The Black Zones: Harnessing Mobile Tech Enabled Community Driven Data, Big Population, Health Data, & Agent-Based Models to Transform the Measures & Metrics around Structural Racism & Black Longevity

Abstract Racial residential segregation (RRS) is a form of structural racism and a key driver of the life expectancy gap for African Americans (AAs) and Black Immigrants (BI) in the U.S. Shorter Black life expectancy is due to negative consequences of RRS, such as food deserts, pollution, low socioeconomic position, and resource

scarcity. The Black Progress Index (BPI) launched by the Brookings Institution in partnership with the NAACP in 2022, comprises 13 social factors that predict longer life expectancy among Black populations at the county level, such as home and business ownership, fathers in the households, and social relationships. The BPI

utilizes secondary data to reveal the factors and locales where Black people are living the longest and begins to (1) identify the environmental, social, cultural, and behavioral factors that drive disparities in life expectancy between AAs and BIs and (2) develop strategies to reduce or eliminate those disparities and promote active life

expectancy and improve the health status of AAs and BIs. However, while secondary data and social environment factors are useful to describe some factors that impact Black longevity, these data do not effectively capture the lived experience of residents at the neighborhood level. Thus, new community-led measures that capture lived experience including intersectionality and other

complex social constructs (e.g., structural racism) that function as risk and resiliency factors that associated with Black life expectancy are needed. Mobile data collection platforms that collect real-time, location-based data on users’ lived experiences and perceptions of their neighborhoods can gather much-needed data to

disentangle negative and positive characteristics Black neighborhoods with relatively low versus high Black progress (e.g., Black longevity) as measured by the Black Progress Index and the impact those characteristics have on AA and BI residents’ life expectancy. We propose a sequential mixed-methods approach that uses mobile technology to enhance community based

participatory research to develop new measures of risk and resiliency factors of structural racism that capture AA and BIs lived experience of segregated neighborhoods and Black longevity. Additionally, we will develop dynamic agent-based models that integrate multiple types of data to capture understudied aspects of the legacy

of structural racism, that includes geography, social context, historical racism, and how public money is spent in Black communities. These models will provide insights on how decision making by government relates to the less visible structural forces that influence Black longevity. For example, our agent-based models will be able to

provide insights on how financial decision making by government entities impacts patters in education funding, health funding, parks and public space, police expenditures, cash assistance, and other pecuniary choices related to structural forces that influence Black longevity. Our research contributes to filling the gap left by

previous studies that have been slow to quantify and measure structural racism's impact on Black longevity, and sheds new light on the extent to which compositional and contextual effects interact and impact the variation of Black life expectancy in different regions across the US.