The Urban Data Research Lab uses novel big data and spatial data science to understand urban inequality at the scale of the neighborhood in order to inform policy and opportunities for more equitable cities.

Measuring Neighborhood Dynamics with Big Data

Much of how researchers, advocates, and policy-makers understand the geography of opportunity is largely based on the residential neighborhood context. To study neighborhood dynamics, especially at a large scale, we have traditionally relied on administrative data such as those produced by the U.S. Census Bureau. This area of research aims to think, measure, and generate data products to describe the wide range of activities and socio-spatial dynamics that make up how people experience their social and environmental context and how these can change across the day or seasonally. We investigate the possibilities of using novel, unstructured data sources such as newer iterations of cell phone location data, in a ground-truthed and statistically rigorous manner, to shift how we think about context in a more holistic, representative way.

Tracing the Legacies of 20th-Century Housing and Urban Policy

Public policy in the U.S. is at a critical turning point where the need to address historical and ongoing housing discrimination calls for more nuanced understandings of longstanding spatial inequalities and their impacts on residential segregation and stratification. We study large-scale historical housing discrimination through such as practices, federal level redlining and urban renewal in the United States, to understand how historical housing policies, practices, institutions, and technologies have influenced urban inequality.

Cabrini-Green, Chicago. Photo by LHOON, CC BY-SA 2.0

Evaluating the Effectiveness of Housing Policy with Novel Data

Housing policy, from inclusionary zoning to voucher programs to public housing redevelopment, is often designed around assumptions about how households move, integrate, and access opportunity. This area of research draws on new data sources, including anonymized human mobility data, historical administrative records, and novel residential mobility data, to test whether these programs achieve their intended effects. We ask whether siting decisions actually produce integration, whether residents displaced by redevelopment programs go on to attain higher-opportunity neighborhoods, and how the geography of policy exposure shapes long-run outcomes.