The growing ethnic and racial diversity of the United States is evident at all spatial scales. One of the striking features of this new mixture of peoples, however, is that this new diversity often occurs in tandem with racial concentration. This article surveys these new geographies from four points of view: the nation as a whole, states, large metropolitan areas, and neighborhoods. The analysis at each scale relies on a new taxonomy of racial composition that simultaneously appraises both diversity and the lack thereof (Holloway, Wright, and Ellis 2012). Urban analysis often posits neighborhood racial segregation and diversity as either endpoints on a continuum of racial dominance or mirror images of one another. We disturb that perspective and stress that segregation and diversity must be jointly understood—they are necessarily related, although not as inevitable binary opposites. Using census data from 1990, 2000, and 2010, the research points to how patterns of racial diversity and dominance interact across varying spatial scales. This investigation helps answer some basic questions about the changing geographies of racialized groups, setting the stage for the following articles that explore the relationship between geography and the participation of underrepresented groups in higher education.
While weather stations generally capture near‐surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta‐related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite‐based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003–2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite‐hybrid mixed‐effects model for each year, regressing Ta measurements against land use terms, day‐specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10‐fold cross‐validation at withheld stations. Across all years, the root‐mean‐square error ranged from 0.92 to 1.92 K and the R2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high‐quality Ta estimates for epidemiology studies in the MCM region.
In the U.S., substantial employment and wage gaps persist between workers with and without disabilities. A lack of accessible transportation is often cited as a barrier to employment in higher wage jobs for people with disabilities, but little is known about the intraurban commuting patterns of employed people with disabilities in relation to their wage earnings. Our study compares wages and commute times between workers with and without disabilities in the New York metropolitan region and identifies the intraurban zones where residents experience higher inequities in wage earnings and commute times. We obtained our data from the Public Use Microdata Sample (PUMS) of the American Community Survey (ACS) for the 2008–2012 time period. We used linear mixed-effects models and generated separate models with log hourly wage or one-way commute time as the dependent variable. We find significant differences in wages and commute times between workers with and without disabilities at the scale of the metropolitan region as well as by intraurban zone. At the metropolitan scale, disabled workers earn 16.6% less and commute one minute longer on average than non-disabled workers. High commute and wage inequalities converge in the center, where workers with disabilities are more likely to use public transit, earn 17.1% less, and travel nearly four minutes longer on average than workers without disabilities. These results suggest that transport options are less accessible and slower for disabled workers than they are for non-disabled workers. Our findings indicate a need for more accessible and quicker forms of transportation in the center along with an increased availability of centrally located and affordable housing to reduce the disability gap in wages and commute times. We also find that workers with disabilities generally seek higher wages in exchange for longer commute times, but the results differ by race/ethnicity and gender. Compared to white men, minority workers earn much less, and white and Hispanic women have significantly shorter commute times. Our findings offer new geographic insights on how having a disability can influence wage earnings and commute times for workers in different intraurban zones in the New York metropolitan region.
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