Key takeaway:
- This study reinforces previous findings that people of racial/ethnic minorities and people of lower socioeconomic status in the US are systematically linked to higher exposure to traffic and related health risks (i.e., air pollution, noise pollution, traffic injuries and fatalities, etc.).
- It also contributes a better understanding of the spatial aspects and patterns of differential traffic exposure.
Results:
- Higher poverty is associated with increased traffic exposure. This study found that income was the most consistent predictor of traffic density.
- This diverges with previous findings that race was a more consistent predictor of traffic density than income.
- It is uncertain whether this departure reflects something unique about the Denver metropolitan region or differences in variables and research methodology.
- Minority racial and ethnic groups have systematically higher exposure to traffic. The spatial pattern of traffic exposure by race and ethnicity shows stronger neighborhood-level effects.
- Lack of college education in the Denver metropolitan region predicts traffic exposure independently of race and socioeconomic status and exhibits a strong regional pattern:
- There is a negative association between college education and traffic exposure, which is especially significant in outer suburbs and seems to exhibit a ring pattern following the outer suburbs around the urban core.
- Differential exposure to high traffic volumes by college education may reflect a regional process.
- This ring pattern around the urban core is a notable finding because: (1) Previous studies have not identified any specific spatial patterns in disparities in traffic exposure, and (2) Initial visual inspection shows that this ring pattern of traffic exposure shows continuity along major transport infrastructure, and not just transport nodes.
Implications:
- The finding that income is the most consistent predictor of traffic density is interesting because it contrasts with earlier research that established race as the most consistent predictor of traffic density. This indicates that regardless of the cause (race, income, education level, etc.), there are serious differential exposures to traffic by socioeconomic and demographic factors. Policymakers must consider how to ameliorate systemic differential traffic exposure if they prioritize reducing health disparities through land-use and transport decisions.
- The spatial pattern of exposure to traffic by race and ethnicity reflected stronger neighborhood-level effects, while the spatial pattern of exposure to traffic by education level reflected stronger regional effects. This suggests that both regional and localized processes drive inequalities in traffic exposure. The challenge, then, is finding the appropriate scale of policy intervention.
- The regional pattern in lack of college education as a predictor for traffic exposure may reflect a changing demographic in which the urban core is more desirable and expensive to live in.
Methods:
- The researchers examined exposure to traffic and socio-demographic characteristics (race/ethnicity, poverty, education level) in the Denver metropolitan region, using traffic data from Department of Transportation average annual daily traffic movements and socioeconomic and demographic data from the census.
Rosenlieb, E.G.; McAndrews, C.; Marshall, W.E.; Troy, A. (2018). Urban development patterns and exposure to hazardous and protective traffic environments. Journal of Transport Geography, 66.
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