Towards real-time emergency response using crowdsourcing

Crowdsourcing has emerged as an attractive paradigm in recent years for information collection for disaster response, which utilizes data received from the human crowd, to provide critical information collection and dissemination during emergency situations and visualize this data to generate emergency maps for the human crowd. In this paper we investigate the use of crowdsourcing mechanisms for real-time emergency response and describe our approach for developing a crowdsourcing tool that can be effectively used to formulate questions and seek answers from the human crowd using a MapReduce programming model, and integrate this information into a novel spatiotemporal data structure and create a visual emergency map. Our experimental evaluation shows that our approach is practical, efficient and can be used for applications with real-time demands.