Breaking the Cycle: Using Data to Advance Racial Justice
By Emma Bonanno and Gopinath Gnanakumar Malathi
Data can tell stories, demonstrate trends, and drive informed decision-making. However, data is not neutral, and our perspectives, positionality, and objectives for collection and use matter. Data has often been used to justify and reinforce systemic racism and discrimination against Black communities, directing resources away from them and concentrating wealth and opportunity elsewhere. For example, the practice of redlining, where banks and insurance companies used maps to define "risky" neighborhoods and refused to provide loans or insurance to residents of those areas, had a devastating impact on Black communities. By denying access to basic financial services, redlining deprived Black families of the ability to build wealth through homeownership.
The 1938 Home Owners’ Loan Corporation map of Brooklyn. Credit: National Archives and Records Administration, Mapping Inequality.
When data is collected and communicated without awareness of biases, the outcomes of analysis and visualization have an outsized capacity to mislead, misrepresent, and harm communities that already experience inequity and discrimination. Because of this, layering quantitative insights with qualitative community knowledge is imperative to “equitable,” anti-racist planning. Through engagement, planners can shape who, how, and where community data is collected and used. Community data is the backbone of any successful and equitable development project, whether an offshore wind farm or a neighborhood park. Making sure it’s collected and used equitably is key.
The following are some best practices for data analysts to approach their work through the lens of anti-racism, according to Urban Institute’s Do No Harm Guide:
Ground data analysis and communication in empathy to build relationships and trust with the communities in focus. This idea can be summarized by asking: “If I were one of the data points on this visualization, would I feel offended?” This includes using personal connections, such as oral histories, individual experiences, and quotes, in tandem with data visualizations to help the audience better connect with the subject group.
Consider and be conscious of the overrepresented and underrepresented groups in your data, especially when aggregating several different groups due to a limited sample size. Omitting specific populations from the data can have significant ramifications for what products or public policies are created and who benefits from those products and policies.
Apply racial equity awareness in choosing active and compelling language for titles, annotations, labels, and notes. Text descriptors on data visualizations present an important opportunity to address the role racism and other forms of oppression have played in the narrative a chart is trying to visualize. Apply the same awareness when choosing color palettes for your data visualizations. As with text, it is crucial to recognize the impact your color palette has toward perpetuating or exacerbating stereotypes and inequities.
Organizations like Data for Black Lives, Boston University’s Center for Antiracist Research Racial Data Lab, New York University’s Center for Critical Race and Digital Studies, Tableau’s Racial Justice Data Initiative, and Data & Society are working to reverse historic trends of inequitable data usage and address systemic bias and discrimination in the data landscape. They are developing datasets, tools, and resources that can be used to advance economic mobility, reduce poverty, improve health outcomes, and advocate for equitable change. Check out some of these exciting tools below.
Mapping Inequality (University of Richmond) Launched in 2016 by researchers at the University of Richmond, Mapping Inequality is an interactive map that overlays historic Federal Home Owners’ Loan Corporation "redlining" maps with current geographic boundaries. The historic 1930s maps were used to assess the creditworthiness of specific neighborhoods—Black and Brown neighborhoods were predominantly “redlined” and deemed hazardous for investments. The maps were later used by the Veterans Administration and the Federal Housing Administration to guide mortgage lending during the post-World War II housing boom, creating unequal access to wealth-building opportunities through homeownership and entrenching patterns of disinvestment that exist to this day. By combining this historical data with current geographic realities, this dataset makes the 1930s redlining maps easily accessible and explorable. It allows researchers to recognize the historical role of discrimination in homeownership and lending practices and guide policy, program-making, and advocacy to close the racial wealth gap.
Black Wealth Data Center (Bloomberg Philanthropies’ Greenwood Initiative) The Black Wealth Data Center aggregates national and county-level data that demonstrates the influence of race on education, employment, asset ownership, and other wealth-building indicators and outcomes. By centralizing data from many sources and partnering with leading organizations across the country, this repository enables practitioners and policymakers to leverage data to make decisions that help repair the harm caused by historical racism and decades of disinvestment. Their interactive tools include data on assets and debt, business ownership, education, employment, homeownership, and other indicators of Black wealth.
Financial Health and Wealth Dashboard; Debt in America Dashboard (Urban Institute) The Urban Institute has created several interactive dashboards that provide policymakers and local stakeholders with quantitative data on the financial wellbeing of diverse communities across the US. These resources also provide examples of strategies to address specific disparities that local leaders can adopt.
COVID Racial Data Tracker (COVID Tracking Project x Boston University) The COVID Racial Data Tracker advocates for collects, publishes, and analyzes racial data on the COVID-19 pandemic across the United States. State-level COVID statistics tell part of the story. Still, many U.S. states are also deeply segregated—meaning different counties in the same state can have vastly different breakdowns by race and ethnicity. This tool uses COVID infection and death rate data from New York Times to track racial inequities and disparities at the county level and identify how communities of color ended up at the frontlines of the pandemic.
EquityNYC (City of New York) EquityNYC compiles cross-cutting data from 55 New York City agencies and partners in order to track specific equity indicators, including education, health and wellbeing, housing, empowered residents and neighborhoods, economic security and mobility, core infrastructure and the environment, personal and community safety, and diverse and inclusive government. This tool helps address racial disparity within communities of color by providing a transparent and accessible means of visualizing and analyzing data, enabling decision-makers to identify and address systemic biases and inequalities
Equitable Development Data Explorer (City of New York) The NYC Departments of City Planning (DCP) and Housing Preservation and Development (HPD) developed this interactive resource that equips New Yorkers with data to navigate challenging issues about housing affordability and displacement that stemmed from historic racial segregation and discrimination against communities of color. It includes two useful tools: a wide range of demographic, housing, and quality of life data to inform public discussions about racial equity and planning for a fairer city, and a Displacement Risk Map that illustrates the level of risk that residents may experience of forced removal from their homes or neighborhood due to economic factors.
Do you have other resources we should add to our list? Please tell us in the comments section below—we are always looking to learn more!
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