At its best, public policy research shines a light in the dark, illuminating social conditions so we can see our way through to solutions. But that light is only as bright as the best research tools and techniques will allow. Recent advances in data science are expanding the range of what’s visible and possible in at least three ways: filling fundamental gaps in knowledge, refining policy design, and clarifying connections between raw data and actionable information.

Filling knowledge gaps

Right now, the regulations that dictate which communities will have access to schools, parks, medical facilities, and any other resource that requires physical space are largely inaccessible, tucked away in municipal offices.

Zoning codes are the scaffolding that opportunities are built on. They dictate how land can be used, influence the cost of real estate development, and affect who can afford to live where. In part because this information is so difficult to access, there is no national dataset comparing zoning codes across jurisdictions. Researchers and policymakers cannot fully quantify the impacts of policy changes on issues like housing affordability and segregation.

To fill this gap, the Urban Institute is exploring the possibility of using property assessment records to generate a model that can predict zoning characteristics. We hope this model will lead to an easy-to-update, publicly accessible database of zoning regulations by municipality that will help policymakers better understand how to shape vibrant, equitable communities.

Designing more effective policies

Traditionally, federal policymakers start with scenarios for different combinations of inputs (specific policy prescriptions) and use models to “score,” or estimate, the outcomes. Scoring is useful, but what if instead we could start with the outcomes we want and work backward to identify the inputs that will achieve those results?

This is not purely hypothetical. At Urban, using the power of cloud computing, we are working to make this vision a reality. Funded by the Alfred P. Sloan Foundation, we are retooling the Urban-Brookings Tax Policy Center’s large-scale microsimulation model to enable researchers to start with a desired outcome in mind, test thousands of scenarios to determine which can achieve the desired effects, and respond to fast-moving policy debates.

Making high-quality information more accessible

Often, researchers wanting to assemble education data are forced to spend hours, if not days, collecting, cleaning, and harmonizing the data for every year they’d like to compare. For policymakers, journalists, community advocates, and other important audiences, these data are accessible only through the reports researchers produce.

To fix this, Urban used an application programming interface to create the Education Data Portal. The portal brings together all the major national datasets on K–12 schools and districts, colleges, and universities and allows open access for everyone. As Urban and outside groups build applications for different audiences, like a point-and-click download tool or data summaries by congressional district, all these applications will be updated as soon as new data are uploaded to the portal.

This new architecture saves time and money and enables institutions like Urban to disseminate up-to-date information to the people who need it most.

Technology is changing the questions policymakers and community members can ask and how precisely and efficiently we can answer them. As Urban enters its next 50 years, we expect technology and data science will empower decisionmakers and communities, improve outcomes, and lead to even more creative uses for sources of information that have been beyond the use of public policy research.

Jessica Kelly, David D’Orio, and Jamila Patterson contributed to this post.