Achieving sustainable development goals requires targeting and monitoring sustainable solutions tailored to different social and ecological contexts. A social-ecological systems (SESs) framework was developed to help diagnose problems, identify complex interactions, and solutions tailored to each SES. Here we develop a data-driven method for upscaling the SES framework and apply it to a context where data is scarce, but also where solutions towards sustainable development are needed. The purpose of upscaling the framework is to create a tool that facilitates decision-making in data-scarce contexts. We mapped SES by applying the framework to poverty alleviation and food security issues in the Volta River basin in Ghana and Burkina Faso. We found archetypical configurations of SES in space, and discuss where agricultural innovations such as water reservoirs might have a stronger impact at increasing food availability and therefore alleviating poverty and hunger. We conclude by outlining how the method can be used in other SES comparative studies.