Redlining, a discriminatory practice created by the federal government in the 1930s to deny mortgages in certain neighborhoods characterized by a higher proportion of Black and immigrant residents, established a lasting legacy of systemic racism and environmental inequities. So how can we better understand how historical redlining impacts our health and environment today?
With funding from the EaRTH Center, California redlining data are now incorporated into the UCSF Health Atlas. Under the direction of Dr. Debby Oh, the UCSF Population Health Data Initiative worked together with Stamen Design to develop this interactive data visualization tool that incorporates 100+ factors including demographic, socioeconomic, environmental, community, neighborhood, health and health care data. Integration of redlining data into the Health Atlas will help researchers analyze and visualize the intersection of social, environmental and health factors that affect communities and inform development of new studies to assess the contemporary influence of historical redlining on health disparities.
How redlining began
Beginning in the 1930s during the New Deal era, the Home Owners’ Loan Corporation (HOLC), a U.S. federal agency, created a ranking system for neighborhoods, now known as redlining. Neighborhoods were given a grade from A (“best”) to D (“hazardous”) based on data including quality of housing, recent values of sales and rentals, and the racial and ethnic identity of a neighborhood’s residents.
The HOLC ranking system created barriers to mortgage financing in neighborhoods considered “hazardous” for banks and lenders to invest. Communities that were ethnically homogenous and White were graded higher, whereas those with higher proportions of Black and immigrant families were graded lower. This practice was a form of structural racism that made it nearly impossible for people in neighborhoods with low grades to buy a home.
Why historical redlining is important
Historical redlining contributed to the wealth gap that exists in the U.S. today by systematically directing financial investment away from racial and ethnic minorities and immigrants and toward White families. In addition to its effect on intergenerational wealth and persistent poverty, historical redlining has also been linked to disparities in built environments. Neighborhoods with lower HOLC grades have more multifamily housing structures, and higher rates of environmental exposure to air pollution and extreme urban heat.
Scientific evidence also suggests that historical redlining is associated with a range of health inequities including poor cardiovascular health outcomes, preterm births, gunshot-related injuries, cancer, asthma, and COVID-19.
These HOLC mortgage security risk maps, which have been digitized by the Mapping Inequality Project, are now incorporated into the Health Atlas to allow users to visualize historical redlining in the following California metropolitan areas:
- San Francisco-Oakland-Hayward
- San Jose–Sunnyvale–Santa Clara
- Los Angeles–Long Beach
- San Diego–Carlsbad
The Health Atlas allows users to map historical redlining data alongside present-day environmental factors and health outcomes. Colors indicate how each census tract corresponds to the historic HOLC risk grade for that area:
- Green = HOLC Grade A (“best”)
- Blue = HOLC Grade B
- Yellow = HOLC Grade C
- Red = HOLC Grade D (“hazardous”)
For those interested in more detail, background information and references on historical redlining are included in a data description. By using the new “Common Areas” feature in the Health Atlas, users can view and export data for specific regions of California.
We encourage researchers, policy staff, and reporters to explore the updated UCSF Health Atlas to better understand how the impact of racist historical redlining persists in communities today.
For more information on redlining and how to use the UCSF Health Atlas, join an EaRTH Center webinar featuring Dr. Debby Oh on Wednesday, June 22, at 1pm PT. Zoom info here: https://earth.ucsf.edu/events
About the authors
Debby Oh, PhD is a Data Scientist at the UCSF Department of Epidemiology & Biostatistics. Her work involves analyzing and visualizing data from the cancer registry, electronic health records, census, and other public data sources.
Kathryn Kemper, MPH is the Project Manager for the UCSF Population Health Data Initiative, which maps clinical data linked with neighborhood-level factors to identify patterns and inform more precise public health interventions across the Bay Area and California.
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