Environmental inequality in the neighborhood networks of urban mobility in US cities

Abstract

Research has made clear that neighborhoods impact the health and well-being of their residents. A related strand of research shows that neighborhood disadvantage is geographically clustered. Because the neighborhoods of low-income and minority populations tend to be more disadvantaged, neighborhood conditions help explain racial and socioeconomic inequalities. These strands of research restrict processes of neighborhood influence to operate only within and between geographically contiguous neighbors. However, we are underestimating the role of neighborhood conditions in explaining inequality if disadvantage extends beyond the residential and extralocal environments into a network of neighborhoods spanning the urban landscape based on where residents move within a city. I use anonymized mobile phone data to measure exposure to air pollution among residents of poor and minority neighborhoods in 88 of the most populous US cities. I find that residents from minority and poor neighborhoods travel to neighborhoods that have greater air pollution levels than the neighborhoods that residents from White and nonpoor neighborhoods visit. Hispanic neighborhoods exhibit the greatest overall pollution burden, Black/White and Asian/White disparities are greatest at the network than residential scale, and the socioeconomic advantage of lower risk exposure is highest for residents from White neighborhoods. These inequalities are notable given recent declines in segregation and air pollution levels in American cities.

Keywords: air quality; environmental justice; neighborhood; network; urban mobility.

Fig. 1.

Neighborhoods shaded by the number of trips from representative neighborhoods (in blue) in Chicago and Los Angeles.

Fig. 2.

Regression-adjusted $P{M}_{2.5}$ (with 95% CIs) in the neighborhoods that residents travel to weighted by the number of trips.

Fig. 3.

Regression-adjusted $P{M}_{2.5}$ (with 95% CIs) in residential neighborhoods (residential), adjacent neighborhoods (adjacent), and the nonadjacent neighborhoods that residents visit (network).

Fig. 4.

Regression-adjusted $P{M}_{2.5}$ (with 95% CIs) in residential neighborhoods (residential), adjacent neighborhoods (adjacent), and the nonadjacent neighborhoods that residents visit (network) by race/ethnicity and poverty status.

Fig. 5.

Within-MSA travel patterns by neighborhood racial/ethnic and poverty type (with 95% CIs). (A) Proportion of trips. (B) Average distance traveled.