#
Singapore Soundscape Site Selection Survey (S^{5}): Identification of Characteristic Soundscapes of Singapore via Weighted k-Means Clustering

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

#### 1.1. Background and Motivation

- crowdsourcing opinions from a large sample of local experts via the administration of a standardized questionnaire,
- accounting for the reliability of each local expert in the sample via the numerical weighting of each opinion, and
- summarizing the crowdsourced opinions via an automatic, replicable clustering algorithm,

#### 1.2. Organization and Scope

- Section 2 provides a brief overview of work related to our study.
- Section 3 describes the study area, the questionnaire used to elucidate locations from the participants of the study, and details on the weighted k-means clustering method we used to obtain locations of the characteristic soundscapes from the locations elucidated from the participants.
- Section 4 presents the results of our proposed clustering method.
- Section 5 analyzes the clusters and characteristic soundscapes obtained to validate the method.
- Section 6 concludes our study and suggests possible directions for future work.

## 2. Related Work

## 3. Materials and Methods

#### 3.1. Study Area and Context

- has resided in Singapore for at least 10 years, or
- is a Singapore Tourism Board (STB)-licensed tourist guide (STB-licensed tourist guides are required to undergo the training described at https://www.stb.gov.sg/content/stb/en/assistance-and-licensing/licensing-overview/tourist-guide-licence.html [accessed on 11 May 2022] before obtaining their license).

^{2}, is also much larger than the city centers that were investigated for the USotW project. The questionnaire must thus be adapted to reduce selection bias in the characteristic soundscapes that will later be identified by the clustering method. To do so, we divide Singapore into the six planning regions as defined by the Urban Redevelopment Authority (URA) (the government agency in charge of land use planning and conservation in Singapore), administer a separate questionnaire for each region, and finally aggregate the points to perform the final clustering in Section 3.5. The planning regions, together with the names of some representative neighborhoods, are shown in Figure 2. The planning region officially designated as the “Central Area” in Figure 2 is also colloquially known as the “Central Business District” (CBD) or “CBD Area”, and will henceforth be referred to as such to prevent confusion with the similarly-named “Central Region”.

#### 3.2. Participants

#### 3.3. Questionnaire

#### 3.4. Weight Assignment Accounting for Reliability

- ${w}_{i}$ denotes the reliability measure of the coordinates ${x}_{i}$ of the chosen location,
- $\sigma (\cdot )$ denotes the sigmoid function,
- ${t}_{i}$ denotes the average duration of each visit to ${x}_{i}$ in minutes.

#### 3.5. Clustering Method

- the latitude of the location ${\phi}_{i}$,
- the longitude of the location ${\theta}_{i}$, and
- the weight, ${w}_{i}$, associated with the location.

#### 3.5.1. Standard k-means Clustering Method

Algorithm 1 Standard k-means clustering method | ||||||||

Inputs: | Set of $n$ points $X=\{{x}_{1},{x}_{2},\dots ,{x}_{n}\}$ to be clustered Number of clusters $k$ | |||||||

Outputs: | Set of $k$ cluster centers $C=\left\{{c}_{1},{c}_{2}\dots ,{c}_{k}\right\}$ Set of $k$ clusters $\mathcal{C}=\left\{{C}_{1},{C}_{2},\dots ,{C}_{k}\right\}$, where ${C}_{i}{{\displaystyle \cap}}^{}{C}_{j}=\varnothing $ for all $i\ne j$ and $\underset{i=1}{\overset{k}{U}}{C}_{i}=X$ | |||||||

Initialization: | ||||||||

$C\leftarrow $ Random $k$-element subset of $X;$ | / / | Each subset chosen with equal probability. | ||||||

while not converged do | / / | Convergence is reached when $\mathcal{C}$ remains | ||||||

unchanged for 1 iteration of the “while” loop. | ||||||||

for$i=1$to$n$do | ||||||||

${C}_{i}\leftarrow \left\{{x}_{j}:\underset{c\in C}{\mathrm{argmin}}{\left({x}_{j}-c\right)}^{2}={c}_{i}\right\};$ | / / | Assign points to the cluster whose center they are closest in Euclidean distance. | ||||||

${c}_{i}\leftarrow \frac{1}{\left|{C}_{i}\right|}{\displaystyle \sum}_{\left\{j:{x}_{j}\in {C}_{i}\right\}}{x}_{j};$ | / / | Update cluster center as mean of all points in cluster. | ||||||

Return: $C,\mathcal{C}$ |

#### 3.5.2. Modification 1: Haversine Distance Metric

- $d\left({x}_{1},{x}_{2}\right)$ denotes the haversine distance between two points ${x}_{1}=\left({\phi}_{1},{\theta}_{1}\right)$ and ${x}_{2}=\left({\phi}_{2},{\theta}_{2}\right)$ on a sphere,
- $R$ denotes the sphere radius (with $R=6371$ [in km] assuming a spherical Earth),
- ${\phi}_{1}$ denotes the latitude of the point ${x}_{1}$ on the sphere,
- ${\theta}_{1}$ denotes the longitude of the point ${x}_{1}$ on the sphere,
- ${\phi}_{2}$ denotes the latitude of the point ${x}_{2}$ on the sphere, and
- ${\theta}_{2}$ denotes the longitude of the point ${x}_{2}$ on the sphere.

#### 3.5.3. Modification 2: Cluster Center Initialization with k-Means++

Algorithm 2 Cluster center initialization with k-means++ (adapted from [32]) | ||||||||

Inputs: | Set of $n$ points $X=\{{x}_{1},{x}_{2},\dots ,{x}_{n}\}$ to be clustered Number of clusters $k$ Distance metric $d\left(\cdot ,\cdot \right)$ | |||||||

Output: Set of $k$ initial cluster centers $C=\left\{{c}_{1},{c}_{2}\dots ,{c}_{k}\right\}$ | ||||||||

Initialization: | ||||||||

$C\leftarrow \varnothing ;$ | / / | Initialize set of cluster centers as empty set. | ||||||

$r\leftarrow \left[\infty ,\dots ,\infty \right]\in {\mathbb{R}}^{n};$ | / / | $r\left[m\right]$ is the distance from the point ${x}_{m}\in X$ to its closest center in $C$. | ||||||

$p\leftarrow \left[\frac{1}{n},\dots ,\frac{1}{n}\right]\in {\mathbb{R}}^{n};$ | / / | $p\left[m\right]$ is the probability that the point ${x}_{m}\in X$ is chosen as an initial cluster center in $C$. Probabilities are initialized uniformly. | ||||||

for$i=1$to$k$do | ||||||||

${c}_{i}\stackrel{\sim p}{\leftarrow}X;$ | / / | Choose ${c}_{i}$ as a random point from $X$, where ${x}_{m}$ is chosen with probability $p\left[m\right]$. | ||||||

$C\leftarrow C{{\displaystyle \cup}}^{}\left\{{c}_{i}\right\};$ | / / | Append ${c}_{i}$ to the set of cluster centers. | ||||||

for$j=1$to$n$do | ||||||||

$r\left[j\right]\leftarrow \underset{c\in C}{\mathrm{min}}d\left(c,{x}_{j}\right);$ | / / | Update the distance from each point to its nearest cluster center in $\mathrm{C}$. | ||||||

for$j=1$to$n$do | ||||||||

$p\left[j\right]\leftarrow \frac{{\left(r\left[j\right]\right)}^{2}}{{{\displaystyle \sum}}_{l=1}^{n}{\left(r\left[l\right]\right)}^{2}};$ | / / | Update the new probability for each point using $r$. | ||||||

Return: $C$ |

#### 3.5.4. Modification 3: Cluster Center Computation with Weighted Means

- ${c}_{i}$ denotes the cluster center of the $i$-th cluster,
- ${C}_{i}$ denotes the set of all points in the $i$-th cluster,
- ${x}_{j}$ denotes the point with index $j$,
- ${w}_{j}$ denotes the weight of the point ${x}_{j}$ computed via Equation (1), and
- $\left\{j:{x}_{j}\in {C}_{i}\right\}$ denotes the set of indices of all the points in the $i$-th cluster.

## 4. Results

#### 4.1. Optimal Number of Clusters

- $\mathcal{C}=\left\{{C}_{1},{C}_{2},\dots ,{C}_{k}\right\}$ denotes the set of clusters,
- $k$ denotes the number of clusters,
- ${C}_{i}$ denotes the set of all points in the $i$-th cluster, and
- $d\left(x,y\right)$ denotes the distance between two points $x$ and $y$.

#### 4.2. Cluster Centers

## 5. Discussion

#### 5.1. Distribution of Cluster Centers

#### 5.2. Characteristic Soundscapes

#### 5.3. Limitations

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

API | Application Programming Interface |

AUC | Area under curve |

CBD | Central Business District |

GPS | Global Positioning System |

ISO | International Organization for Standardization |

MRT | Mass rapid transit (train network) |

NTU | Nanyang Technological University (Singapore) |

SD | Standard deviation |

STB | Singapore Tourism Board |

URA | Urban Redevelopment Authority (Singapore) |

UK | United Kingdom |

USotW | Urban Soundscapes of the World (database) |

WNSS | Weinstein Noise Sensitivity Scale |

## Appendix A. Questionnaire

- Considering public open spaces (e.g., streets, squares, parks, etc.) in the <Planning Region> of Singapore, where do you experience the soundscape to be most <Perceptual Attributes>?
- Coordinates of your chosen location.
- Please explain and elaborate on your choice of location. For example, you can indicate the typical time and day of the week, etc., that you find the location to have a soundscape that is <Perceptual Attributes>.
- How often do you visit your chosen location or pass by it on foot?
- How many times have you visited your chosen location or passed by it on foot?
- On average, how long do you spend at your chosen location or pass by it on foot?

Region | Latitude (Degrees) | Longitude (Degrees) | Description |
---|---|---|---|

Central Area (“CBD Area”) | 1.2830173 | 103.8513365 | Raffles Place MRT Station |

Central Region | 1.3199584 | 103.8259427 | Stevens MRT Station |

East Region | 1.3532359 | 103.9452235 | Tampines MRT Station |

North Region | 1.4273512 | 103.7931482 | Woodlands South MRT Station |

North-east Region | 1.3829481 | 103.8933582 | Buangkok MRT Station |

West Region | 1.3376415 | 103.6968990 | Pioneer MRT Station |

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**Figure 1.**ISO 12913-3:2019 circumplex model of soundscape perception with descriptors for each quadrant drawn from [6].

**Figure 2.**Major planning regions of Singapore as defined by the Urban Redevelopment Authority (URA) of Singapore (adapted from [25]).

**Figure 3.**Aggregated demographic information of participants ($n=67$) in S

^{5}, with the numbers above the bars in histograms denoting the exact number of participants in that bin: (

**a**) age distribution (Mean: 45.3, Standard Deviation (SD): 14.8); (

**b**) distribution of length of residence in Singapore (Mean: 43.9, SD: 15.7); (

**c**) whether the participant is an STB-licensed tourist guide; (

**d**) gender distribution; (

**e**) distribution of normalized WNSS-21 score on a scale of 1 to 5 (Mean: 2.62, SD: 0.50); (

**f**) Distribution of participants’ main residence by URA planning region, with numbers in each region denoting the number of participants whose main residence is in that region.

**Figure 4.**Weights used for clustering against the cumulative proportion of responses (and corresponding actual sorted response numbers in increasing order) at locations identified for each set of perceptual attributes (labeled with area under curve [AUC]).

**Figure 5.**Maximum Dunn index values by number of clusters (with optimal number represented by red circles and dashed lines) for points labeled as (

**a**) full of life and exciting; (

**b**) chaotic and restless; (

**c**) calm and tranquil; and (

**d**) lifeless and boring.

**Figure 6.**Cluster centers (black crosses) and points (marked with a unique color for each cluster), superimposed on a map of Singapore and representing locations considered by participants to be the most: (

**a**) full of life and exciting; (

**b**) chaotic and restless; (

**c**) calm and tranquil; or (

**d**) lifeless and boring. Regions are color-coded in the same manner as in Figure 2.

**Figure 7.**Illustration of how the environmental and perceptual characteristics of cluster center locations may differ from actual locations identified by participants. Here, the actual locations identified are busy traffic intersections, but the cluster center is in the middle of a park.

Study (Year) | Area(s) Stimuli Originated | Rationale for Choice | Perceptual Attribute(s) under Study |
---|---|---|---|

Axelsson et al. (2010) [6] | London (UK), Stockholm (Sweden) | Variety in overall sound pressure level, types of sound sources | Agreement with 116 different affective attributes (for example, “pleasant” and “calm”) |

Axelsson (2015) [7] | Sheffield, London, Brighton (UK) | Variety in types of urban and peri-urban areas | Agreement with adjectives “pleasant”, “vibrant”, “eventful”, “chaotic”, “annoying”, “monotonous”, “uneventful”, “calm” |

Puyana Romero et al. (2016) [8] | Naples (Italy) | Variety in conditions of road traffic flow | Perceived soundscape quality |

Aumond et al. (2017) [9] | Paris (France) | Variety in types of urban areas | Pleasantness |

Fan et al. (2017) [10] | Mixed (from Freesound [11]) | Variety in types of sound sources | Valence, arousal |

Puyana Romero et al. (2019) [12] | Naples (Italy) | Variety in types of urban spaces | Agreement with adjectives “pleasant”, “unpleasant”, “monotonous”, “exciting”, “eventful”, “uneventful”, “chaotic”, calm” |

Masullo et al. (2021) [13] | Mixed (from IADS-E database [14]) | Variety in types of urban sound sources | 2 sets of attributes (17 and 12 attributes) related to emotional salience |

Hasegawa and Lau (2022) [15] | Singapore (Singapore) | Presence of common noise sources and greenery, resident demographic similarity | Pleasantness, eventfulness, satisfaction |

Response (Number of Times Visited) | Frequency Weight |
---|---|

1 to 3 | 1 |

4 to 6 | 2 |

7 to 9 | 3 |

10 or more | 4 |

ID | Region | Latitude (Degrees) | Longitude (Degrees) | Description |
---|---|---|---|---|

A01 | CBD | 1.291598203 | 103.8465300 | Opposite Clarke Quay Shopping Mall |

A02 | East | 1.354207500 | 103.9435079 | Tampines Bus Interchange |

A03 | East | 1.363875914 | 103.9914004 | Changi Airport Terminal 1 |

A04 | Central | 1.263173177 | 103.8228356 | VivoCity Shopping Mall |

A05 | Central | 1.301498905 | 103.9049564 | Parkway Parade Shopping Mall |

A06 | Central | 1.311034361 | 103.7943141 | Holland Village Market & Food Centre |

A07 | Central | 1.350677442 | 103.8494603 | Junction 8 Shopping Mall |

A08 | North | 1.404012379 | 103.7934915 | Singapore Zoo (Ah Meng Restaurant) |

A09 | North | 1.429740500 | 103.8351859 | Yishun MRT Station |

A10 | North | 1.437221700 | 103.7861714 | Woodlands MRT Station |

A11 | North | 1.446914441 | 103.7301914 | Sungei Buloh Wetland Reserve (Mangrove Boardwalk) |

A12 | North-east | 1.392070753 | 103.8956615 | Compass One Shopping Mall |

A13 | West | 1.333243872 | 103.7414451 | Jurong East MRT Station |

A14 | West | 1.336767900 | 103.6941672 | Jurong West Sports Hall (facing Jurong West Street 93) |

A15 | West | 1.343433486 | 103.6351438 | Raffles Marina |

ID | Region | Latitude (Degrees) | Longitude (Degrees) | Description |
---|---|---|---|---|

B01 | CBD | 1.300102657 | 103.8459222 | Handy Road (Opposite Plaza Singapura Shopping Mall) |

B02 | East | 1.324737167 | 103.9306484 | Bedok Interchange Hawker Centre |

B03 | East | 1.359156559 | 103.9407174 | Tampines Central 7 (Road) |

B04 | East | 1.364476558 | 103.9915721 | Changi Airport Terminal 1 |

B05 | Central | 1.310991457 | 103.7947432 | Holland Village Market & Food Centre |

B06 | Central | 1.335196760 | 103.8844747 | Harrison Industrial Building |

B07 | Central | 1.350930707 | 103.8480879 | Bishan MRT Station |

B08 | North | 1.429664842 | 103.8341680 | S-11 Yishun 744 Hawker Centre |

B09 | North | 1.442881682 | 103.7756387 | Opposite SPC Admiralty (Petrol Station) |

B10 | North-east | 1.391455032 | 103.8955306 | Sengkang Bus Interchange |

B11 | West | 1.333995645 | 103.6346393 | Intersection of Tuas West Drive & Pioneer Road |

B12 | West | 1.337641500 | 103.7036367 | Intersection of Jurong West Street 63 & Jurong West Street 64 |

B13 | West | 1.334852761 | 103.7461658 | IMM Shopping Mall |

B14 | West | 1.379686712 | 103.7606068 | Intersection of Woodlands Road & Choa Chu Kang Road |

ID | Region | Latitude (Degrees) | Longitude (Degrees) | Description |
---|---|---|---|---|

C01 | CBD | 1.290393318 | 103.8510017 | National Gallery Singapore (Museum) |

C02 | East | 1.362116882 | 103.9467685 | Tampines Eco Green Park |

C03 | East | 1.388507653 | 103.9884539 | Changi Beach Park |

C04 | Central | 1.301187849 | 103.9156572 | East Coast Park (Area C) |

C05 | Central | 1.320559055 | 103.8162867 | Botanic Gardens Eco Lake |

C06 | North | 1.404355599 | 103.8036195 | Upper Seletar Reservoir |

C07 | North | 1.441122864 | 103.7228199 | Sungei Buloh Wetland Reserve (Buloh Besar River) |

C08 | North | 1.446485425 | 103.7805025 | Admiralty Park |

C09 | North | 1.451390510 | 103.8405410 | Sembawang Park |

C10 | North-east | 1.374836700 | 103.8455383 | Ang Mo Kio Town Garden West |

C11 | North-east | 1.408367898 | 103.9072628 | Punggol Waterway Park |

C12 | West | 1.334123398 | 103.7277980 | Jurong Lake Gardens |

C13 | West | 1.344436500 | 103.6339522 | Johor Straits Lighthouse |

C14 | West | 1.348941989 | 103.6876865 | NTU Sports and Recreation Centre |

C15 | West | 1.354816300 | 103.7762985 | Bukit Timah Hill Summit |

ID | Region | Latitude (Degrees) | Longitude (Degrees) | Description |
---|---|---|---|---|

D01 | CBD | 1.287693895 | 103.8514652 | Asian Civilisations Museum |

D02 | East | 1.321819702 | 103.9144639 | Jalan Senyum (Road) |

D03 | East | 1.342467129 | 103.9633338 | Singapore University of Technology and Design Staff Housing |

D04 | East | 1.372735405 | 103.9496974 | White Sands Shopping Mall |

D05 | Central | 1.305542629 | 103.8222091 | Napier Road |

D06 | Central | 1.336814235 | 103.7931607 | The Grandstand Shopping Mall |

D07 | Central | 1.344033838 | 103.8470656 | Bishan Harmony Park |

D08 | North | 1.407594732 | 103.7576143 | Mandai Estate |

D09 | North | 1.417440810 | 103.8332204 | Khatib MRT Station |

D10 | North | 1.443074739 | 103.7904874 | Woodlands North Plaza |

D11 | North | 1.448458900 | 103.8223306 | Intersection of Canberra Road and Old Nelson Road |

D12 | North-east | 1.358085773 | 103.8887448 | Hougang Block 236 (Residential Building) |

D13 | North-east | 1.399313682 | 103.8852278 | Sengkang Riverside Park |

D14 | West | 1.282380986 | 103.6306377 | Tuas South Avenue 7 |

D15 | West | 1.321123540 | 103.7405868 | Teban Neighborhood Park |

D16 | West | 1.332750479 | 103.6394783 | Tuas West Road MRT Station |

D17 | West | 1.336054064 | 103.6840244 | Singapore Discovery Centre (Museum) |

D18 | West | 1.391549335 | 103.6987229 | Lim Chu Kang Road |

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## Share and Cite

**MDPI and ACS Style**

Ooi, K.; Lam, B.; Hong, J.-Y.; Watcharasupat, K.N.; Ong, Z.-T.; Gan, W.-S.
Singapore Soundscape Site Selection Survey (S^{5}): Identification of Characteristic Soundscapes of Singapore via Weighted *k*-Means Clustering. *Sustainability* **2022**, *14*, 7485.
https://doi.org/10.3390/su14127485

**AMA Style**

Ooi K, Lam B, Hong J-Y, Watcharasupat KN, Ong Z-T, Gan W-S.
Singapore Soundscape Site Selection Survey (S^{5}): Identification of Characteristic Soundscapes of Singapore via Weighted *k*-Means Clustering. *Sustainability*. 2022; 14(12):7485.
https://doi.org/10.3390/su14127485

**Chicago/Turabian Style**

Ooi, Kenneth, Bhan Lam, Joo-Young Hong, Karn N. Watcharasupat, Zhen-Ting Ong, and Woon-Seng Gan.
2022. "Singapore Soundscape Site Selection Survey (S^{5}): Identification of Characteristic Soundscapes of Singapore via Weighted *k*-Means Clustering" *Sustainability* 14, no. 12: 7485.
https://doi.org/10.3390/su14127485