Mapping Driver Training Deserts: A Data-Informed Decision-Support Tool for Driver Training Resource Allocation

Driver education and behind-the-wheel (BTW) training are required for junior licensure in thirty-one U.S. states, including Ohio, to support the acquisition of safe driving skills. Yet spatial and structural barriers can delay licensure for youth in disadvantaged communities. In Ohio, high poverty rates and long travel times to licensed driving schools create driver training deserts (DTDs), limiting access to graduated driver licensing programs. This study develops and evaluates a statewide mapping tool to identify DTDs and support more equitable resource allocation. The tool integrates U.S. Census data with travel-time estimates to the nearest BTW provider, creating tract-level indicators of socioeconomic disadvantage and service accessibility. The framework identifies concentrations of DTDs in rural southeastern counties and high-poverty urban neighborhoods. The interactive platform allows users to compare alternative threshold scenarios and visualize areas of compounded need. Practitioner feedback highlighted the tool’s value in supporting expanded services, while also underscoring challenges such as the need for updated service provider data. Findings demonstrate how iterative, participatory design and applied geographic analysis can translate spatial inequities into operational decision-making. This tool offers a scalable spatial decision-support framework for targeting investments in underserved areas and can be extended to incorporate additional data and policy contexts.