1. Introduction
Neighborhood noise is an everyday reality and has increasingly become a prevalent neighborhood problem and public concern (Alexander and Stokoe 2019; Dimitrova and Dzhambov 2017). Noise can impose both physiological and psychological impacts on urban residents in the form of hearing impairment, sleep disturbance, cognitive and concentration disorders, stress, annoyance, and anger (van Kamp et al. 2013). Neighborhood noise can lead individuals to lodge complaints to government authorities (Liu et al. 2019), trigger more aggressive behaviors such as disputes between neighbors and crime (Kwon et al. 2016; Paiva et al. 2016), and in the longer term has adverse impacts on daily activities (Lee et al. 2019), quality of life (Cheshire et al. 2019), physical and mental health of residents (van Kamp et al. 2013), relocation of residence (Kwon et al. 2016), and change in the socioeconomic profile of the neighborhoods (Paiva et al. 2016). The limited empirical scholarship examining the drivers of noise in neighborhoods has pointed to a number of important factors, including the distance to noise sources, the socioeconomic profile of neighborhoods, and the design of neighborhood environment (Stansfeld et al. 2000). While we know that the built environment matters, what remains less explored is how the natural environmental and, in particular, weather shape the neighbor complaints about noise in neighborhoods.
There exists a rich scholarship that has explained the link between weather and human behaviors in the domain of crime (Anderson et al. 2000), transport (Wei et al. 2019), travel (Cools et al. 2010), and noise (Lee et al. 2014). In particular, noise-related studies examine the impact of weather, most commonly temperature, on the perceived comfort and sensitivity to surrounding noise from the perspective of environmental stress and social psychology. Such noise-related literature covers a diversity of noise sources from trains, aircraft, road traffic, factories, and building sites (e.g., Croy et al. 2013; Lee et al. 2014; Brenguier et al. 2019; Basner et al. 2017; Li and Clarke 2010; Paiva et al. 2016; Recio et al. 2016; Ware et al. 2015; Deb et al. 2018; Jain et al. 2017; Aliabadi et al. 2015; Kwon et al. 2016, 2019; Lee et al. 2019). However, scant attention has been given to noise occurring between neighbors, which might be different across space and time.
In theory, a number of well-established theories have been employed to explain the relationship between weather and human behaviors. For example, the theory of planned behavior has been widely used in the domain of transport and travel (Wei et al. 2019); in the domain of crime, affective aggression theory (Anderson et al. 2000; Anderson and Anderson 1998; Anderson and DeNeve 1992), excitation transfer theory (Zillmann 2008), and routine activity theory (Cohen and Felson 1979) are commonly employed for interpretation of weather–crime associations; environmental stress theory (EST) is advocated by scholars studying in noise and environment (Shepherd et al. 2010; Lee and Wang 2018). However, whether the above theories applied in multiple domains can be applied to explain noise complaints between neighbors remains unknown.
To advance empirical studies on neighborhood noise, we adopt EST in this paper to provide the theoretical explanation on the effect of weather on noise complaints occurring between neighbors over seasons. We take Brisbane, the capital city of the State of Queensland in Australia, as the study area to address two interrelated research questions: 1) Is there an association between weather and noise complaints between neighbors? 2) To what extent does weather matter in the prevalence of noise complaints between neighbors that may be coherent to built-environment characteristics? Drawing on a large-scale administrative dataset of residents’ complaints, we geocode their locations in a geographic information system, statistically posit their occurrence on yearly and seasonal time frames, and employ a principal component analysis and a stepwise multiple regression to analyze the extent to which five typical weather factors—rainfall, wind, temperature, humidity, and pressure—individually and collectively affect noise complaints between neighbors. Our findings imply that there are seasonal patterns of noise complaints, and weather conditions may serve as a psychological moderator to change people’s tolerance and sensitivity to noise, alter their routine activities and exposure to noise sources, and further increase the likelihood of imposing noise complaints between neighbors, particularly on hot summer days and rainy winter days.
The rest of the paper is organized as follows: In the next section, we review how the built environment and weather affect human behaviors to forge the potential mechanisms to explain the relationship between weather conditions and noise complaints made by neighbors, on the basis of which we further propose the research design. What follows is a description of the study context, data, and method. The results on the temporal variations of the noise complaints and weather conditions as well as their interrelationship are presented, followed by the discussion on conceptual insights and future study. A conclusive summary is presented in the last section.
2. Neighborhood noise, complaints between neighbors, and the built environment
Neighborhood noise, typically defined as unwanted disturbing sound, has been perceived as a pollutant and a type of environmental stressor that causes adverse health and social effects to urban residents (Park and Lee 2019; Schexnayder and Shane 2010; Lee et al. 2015). The exposure to noise may be from many different sources in both the built environment (e.g., Tong and Kang 2021; Croy et al. 2013; Lee et al. 2014; Brenguier et al. 2019; Basner et al. 2017; Stansfeld et al. 2005, 2009; Li and Clarke 2010; Paiva et al. 2016; Recio et al. 2016; Ware et al. 2015; Deb et al. 2018; Jain et al. 2017; Aliabadi et al. 2015) and the natural environment (e.g., Peters et al. 2014; Seidman and Standring 2010; Lee et al. 2015; Knopper and Ollson 2011). The World Health Organization (2011) has documented seven categories of adverse effects caused by excessive levels of noise: hearing impairment, interference with spoken communication, cardiovascular disturbances, mental health problems, impaired cognition, negative social behaviors, and sleep disturbances (Halperin 2014; McGuire and Basner 2018). The mechanism of how noise as an external stressor affects internal responses of human beings, both physiologically and psychologically, has been predominantly discussed by the advocacy scholars guided by the environmental stress theory (Benita and Tunçer 2019; Lazarus and Folkman 1984; Bechtel 1997; Hartig et al. 1997).
Initially developed by Lazarus and Folkman (1984) and further extended by Bechtel (1997), EST recognizes not only the cognitive appraisal of environmental surrounding from the perspective of different individuals (Hartig et al. 1997); it also enables researchers to develop empirical models of how the built or natural environment affect both physical and mental health of human beings (Benita and Tunçer 2019). With regard to noise, the EST generally describes noise as a nuisance, intruding into personal privacy, causing disturbance of concentration and communication disorders, triggering annoyance and decline in the quality of life, and hence leading to stress responses and symptoms, and even possibly overt illness or complaints (Benita and Tunçer 2019; Dreger et al. 2015; Van Kamp and Davies 2008; van Kamp et al. 2013). However, it is noteworthy that annoyance and complaints are different phenomena, the former being a subjectively held opinion and the latter being an overt action (Basner et al. 2017). In other words, although annoyance trigged by noise inside and outside of the home may disturb the activities of restoration (e.g., sleeping and relaxing) or concentration (e.g., reading or studying), it may not necessarily result in complaints unless such annoyance exceeds a certain extent that one can endure (Pedersen 2011; Lee and Wang 2018). Different individuals can exhibit different annoyance reaction to the same noise, and these individual differences can be ascribed partly to differences in noise sensitivity and cognition (Shepherd et al. 2010; Lee and Wang 2018).
A large body of studies on noise include the effects of noise on individuals (Fyhri and Aasvang 2010; Montazami et al. 2012; Torija and Flindell 2014), measurement of occupational noise exposure (Chung et al. 2012; Caciari et al. 2013; Jang et al. 2015), noise prevention and control (Huang et al. 2015; Castineira-Ibanez et al. 2015; Kwon et al. 2016), and the socioacoustic effect of noise investigation at both individual and community levels (Tong and Kang 2021; Fields et al. 2001; Yano and Ma 2004; Gille et al. 2016). While the sources of noises reported in these studies vary—mechanical, occupational, industrial, and transport generated (Croy et al. 2013; Lee et al. 2014; Brenguier et al. 2019; Basner et al. 2017; Stansfeld et al. 2005, 2009; Li and Clarke 2010; Paiva et al. 2016; Recio et al. 2016; Ware et al. 2015; Deb et al. 2018; Jain et al. 2017; Aliabadi et al. 2015)—they share a common feature; that is, the noises are mediated by the built environment.
The density, design, and diversity of the urban built environment have been widely recognized to affect the way in which human response to surrounding environment (Stansfeld et al. 2000). Neighborhoods with high-density buildings and road networks, and correspondingly high concentrations of population and vehicles, and mixed land use of industry, commerce, and residence are found to have a higher likelihood of exposure to noise from road traffic, construction, and public works, social, industrial, and economic activities, and thus higher likelihood for stress-related psychosocial symptoms such as annoyances and complaints (Gidlöf-Gunnarsson and Öhrström 2007). The design of noise-preventing landscape, large green spaces, less compact streets and block spaces, or the configuration of fences and road noise barriers (Billera et al. 1997) masks noise sources and diminishes long-term noise prevalence and nuisance, thus reducing psychosocial distress and likelihood for complaints. The diversity of neighborhoods, normally reflected by the demographic or socioeconomic heterogeneity in a certain area (e.g., socioeconomic status, ethnicity, and age structure), is also incorporated as statistical controls in the examination of the impact of noise on human psychosocial response (Tong and Kang 2021). For example, children are found to have a higher biological risk and be more sensitive to traffic noise from roads and trains (Evans et al. 2001); likewise, the elderly are more likely to be interrupted by road traffic during rest and relaxation (Azuma and Uchiyama 2017). Further, neighborhoods with a higher proportion of young and single residents or with diverse ethnicities and religions were observed to have more noise complaints in England (Tong and Kang 2021). However, few studies have attempted to posit how the natural environment—in particular, weather conditions—affects noise complaints, including temperature, wind, pressure, humidity, and rainfall. To this end, our study controls for the effect of the built environment on noise complaints and further examine the extent to which weather conditions increase or decrease the likelihood of noise complaints between neighbors.
3. The effect of weather on noise complaints between neighbors
Central to EST, our theoretical expectation on the relationship between weather and noise complaints is that weather serves as a psychological moderator to change people’s tolerance and sensitivity to noise and further affects the likelihood of noise complaints between neighbors. But the psychological reactions of individuals to weather are complex; it is also possible that weather alters individuals’ routine activities and exposure to noise sources. Hence, our study incorporates the advocacy of EST and routine activity theory to construct several pathways through which various weather conditions explain the prevalence of noise complaints. Next, we discuss each weather factors in turn and introduce the findings from the existing crime and noise scholarship.
a. Temperature
Temperature has been most commonly discussed in the crime literature, causing so-called heat stress (Benita and Tunçer 2019, p. 2) and crankiness (Anderson 2001, p. 36) to influence human behaviors (Guo et al. 2007; Singhal et al. 2014; Kashfi et al. 2015). The temperature–aggression or heat hypothesis asserted that hot temperatures cause increases in aggressive motivation and, under the right conditions, in aggressive behavior, and this has been empirically tested from a variety of sources and in various centuries and continents (Akrami et al. 2014; Alexander and Stokoe 2019; Shepherd et al. 2010; Pedersen 2011; Benita and Tunçer 2019; Yang et al. 2019; Nagano and Horikoshi 2005). However, whether temperature plays a direct causal role has been the question stimulating most recent research in this area. In the domain of noise, the existing literature explores the combined effects of noise and temperature on the subjective assessment of human such as body discomfort (Deb et al. 2018; Pellerin and Candas 2003; Yang et al. 2019; Nagano and Horikoshi 2005). For instance, Yang et al. (2019) found significant effects of air temperature on the perception of noise; loudness and complaints of noise increased with increasing temperature, leading to a peaking annoyance in a thermal environment. In the manufacturing industry, high temperature has proven to create adverse effects on workers’ health and productivity by lowering their concentration and increasing their attention to the concurrent prevalence of other mechanical noises, which increased the complaints about headache as a type of annoyance (Deb et al. 2018). These studies usually considered temperature as a part of a comprehensive thermal comfort assessment to capture the environmental effects on human body and mental reactions through a series of indices such as the universal thermal climate index, the heat stress index, and the wet-bulb global temperature (Lam et al. 2018; Zare et al. 2018). The noise–thermal mechanism revealed from these preceding studies can be extended to understand the effect of temperature on noise complaints between neighbors across seasons: hot temperature may magnify people’s sensitivity to physical conditions, to decrease their psychological comfort, and to further impose complaints to neighbors on summer days, while such effects in other seasons need more empirical investigations.
b. Rain
In comparison with temperature, there is a less consistent relationship between rainfall and noise complaints possibly because the magnitude of rainfall would affect human sensitivity and psychological response differently and also alter people’s routine activities and their exposure to noise sources. Existing studies show that increase of rainfall generates interior background noise in and across buildings, affecting their outside wall and roof surfaces (Akrami et al. 2014), transmitting noise from space in backyard or front yard, and inducing a decrease of speech intelligibility (Guigou-Carter et al. 2002; Alexander and Stokoe 2019). Such a physical exposure to the increase of rainfall may render people to be more sensitive and less tolerant to noise, psychologically stimulating their annoyance and nuisance and further inducing noise complaints between neighbors. However, in some circumstances, it is possible that a moderate level of rainfall induces a drop in temperature and improves physical comfort to the surrounding environment, and thus indirectly increases their positive mood and reduces annoyance, especially on hot summer days (Denissen et al. 2008; Akrami et al. 2014; Alexander and Stokoe 2019). From these inconclusive findings, we make a simple proposition that the association between rainfall and the likelihood of noise complaints is subject to the volume of rainfall and seasonal variations.
c. Wind
The extent to which wind affects human behaviors is less explored relative to temperature and rainfall, although some existing studies acknowledged that strong wind leads to greater climatic noise at the systematic level, which can be annoying to human beings and can be associated with stronger effects on reported health (e.g., sleep disturbance), especially when wind-induced sound level is greater than 40 dB (Pedersen 2011; Knopper and Ollson 2011). Moreover, annoyance occasionally appears to be more related to visual cues and an individual’s attitude than to the noise itself (McNett et al. 2010). Strong wind brings visual chaos, messy fabrics, and disorder to the neighborhood environment; residents are more likely to attribute an annoyed state to this physical manifestation than to wind itself, and these visual effects can raise their sensitivity and awareness to surrounding noises (Shepherd et al. 2010; Pedersen 2011). This is particularly the case in neighborhoods with relatively worse visual amenities and more unmanaged waste disposal, or in areas of concentrated high-density or high-rise buildings, where strong wind may increase irritable and distressed feelings of residents and indirectly be associated with a higher likelihood of neighbor complaints about noise. Although such a cause-and-effect relationship has not been proven with clear evidence, it is reasonably speculated from early work (e.g., McNett et al. 2010; Shepherd et al. 2010; Pedersen 2011) that visual and physical changes in the environment in windy weather would increase people’s sensitivity to noise and feelings of annoyance and nuisance, which seem to be more associated with reported noise complaints.
d. Other weather factors
Beyond the three key weather factors, humidity and air pressure are also commonly considered as indicators of weather effects (Collard and Healy 2003; Feddersen et al. 2016), although they are relatively less captured in the noise-related literature. A change in air pressure and humidity might be coherently associated with a change of wind, temperature, and rainfall, which may affect human comfort and sensitivity to stimuli in the surrounding environment and indirectly influence their tolerance of noise (Collard and Healy 2003). The impacts of humidity on human mobility have been observed: people tend to have more movement and outdoor activities on dry, comfortable days and less in moist, cold conditions (Kalkstein et al. 2009; Zhou et al. 2017). Some scholars combine several weather factors into one single indicator, such as the “apparent temperature” (Corcoran and Tao 2017, p. 76) or a thermal index of “physiologically equivalent temperature” (Creemers et al. 2015, p. 187) as a reflection of perceived human comfort and physical responses to the weather effects. In this sense, we construct a series of combined weather factors in our research design to integrate humidity and pressure with temperature, wind, and rainfall to explore how they affect noise complaints between neighbors.
In addition, it is noteworthy that the impact of weather on animals is different from that on human beings (Graunke et al. 2011; Das et al. 2016). It has long been documented that the change of environment has profound effects on the behavioral, physiological, and psychological responses of animals, though their psychological reactions to their environment is difficult to measure (Sales et al. 1997). In this regard, scientific experiments tend more to treat weather conditions as a whole set of physical stressors so their impacts on animals can be monitored, including the change of sleep or wake cycles, biological functions, interaction with human beings, and the healthy equilibrium of animal bodies (Wei 1969). Some scientific results show that the hearing of animals differs from that of humans; dogs have much better hearing and can hear sounds that are quieter by up to a factor of 4 than can the human ear (Sales et al. 1997). As such, the increasing levels of noise from heavy rainfall and strong wind would have stimulate animals’ sensitivity to surrounding environment and might trigger dog barking, which constitutes the major source of noise complaints between neighbors. Moreover, weather can also alter the intensity of animal movement; people may feel reluctant to go outdoor for dog walking on rainy winter days, thus increasing the time of animals staying at home and the likelihood of animal barking as a result of constraint of movement. In this regard, the differentiation of noise complaints on animals and nonanimals is also considered in our research design to form a more holistic picture between weather and neighbor complaints on noise.
In sum, our study focuses on a particular type of human reaction to noise—noise complaints made by neighbors—as an overt action to reflect the impact of noise on the mental health of human beings and explore how noise complaints may relate to weather conditions. The proposed mechanisms are tested in our seasonal analysis given that the variability of weather conditions is more distinct across seasons in subtropical Brisbane, and the change of people’s mentality tends to be more connected to specific seasons (Petrov et al. 2015; Berry et al. 2010; Wehr et al. 2001). Figure 1 shows the research design and analytical workflow of this study. Although our analysis may not be able to confirm the casual direction between weather and action of complaints, it provides new insights to enhance the previously inconclusive discussion of the endogeneity and causality, and it contributes empirical evidence to our theoretical expectations alongside which future studies can be developed.
Research design and analytical workflow.
Citation: Weather, Climate, and Society 13, 4; 10.1175/WCAS-D-21-0020.1
4. Study context, data, and method
a. The Brisbane context
We take the Brisbane Local Government Area (LGA) in southeast Queensland as the study area where a population of 1.13 million inhabited a low-density area of 1342 km2 in 2016 (ABS 2016). Much of population growth concentrates in the outer suburbs, mostly with low-density dwellings, while the inner city and adjacent waterfront areas with high-density properties (e.g., high-rise apartments) have also experienced higher-than-average population growth as a result of urban renewal programs (Office of Economic and Statistical Research 2010). Noise complaints have been observed to increase from 2007 to 2014 and are expected to continue increasing in the future as a result of the increase of population and housing density (Cheshire et al. 2019). Such an increase of noise complaints has been also observed in other cities internationally (Benocci et al. 2020). As the capital city of Queensland, known as the sunshine state, Brisbane has a humid subtropical climate that makes it Australia’s second-hottest capital city after Darwin (Bureau of Meteorology 2016). The average maximum temperatures of above 26°C persist from October through to April; because of its proximity to warm ocean currents, Brisbane has never recorded a subzero minimum temperature; summers in Brisbane are long, hot, and wet, but temperatures very occasionally reach 35°C or more; while winters are short, dry, and moderately warm, with average about 22°C (Bureau of Meteorology 2016). From November to March, thunderstorms are common over Brisbane, with the more severe events accompanied by large damaging hail stones, torrential rain, and destructive winds. On an annual basis in 2016, Brisbane experienced an annual mean minimum of 16.6°C and mean maximum of 26.6°C, with mean precipitation of 1018.3 mm, maximum wind speed of 157 km h−1 appearing in November, and mean relative humidity of 52% (Bureau of Meteorology 2016).
b. Data
We used large-scale administrative data (LAD) supplied by Brisbane City Council’s Compliance and Regulatory Services division (CARS) in 2016. This CARS dataset contains complaint records from residents who lodge through the council’s 24-h call center, in writing, through an online portal, in person, or via social media. As LAD became a newly emerging source for neighborhood studies in social science (e.g., data of “311” requests, recorded police activity, digitized emergency records, and hotline calls) a common issue involved in the usage of LAD in early studies is the propensity of local residents to report problems (O’Brien et al. 2014; Minkoff 2016; White and Trump 2018; Lerman and Weaver 2014). People would have different responses, reactions, and attitudes to the same urban problems or neighborhood issues. As such, it is possible that a neighborhood with the most reported cases has the fewest actual problems simply because of a high likelihood of reporting problems. Thus, analytical results by LAD would be less convincing without the consideration of reporting propensity, namely, the likelihood of people to report problems among the total population in a neighborhood (Minkoff 2016). In this regard, the analyses and data interpretations in our study will be propensity controlled to achieve more rigorous and robust research design.
A common approach used in the LAD-related social studies [e.g., the widely cited 311 data studies done by Minkoff (2016)] is to locate or geocode the locations of complainers to reveal the spatial distribution of urban problems reported by them. However, these early studies made an arbitrary assumption that the locations of complainers are the places where urban problems occur, given that citizens who regularly visit other neighborhoods for work, recreation, and shopping may complain and report problems at that locale far away from the neighborhood where they reside (Sastry et al. 2006). This drawback may be caused by the limitation that 311 data are only available for complainers’ locations, which, however, can be conquered by the CARS dataset used in our study. The spatial and temporal information of CARS data recorded at the individual level enables us to measure the distance between complainers and offenders (people who are complained about) to further define neighbor complaints as complaints made by a resident to neighboring people within 100-m distance of their home and nonneighbor complaints as the ones beyond 100-m distance.
These CARS data provided records of complaints at individual level, including the nature, type, description of reported complaints, time of complaints (year, month, date, hour, and minute), and home addresses for both the complainant and the target of the complaint. The CARS dataset was made available to the research team under a signed confidentiality agreement following extensive negotiations given the sensitive nature of the dataset in identifying who had lodged a complaint about whom. Since the CARS data encompassed a range of problem types, not all relating to between-neighbor problems, we commenced with defining and deriving neighbor-focused complaints by using a spatial data-mining approach that was detailed in the previous paper (Liu et al. 2019). Then we filtered the overall neighbor complaints by selecting the records with complaint nature of “noise pollution-residential,” which ended up with total 8338 noise-related records. The temporal information in this dataset enabled us to explore seasonal and monthly variations of noise complaints. These noise data at individual level were aggregated to the Statistical Area level 2 (SA2) by season to be used in the regression analysis at the later stage. The SA2 is a spatial unit that generally has a population range from 3000 to 25 000 persons (Australian Bureau of Statistics 2011), and the size of SA2 is appropriate in our analysis to indicate the spatial variation of weather conditions and noise complaints.
Weather-condition data at half-hourly intervals were retrieved from the Bureau of Meteorology in 2016, collected in nine weather stations in and around the Brisbane local government area, including Amberley Base, Archerfield Airport, Brisbane City, Brisbane Airport, Redland, Redcliffe, Beaudesert, Gatton, and Cape Moreton Bay. We selected five variables from this weather dataset (Table 1), because they are commonly used in the previous studies (Richardson 2007; Barry and Chorley 2009). We then aggregated the half-hourly weather data by station and by day and season. Accordingly, weather data aggregated by day were used in the 1-yr analysis, and weather data aggregated by season were used in the seasonal analysis. We employ the term “seasonal weather” in the data interpretation of the seasonal analysis that uses the aggregated mean value of a weather variable (e.g., rainfall) to represent the climatic pattern in the period of that season. We further assign the weather data collected from nine weather stations to 128 SA2 in Brisbane by kriging, a spatial interpolation method using the surrounding measured values from points to predict values at unmeasured locations, which has been commonly used in literature. kriging interpolation method was conducted in the ArcGIS software using a kriging geoprocessing tool, generating a weather dataset at SA2 level.
Data and data sources.
c. Method
We commenced with a descriptive analysis of noise complaints by type and weather conditions by month and by season. We then conducted a principal component analysis (PCA) as a common approach to reduce the dimensionality of built environment and weather factors while retaining most of the variation within the dataset (technical details provided in Wold et al. 1987). The results of the PCA for the built environment and weather factors are provided in the online supplemental material. Overall, PCA identified five components of the built environment, with each containing one or multiple built-environment variables (see Tables S1 and S2 in the online supplemental material). PCA also identifies five combinations of weather factors capturing their combined effects on noise complaints, including temperature × pressure, humidity × rain, humidity × wind, rain × wind, and humidity × rain × wind (see Tables S3–S12 in the online supplemental material).
Then, we employed a stepwise multiple regression modeling approach to explore the association between noise complaints, built environment, and weather conditions (single and combined factors). The stepwise selection procedure enables us to adjust the order that the candidate independent variables are entered into the regression model to generate the highest R square (correlation coefficient squared) between the dependent and independent variables. The stepwise selection is a combination of the forward and backward selection techniques (Armstrong and Hilton 2010). It checks all candidate variables in the model after each step in which a variable was added to see if their significance has been reduced below a specified tolerance level. If a nonsignificant variable is found, it is removed from the model. Such a stepwise selection method requires two significance levels: one for adding variables and one for removing variables. After multiple iterations, the cutoff probability for adding variables should be less than the cutoff probability for removing variables to achieve the best selection of variables with the highest model performance.
5. Results
a. Descriptive analysis: The temporal variation of noise complaints and weather conditions
Animal noise is the major source of noise complaints, accounting for 76.2% of total complaints (Table 2). Nonanimal noise complaints (23.7% of the total) are mainly from builders working out of hours as the external source as well as internal sources such as air conditioners, refrigeration equipment, amplifier devices, and ancillary domestic noise.
Summary of noise complaints.
Figure 2 shows the temporal variation of noise complaints and weather conditions by month and by season. The number of animal (solid line) and nonanimal (dash line) noise complaints are read in the y axis on the right. Animal noise complaints appear to be highest in May and then March, August, and October whereas nonanimal noise complaints are observed to be highest in March followed by November and least in June. Seasonally, animal noise complaints increase largely in autumn (February–April) to achieve a peak in May and relatively keep stable in spring (August–October) and decrease in summer (November–January), whereas nonanimal noise complaints increase in spring and have more fluctuations within each season.
Temporal variation of noise complaints and weather conditions.
Citation: Weather, Climate, and Society 13, 4; 10.1175/WCAS-D-21-0020.1
For weather conditions, each box plot shows the value range, mean, and standard deviation of a certain weather factor in one month that can be read in the y axis on the left (Fig. 2). Rainfall, temperature, and pressure are observed to have more substantial variations across months than wind and humidity. The fluctuation of rainfall is large in winter (May–July), with June having the highest mean value and widest range of rainfall throughout the year and May and July having the much lower mean value and smaller range of rainfall (Fig. 2a). The mean value of temperature decreases from autumn to winter, hitting the lowest in July, and then increases from spring to summer, hitting the peak in December (Fig. 2b). Pressure has large variations within each season and across the whole year, with the highest mean value in August followed by April and July (Fig. 2e). The variation of mean value of wind is relatively small across months while the value range of wind in each month is large (Fig. 2c). The mean value of humidity changes more obviously across months than that of wind, being highest in March and lowest in October, and the variation of humidity within a month is relatively large in summer, particularly in November and December, when compared with other seasons (Fig. 2d).
b. Relationship between noise complaints and weather conditions
The final regression outcome (Table 3) shows that the R squares in regression results for different seasons and types of noise complaints are all increasing from model 1 to model 2 and 3, indicating the improvement of model performance by involving single or combined weather factors. In model 1, PCs 2, 3, and 5 are negatively (p < 0.05) associated with all noise complaints (PC 2: β from −0.304 to −0.15; PC 3: β from −0.288 to −0.155; PC 5: β from −0.362 to −0.258) and non-animal-related noise complaints for a 1-yr period and for spring, summer, and winter (PC 2: β from −0.283 to −0.235; PC 3: β from −0.165 to −0.275; PC 5: β from −0.267 to −0.364). PCs 4 and 5 are significantly (p < 0.05) associated with animal-related noise complaints for a 1-yr period (PC 4: β = 0.454; PC 5: β = 0.177), whereas only PC 4 is positively (p < 0.01) associated with animal-related noise complaints for four seasons (PC 4: β = 0.305–0.454). Similar to model 1, PCs 4 and 5 in models 2 and 3 are significantly (p < 0.01) associated with all types of noise complaints for a 1-yr period and for summer, autumn, and winter. Specifically, PC 4, to which cultural diversity highly contributes (loading factor = 0.955), is positively associated with all complaints and animal-related noise complaints but negatively associated with non-animal-related noise complaints; PC 5, which is highly influenced by land diversity (loading factor = 0.976), is negatively associated with all types of noise complaints. In other words, more multiethnic suburbs tend to have more animal noise complaints but fewer non-animal-related noise complaints in a yearly time frame and in different seasons. It may be because multiethnic suburbs, usually ethnic enclaves, are more maturely developed with less construction and thus are less likely to have noise from working builders. Furthermore, suburbs with less land-use diversity tend to have more noise complaints in both types across seasons. It is possibly because residential suburbs (in single land use) are more likely to have households with pets and thus higher exposure to animal noise and noise from other onsite construction or home appliances.
Results of a stepwise multiple regression. An em dash indicates that this combined weather factor is excluded in the regression process to optimize the result. One, two, and three asterisks indicate significance at p < 0.1, p < 0.05; and p < 0.01, respectively. Principal component 1 contains SEIFA (α = −0.922) and lot density (α = 0.895); component 2 contains housing diversity (α = 0.798) and age above 65 (α = 0.792); component 3 contains green space (α = 0.871) and population density (α = 0.727); component 4 contains cultural diversity (α = 0.955); component 5 contains land diversity (α = 0.976).
Controlling built-environment factors in model 2, we further add in single weather factors to explain how weather affects noise complaints. For a 1-yr period, it is surprising to observe that all weather factors are significantly (p < 0.1 for wind; p < 0.01 for the other four) associated with all complaints and nonanimal noise complaints but not with animal noise complaints. More specifically, less rainfall and stronger wind, as well as higher temperature, humidity, and air pressure, are associated with all and nonanimal noise complaints. However, when broken down to different seasons, the effects of seasonal weather on noise complaints vary. Rainfall is positively (p < 0.05; β = 0.391 and 0.489, respectively) associated with animal noise complaints in spring and summer, whereas it is negatively (p < 0.1; β = −0.362 and −0.188, respectively) associated with nonanimal noise complaints in summer and winter. Next, a higher level of wind is significantly (p < 0.05; β = 0.501) associated with more nonanimal noise complaints only in summer but less in autumn and winter. Moreover, strong wind in cold winter days would decrease the likelihood of construction workers as the major source of nonanimal complaints. Further, temperature is positively (p < 0.01; β = 0.274–1.116) associated with nonanimal noise complaints in four seasons but is only negatively (p < 0.01; β = −0.249) associated with animal noise complaints in autumn. Humidity is positively (p < 0.1) associated with all complaints and nonanimal noise complaints only in spring (β = 0.668 and 0.506, respectively), and pressure is positively (p < 0.05; β = 0.616) associated with animal noise complaints in spring but is negatively (p < 0.1; β = −0.444) associated with nonanimal noise complaints in summer.
The result of model 3 shows how combined weather factors affect noise complaints. A higher level of humidity and rain is significantly (p < 0.01; β = −3.8) associated with a lower level of nonanimal noise complaints only in summer, indicating that nonanimal noise complaints are less prevalent on humid and rainy days in summer. On humid and windy days, there are observed fewer nonanimal noise complaints in summer and winter but more in spring. However, on humid, windy, and rainy days, there are more nonanimal noise complaints in summer and winter. It indicates that rain play an important role to distinguish seasonal patterns of nonanimal noise complaints and may increase humans’ sensitivity to surrounding noise on hot summer days; it also may be because rain, especially heavy in June in winter, presents obstacles or reduces the outdoor activities of residents who spend more time at home and are more likely to be exposed to nearby noises made by neighbors. Relative to the nonanimal noise complaints, the combined weather factors have more obvious impacts on animal noise complaints in autumn. In particular, a higher level of humidity, wind, and rain is significantly (p < 0.05 and β = 3.975) associated with a higher level of animal noise complaints in autumn, indicating that animals (e.g., pets) may make more noise on humid, windy, and rainy days and be more likely to interrupt neighbors when they stay at home on rainy days.
6. Discussion
We consider what the above findings mean by linking to the early theoretical expectations. To begin with, higher temperature is observed to the more prevalence of nonanimal noise complaints in a yearly time frame, leading to peaking annoyance and nuisance in a thermal environment and an increasing likelihood of complaints about surroundings (Deb et al. 2018; Pellerin and Candas 2003; Yang et al. 2019). Our finding is in alignment with the temperature–aggression model and heat hypothesis widely discussed in social psychology and urban criminology—the mechanism of how temperature affects general aggressive reactions and crime helps us to understand the noise complaints by neighbors. A higher level of temperature detrimental to physical comfort magnifies people’s sensitivity to their environment, decreases their psychological tolerance, and generates annoyance that potentially triggers complaints in the face of a conflicting situation or noise from near neighbors. It is particularly the case for the high-density living in inner cities with spatial proximity in the form of shared walls, floors, and walkways, and through the transfer of noise around buildings and from animals (Power 2015; Cheshire et al. 2019; Liu et al. 2019).
To explore further, more intensive rainfall is found to be associated with more animal noise complaints but fewer nonanimal noise complaints only in spring and summer. It may be explained by the fact that spring and summer in Australia has more rainy days. Rainfall, especially in the middle of daytime, acts to reduce the physical discomfort and diminish psychological tolerance of human beings to the surroundings but to bring nuisance and interruptions to animals (i.e., pets in a family) and cause animal noise to disturb neighbors. Another possibility may be that building workers tend to work longer outdoor on sunny days with higher temperatures, and thus may contribute to more nonanimal noise complaints. This finding is different from what the literature revealed, which is that people may tend to be more sensitive to noise and make more complaints about noise with the increase of rainfall (Guigou-Carter et al. 2002; Alexander and Stokoe 2019; Akrami et al. 2014), although some early studies indicated that people may tend to be less sensitive to surrounding noise when they get used to rainfall as a noisy background (Akrami et al. 2014; Peters et al. 2014; Lee 2006); in that sense it may reduce annoyance and complaints. Thus, the effect of rain on human reaction seems to be ambiguous and subject to its magnitude, concurrent weather conditions, and seasonality.
Under conditions of more intense wind, the likelihood of noise complaints is observed to be higher particularly in summer, which is in alignment with the finding of some literature (Pedersen 2011; Shepherd et al. 2010; Pedersen 2011). Wind is recognized to be associated with increasing noise and personal annoyance by affecting visual surroundings, personal attitude, and sensitivity to noise (McNett et al. 2010), and thus stimulates annoyance or complaints among neighbors. The possible explanation may come down to the Australian context where stronger wind in summer brings warm air circulating around and magnifies people’s sensitivity to high temperatures, decreasing their psychological comfort and increasing the likelihood of nuisance, annoyance, and complaints. Another possibility may be that strong wind in cold winter days would decrease the likelihood of construction workers as the major source of nonanimal complaints. In this regard, it is less persuasive to conclude the effect of wind on noise complaints without consideration of seasonal variations and noise sources.
Although a single weather condition (e.g., high temperature) may affect human behaviors explicitly, it is more common that weather conditions function collectively with a combined effect on reaction and response (Sanders and Brizzolara 1982; Howarth and Hoffman 1984). Specifically, humidity and pressure usually change with rainfall, wind, and temperature coherently and dependently, which may not influence human’s routine activities as heavy rain and strong wind may do so. In the subtropical context of southeast Queensland where oceanic climate along the Australian coast mixing with the desert climate in outback causes most days in Queensland to be dry (low humidity), it is interesting to observe the comparison that fewer nonanimal noise complaints appear on humid and windy days but more appear on humid, windy, and rainy days in summer and winter, and such a comparison is not observed in spring and autumn. It seems that in a similar condition of humidity, pressure, and temperature, rain play a relatively important role to distinguish seasonal patterns of nonanimal noise complaints; it also may be because rain, which is especially heavy in winter, reduces the outdoor activities of residents who spend more time at home and increases the likelihood of hearing nearby noises made by neighbors.
In addition, it has been observed that weather effects on animal noise and nonanimal noise complaints are distinct. This finding is in line with the observations in existing studies (e.g., Graunke et al. 2011; Das et al. 2016) documenting that the change of environment has profound effects on the behavioral, physiological, and psychological responses of animals. It may be explained by the sensitivity, hearing, and sight of animals that differ from that of humans (Sales et al. 1997). It is possible that increasing the levels of noise from heavy rainfall and strong wind would stimulate animals’ sensitivity to the surrounding environment and therefore might trigger dog barking as a major source of noise complaints by neighbors. Furthermore, weather may alter the intensity of animal movement especially on rainy winter days given that people may feel reluctant to go outdoor for dog walking and may lead to a higher likelihood of animal barking due to the constraints of movement. Our findings on animal-related noise complaints reflect the change of animal behaviors over different weather conditions, providing evidence for future study on animal behaviors.
Our findings hold a number of practical policy and planning implications for government planning agencies, building and construction authorities, and community organizations in terms of urban governance, building management, and community interventions. For instance, government planning agencies could take into account the seasonal patterns of noise complaints to better regulate the design and management of the neighborhood environment to be more noise resilient across a broad suite of prevailing weather conditions. Detailed community management plans are needed to better regulate working hours of local construction projects and to increase the frequency of collecting abandoned goods, waste, and building debris in neighborhoods as those items may create more noise on rainy and windy days. On the other hand, the building and construction authorities could consider setting up relevant construction regulations and building standards to promote the usage of noise-proof fences, doors, and windows to prevent noise across neighboring properties. To facilitate urban governance, various platforms and applications need to be developed for residents to report local noise problems, including hotline calls, online forums, or mobile phone apps so that residents would get instant responses and interventions from the administrative authorities.
Our findings also point to a number of avenues for future studies to investigate and unpack the way in which environmental stress and social psychology intertwine with public health. Given the prevalence of neighborhood noise in the urban environment, it is clearly a threat to the health of local residents as it disrupts the lives of those being affected, reduces the quality of life, and has an adverse impact on their mental and physical well-being (Stansfeld et al. 2000). Future studies on environmental stress and social psychology could explore the pathophysiological mechanism of weather–noise relationship and the coping strategies of seasonal mentality corresponding to weather change (Stansfeld et al. 2000). For example, a comparative study between tranquil and noisy areas can help with physical and psychological restoration of noise affecting residents; other positive health virtues of noise control should be explored. Active coping strategies to noisy environment should be developed to mitigate any ill effect and prevent long-term exposure to noise. On most occasions, noise among neighbors is not under the individual’s control, and it is more harmful in an urban environment that has a combination of social and environmental stressors, where communities are already battling other disadvantages (Gidlöf-Gunnarsson and Öhrström 2007). Further research to examine multiple social and environmental stressors is needed.
So far, the findings in our study are largely consistent with the preponderance of evidence from early studies though presenting a few contradictories to the previous conceptual expectations. When such discrepancies arise, the answer is not to arbitrarily conclude the matter settled, but to call for more research to redress some limitations in our study and elaborate our findings in different contexts and directions. First, the Brisbane Council CARS data used in our study are limited in personal details about the complainants themselves, such as family composition, gender, age, ethnicity, housing ownership, or of the neighbors who annoyed them. These personal features contribute to the extent and frequency of complaints they impose. Second, our definition and measure of “noise complaints on neighbors” may be argued as to the selection of 100 m, which can be tested by different thresholds. Third, the relationship between neighbor complaints on noise and weather conditions is not simply liner, but complex linking to personal conditions such as tolerance, endurance, and habits. More robust experimental tests are needed to discover that “turning point” beyond which people would convert their privately held opinions to overt action of complaints. Fourth, the stepwise regression we use in our analysis combines the forward and backward variable selection methods to obtain the best variables that contribute most to the model. However, this approach is subject to some methodological drawbacks. For example, the stepwise method aims to achieve the maximum model performance; depending on the significance level set by researchers at which variables can enter the model it may include variables that are not necessary for the model. We also need to exclude the variable outliers that may impact on the stepping procedure before the modeling commences. Fifth, the preceding data interpretations are tentative and hypothetical, and cannot indicate the causal direction between neighbor complaints on noise and weather conditions. Qualitative and causality studies targeting broader population and geographic contexts should be on the future agenda.
7. Conclusions
This study examines the extent that weather affects noise complaints between neighbors; it provides theoretical explanations on the weather–complaint relationship and contributes an empirical study in Brisbane. With the understanding of the nature of noise complaints on neighbors and its temporal variation across months and seasons, our findings reveal that the effects of weather on noise complaints are complex, subject to noise sources, and further mediated by built environment in terms of the density, design, and diversity of neighborhoods. In a yearly frame, a higher level of wind, humidity, and temperature is observed to associate with a higher likelihood of noise complaints, while such weather effects in different seasons are less explicit. Thus, it may be arbitrary to have a confirmative conclusion of the effects of weather on noise complaints without consideration of noise sources, complaint types, built environment, and seasonal variations. As a subjective experience and common reaction to noise from neighbors, complaints may be imposed differently, ascribed partly to differences in personal annoyance, noise sensitivity, and tolerance, as well as exposure to the noise sources, all of which are directly and indirectly associated with weather conditions as exogenous factors affecting people’s everyday lives. Although the evidence revealed in our study is too limited to afford greater generalizability and establish solid causality between weather conditions and noise complaints, we make an initial attempt to link the complexity of human relationship to environmental stressors. Our speculative explanations of how the natural environment influences human reactions to their surroundings and neighborhood relationships points the way to further advances in different geographic and climatic contexts.
Acknowledgments
This research is funded by an Australian Research Council Discovery Grant (DP150100457). The authors acknowledge the support of the Brisbane City Council Compliance and Regulatory Services Branch and Australian government Bureau of Meteorology for making their data available for analysis. We also thank the anonymous reviewers for their constructive comments on an earlier version of this paper.
Data availability statement
The complaint data used in this study were retrieved from Brisbane City Council’s Compliance and Regulatory Services Division with confidential information of complainers at the individual level. Therefore, this complaint dataset cannot be shared and accessed by the public. Weather data were retrieved from the Bureau of Meteorology and can be accessed via users’ application. If there are any questions about the data, please contact the corresponding author.
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