The AidData Center for Development Policy

Official Logo of the United States Agency for International Development

Funded through a five-year, $25 million cooperative agreement with the U.S. Global Development Lab, the AidData Center for Development Policy provides geospatial data and tools that enable the global development community to more effectively target, coordinate, and evaluate aid. The Center is a consortium of five partners: AidData at the College of William and Mary, Development Gateway, Brigham Young University, the University of Texas at Austin, and Esri.

Working in partnership with USAID country missions, host governments, and civil society groups, the Center pinpoints the precise geographic locations of development projects and creates subnational maps and dashboards that overlay geocoded project data with high-resolution spatial data on poverty, disease, violence, environmental degradation, and governance. These data and tools make it possible to visualize and analyze where funds are going at the subnational level compared to the areas of greatest need and opportunity. Read more about the Center's activities here.

Our Focus Areas

Headquartered at the College of William and Mary in Williamsburg, Virginia, the AidData Center for Development Policy has five main focus areas.

  1. We pinpoint the locations of development activities, visualizing and analyzing this geocoded information to support monitoring of aid distribution and impact.

  2. We mobilize experts through the AidData Research Consortium to analyze development trends using geospatial information, and inform evidence-based policy decisions.

  3. We boost the capacity of governments to capture granular information on their development portfolio and use this information to manage development finance flows.

  4. We equip students and faculty with tools and training to help governments and NGOs to map the distribution of development resource flows and monitor results.

  5. We design new tools for researchers, policymakers, and practitioners to visualize multiple data sources and uncover otherwise difficult-to-observe relationships.