Visible communities : designing a socio-spatial map

Abstract

This thesis presents a collaborative human-machine crowdmapping approach to creating socio-spatial maps that represent both spatial and social aspects of communities. Our implemented system combines satellite image analytics, a mobile mapping app, and social survey data. The system is designed to provide an end user experience that aligns institutional interests with grassroots interests, resulting in a self-sustaining system. In collaboration with the global health organization Partners in Health, we tested our approach with local health workers in Rwanda. Better maps can improve local visibility and empower communities to share knowledge, trade goods, and access medical services. Assisted by automatically annotated satellite maps, the community-driven mapping resulted in detailed spatial and social maps for four rural villages. With the collected data, we designed a novel socio-spatial map for this community that combines knowledge about household locations, paths, inhabitants of those homes, and social relations between residents. Generalizing from this map, we propose a framework to organize people, places, paths, and relationships to reason about the intersection of social and spatial mapping. Furthermore, we derive design characteristics of our human-machine mapping system that can guide the development of new systems in related contexts. Socio-spatial maps have the potential to be used as critical decision-making tools for individuals and organizations alike.