Mapping the unequal impact of COVID-19 by race — without the data.
New dot density map helps illuminate which communities have been hardest hit
Explore the map at uscovidatlas.org
COVID-19 is a global pandemic, but we experience it locally. Seeing loved ones, going to the grocery store, traveling to a vaccine clinic, and for many staying put at home. It’s important that we’re able to zoom in on data to get a better picture of what’s really happening — good county data tells us more than state level data, and zip-code or census block group even more. The US Covid Atlas team has been working to incorporate detailed data and context about the pandemic to give a more robust perspective of what communities are grappling with. We’ve been thinking a lot about the ways in which the pandemic has affected and continues to affect regions differently, particularly across racial and socioeconomic lines. Recent additions to the Atlas include metrics that explore mobility behaviors (at home, full time and part time work behaviors) and the drivers behind those decisions (essential workers). Dr Lawrence Brown’s article on the long history of inequities in spatial epidemiology calls for better linking of the current pandemic crisis and our country’s lineage of colonialism, hypersegregation, and mass incarceration. Laura Chen details the real pandemic costs of hypersegregation that persists across major US cities.
But COVID-19 data that is disaggregated by race is available only at the state level, meaning that data insights on community impact at the county level (or below) nationally aren’t around yet. Under these limitations, how can we figure out where disparities exist? In time, it’s likely that we’ll get more detailed information on race, ethnicity, age, and COVID-19 impact across the country. But right now, we need insights and actions. That’s basically problem 1: we just don’t have all the data we need yet.
There’s another issue, too. Traditional statistical methods might rely on the percent of geographies made up of different American Community Survey (ACS) race or ethnicity groups. These typologies have limitations of their own, but that’s a topic for another day. In other studies, we work to identify areas with significant populations representing different ACS groups and also high COVID-19 impact, but that method alone sometimes misses smaller minority communities distributed around the country. In short, within each county, there are a lot of different racial and ethnic groups that make up the community, and standard methods can leave folks behind — that’s problem 2: patterns of race and ethnicity based on where people tend to live are more complex than typical stats tend to reveal.
Enter the dot density map:
Unlike traditional choropleth maps — with geographies symbolized with colors representing different metrics — dot density maps show dots representing concentrations of people. There are still some limits here, a 1:1 person to dot map is technically challenging (so for the web we settled for 1:500), and we have to make some assumptions about where people actually are — in the map above we take the makeup of Census Tracts and add random dots to represent the different racial and ethnic groups that ACS reports there.
This is great to illustrate the complex texture of where people tend to live, bring to light continuing histories of segregation, and understand what the landscape looks like, as reported by our best estimates. It’s a visualization strategy to fill the gaps of traditional methods. But how does this help understand racial impact of COVID-19?
While we wait for data that can tell us a more complete picture, we can start understanding patterns of where communities have been impacted most and where race and ethnic groups tend to live.
First, we can create a dot density map and include the ACS groups we’d like to highlight. Here’s Black or African American and Hispanic or Latinx folks across the Southern US:
Next, we can add a variable that represents COVID-19’s toll and impact. Here is the per capita number of lives lost over the course of the pandemic.
By using both of these mapping strategies together, we can map where people tend to live and what the COVID-19 impact was like for that county. The map below again shows per capita lives lost, but the dot density map is now colored based on the COVID-19 data, rather than the ACS race and ethnicity groups. We can filter different ACS groups we’d like to highlight, showing where those communities tend to live and what the experience of COVID-19 was like for those communities (at the county level).
This map highlights populations in counties throughout the South who have shoulder some of the biggest burdens of COVID-19. It starts to reveal that many rural, predominantly Black or African American communities across the ‘Black Belt’ region of the south have faced some of the most intense rates of mortality in the US. The map below highlights more specifically the counties in the black belt, showing where Black and African American communities tend to be, and the county-level COVID-19 data.
This map shows where Black or African American communities live in Black Belt counties, and the COVID-19 mortality the people in those counties have faced. It points the structural issues underlying health disparities of COVID-19: access to care and mobility, economic security and support to make safe decisions, and much more. The pandemic has elevated and illustrated what happens when the long, long histories of inequality and oppression go unaddressed.
The ability to focus on where people tend to live and different ACS groups doesn’t tell us the full story of COVID-19’s unequal impact, but it does start to fill the gap. The US Covid Atlas team recently added these features to interactively explore dot density maps. They start to fill the gap for the missing race-disaggregated, reveal new insights, and give a more human view of the data.
Explore now at uscovidatlas.org
Contributors: Dylan Halpern, Lawrence Brown, Susan Paykin
Dr. Lawrence Brown is the founder and directory of The Black Butterfly Project and author of The Black Butterfly.
