Wt (A) Wt (P) Road Rd (A) Grd (A) RAF EGR

37

ACCEPTED MANUSCRIPT

Table 7. Correlation coefficients between perceived sound sources and morphological indices (*p<0.05, **p<0.01)

P2 P3

0.05

0.04

-0.05

-0.09

-0.01 0.00 -0.12

0.03

0.03

0.01

-0.01

-0.05

P1

0.18*

0.06

0.21*

0.13

0.02

-0.19

*

-0.15 -0.12 -0.15

**

-0.01

Gr(P)

Op(A)

Op(P)

-0.15

-0.20

*

*

-0.22

*

-0.11 -0.11 -0.12

*

-0.06

-0.14 -0.18

**

-0.13

-0.20

OSR -0.22

*

Wt(A)

Wt(P)

Rd(A)

*

*

0.45

**

-0.27

**

-0.18 -0.22

*

**

-0.21*

-0.25

**

0.45

**

0.45

**

0.45

**

-0.11

-0.06 *

-0.20

-0.16

-0.21

**

-0.14

-0.23

**

-0.20

-0.21

*

**

-0.13

-0.20 -0.24

**

-0.22 -0.22

*

**

Grd(A)

RAF

EGR

0.36

**

-0.14

0.34

**

-0.13

0.37

**

-0.12

-0.06

0.36

**

-0.13*

0.13

-0.06 -0.05 -0.05

0.10

0.16

-0.26**

0.11

0.15

0.11

0.04

0.06

0.03

0.02

0.00

0.02

0.02

0.01

0.18

P3

0.32**

0.37**

0.30**

0.32**

0.32**

-0.14

-0.18*

-0.11

-0.14

-0.11

-0.06

-0.06

0.08

-0.20*

0.07

-0.23*

Total

0.22**

0.18**

0.22**

0.19**

0.13*

-0.03

-0.07

-0.08

-0.12*

-0.08

-0.06

-0.06

0.06

-0.09

0.04

-0.13*

P1

0.20*

0.18*

0.25**

0.20*

0.19*

-0.12

-0.14

-0.12

-0.08

-0.10

0.11

0.09

-0.19*

0.09

-0.18

0.09

P2

*

*

0.27

**

0.17

0.16

-0.11

-0.09

-0.07

-0.07

-0.06

0.02

0.01

-0.09

0.02

-0.09

0.00

0.47

**

-0.19

*

-0.10

-0.11

-0.05

-0.01

-0.11

-0.06

0.33

**

-0.12

*

-0.01

*

0.03

*

0.00

-0.42

**

0.09

-0.32

**

0.04

-0.25

**

-0.04

-0.33

**

0.04

P1 P2 P3

0.41

**

0.27

**

-0.02 -0.10 -0.03

Total

-0.05

P1

-0.36**

P2

-0.31

**

-0.33

**

-0.33

**

P3 Total

0.19 0.40

**

0.26

**

-0.18 -0.24

**

-0.13 -0.18

**

-0.01 0.04

0.40

**

0.26

**

-0.06 -0.14

-0.01

-0.04

0.01

-0.08

-0.30**

-0.27**

-0.35**

-0.24

**

**

**

-0.32

**

-0.28

**

-0.26

-0.21 -0.25

-0.28

0.36

**

-0.13

0.23

**

*

*

**

-0.31

**

-0.31

**

-0.12

M AN U

0.14

0.19

-0.21 -0.28

*

**

-0.15 -0.21

**

0.42

**

0.41

**

0.31

**

0.37

**

-0.27**

0.13

*

-0.17

**

*

-0.20

-0.14 0.37

**

0.36

**

0.20

0.32

*

**

0.16

-0.12 0.43

**

0.45

**

0.38

**

0.40

**

0.25

**

0.30

**

0.42 0.36

**

0.38

**

-0.02

-0.04

-0.28

0.08

-0.24**

0.24**

-0.15

0.04

-0.24

**

**

-0.16

-0.25

**

-0.24

**

-0.04 -0.01 -0.02

-0.06 -0.02 -0.04

0.30

**

*

0.69

**

0.68

**

0.36

**

0.73

**

0.74

**

0.34

**

0.68

**

0.68

**

0.11

-0.28

0.20

0.13

*

-0.25

**

0.13

*

*

0.23 0.17

**

0.20 0.33

**

0.25

**

-0.35

**

-0.28

**

-0.12

**

0.62**

0.07

-0.11

0.39

0.62**

*

38

0.34

**

0.00

**

0.25**

0.06

P1 (09:00-11:00), P2 (13:00-15:00) and P3 (18:00-20:00)

0.32

**

0.36**

**

0.12

-0.13

*

0.19*

*

-0.19

-0.24

**

-0.20

-0.10

*

P2

Total

Water

-0.05

0.00

Total

P3 Bird

-0.04

0.09

0.02

Gr(A)

TE D

Music

0.09

0.06

CAR

EP

Human

0.04

BPAF

RI PT

P1

Bldg(P)

AC C

Traffic

Bldsf(A)

SC

Bldg(A)

-0.22

*

**

-0.08 0.02

0.14 0.07 -0.02 0.07

0.23

0.14 0.20

**

-0.16 -0.15**

ACCEPTED MANUSCRIPT

Table 8. Standardized regression coefficients from multiple linear regression analysis for sound sources using acoustic and morphological indicators (*p<0.05, **p<0.01)

P1

P2

P3

Total

P1

P2

P3

Total

P1

P2

P3

Total

P1

P2

P3

0.47

0.44

0.49

0.55

0.15

0.14

0.24

0.25

0.32

0.51

0.46

0.21

0.51

0.62

0.51

0.57

0.45

0.36

0.48

0.47

0.03

0.03

0.05

-0.01

-0.37

-0.34

-0.53

-0.27

0.14

0.44

0.19

0.01

**

**

**

**

**

**

**

*

*

**

*

0.23

0.13

0.19

0.34

-0.33

-0.33

-0.41

-0.26

0.00

0.05

-0.20

0.10

-0.11

0.05

-0.11

-0.09

*

**

**

**

**

**

0.17

0.27

-0.09

-0.08

-0.10

-0.10

0.10

0.19

-0.03

0.11

-0.17

-0.20

-0.16

-0.13

*

**

**

*

Sharpness

-0.02

-0.02

0.12

0.59

0.06

0.00

**

**

0.13 ** -0.24

0.01

0.10

0.16

-0.20

-0.23

-0.21

**

**

**

**

0.08

0.15

0.08

0.04

0.19

0.14

**

-0.03

-0.16

Morphological

Road Water feature

-0.11

-0.06

0.02

*

-0.08

-0.04

-0.02

*

0.14

0.15

**

0.14 *

0.05

0.02

-0.18

0.17

**

**

-0.04 0.09

EP

Building

-0.07

0.12

0.11

AC C

Open space

-0.10

M AN U

L10-90

**

-0.23

-0.22

-0.23

-0.23

**

**

**

**

-0.14

TE D

LCeq-Aeq

Water

Total

Acoustic LAeq

Bird

-0.04

SC

R2

Human

RI PT

Traffic

-0.05

0.10 0.09

-0.19

0.03

*

-0.04 0.35

0.30

-0.04

-0.02

**

0.30

0.28

**

**

-0.01

0.00

0.29 ** -0.07

0.28 ** 0.06

**

0.24

0.16

-0.10

-0.09

-0.04

-0.06

*

-0.09

0.01

P1 (09:00-11:00), P2 (13:00-15:00) and P3 (18:00-20:00)

39

-0.01

-0.02

*

*

-0.01

-0.07

0.02

0.59

0.50

0.61

0.67

**

**

**

**

ACCEPTED MANUSCRIPT Table 9. Standardized regression coefficients from multiple linear regression analysis of soundscape perception using acoustic and morphological indicators (*p<0.05, **p<0.01) Pleasantness Total

P1

P2

P3

Total

P1

P2

P3

0.49

0.54

0.53

0.50

0.13

0.08

0.22

0.21

-0.67

-0.45

-0.65

-0.81

0.22

**

**

**

**

**

-0.05

0.13

-0.10

-0.11

-0.17

-0.30

-0.24

**

*

*

-0.11

-0.09

-0.15

-0.09

-0.09

-0.05

-0.04

-0.01

0.17

-0.01

Acoustic LAeq LCeq-Aeq

0.12

L10-90

0.16

0.06

0.13

0.27

-0.07

-0.13

-0.03

**

Morphological Open space Building

0.12 0.00 0.00

Road

0.14

-0.01

0.05

0.04

-0.02

-0.06

-0.02

0.09

0.07

0.26

0.24

0.27

0.25

**

**

**

**

EP AC C 40

0.32

0.17

**

*

TE D

Water feature

0.12

**

-0.06

M AN U

Sharpness

0.02

SC

*

RI PT

R

2

Eventfulness

-0.04

*

0.11

-0.06

0.11

*

0.10 0.10

0.25 **

0.05

0.08

0.23 *

0.09

0.10

0.14

ACCEPTED MANUSCRIPT Figure captions Figure 1. Case study area in Seoul, Korea: (a) 122 sampling locations of the study area and sampling zone A-I for soundscape data collection; (b) morphological components.

RI PT

Figure 2. Spatiotemporal variation in perceived sound source types for three different sampling periods: (a) traffic, (b) human, (c) music, (d) bird, and (e) water sounds Figure 3. Temporal variation in the dominant sound source types for three different sampling periods and the main place functions: (a) traffic, (b) human, (c) music, (d) bird, and (e) water sounds. The perceived value of sound sources refer to the percentages of records that indicated “Heard a lot” or “Dominates completely” for a given sound source type.

SC

Figure 4. Spatiotemporal variation of: (a) pleasantness and (b) eventfulness for the three sampling periods

M AN U

Figure 5. Temporal variation of the mean PCA component scores for the three sampling periods and the main functions of places: (a) pleasantness and (b) eventfulness scores

AC C

EP

TE D

Figure 6. PCA component scores for morphological factors according to the main space functions

41

ACCEPTED MANUSCRIPT Appendix A: Questionnaire for soundscape data collection (Translated from Korean) 1. To what extent do you presently hear the following types of sounds? Please tick off one response alternative per type of sound A little

Moderately

□

□

□

□

□

□

□ □ □ □ □

□ □ □ □ □

□ □ □ □ □

2. Sounds from human activities 3. Natural sounds - Water sounds - Bird sounds - Wind sounds 4. Music 5. Other sounds

Dominates completely

□

□

□

□

□ □ □ □ □

□ □ □ □ □

SC

1. Traffic noise

A lot

RI PT

Do not hear at all

M AN U

Type of sounds

2. To what extent do you agree with eight attributes below on how you experience the present surrounding sound environment? Please tick off one response alterative per attribute. Slightly Disagree

Neither disagree, nor agree

Slightly Agree

Strongly Agree

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

□

Monotonous

□

□

□

□

□

Calm

□

□

□

□

□

□

□

□

□

□

Unpleasant Eventful Uneventful

AC C

Exciting

TE D

Pleasant

Strongly Disagree

EP

The sound environment is:

Chaotic

42

RI PT

ACCEPTED MANUSCRIPT

B

C

E

F

D

Seoul

Sampling zones: A-I (a)

TE D

H

I

EP

Residential area High-density commercial area Low density commercial area Central business area Urban public space

AC C

Sampling locations

G

M AN U

A

SC

Korea

(b)

Green / park

Square

City stream

Building

Ground

Road

Sampling period 1: 09-11

Sampling period 2: 13-15

Sampling period 3: 18-20

SC M AN U TE D EP AC C

(e) Water sounds

(d) Bird sounds

(c) Musicsounds sounds Human

(b) Human sounds

RI PT

(a) Traffic sounds

ACCEPTED MANUSCRIPT

1.0-1.4 1.4-1.8 1.8-2.2 2.2-2.6 2.6-3.0 3.0-3.4 3.4-3.8 3.8-4.2 4.2-4.6 4.6-5.0

Not at all heard

Dominates completely

ACCEPTED MANUSCRIPT

80

80

30 20

50 40 30 20 10

0

0 P1

P2 Sampling period

P3

P1 80

80

P2 Sampling period

60 50

50

30 20

P1

P2 Sampling period

P3

20

P1

P2 Sampling period

Low-density commercial Residential

40

Recreational

30

Sampling periods

20

P1(09:00-11:00) P2(13:00-15:00) P3(18:00-20:00)

0

0

30

High-density commercial

10

10

40

Business

AC C

40

60

50

Space functions

EP

Prominant sound source [%]

70

60

0 P3

(e) Water 70

70

10

TE D

(d) Bird

Prominant sound source [%]

40

RI PT

50

60

SC

60

(c) Music

70

M AN U

70

10

Prominant sound source [%]

80 (b) Human

Prominant sound source [%]

Prominant sound source [%]

(a) Traffic

P1

P2 Sampling period

P3

P3

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Legend

Pleasant

Uneventful

EP

TE D

M AN U

SC

Unpleasant

AC C

(b) Eventfulness score

Sampling period 3: 18-20

RI PT

Sampling period 2: 13-15

(a) Pleasantness score

Sampling period 1: 09-11

Eventful

(a)

1.0

1.0

(b)

SC

1.5

RI PT

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M AN U

0.0

Business Low-density commercial Recreational

P1

High-density commercial Residential

EP

-1.5

TE D

-0.5

-1.0

Eventfulness score

0.5

P2

AC C

Pleasantness score

0.5

Sampling period

P3

0.0

-0.5

-1.0

Business Low-density commercial Recreational

High-density commercial Residential

-1.5 P1

P2 Sampling period

P3

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RI PT

6 Open space Building Road

SC

Water feature

M AN U

4

TE D

2

-2

Business

AC C

EP

0

High-density commercial

Low-density commercial Space functions Main space functions

Residential

Recreational

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Highlights Spatiotemporal patterns in soundscapes were collected in an urban area. Intercorrelations were found among soundscape, acoustic and morphological factors.

RI PT

A combination of acoustic and morphological indices can be applied to describe urban

AC C

EP

TE D

M AN U

SC

soundscapes.