Imagine trying to navigate an unfamiliar city with a broken compass. The needle appears steady and reliable, instilling confidence in each turn you take. But unbeknownst to you, every step leads you further off course. You keep walking, trusting the instrument in your hand. But your confidence is misplaced: The compass is broken—faulty and unreliable, and you’re steadily moving in the wrong direction. This broken compass is much like the civic data we use in the United States today, demonstrating the vital importance of civic data equity.

Civic data attempts to capture and describe the realities of public life and community well-being. Data sources like census surveys and administrative records—such as tax filings, traffic data, school standardized testing results, and labor statistics—are used to determine which neighborhoods will be funded and which will be starved of resources, which communities will have roads and public transportation built and which will remain isolated, which schools will receive investment and which will be left to fail, and which neighborhoods will have jobs and economic opportunity while others face disinvestment and decline. Civic data influences where we live, how we live, and quite literally, how long we live, underscoring the importance of ensuring civic data equity.

But data is never neutral or objective. Data systems are designed by people working within institutions, and when those institutions are built on structural racism, patriarchy, and other systems of oppression, the data they produce will reflect and reinforce those same inequities. To this day, we’ve been relying on civic data that appears scientific and trustworthy, but in truth, it has been rooted in multiple systems of oppression, including structural racism, colonialism, and white supremacy. This means that the traditional forms of civic data we’ve relied on to shape nearly every aspect of our existence have been undermining our community health goals broadly but have been explicitly weaponized against communities of color.

When Civic Data Tells Lies

Consider the Home Owners’ Loan Corporation (HOLC), a federal agency created during the Great Depression to stabilize the housing market. To assess the perceived “risk” of lending in neighborhoods across the nation, HOLC began developing detailed maps of US cities in the 1930s, ultimately color-coding neighborhoods in hundreds of cities based on their perceived investment worthiness—and using the race of residents as a proxy for lending risk. HOLC applied one of four color-coded grades: “A” or “Best” (green), “B” or “Still Desirable” (blue), “C” or “Definitely Declining” (yellow), and “D” or “Hazardous” (red). Driven by white supremacy, racism, nativism, classism, and anti-Semitism, HOLC deemed all-white neighborhoods most “desirable” while marking neighborhoods with Black, immigrant, and other marginalized residents as “declining” or “hazardous,” systematically denying them access to loans and investment. This policy and practice became known as redlining.

Read the full article about civic data equity, justice, and sovereignty by Jamila M. Porter, Zamir Bradford, and Lynne Le at Stanford Social Innovation Review.