A Primer on Geospatial Impact Evaluation Methods, Tools, and Applications

The growing availability of georeferenced data on development investments and outcomes has opened up new opportunities to understand what works, what doesn’t, and why at a substantially lower time and financial cost. When precisely georeferenced intervention data are fused with in-situ and remotely sensed data on outcomes like poverty, child mortality, deforestation, and governance, quasi-experimental methods of causal inference can be used to control for potential confounds and omitted variables at fine geographic levels. We introduce these geospatial impact evaluation (GIE) methods, review their advantages and disadvantages, and describe their relevance and use across countries, sectors, intervention types, and development organizations.

Funding: This study was made possible through generous financial support from the William and Flora Hewlett Foundation (Grant # 2015-3240) and a cooperative agreement (AID OAA-A-12-00096) between USAID’s Global Development Lab and AidData at the College of William and Mary under the Higher Education Solutions Network (HESN) Program.