Numina is a startup founded in 2014 with the mission of empowering cities with data to become more responsive and equitable. Numina offers the only computer vision sensor solution purpose-built for streets—quantifying the granular behaviors of pedestrians, bicyclists, and vehicles anonymously and in aggregate—and has deployed its technology in 25 cities globally. Numina enlisted a Capstone team to better understand the financing dimension of street data initiatives and to identify potential solutions to sustainability challenges. The team analyzed city spending and current street data procurement practices, determined cost-sharing opportunities with other end users, and identified factors that affect city decisions to open or sell data. The team interviewed city transportation department officials and conducted a document review of city budgets and contracts with data vendors. The team analyzed its findings to identify pathways forward to facilitate equitable cost-sharing mechanisms for street data investments.

The Supportive Housing Network of New York (SHNNY) promotes supportive housing development by providing policy analysis, advocacy, and assistance to organizations managing supportive housing units across the state. With advocacy being central to SHNNY’s work in securing funding at the local, state, and federal levels, the organization enlisted a Capstone team to help determine costs associated with homelessness. The team calculated the real per-day costs of different settings and contexts that homeless individuals engage with in the absence of supportive housing. The team consulted with various experts in the field and reviewed a variety of prior research, financial reports, and local government budgets to create a series of cost estimates in five New York cities: Albany, Buffalo, New York City, Rochester, and Syracuse. The team’s findings and estimates illustrate the high cost of a lack of investment in permanent supportive housing and inform SHNNY’s future engagement with policymakers, media, other stakeholders, and the general public.

CARMERA is an autonomous vehicle (AV) high-definition (HD) mapping company that specializes in roadway change management. It is critical for public safety that HD maps have granular and up-to-date information on road features to anticipate the road ahead. CARMERA enlisted a Capstone team to review data sharing policies at the federal, state, and city levels and help identify connections between roadway changes collected by cities and private AV firms. The team utilized a spatial regression model to examine whether public datasets can help predict the observed roadway changes from CARMERA’s pilot program in San Francisco. Its preliminary findings indicate an opportunity for the optimization of private company data gathering based on public datasets. The team produced a final report detailing ways to use public data to inform policies and recommendations for cities and private AV firms with the shared goal of coordinated and efficient capture of roadway changes on HD maps.