PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Noise complaints and its relation to socio-economic factors atcity/region scale in England
Huan TONG1; Jian KANG21,2

UCL Institute for Environmental Design and Engineering, The Bartlett, University College London, the UnitedKingdom

ABSTRACTReporting noise complaints is an important part of informing noise legislation. While the effects of various
socio-economic factors on noise perception have been investigated in previous studies, the aim of this study
is to examine relationships between the rate of noise complaints and socio-economic factors. The noise
complaints and socio-economic datasets from the government open data source at district and unitary
authority levels across the England are used. The socio-economic dataset is categorised into four groups inthis study, namely demographic, working, property and deprivation factors. Correlation analysis is conducted
between noise complaint rate and socio-economic factors, and the results suggest that the correlations aregenerally significant. Cities/regions with a higher proportion of young and single residents are likely toreceive more noise complaints, and so are cities/regions with a higher unemployment rate and higher
proportion of residents living in flats. The deprived city/regions, measured by the English Index of Multiple
Deprivation, tend to have a higher noise complaint rate.
Keywords: Noise complaint rate; demographic factors; property factors; job-related factors; deprivation
factors.

1. INTRODUCTION
Neighbour complaints appear to be a growing phenomenon, and are a key feature of contemporary
urban living. They have significant effects on the quality of life for residents, the level of health , andneighbourhood cohesion (1). To reduce the impact of neighbour complaints, public complaint
platforms have been established in many countries, such as those in Australia, the U.S. and the U.K., todeal with problems relating to complaints for residents and to provide data for government
decision-making. Among the neighbour complaints, the volume of complaints about noise is thegreatest. Socio-economic factors play an important role in neighbour complaints. Complaining aboutnoise is a behaviour based on residents’ annoyance with noise and urban noise levels. From theperspective of sound environments, previous studies have examined the impact of socio-economicfactors on noise annoyance and noise level (2). However, the relationships between noise complaints
and socio-economic factors have not been fully investigated.
Using a combined questionnaire and noise measurement survey in Great Britain, Fields and Walker(3) conducted research to examine the impact of about 35 demographic factors on annoyance arisingfrom railway noise. The results show that there are significant relationships between noise annoyanceand older dwellings, older respondents, and life-time residents. Xie and Kang (4) conducted a morecomprehensive study to examine the relationships between environmental noise levels andsocio-economic factors, focussing on levels in neighbourhoods and boroughs throughout London
while including more socio-economic factors. At the neighbourhood level, fewer students andpart-time residents lived in quieter areas, and neighbourhood noise levels were negatively associatedwith their total deprivation rankings. At the borough level, income levels were generally higher in thenoisier boroughs. Yu and Kang (5) focused on subjective evaluations of the sound level in an urban
open space. Aletta et al. (6) analysed the effect of demographic factors on sound perception using acase study of a cycling path. A range of correlations have been revealed through such small -scale
1
2

Huan.tong.18@ucl.ac.uk
J.kang@ucl.ac.uk

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research.
Although the relationships between socio-economic factors and noise level and between
socio-economic factors and noise annoyance have been investigated, the relationships between
socio-economic factors and noise complaints has not been adequately explored, especially on a larger
scale. Kang (2) stated that noise complaints are strongly related to noise annoyance when he analysednoise standards and regulations in Europe. Furthermore, Legewie and Schaeffer (7) found that therelationship between ethno-racial diversity in a neighbourhood and the number of noise complaintcalls was significant.
Therefore, the aim of this research is to examine the relationship s between noise complaints andsocio-economic factors. The socio-economic factors are categorised into four groups: demographic
factors, property factors, job-related factors and deprivation factors. The noise complaints andsocio-economic factors datasets from the government open data source are used th rough statisticalanalysis in all district and unitary local authorities across England.

2. METHODOLOGY
2.1 Geographical samples
This study is based on district and unitary administration level across whole England as data
availability. City/region, including urban, semi-urban, and rural areas, as a strategic and political levelof administration and policymaking, was used to refer to district and unitary authority level (8). Thereare 325 samples included in this study.
2.2 Noise complaints dataset
The reporting of noise complaints is carried out in England as a part of environmental legislation,providing data for government decision-making. The noise complaint data can be downloaded from
Public Health England. The rate of noise complaint data was selected to conduct the correlation
analysis, with the aim of comparing a large number of cities/regions across various scales.
The range of data on noise complaint rates is available from 2010 to 2015. A strong relationshipwas found within these years and the relative value of noise complaints between each city/region did
not change dramatically. Therefore, the relationships between socio -economic factors and noise
complaints during that period will be similar. As the census of 2011 has the most recent and detailedsocio-economic dataset, the 2011 rate of noise complaints was selected for the statistical analysis.
2.3 Socio-economic factors dataset
As previous studies argue, there is a wide range of socio-economic factors that can have an impact
on sound environment evaluation. On the basis of the literature review and data availability, 78 factors
were selected to conduct the correlation analysis. They are categorised into four groups: demographic,
job-related, property, and deprivation factors. The detailed factors are shown in Table 1.
Table 1- Indicators of socio-economic factors.
Demographic factors

Job-related factors

Job-related factors

Deprivation factors

AgeSexMarital status
Household size

Economic activity

Qualification

Hours worked

HealthOccupation

Social grade

Accommodation size
Central heating
Accommodation type and tenure--Car or van availability

Religion diversity
Ethnic diversity
All socio-economic factors are extracted from Census 2011. To compare cities/regions acrossvarious scales, all other indicators are presented by percentage, excluding deprivation factors, which7044

are shown as rank, mean age, median age, car or van availability, and religious and ethnic diversity. In
terms of ethnicity and religion, the diversity is calculated using Simpson’s Diversity Index, which isuniversally accepted (9,10). The formula is:ேଶ‫ ܦ‬ൌ σௌ௜ୀଵ ቀ ೔ቁ(1)ே

where D is Simpson’s Diversity Index; Ni is the population by ethnicity or religion i. Religions
include Christianity, Buddhism, Hinduism, Judaism, Islam, Sikhism, and Other. Ethnic groups include
White, Mixed/Multiple, Asian/Asian British, Black/African/Caribbean/Black Briti sh, and Other. N is
the total population.

3. RESULTS
3.1 Demographic factors
The correlation analysis between demographic factors and noise complaints are examined in thispart, including age, marital status, qualification, health, religion and ethnic diversity. The rate of noise
complaints has modest negative correlations with mean and median age, with coefficients of about -0.5.
Noise complaints have a positive and weak relationship with the proportion of males, with a 0.194
correlation coefficient at a 0.01 significance level. A strong positive relationship is found between
noise complaints and the proportion of single residents, with a higher coefficient value at 0.612.
Significant correlations are found between residents’ highest level of qualificat ion and noise
complaints, as shown in Table 2. The cities/regions with a higher percentage of residents with a higher
education tend to receive more noise nuisance reports.
Table 2-Correlation coefficients between the percentage of residents with various qualification levels andnoise complaints (** indicates p<0.05, * indicates p<0.01).
No

Level 1

Level 2

qualification

qualification

qualificationss

s-0.132*-0.318**-0.533**

Apprenticeshi
p-0.498**

Level 3

Level 4

Otherqualification

qualification

qualification

ss and aboves-0.149**0.255**

0.562**The impact of sound environment on health problems has been intensively investigated . In thisstudy, the noise complaints are generally related to general health, long -term health problem ordisability, and provision of unpaid care per week, as shown in Table 3.
Both ethnic and religious diversity have positive modest relationships with noise complaints (thecorrelation coefficients are 0.554 and 0.474 at a 0.01 significance level). In a diverse society, the refore,
there could be more conflicts. This finding is consistent with that of Legewie and Schaeffer (7), whofound that residents living between racial enclaves tend to complain more about noise than those wholive within clearly defined racial boundaries.
Table 3-Correlation coefficients between health factors and noise complaints (** indicates p<0.05, *indicates p<0.01).
General HealthLong-term health problem or disability

GoodFair

Bad

0.093

-0.214**0.083

Provision of unpaid care perweek

Day-to-day activities

Day-to-day activities

No unpaid

50 or more

limited (all residents)

limited (workers)

care

hours

-0.206**0.119*

0.512**-0.184**

3.2 Job-related factors
There are three categories of job-related factors: economic activity, hours worked, and occupation.
Economic activity is an indicator of residents’ status of employment. In terms of being economically7045

active, the percentage of residents with part-time jobs has negative relationships with the rate of noisecomplaints, with a coefficient of -0.523 (Table 4). Noise complaints do not show correlation with thepercentage of residents having full-time and self-employed jobs. As for the percentage of unemployedresidents, noise complaint is positively related, with coefficient values of 0.406 . As for economicallyinactive residents, a significant negative relationship is found between noise complaints and thepercentage of retired residents, with a coefficient of -0.522. Positive relationships are found between
noise complaints and all remaining factors, including the percentage of students and disabled residents.
The results indicate that cities/regions with a higher propo rtion of unemployed residents might befacing more serious noise complaint problems. In addition, the cities/regions with a higher percentageof residents who work more hours tend to receive more noise nuisance reports.
Table 4-Correlation coefficients between economic activity status and noise complaints (** indicates p<0.05,* indicates p<0.01).

Economically active
Unemployed

Economically inactive

Part-time-0.523**Full-time

0.013

Self- employed-0.065

Total unemployed residents

0.406**

Unemployed male

0.339**

Unemployed female

0.488**

Retired-0.522**Student

0.411**

Looking after home or family

0.340**

Long-term sick or disabled

0.175**

Other

0.555**In terms of residents’ occupation, the proportion of residents with professional or senioroccupations has positive relationships with noise complaints, while residents with entry-level orblue-collar occupations have a negative relationship. As the share of professional, associate
professional, and technical occupations increases, the rate of noise complaints also increases. In
contrast, such occupations – administrative and secretarial, skilled trades, caring, leisure and otherservice occupations, and process plant and machine operatives – have negative values in terms of noise
complaint rates. The remaining occupations do not show statistically significant correlations. Overall,noise complaints have a positive relationship with the percentage of residents in professional -leveloccupations.
3.3 Property factors
There were generally significant correlations between property factors and noise complaints. The
results show that noise complaints have a negative relationship with the average number of rooms andbedrooms per household, with coefficient values of -0.610 for rooms and -0.581 for bedrooms. In
terms of central heating, the percentage of centrally heated households is negatively related to the rateof noise complaints, with a correlation coefficient value of -0.167. In addition, as the average numberof car or van per household increases, the noise complaint rate tends to decrease.
In terms of accommodation type, negative relationships are found between the proportion ofresidents living in a whole house or bungalow and noise complaints, with a coefficient value of -0.653(Table 5). Detached and semi-detached dwellings also have negative relationships with noisecomplaints, with lower coefficient values of -0.500 and -0.456, respectively. Noise complaints,
however, are positively related to the proportion of residents living in terraced houses. Similar
relationships appear for flats: noise complaints are generally positively related to the proportion ofresidents living in a flat, with coefficient values of 0.627, 0.606, and 0.360, for purpose -built blocks offlats or tenements, for part of a converted or shared house, and for commercial buildings, respectively.
To some extent, terraced houses are more similar to flats in spatial patterns as the rooms are contiguous,
although they are categorised under whole house or bungalow. Therefore, they show similar

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relationships with noise. As for the last variable, caravan or other mobile or temporary structure, it ispositively related to noise complaints. The results show that cities/regions with more residents livingin flats have an increasing rate of complaint activity.
Table 5-Correlation coefficients between accommodation type and tenure, and noise complaints (** indicatesp<0.05, * indicates p<0.01).

Accommodation type

Accommodation Tenure

Whole house or bungalow-0.653**

Detached-0.500**Semi-detached-0.456**

Terraced

0.164**

Flat, maisonette, or apartment

0.653**

Purpose-built block of flats or tenement

0.627**Part of a converted or shared house0.606**

In commercial building

0.360**

Caravan or other mobile or temporary structure-0.253**

Owned-0.668**

Shared ownership

0.242**

Social rented

0.532**

Private rented

0.594**

Rent free

0.039

The results of tenure are shown in Table 6. It can be clearly seen that there is a significant andstrong inverse relationship between noise complaints and the percentage of a household that owns theaccommodation it occupies, with a coefficient value of -0.668. However, noise complaints appear to bepositively related to the percentage of households who share ownership with others, with a lower
coefficient value of 0.242. Households living in rented accommodation are classified by the type oflandlord who owns or manages the accommodation. Noise complaint has a positive relationship withthe percentage of households that rents from social and private properties, with higher coefficientvalues of 0.532 and 0.594, respectively.
3.4 Deprivation factors
In terms of deprivation factors, the first ranking cities/regions represent the most deprived
cities/regions, namely disadvantaged areas. The results show a negative relationship between total
deprivation and noise complaints, with a coefficient value of -0.373, indicating that more deprived
cities/regions tend to have more noise complaints. In terms of barriers to housing and services, crime,
living environment, and income deprivation all have negative relationships with noise complaints,
with similar coefficient values, compared to total deprivation. Emplo yment and health are also
negatively related to noise complaints, but with lower coefficient values.

4. CONCLUSIONSThis study examines the relationships between noise complaints and socio -economic factors,
including demographic, job-related, property, and deprivation factors based on statistical analysis in
the district and unitary local authority-levels in England. The results of this investigation are asfollows;
1. From the perspective of demographic factors, complainants are likely to have a higher educa tionlevel and live in an area with diverse religions and ethnicities. Cities/regions with a higher proportionof single individuals are prone to receive more noise complaints.
2. If the unemployment rate of the cities/regions is higher, residents tend to report more noiseissues. The unemployment rate of females has a closer relationship with noise complaints than that of7047

males.
3. In terms of property factors, if there are more flats or rented houses in an area, noise problems
become considerably significant.
4. More deprived cities/regions tend to have more noise complaints in terms of each aspect in the
deprivation index: housing and services, crime, employment, health, environment, and income.
This study has revealed the strengths of the relationships between each socio-economic factor andnoise complaints, and it can help predict the noise complaint rate. Furthermore, these results can help
government organisations to prioritise resources in terms of noise complaints as a part of neighbour
complaints, both geographically and socio-economically. For instance, if a city/region has a higher
unemployment rate, it tends to have a higher noise complaint rate. Therefore, more resources could beallocated in such cities/regions.

ACKNOWLEDGEMENTS
This research was supported by the China Scholarship Council.

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