A Day in the Neighborhood
Visualizing health justice in Baltimore City.
We present a visualization approach to examining health justice variations across Baltimore City neighborhoods. The approach is built on the Social Determinants of Health and Baltimore’s Black Butterfly models, with the hypothesis that health outcomes correlate to the demographic and social factors of individual neighborhoods. Data from Open Baltimore was layered on choropleth maps of the city to produce a spatial visualization dashboard. We point out interesting connections between health-related outcomes and social divides based on seemingly unrelated geography. Visualization case studies like this can potentially educate incoming medical professionals about the city and allow them to better serve their communities. The visualization can be viewed on Tableau.
Social Determinants of Health
Public health science utilizes theory to develop campaigns and approaches to investigating and improving health outcomes in communities. One that has gained traction and changed medical care practice is the Social Determinants of Health model (SDoH). Although ever-evolving with new additions, the core model provides 5 categories of factors that can impact a person’s health: their education, health care access, neighborhood, social context, and economic stability. This model is an answer to the individualized perspective of health care and argues that there are factors beyond the control of the individual that impact their health outcomes.
Unfortunately these factors play a critical role in mortality. Several hundreds of thousands of deaths in the United States can be stated as preventable, especially considering factors such as low education and low social support.
For Baltimore, this model has held true time and again. In the Southwest Baltimore neighborhood, researchers found that when Black and White residents are living under the same socioeconomic factors, health outcomes are more equalized. This suggests that the resources of a neighborhood largely determines the health outcomes for its residents. A specific mechanism of neighborhood resources is walkability and inclusion of other pedestrian-friendly design elements.
Combined with neighborhood safety, the planning of a zip code can determine whether residents are allowed access to daily exercise. Adding environmental stress as a factor, which consists of psychosocial hazards and safety, was shown to be associated with cardiovascular disease in older adults living in Baltimore. Although the mechanism is not known of how these stresses play a role, it is important to consider these non-health related qualities of a neighborhood on its residents.
Another health context where social determinants play a role within the Baltimore City environment is in sexual health and adolescent pregnancy. While individual and community norm factors impact this health crisis, other environmentally attributable factors including lack of educational resources and poverty also have a large impact.
Case studies of individual determinants affecting individual outcomes proves the efficacy of the SDoH model. There is now a need to apply the model in a preventative and layered perspective, explored in this visualization approach.
Visualizing Baltimore
Depictions of Baltimore neighborhoods have varied in purpose and execution. Examining older visualizations (and the purpose they were created for) reflects an appalling past. The city has a very long history of racist zoning policies that shaped the divides and outcomes of its neighborhoods today, including apartheid-like policies of preventing Black families from moving into White-zoned neighborhoods such Northwest and North Baltimore. The history has shaped the make-up of today’s neighborhoods and continues to impact the future of the city.
Contemporary analysis of neighborhoods have produced stark findings. One key visual hypothesis is Baltimore’s “Black Butterfly vs. the White L,” developed by equity scientist Dr. Lawrence Brown. The model shows how Baltimore neighborhoods are divided along racial lines in an overall butterfly pattern. The Black Butterfly includes neighborhoods such as Mondawmin and Loch Raven, which flank White L neighborhoods such as North Baltimore and Inner Harbor/Fells Point on either side of the city lines. Adding layers of other attributes, such as bike paths or Zipcar locations, shows how these attributes fall along the “White L,’’ of Baltimore. A full visualization dashboard of this phenomenon created using Dr. Lawrence Brown’s findings can be found on Tableau’s Data Equity Hub, which analyzed social determinants such as childhood poverty and internet access along neighborhood lines.
The Black Butterfly serves as the theoretical underpinning for our visualization, “A Day in the Neighborhood,” as we compare various metrics of the SDoH against the racial breakdown of each neighborhood. Strong arguments have been made that “place, not race” has more impact on health outcomes; however Baltimore’s unique racial history provides an interesting opportunity to compare race and other social factors. Previous work has compared neighborhood divides against quality of life outcomes, however this visualization approach specifically focuses on health justice using SDoH.
Visualization Development
Initial sketches of a visualization approach were inspired by descriptions of SDoH as a “kaleidoscope of factors” that can impact an individual’s health. The key idea developed was to convey multiple factors together to see how they inter-mingle, and where they fall along the already-known Black Butterfly and White L neighborhoods.
Data was pulled from the City of Baltimore government data hub, Open Baltimore, which provides researchers and designers with thousands of open-source datasets. The datasets chosen were included by the following criteria: Available Year (2011, 2010 for Census Data), Breakdown by Community Statistical Area (CSA), and Percentage Metric (to ensure accurate comparisons between determinants based on units). All datasets were sourced from the Baltimore Neighborhood Indicator Alliance. Additionally, representative datasets were chosen to fit each of the 5 categories described within SDoH:
- Education Access: % of High School Completion Rate
- Neighborhood Context: % of Households with No Vehicle
- Social/Community Context: % of Students Receiving Free Meals
- Economic Stability: % of Households Earning Less Than $25,000
- Health Quality: Life Expectancy (excluded for comparisons)
Visualizations were built using Tableau with the main dashboard showing the SDoH datasets above layered over a choropleth map of the % of Black residents within each neighborhood.
Trade-Offs
Our case study visualization came across several challenges and tradeoffs during its design and development. Using CSA as the geographic value helped create consistency across the individual datasets. However this data type created only 55 distinct regions. In reality, Baltimore has over 200 culturally distinct neighborhoods. Although clumping neighborhoods together loses cultural granularity, it still provides enough distinction to make out the Black Butterfly model clearly.
The type of comparison chart used in the tooltip of each neighborhood produced another challenge. Radar or spider charts were intended to be shown, however bar charts of each determinant were shown instead due to their readability and decreased build complexity in Tableau. In future iterations, radar charts can be integrated as glanceable visualizations to compare between neighborhoods.
Choosing a representative dataset for each SDoH category was also difficult. The sets chosen were best due to their availability, nature of the datatype, and year collected, however, clinicians and users would likely want to see more relevant metrics based on the context of use.
Finally, due to the complexity of showing multiple attributes on spatial charts like maps, we had to produce individual choropleth maps of each SDoH determinant on its own for focused readability.
Applications
Baltimore and its surrounding neighborhoods are home to health education institutions such as Johns Hopkins and University of Maryland that are responsible for recruiting new medical professionals to the city every year. Building empathy and situational knowledge for their new community is an important skill that visualizations like this can help develop for budding physicians, nurses, therapists, dentists, and more.
Integrating SDoH into more formal health technology is an opportunity to increase acceptance and application of the model. One way is through the Electronic Health Record (EHR). These systems provide clinicians with dashboard views of their patients, conveying information like their medical history, co-morbidities, family history, medications, and risk factors. Adding the patient’s “SDoH context” to their digital chart can give clinicians an informed perspective and positively influence the impact of their prescribed treatments. For example, if a patient needs to increase their daily exercise but lives in a neighborhood with a low walkability score, clinicians can alter their treatment plan to account for alternative exercise plans.
Final Thoughts
Visualization is a powerful tool for amplifying our knowledge and improving outcomes in the real world. Using visualizations to describe existing issues in our society, in neighborhoods near and far, is a potent source for solving these problems.
“If you never leave your neighborhood and you never talk to people that are different from you, you will remain stupid. There are no smart racists.”
- John Waters for the Baltimore Sun
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