Imagery-based modeling of social, economic, and governance indicators in sub-Saharan Africa

Abstract

Many policy and national security challenges require understanding the social, cultural, and economic characteristics of a country or region. Addressing failing states, insurgencies, terrorist threats, societal change, and support for military operations require a detailed understanding of the local population. Information about the state of the economy, levels of community support and involvement, and attitudes toward government authorities can guide decision makers in developing and implementing policies or operations. However, such information is difficult to gather in remote, inaccessible, or denied areas. Draper's previous work demonstrating the application of remote sensing to specific issues, such as population estimation, agricultural analysis, and environmental monitoring, has been very promising. In recent papers, we extended these concepts to imagery-based prediction models for governance, well-being, and social capital. Social science theory indicates the relationships among physical structures, institutional features, and social structures. Based on these relationships, we developed models for rural Afghanistan and validated the relationships using survey data. In this paper we explore the adaptation of those models to sub-Saharan Africa. Our analysis indicates that, as in Afghanistan, certain attributes of the society are predictable from imagery-derived features. The automated extraction of relevant indicators, however, depends on both spatial and spectral information. Deriving useful measures from only panchromatic imagery poses some methodological challenges and additional research is needed.