Traffic Noise and Inequality in the Twin Cities, Minnesota

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Traffic Noise and Inequality in the TwinCities, MinnesotaTsegaye Habte Nega a , Laura Chihara b , Kimberly Smith a & MallikaJayaraman aa Environmental Studies Department , Carleton College , Northfield ,MN , USAb Mathematics Department , Carleton College , Northfield , MN , USAAccepted author version posted online: 06 Jun 2012.Publishedonline: 04 Apr 2013.

To cite this article: Tsegaye Habte Nega , Laura Chihara , Kimberly Smith & Mallika Jayaraman (2013)Traffic Noise and Inequality in the Twin Cities, Minnesota, Human and Ecological Risk Assessment: AnInternational Journal, 19:3, 601-619, DOI: 10.1080/10807039.2012.691409

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Human and Ecological Risk Assessment, 19: 601–619, 2013Copyright C© Taylor & Francis Group, LLCISSN: 1080-7039 print / 1549-7860 onlineDOI: 10.1080/10807039.2012.691409

HAZARD ASSESSMENT ARTICLE

Traffic Noise and Inequality in the Twin Cities,Minnesota

Tsegaye Habte Nega,1 Laura Chihara,2 Kimberly Smith,1 and Mallika Jayaraman1

1Environmental Studies Department, Carleton College, Northfield, MN, USA;2Mathematics Department, Carleton College, Northfield, MN, USA

ABSTRACTIt is widely known that prolonged exposure to high levels of traffic noise has

several health effects. While scholarship in environmental justice has explored theenvironmental equity hypothesis in a wide range of areas, whether the spatial dis-tribution of traffic noise is equitable among different racial and socioeconomicgroups has rarely been explored, especially in the United States. This article ad-dresses this lacuna by examining this relationship in the Twin Cities Metro Region,Minnesota. Traffic data from the Minnesota Department of Transportation wereused to model the propagation of traffic noise over the study area and aircraft noisecontour lines were added to account for aircraft noise. Inequities associated withexposure to chronic traffic noise were investigated using selected demographic andsocioeconomic variables from the U.S. Census 2000. Statistical analysis was based ona regression model that addressed spatial autocorrelation. Results indicate that thereis an association between noise levels and household income, median householdvalue, the percentage of non-white residents, and the percentage of the populationless than 18 years of age.

Key Words: traffic noise, environmental justice, health, spatial statistics, GIS.

INTRODUCTION

Prolonged exposure to high levels of traffic noise affects people in a number ofways, ranging from simple annoyance (Miedema and Oudshoorn 2001; Ouis 2001),to sleep disturbance (Netherlands 2004), to increasing risk for stroke (Sorensenet al. 2011), hypertension (Bodin et al. 2009; Jarup et al. 2008), and myocardial

Received 29 September 2011; revised manuscript accepted 2 January 2012.Address correspondence to Tsegaye Habte Nega, Environmental Studies Department,Carleton College, One North College St., Northfield, MN 55057, USA. E-mail: [email protected]

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infarction (Babisch et al. 2005; see Babisch 2006; Babisch 2008 for reviews; Clarkand Stansfeld 2007; Passchier-Vermeer and Passchier 2000). The noise level at whichsuch effects are observed does not have to be high. For example, Barregard et al.(2009) modeled traffic noise exposure at residential locations and found that thatpeople exposed to traffic noise with a 24-hour average of >55 dBA had an oddsratio (OR) of 1.9 (95% CI 1.1 to 3.5) for hypertension. Similarly, in a study ofpopulation-based cohorts of 50,753 people, Sorensen et al. (2011) found an adjustedIncidence Rate Ratio (IRR) of 1.27 (95% CI 1.13 to 1.43) among those who are64 years old and older. The impact on health is expected to grow worse with theincreasing number of vehicles in the urban road network and the diminishingnumber of nighttime quiet hours (Goiness and Hagler 2007). This increase intraffic noise may have a significant environmental justice dimension. Environmentaljustice is centrally aimed at preventing or rectifying disproportionate exposure to theeffects of environmental degradation, with particular concern for those inequalitiesthat affect the poor and the historically disadvantaged or marginalized populations(Schlosberg 2007).

Transportation has long been recognized as an area of concern in the environ-mental justice literature. This literature has pointed out that low income and minor-ity groups participate less in transportation related decision-making (Denmark 1998;Khisty 2000; Lee 1997), receive less mitigation and protection from the enforcementof environmental regulations (Lee 1997; Shepard and Sonn 1997), and are oftentargeted to host unwanted transportation facilities without enjoying their benefits(Bullard 2007; Bullard et al. 2004; Powell 2007). However, with the exception of afew studies that have focused on air transport noise pollution (Morello-Frosch et al.2001; Pfeffer et al. 2002), the question of whether low-income and minority commu-nities are disproportionately affected by road traffic noise has rarely been examinedin the United States, even though road traffic noise is perhaps the greatest sourceof noise in residential neighborhoods (Barber et al. 2010).

A few studies conducted outside the United States have attempted to moresystematically explore the relationship between socioeconomic status and roadtraffic noise. The results were suggestive but inconclusive. For example, in a studyconducted in the United Kingdom, Brainard et al. (2004) found that, with theexception of blacks (p < .1), no other minority group was exposed to higher trafficnoise levels. Similarly, Lam and Chan (2008) found that in Hong Kong, there wasno correlation between exposure to high level of traffic noise and socioeconomicstatus. Contrary to these findings, Havard et al. (2011) found that the wealthy inParis are more exposed to higher traffic noise levels than the rest of the population.

One major reason for the thinness of the scholarly literature on this topic lies inthe difficulty of modeling noise propagation over the landscape. To properly analyzethe spatial association between socioeconomic status and exposure to traffic noiseat a landscape level, it is necessary to create a noise surface map at sufficiently de-tailed spatial resolution to account for the complex and heterogeneous interactionbetween the noise source and the resistance of the landscape to noise propagation.Implementing such a model can be very difficult. For one thing, the data neededfor the model are very extensive and may not even be readily available (e.g., build-ings’ footprint and height data). Furthermore, it is computationally very intensive,requiring more computing resources than a desktop computer. Fortunately, recent

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Traffic Noise and Inequality in Twin Cities, MN

developments in geographic information systems (GIS) and distributive computinghave reduced these difficulties, making it much easier to create a noise surface mapat landscape level.

A second problem with the spatial analysis of traffic noise is the use of inappro-priate statistical techniques for spatial data. For example, of the studies mentionedabove, only Havard et al. (2011) used a statistical technique appropriate for spatialdata that accounts for spatial autocorrelation. While environmental justice studieshave recently recognized the importance of incorporating analytical techniques thatare sensitive to spatial processes and effects (Chakrborty 2009; Haynes et al. 2001;Mennis and Jordan 2005; Pastor et al. 2005), the quantitative study of environmentaljustice issues in general is characterized by its lack of attention to the effect of ge-ographic methodology on the conclusions that can be drawn from spatially relatedcorrelated data.

In this article, we seek to contribute to the quantitative analysis of environmentaljustice issues by evaluating inequalities in exposure to traffic noise. We approach thisissue by means of an empirical case study of the Twin Cities Metro Region (TCMR),Minnesota. In particular, we model traffic noise propagation using a spatially ex-plicit approach instead of indirectly measuring it based on population densities(Miller 2003) and then we use a statistical model that accounts for spatial autocor-relation to determine the relationship between noise levels and socioeconomicallydisadvantaged groups in the region.

STUDY AREA

The TCMR is located in the east-central part of the state of Minnesota and iscomposed of seven counties (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, andWashington). The region is an ideal place for the proposed study for three reasons.First, the region, which includes the Twin Cities area, is home to two-thirds of thestate’s population (3.2 million people). Second, the region has been growing morerapidly than any other metropolitan area in the upper U.S. Midwest. Almost all thisgrowth has occurred in developing suburbs and exurban areas. This is true evenin the most densely settled parts of the region. For example, between 1986 and2002, the amount of urbanized land in the seven-county metropolitan core grewfrom 182,109 to 252,929 hectares (38% increase) while population grew by just 29%(MNDNR 2006). That is, the growth rate in urbanized land was 53% greater thanthe population growth rate. Moreover, current projections indicate that more than1 million people will be added to the region in the first three decades of the 21st cen-tury (MNDNR 2006). Finally, the region is becoming increasingly diverse. Accordingto the Census 2010 data, the white population (both non-Hispanic and Hispanic)is approximately 81% of the population, compared to 84.5% in 2000. People ofcolor now comprise 19% of the region’s population compared to 14% in 2000, andare settling in a larger number of neighborhoods and communities. One inevitableconsequence of the general growth of the metro’s population is the increase intraffic volume. For example, from 1992–2009 Vehicles Miles Traveled (VMT) for theregion increased by 37%. Thus, unless measures are taken to assess and mitigate thedetrimental effect of this growth, exposure to traffic noise may become a seriousproblem.

Hum. Ecol. Risk Assess. Vol. 19, No. 3, 2013 603

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METHODS AND MATERIALS

Traffic Noise Modeling

To determine the relationship between socioeconomic status and exposure tohigh traffic noise, we first created a traffic noise exposure surface for the entireTCMR by calculating the propagation of traffic noise over the landscape usingthe Federal Highway Authority (FHWA) 1978 standard (Barry and Regan 1978).Following Barry and Reagan (1978), the traffic noise level at any given location onthe landscape is given by:

LEQ(i) = L̄0(i) + 0.115 σ 2i +10 log

NiπD0

T ∗ Si

+ 10 log[

D0

D

]1+α+ 10 log

(ψα(ϕ1,ϕ2)

π

)+�gradient +�shielding (1)

where LEQ(i) is A-weighted hourly energy equivalent noise level in dBA, which iscalculated for each class i of vehicle (automobile and trucks); L̄0(i) is the meanSound Pressure Level (SPL) at the reference distance for class I; σ i is the standarddeviation of the SPL for each class of vehicle; Ni is the number of vehicles of the ithclass passing during the relevant hour; D0 is the reference distance (usually 15 m);D is the perpendicular distance from the road center line to the receiver; α is a siteparameter (soft and hard surface), 0 < α < 1; Si is the mean speed of the ith class;T is the duration, usually 1 hour; ϕ1 and ϕ2 are the angles from the perpendicularof the limits of the observer’s view of a section of the road. They are used to accountfor only the energy coming from a portion of the roadway; �gradient is an adjustmentfor road surface gradient; �shielding is a shielding adjustment (land cover, buildings,noise barriers).

To calculate the traffic noise map for the region using the above model, severaldata sources were used. A 2007 road centerline and the associated traffic character-istics (volume and proportion of trucks and vehicles) for the region were obtainedfrom the Minnesota Department of Transportation (MNDoT). We converted theposted speed of each road into a GIS layer. For all roads without recorded trafficvolume (i.e., residential), we assigned 100 vehicles/day as a minimum estimate. Thisestimate was based on factors identified in the literature as important for estimatingtraffic volume, such as census data on commuting population, tract area, the sizeof a standard street block, and road type (Cheng 1992; Fricker and Saha 1987).In order to assess the sensitivity of our results to our estimate of traffic volume forlocal roads, we conducted a sensitivity analysis by running the model twice, onceusing 200 and once using 300 vehicles/day, for one of the counties that make upthe TCMR. With the exception of a few areas in the rural part of the county, therewas less than 0.05 dBA difference at 30 m from the road centerline, suggesting thatthe estimated values are not sensitive to the traffic volume variations used for thesensitivity analysis.

Data on traffic volume and the proportion of trucks and vehicles on each seg-ment of the road were averaged over a 24-h period, making it impossible to accountfor a day- and nighttime variation in traffic noise. Following Arditi et al. (2007), wedivided the total traffic volume into day and nighttime periods by assuming that80% of vehicles and 65% of trucks are driven during the daytime (6 a.m.–10 p.m.)


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and 35% of trucks and 20% of vehicles are driven during the nighttime (10 p.m.–6 a.m.). Values for ground absorption were chosen by assuming that all road surfacesconsisted of impervious bitumen. For computational simplicity, we created the roadsurface gradient by re-sampling U.S. Geological Survey 30 m resolution Digital Eleva-tion Model (DEM) to 100 m resolution. We used three data sources for the shieldingadjustment: buildings, foliage, and noise barriers. We extracted the perimeter andheight of 818,500 buildings using aerial photography and LiDAR data. For the areaswhere LiDAR data are available, we used LiDAR AnalystTM software to extract build-ings’ footprints. For the areas of the region where only aerial photos are available, weused Feature AnalystTM software to extract buildings’ footprints. For measurementsextracted using aerial photography, height was determined using a combination ofparcel data and field measurements.

The locations chosen for the traffic noise levels were the 818,500 buildings in theTCMR. Thus, we used the perimeter and height of these buildings to account forthe shielding effect of buildings. To account for foliage shielding (and to make thecomputation manageable), we assumed that only forest patches that are at least 10 mhigh and have an area greater than 10,000 m2 will have a significant effect on noiseattenuation. Accordingly, we extracted forest polygons with these characteristicsusing a combination of year 2005 land use data and 2001 National Land CoverData (NLCD). To account for noise barrier shielding, we used a noise barrier layerobtained from MNDoT, which contains the locations, width, and height of noisebarriers in the region.

We used the SoundPlanTM noise modeling software to implement the model givenby Eq. (1). Predicted traffic noise output is calculated at a grid resolution of 10 m2.We conducted a preliminary validation of the model output by comparing it withobserved noise levels at 134 locations that were sampled along major highways.For each location, two readings were taken very close to the highway, one between9 a.m. and 10 a.m. and another between 1 p.m. and 2 p.m. The noise level for eachlocation is then determined by averaging the two observed values, and this averageis then compared with the predicted noise level.

The relationship between the mean observed and predicted noise levels is linearand moderately strong with a correlation of 0.76 (Figure 1). It is not surprisingthat the relationship is not stronger given the size and complexity of the model. Inparticular, the traffic volume data that we used for modeling traffic noise propagationis a 24-h average, which requires more time-interval sampling per location than justthe two used in the validation. Still, this preliminary comparison serves as a goodindicator of how well this model captures traffic noise levels.

In order to incorporate noise produced by aircraft into the overall noise exposure,we obtained the aircraft noise contour lines of the region from the Twin CitiesMetropolitan Airports Commission. The contour line noise values coincident withthe traffic noise surface then were added following rules of logarithmic addition.For example, adding 52 dBA from aircraft contour line into the noise surface wherethe noise value is 51 yields 53.5 dBA. No attempt was made to incorporate noiseproduced by other sources, such as trains or ships. Thus, our noise measurementsfor the 818,500 locations incorporate both traffic noise and aircraft noise. We thendetermined the noise level for each census block group by computing the medianof the noise levels for those buildings in a given block group.


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Figure 1. Mean observed noise level against predicted noise levels (dBA) at the134 sampled locations.

Socioeconomic Data

Following the literature in environmental justice (Boer et al. 1997; Pastor et al.2005), we extracted relevant socioeconomic indicators from the U.S. Census 2000at the block group level to characterize the population living in the study area(Table 1). (Income data at the block group level is not available in the 2010 census.)Most environmental justice research focuses on tracing environmental inequalities

Table 1. Summary statistics of socioeconomic data (U.S. Census 2000).

Min Median Mean Max Std dev

Percent less than 18 years ofage (% < 18)

0.00 25.45 25.40 60.63 8.87

Percent greater than 65 yearsof age (% > 65)

0.00 8.654 10.34 72.19 8.23

Median household income(MHI) (in dollars)

8421 53,550 57,137 200,001 23,124

Median house value (MHV)(in dollars)

12,500 135,650 150,426 953,100 69,221

Per capita income (PCI)(in dollars)

3603 24,768 26,733 127,170 11,187

Percent non-white (% NW) 0.00 8.69 16.12 98.81 19.30Percent non-citizen (% NC) 0.00 2.41 5.04 60.91 7.14Percent with no high school

degree (% NHD)0.00 4.90 6.34 42.35 5.22

Percent unemployed (% Un) 0.00 2.121 2.737 42.689 2.812


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in race and socioeconomic status. More recent research, however, also considers age,since the very young and the very old are more susceptible to, and often have lessability to cope with, environmental stressors such as ambient noise (Cutter 2006).Therefore, our analysis focuses on race, socioeconomic status, and age. Two differentmeasures of income were examined: median household income and per capitaincome. Because this study focuses on noise levels around residential buildings,median household income is a logical measure to use. However, this measure doesnot take into account household size, which is why per capita income was examinedas well. We also decided to include the median value of the houses in a block group.We also considered the percentage of the population of a block group less than18 years of age as well as the percentage of the population of a block group greaterthan 65 years of age. Race was measured as the percentage of non-white persons livingin each block group, education was measured by percentage of people without ahigh school degree, and citizenship was measured by the percentage of non-citizens.

Statistical Analysis

Our objective is to model the median noise level as a function of various de-mographic and socioeconomic variables. The traditional regression model (OLS)requires that the observations be independent, an assumption that is violated here:neighboring block groups will most likely be similar in demographic socioeconomiccharacteristics, and the noise levels will be correlated. Because of the spatial auto-correlation of the variables for neighboring block groups, we use a simultaneousautoregressive model (SAR) (Waller and Gotway 2004).1

The ordinary multiple regression model can be expressed as

Y (si ) = β0 + βi1X1(si ) + βi2X2(si ) + · · · + βip Xp (si ) + ε(si ), i = 1, 2, · · · ,N , (2)

where Y (si ),Xj (si ) denote the dependent and independent variables and ε(si ) isthe residual error for the ith observation (here, si , the ith location). The residualsare assumed to be independent and from a standard normal distribution. For theSAR model, we regress each residual onto the others

ε(si ) =N∑

j=1,j �=i

b i j ε(s j ) + v(si ) (3)

where the bi j , (i = 1, 2, · · · ,N , j = 1, 2, · · · ,N ) are parameters that represent spa-tial dependence—that is, they measure the contribution of the other observations,ε(s j ), j �= i, to the variability of ε(si ). Note that bii = 0, since we do not regress eachresidual onto itself. If bi j = 0 for all i, j, then we are back to the ordinary multipleregression setting with independent errors. The v(si ) are the residuals for the re-gression model (3), and are assumed to be independent and normally distributedwith mean 0 and diagonal variance-covariance matrix v.

Using matrix notation, where Y denotes the vector for the dependent variable,X the matrix of covariates, β the vector of regression parameters, ε the vector

1In the geography and economics literature, this particular model is called a spatial error model(Anselin 2001; Fotheringham et al. 2000).


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of residuals, and B the matrix of spatial dependence parameters, v the vector ofresiduals for the residual regression, the SAR model can be expressed as

Y = Xβ + ε (4a)

ε = Bε + v (4b)

We consider the special case ofv = σ 2I and the spatial proximity measure B = λW ,

where W is the spatial proximity matrix using queen adjacency (Schabenberger andGotway 2004). Thus, the model can be expressed as

Y = Xβ + λW ε + v (5a)

v ∼ N (0, σ 2I ) (5b)

In particular, we assume that the spatial correlation between the observations occursin the error process, because we cannot measure all relevant aspects of a region orbecause the boundaries do not reflect the true dependencies.

We use maximum likelihood to test for the presence of spatial correlation:

H0:λ = 0 versus HA:λ �= 0

The test statistic is LRT = 2(LLspatial − LLOLS), where LLspatial denotes the log-likelihood for the model with λ �= 0 and LLOLS denotes the log-likelihood for themodel with λ = 0. If there is no spatial autocorrelation, then LRT has approximatelya chi-square distribution with 1 degree of freedom.

In comparing two nested (hierarchical) models to see if a subset of a set ofcovariates is adequate, we again use the likelihood ratio test. If LLfull and LLred denotethe log-likelihoods of the full and reduced models, respectively, then the likelihoodratio test statistic is LRT = 2(LLfull – LLred). If the reduced model is correct, thenLRT has approximately a chi-square distribution with degrees of freedom equal tothe difference in the number of parameters in the full and reduced models (seeSchabenberger and Gotway 2004; Waller and Gotway 2004 for further details).

For our analysis, the response variable is the median noise levels (dBA), and weinitially considered all the variables in Table 2 as covariates. Of the 2023 block groups,1994 of them had complete data. We used the freeware software R (http://www.r-project.org/) and the library spdep to perform the simultaneous autoregression(Bivand et al. 2008).

Table 2. Correlation matrix of the independent variables.

% < 18 % > 65 MHI MHV PCI % NW % NC % NHD % Un

% Un 0.17 –0.17 0.02 –0.03 –0.09 0.02 0.00 0.03 1.00% < 18 1.00 –0.48 0.23 –0.05 –0.19 0.27 0.1 0.04 0.17% > 65 –0.48 1.00 –0.19 –0.02 0.10 –0.16 –0.15 0.22 –0.17MHI 0.23 –0.19 1.00 0.71 0.76 –0.53 –0.46 –0.64 0.02MHV –0.05 –0.02 0.71 1.00 0.82 –0.42 –0.28 –0.52 –0.03PCI –0.19 0.10 0.76 0.82 1.00 –0.52 –0.41 –0.57 –0.09% NW 0.27 –0.16 –0.53 –0.42 –0.52 1.00 0.75 0.63 0.02% NC 0.1 –0.15 –0.46 –0.28 –0.41 0.76 1.00 0.54 0.00% NHD 0.04 0.22 –0.64 –0.52 –0.57 0.63 0.54 1.00 0.03


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Figure 2. Distribution of median noise level across census block groups. (Colorfigure available online.)

RESULTS

We first performed some exploratory analysis on our data set to examine therelationship of each explanatory variable to the dependent variable (i.e., mediannoise level). We highlight some of our findings. The distribution of the mediannoise levels in the region shows a clear spatial pattern (Figure 2). Not surprisingly,urban areas (center of map) are more exposed to higher traffic noise than the rest ofthe region, which is dominated mainly by agriculture. In addition, the block groupswith the highest noise exposure (brown) are found near airports and along majorhighways. The median noise level for the TCMR block groups ranged from 30.3 to72.32 dBA with a mean and median noise level of 53.77 and 53.63 dBA, respectively,and standard deviation of 5.73 (Figure 2, top left).

According to the year 2000 census, there are 2,641,985 residents in the TCMR.Of these, 27.8% live in block groups where the median noise level is between 55 and60 dBA, 9.5% live in block groups where the median noise level is between 60 and65 dBA, and 2.2% live in block groups where the median noise level is greater than65 dBA. In particular then, 39.5% of TCMR residents live in block groups wherethe median noise levels put them at higher risk for hypertension (i.e., greater than55 dBA).


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Figure 3. The cluster of block groups where traffic noise is relatively high andwhere the percentage of non-white population, median household in-come, and median house value is high (Figures 3, 4, and 7). (Colorfigure available online.)

In Figure 2, note a cluster of census block groups concentrated in an “L”-shapedregion that passes through the center of the map (Figure 3). This region, whichincludes downtown Minneapolis, consists of 506 block groups with about 548,138residents. In this cluster, 34.4% of the people live in block groups where the mediannoise level is between 55 and 60 dBA, 20.7% in block groups with median noiselevels between 60 and 65 dBA, and 7.2% in block groups with median noise levelgreater than 65 dBA. The percentage of non-white residents in the block groupsat each of these exposure levels is 41.4%, 40.7%, and 51.3%, respectively. Of thethree largest non-white populations in this area (i.e., African Americans, Asians,and Hispanics), African Americans appear to be the most affected. For example, inthis region, African Americans represent 16.9% of the population, Asians 10%, andHispanics 8.5%. But looking at the block groups with median noise levels greaterthan 60 dBA, we find that 21.1% of the residents are African American, 8.9% areAsian, and 9% are Hispanic.

As can be seen at the center of Figure 4, there are two clusters of block groupsthat are starkly different in the percentage of the population less than 18 years ofage. These clusters are exposed to the same level of traffic noise (an average of60 dBA) and are similar in both measures of wealth (low median income and low


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Figure 4. Distribution of percent of population less than 18 years of age acrosscensus block groups. Top left: partial residuals plot of median noiselevels against percent of population less than 18 years of age (controllingfor the other eight socioeconomic variables). (Color figure availableonline.)

median house value). Yet, in one cluster the percentage of the population less than18 is only 7% whereas in the other it is 41%. The main difference between thetwo clusters is that in the former more than 70% of the population is white andin the latter the percentage of the population that is not white is 74. Within thisnon-white population 47% and 16% are African Americans and Asians, respectively.This finding suggests that white residents with children may be better able to avoidneighborhoods with high traffic noise than are minority residents with children.Otherwise, Figure 4 suggests that there is a negative relationship between noiselevels and percentage of the population less than 18 years of age.

In the entire TCMR, there are 283 block groups where the median income isless than $35,000 (U.S. Census Bureau mean poverty threshold for the year 2000).There are 342,243 people who live in one of these 283 block groups. Of these, 35.8%live in these block groups where the median noise levels is between 55 and 60 dBA;25.5% live in block groups with median noise levels in the 60 to 65 dBA range;and 11.2% live in block groups with median noise levels greater than 65 dBA. Inparticular then, 72.5% of residents living in block groups with median income inthe poverty range are at higher risk for hypertension (i.e., greater than 55 dBA).


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Figure 5. Distribution of percentage of non-white population across census blockgroups. Top left: partial residuals plot of median noise levels againstpercentage of non-white residents in block groups (controlling for theother eight socioeconomic variables). (Color figure available online.)

Also, note in Figure 6 a cluster of block groups with the lowest median householdincome and a similar cluster of block groups with a high percentage of non-whitepopulation (Figure 5). The same pattern can be observed with median householdvalue (Figure 7).

Although the above exploratory analysis provides a preliminary understanding ofthe nature of the association between each socioeconomic characteristic and noiselevel, we need to see how these covariates together are associated with noise levels.Thus, in the second phase of our analysis we used a simultaneous autoregressionmodel with median noise level as the response variable and with all the covariatesgiven in Table 1. The results indicate that the percentage of residents more than65 years old, the percentage of non-citizens, the percentage of unemployed, andthe percentage of residents with no high-school degrees as well as the per capitaincome (log transformed, base 2) do not contribute significantly to the model.However, median household income, median house values (both log-transformedto base 2), percentage of residents less than 18 years of age, and percent non-whitewere significant. The log-likelihood for this model is –5366.185 with 11 parametersestimated. The likelihood ratio test for testing the presence of spatial correlation


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Figure 6. Distribution of median household income across census block groups.Top left: partial residuals plot of median noise levels against log (base2) of median household income (controlling for the other eight socioe-conomic variables). (Color figure available online.)

yields the estimate λ̂ = 0.7977 (LRT = 1180, df = 1, P -value < 0.0001), whichconfirms the existence of spatial autocorrelation.

We then considered a reduced model with the four covariates found to be sig-nificant in the above model. The log-likelihood is –5368.066 with 7 parametersestimated. Thus, the likelihood ratio test indicates that the smaller model is ade-quate (LRT = 3.762, df = 4, P -value = 0.439). Coefficient estimates of the variablesfor this reduced model are given in Table 3. Thus, an increase in the percentage

Table 3. Estimates from the SAR model for median DBA. Log likelihood:–5368.1, residual variance: 3.3183, 1994 observations.

Variable Estimate Std error z value p-value

Intercept 101.1459 4.4498 22.7301 <.0001Pct less than 18 years –0.0401 0.0129 –3.0968 .0019log (base 2) of median household income –1.7023 0.2393 –7.1137 <.0001log (base 2) of median house value –1.2022 0.2537 –4.7380 <.0001Pct non-white 0.0273 0.0089 3.0462 .0023


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Figure 7. Distribution of median house value across census block groups. Top left:partial residuals plot of median noise levels against log (base 2) medianhouse values (controlling for the other eight socioeconomic variables).(Color figure available online.)

of non-whites in a block group is associated with an increase in median noise level,while an increase in residents less than 18 years of age or median household incomeor median house value is associated with a decrease in median noise level.

More specifically, all other variables held constant, every 1% increase in thenumber of non-white residents in a block group is associated with approximately a0.03 dBA increase in the expected median noise level (Table 2). There is a negativerelationship between median noise level and the percentage of the population lessthan 18; that is, for every 1% increase in the number of residents less than the ageof 18 years in a block group, there is a 0.04 dBA decrease in the expected mediannoise level (all other variables held constant). Note that while the percentage ofnon-whites and the percentage of residents less than 18 years of age are significantin the model, the values of the estimated coefficients (the rates of change) are small.

The negative relationships between median noise levels and median householdincome, and between median noise levels and household value, are displayed inFigures 6 and 7 (upper left). Other variables held constant, a doubling in themedian household income in a block group is associated with a nearly 60% drop inthe expected median noise level. A doubling of the median house value in a block


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group (other variables held constant) is associated with a nearly 57% drop in theexpected median noise level.

DISCUSSION AND CONCLUSION

The main purpose of this study is to estimate traffic noise exposure at the land-scape level and use this to explore whether there is a relationship between exposureto high noise and demographic and socioeconomic status. Thus, the case studydemonstrates how the combination of these two approaches can be used to addressan important but a neglected area in environmental justice literature as well asaddress the methodological limitations associated with quantitative analysis of en-vironmental justice issues. The results suggest that in census block groups, thereis a statistically significant relationship between certain demographic and socioeco-nomic characteristics and noise levels. Before discussing the possible implicationsof our results, four methodological caveats are worth making explicit.

First, we recognize that while other anthropogenic noise sources (e.g., trains,ships) may have a significant contribution to overall noise level, our model doesnot incorporate such noise sources. This means that our estimate of the noise levelmay be too conservative. Second, because of the strong influence of weather onnoise propagation, traffic noise is highly variable. Thus, it is practically impossibleto model noise levels that reflect this variability when the only noise variable is the24-hour average traffic volume. While we saw a moderately strong relationship (R =0.76) between observed noise levels and those predicted by the traffic noise model(Eq. (1)), a more robust effort should be undertaken to improve this model. Third,this study is an observational study. In particular, while we find a negative associationbetween noise levels and two measures of wealth (median household income andmedian house value), we cannot say what is the cause and what is the effect: Dogovernments build highways in lower income areas or do those with lower incomelive in noisier areas? This is an area of much discussion within the environmentaljustice literature (Mohai et al. 2009; Pastor et al. 2001). However, our results serve asa rationale to undertake further research to determine causation.

In addition, given enormous differences in the lifestyles of individuals, it is impos-sible to establish true exposure to noise without undertaking a large and detailedsurvey in the study area. Even if traffic noise is high at street level (as measured inour study), it does not mean that everyone is exposed to it. A more accurate analysisof noise exposure would require taking into account factors such as the proportionof time people spend inside their home versus being outside, the distance betweenhome and workplace (that is, commuting time), the amount of sound proofing ofthe residences, the floor level at which people live, and so on.

We must also keep in mind that the demographic and socioeconomic data comefrom the year 2000 census (since income data at the block group level is not availablefor 2010), the inputs into the noise model (Eq. (1)) come from sources in differentyears, and the sound measurements were made in 2007. Finally, we must be awareof the modifiable area unit problem (MAUP): the demographic and socioeconomicdata are aggregated data with the block group regions not chosen by the authors.Thus, the results of the spatial model should be interpreted at the block group leveland not at the individual level.


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With these caveats in mind, our study contributes to the environmental justiceliterature in three important ways. First, unlike much of the environmental justiceliterature that focuses on stationary sources of pollution (e.g., industrial manufac-turing, hazardous waste facilities), our study advances the environmental justicescholarship by focusing not only on mobile (road and air traffic) sources of pol-lutants, but also on traffic noise, which has rarely been explored, especially in theUnited States. While our study focuses on the racial and economic disparities asso-ciated with traffic noise exposure, our findings are supported by a growing body ofevidence that suggests that socially disadvantaged groups are more likely to residein areas close to transport-related pollutants (Green et al. 2004; Apelberg et al. 2005;Pearce et al. 2006). Similarly, a number of studies have shown that neighborhoodscontaining a higher percentage of minority or low-income residents are more likelyto be located in areas of high traffic density (Guiner et al. 2003; Houston et al. 2004;Jacobson et al. 2005). Given the known relationship between unwanted noise andhealth and given that the environmental justice movement is concerned with thehealth impacts of environmental inequalities, then our study serves to underscorethat exposure to traffic noise should fall within the purview of environmental justiceinquiries.

The second contribution is methodological. Nearly all previous environmentaljustice studies using multiple regression have assumed that the observations areindependent across the study area. In other words, spatial autocorrelation is rarelyincorporated in the analysis of environmental inequity, which leads to type I errors(i.e., explanatory variables may be deemed significant when in fact, they are not).By explicitly taking into consideration spatial autocorrelation and combining itwith choropleth mapping, this study represents a methodological improvementthat serves to advance a quantitative exploration of environmental justice issues.

Finally, some of the properties of the noise surface itself can be used to integrateenvironmental justice principles into the transportation planning process. Despiteits intensive data requirement and computational demand, creating the noise sur-face is relatively easy. Because such a surface allows for visual presentation of avariety of data (e.g., identifying areas exceeding certain noise levels or estimatinghuman noise exposure), it can be used to determine the magnitude and extent ofnoise-affected areas. This, in turn, can serve as a basis to set realistic targets for noisereduction and for developing effective noise mitigation measures, and to identifyand protect quiet areas. In other words, the noise surface itself can be used to in-tegrate environmental justice principles into the planning process. For example,what is the rate at which traffic disturbance change as a function of distance? Arethere patches of land in the region that are not vulnerable to traffic disturbancebut are surrounded by locations where traffic disturbance is high? More precisely,are there “islands” of quiet areas in otherwise noisy areas? And what are the sizeand spatial distribution of such areas? Determining the slope of the noise surfaceprovide information on both the size and the spatial distribution of the areas wheretraffic disturbance changes rapidly. This in turn can be used to identify quiet areas.Furthermore, determining the slope of the noise surface (i.e., the second derivativeof the slope map or the curvature of the noise surface) provides another insight:The profile curvature of the noise surface shows where the change in traffic noise isvery rapid, then slow, then rapid again. In other words, it creates a surface roughness


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map. The planiform curvature, on the other hand, shows where traffic noise con-verges and diverges. This information can be used to identify and assess possiblelocations for placing noise barriers or traffic calming devices.

In sum, we believe this study offers a substantial improvement over previous effortsto measure the impact of noise on environmental justice communities. Despite itslimitations, it supports the hypothesis that people living in low-income and predom-inately minority neighborhoods may bear a disproportionate burden of the negativeenvironmental consequences of our transportation system. Traffic noise is only oneof many consequences, ranging from hazardous streets to air pollution, that com-bine in complex ways to potentially affect residents’ health, economic prospects,and even political power (Massey and Denton 1993). Improving our ability to modelnoise is one of the many methodological innovations that will be required to assessthese impacts and to undertake mitigating measures.

ACKNOWLEDGMENTS

Research for this article was supported through awards from the Henry LuceFoundation. We thank Wei-Hsin Fu and the Carleton College GIS lab student workersfor providing GIS support.

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Documents

Traffic Noise and Inequality in the Twin Cities, Minnesota