Toward Scalable Local Mapping and Tagging for Rural Africa Using Mobile Devices
In this paper we present a context-aware tool designed for mapping and tagging objects and places of importance to rural communities using sensor-enabled mobile devices. These data sets comprise comprehensive models of specific environments which we use for creating interactive visualized knowledge sharing platforms for indigenous knowledge in Southern Africa. The tool was originally created for researchers to efficiently capture large amounts of data in the field, but we realized that true scalability of the approach would only be attained when including local users. The tool has been through multiple design iterations and in-situ evaluations across several locations in Namibia, and this paper presents findings from our research into the feasibility and effectiveness of the tool to capture meaningful localized data in an efficient and scalable way. From this we conclude that it is very promising when used by trained researchers, but that the interface will need to be significantly redesigned and appropriated for local community members.
Nordichi '12: Proceedings of the 7th Nordic Conference on Human-computer Interaction: Making Sense Through Design, 2012, p. 631-634
Indigenous knowledge; mobile; Context-awareness; pervasive; sensor-based; data capture; mapping; 3D visualization; ICT4D; HCI4D