Before becoming a public health researcher, I trained as a geologist. That may seem like an unusual career trajectory, but it taught me that you can’t really understand how something exists today without weaving in the stories of its past.
Covid-19 has shown public health researchers that they need to expand their vision at a time when maps showing raw data have emerged as a bible for this field and for policymakers. Well-intentioned plans supported by sophisticated data and models can quickly fall apart if there’s a lack of understanding about place and the history of the people affected.
Years ago, I volunteered as a field mapper for a project with an international humanitarian team, helping engineers decide where flush toilets could be built on the site. I studied the geology of the area and found a history of complex plate tectonics and underground caves, which indicated the existence of earthquakes and unstable ground. The team was surprised, as a company geologist had said that all we needed for a plan was to know the specific rock types underlying the area.
Instead of relying solely on rock samples, I asked community members if they remembered any earthquakes. “Oh, 40 years ago — our families still talk about the damage!” was a common response. So I looked deeper and found that the entire site was cross-hatched with fault lines and mottled with sinkholes. I also learned about several sacred locations connected to sinkholes the team hadn’t known about. I translated all this information, ran models, and generated a suitability map that was engineer-ready — but also reflected human reality.
The lesson I learned from that experience was clear: Focusing on sound science and building infrastructure are important, but spatial awareness and respect for history creates the most stable outcomes.
It’s a lesson that applies to Covid-19. Improving public health outcomes during the pandemic depends on the history of specific communities and incorporating into the data the access issues that have plagued them for generations.
While the pandemic is occurring globally, it’s experienced locally. That’s why we need to engage thinkers who can integrate place and context into models and “scientific” data. A place-based perspective may be radical, but it’s also necessary for the pandemic recovery and future prevention efforts.
Not all places have the same stories. In Chicago, where I live and work, some neighborhoods were intentionally segregated by redlining, a practice also used in many other cities. Across the country, some neighborhoods or regions haven’t recovered from the last recession, or the loss of manufacturing jobs, or the legacy of slavery and Jim Crow, or centuries of fighting for First Nations recognition and survival.
Ignoring these factors can change the magnitude and direction of modeling outcomes for the worse, leading to faulty understandings.
While aggregating health data may be a common practice, it also can average out and paper over emerging crises. This happened with the water crisis in Flint, Mich., when ZIP code-level data made it appear that children in the city didn’t have higher levels of lead in their bodies than children who lived outside the city. It also occurred at the start of the pandemic, when the mainstream science community and media focused on state-wide reports while largely avoiding county-level data. Surging, cross-border hotspots in the Delta region, as well as in tribal communities in the Southwest, were missed until they grew out of control.
To improve public health outcomes from Covid-19 and other nonpandemic concerns, social determinants of health must be more than an industry buzzword. They must guide the science that generates public health data.
For the US Covid Atlas project at the University of Chicago, which I direct, our team works to bring these factors into perspective. We start by making data available from state and county scales, from multiple sources, and across the entire time of the pandemic. We include statistical hot spots, historical place-based filters, dozens of community characteristics, and overlay other resources, like federally qualified health centers, for exploration. A viewer could investigate, for example, how the pandemic spread in counties with varying food insecurity, income inequality, or life expectancy rates; explore spillovers across state lines as residents took advantage of different stay-at-home policies; or track vaccine trends alongside access to mass vaccination centers.
Covid-19 data gathered from across the country at community-level scales would improve our understanding of the pandemic immensely, though there is no such federal mandate for reporting this information as publicly available data.
Covid-19 has exploded the mapping industry, and everyone seeks to share their own maps (including me). But connecting this work to history and place is a nascent practice. Maps often reflect a single point in time or one spatial scale, like a county or state, and are generally removed from place data that help explain the patterns that appear. Universities and government entities need to rethink the work that generates these maps and how they can be used for policymaking.
Mapping is usually thought of as a simple visual tool that’s disconnected from serious research. Only a handful of university public health schools or departments have hired geographers and spatial thinkers to foster interdisciplinary approaches that integrate history and place into data science. If others invested in this kind of geographic thinking, I believe we could more rapidly advance public health in the U.S. and elsewhere.
Unfortunately, this approach is often politicized. Consider recent attempts by the federal government to change or limit data from the U.S. Census — which helps tell the stories of place — as well as making it illegal for federally funded researchers to share maps of racial disparities made from census data. In 2017, the federal Department of Housing and Urban Development suspended key components of its mapping assessment tool.
At the same time, publicly available “place” data continue to be under threat, as personal location data becomes increasingly privatized. One of the most valuable datasets for understanding Covid-19 has been mobility data based on aggregated cellphone traces, repackaged by big-tech companies that collect it. The private sector has continued to take advantage of spatial data to increase the prediction accuracy of algorithms — and profits — often pushing the boundaries of privacy. At the same time, public and academic communities continue to miss out and underinvest in how understanding place can better inform policy and practice.
To combat these challenges, the U.S. needs to rebuild its capacity in geographic thinking. We need multidisciplinary teams and approaches that consider “history as a data point,” as public health scholar Lawrence Brown writes in his book “The Black Butterfly.” Vaccine distribution, for example, is not just about the number of people vaccinated, but also about places. A website-based scheduling system may be effective for a middle-class, historically well-resourced area, but far less so for regions with higher concentrations of aging populations or neighborhoods of stressed essential worker families.
We are trying to build stable structures for fighting Covid-19 on a landscape torn by the fault lines of racism and in towns threatened by sinkholes of economic uncertainty. Without accounting for history, we are like goldfish swimming in bowls, forgetting all the lessons learned and wisdom the environment and communities have to offer at each turn.
Without the context of history and place, data science can minimize place as a simple “latitude longitude” coordinate for prediction. Integrating a deeper geographic perspective is currently seen as radical and political — but it’s also essential.
Marynia Kolak is a health geographer leading the Healthy Regions and Policies Lab at the Center for Spatial Data Science and a senior lecturer at the University of Chicago.
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